Communication between distributed information brokers within a data and energy storage internet architecture

Communication between distributed information agents in the Internet architecture of data and energy storage

[0001] Cross-application of related applications

[0002] This application requires the priority of the prior application of US Patent Application No. 14/970,906 filed on December 16, 2015 with the title of "Communications between Distributed Information Agencies in the Internet Architecture of Data and Energy Storage" Right, the content of the earlier application is incorporated into this article by way of introduction.

Background technique

[0003] Intelligent control is important for managing computing systems and networks. You can use analysis and feedback to optimize scheduling and resource allocation to achieve multiple goals, such as adjusting scheduling priority, memory allocation, and network bandwidth allocation. Control theory contains a large number of practical system-control-design principles and provides a systematic method for designing closed-loop systems. The closed-loop system is stable because it can avoid natural oscillations, and because it reaches the expected output, it is accurate and quickly stabilizes to a steady state value. Control theory is a branch of theory and applied mathematics, used for the design of many aspects of calculation. In addition, control theory is used to analyze and design feedback loops in mechanical, electrical, aerospace, and other engineering disciplines. Therefore, control theory has had a profound impact on the design and development of a large number of systems and technologies from airplanes, spacecraft, other transportation and transportation systems to computer systems, industrial manufacturing and operating equipment, machine tools, processing machinery, and consumer equipment.

[0004] Together with the input of the man-machine interface, intelligent control can analyze people's preferences and behaviors, and provide a tailor-made solution for each person. Usually, the task of an intelligent controller is to control a single device or system under strict restrictions to achieve conflicting goals. Designers, manufacturers, and users of intelligent control have been seeking solutions for effective control systems, and at the same time, they also collaborate with each other to achieve better resource sharing and collaboration.

Summary of the invention

[0005] In one embodiment, the present invention includes a distributed system, the distributed system includes a plurality of network elements (network elements, NE for short), the plurality of network elements are used for distributed data and energy storage Internet As an ensemble information broker (EIB) in the architecture; each NE is used to collect device data about the energy flow through multiple devices in the intelligent system associated with the NE in a specific time period, the The amount of energy and data consumed by the device during the specific time period, and the amount of energy and data generated by the device during the specific time period; collecting information about the users presence in the smart system during the specific time period Human presence data appearing in the intelligent system; collecting human activity data in the intelligent system regarding the user's input to the device and the user's interaction with the device in the specific time period; by applying a prediction algorithm to The device data, human presence data, and human activity data determine the future energy and data consumption and generation requirements of the smart system; the future is based on the dynamic human-centered cost function applied to the smart system Energy and data consumption and generation requirements, generating a set of control commands for the devices in the smart system, wherein the dynamic human-centered cost function is based on the smart The human presence data, human activity data, and user-defined preference levels of the system can be adapted to weighted objective metrics; the control command set is transmitted to the corresponding device in the intelligent system; the broadcast message is transmitted to determine the assigned environment or network And external NEs within the Internet architecture of distributed data and energy storage to establish a connection as a friend based on user preferences; transmit a request to establish a connection with a friend of the determined NE, wherein, when a confirmation from the external NE is received The friend connection is established; when the friend connection is established, the human presence data is transmitted to the external NE. In some

In the embodiment, each NE is also used to: when the friend connection is established, periodically send a heartbeat message to maintain the connection state with the external NE, and/or when the friend connection is established, from the external NE Receive external environment presence data, where the external environment presence data indicates interactions between multiple external users and multiple external devices in the external intelligent system associated with the external NE; and/or from a second external NE at a specific time Receive a request for energy or data storage; determine the qualification of the second external intelligent system associated with the second external NE based on the qualification algorithm determined by whether the second external NE has been established as a friend connection; The power generating equipment sends a command to enable the power generating equipment to determine that the second external intelligent system is qualified to receive energy or data, and provide the second external intelligent system at the specific time to satisfy the energy or data storage The requested amount of energy or data generated, and/or provide an interface to change or enter the values used by the energy cost function and the user productivity function. In some embodiments, the present invention further includes: when the second external NE is not established as a friend connection, the qualification algorithm is a credit scheme based on points, wherein the credit scheme based on points compares the energy sent to the external intelligent system Or the amount of data sent and the amount of energy or data received from an external intelligent system; the credit-based credit scheme includes a weight function, and the weight function includes a weight function based on the specific time and minutes. The weight value determined by the time distance between the peak time of energy or data consumption in the smart system allocated to the NE; when the second external NE is established as a friend connection, the qualification algorithm is the established friend Solution, the established friend solution determines that when the second NE is established as a friend connection, the second external intelligent system is eligible to receive energy or data; and/or the dynamic human-centered cost The function includes a human-centered multi-modal representation of the weighted sum of an energy cost function and a user productivity function, the energy cost function represents the operating state of the device, and the user productivity function represents the human comfort level in the intelligent system and The level of human productivity, and includes a noise function, a temperature drop function, a temperature rise function, a freshness function, and a humidity function related to the operating state of the device.

[0006] In another embodiment, the present invention includes a network element (NE) used as an ensemble information broker (EIB) in a distributed data and energy storage Internet architecture; The NE includes a processor for collecting device data about energy flow through multiple devices in the smart system associated with the NE in a specific time period, and the amount of energy consumed by the device in the specific time period And the amount of data and the amount of energy and data generated by the device in a specific time period; collect the human presence data in the smart system about the user's appearance in the smart system during the specific time period; collect the Human activity data on the users input to the device and the users interaction with the device in the intelligent system within a specific time period; predict by applying a prediction algorithm to the device data, human presence data, and human activity data The future energy and data consumption and generation requirements of the intelligent system; based on the future energy and data consumption and generation requirements applied to the dynamic human-centered cost function associated with the intelligent system, the generation requirements in the intelligent system are generated The control command set of the device, wherein the dynamic human-centered cost function is based on the human presence data and human activity data of the intelligent system And the user-defined preference level to adapt the weighted objective metric; the energy cost function represents the operating state of the device, the user productivity function represents the human comfort and human productivity level in the intelligent system, and includes information about all The noise function, temperature drop function, temperature rise function, freshness function and humidity function of the operating state of the device; a transmitter coupled to the processor for transmitting the control command set to the corresponding device in the intelligent system. In some embodiments, the NE further includes a receiver coupled to the processor for receiving from a plurality of external NEs an external intelligent system indicating that a plurality of external users are associated with each NE of the plurality of external NEs There are data in the external environment of the interaction of multiple external devices within, where the multiple external NEs are used to serve as external EIBs established as friend connections in the data and energy storage Internet; the processor is also used to The subset of the external NE is determined based on the analysis of the external environment presence data, wherein the external environment presence data indicates that each of the external intelligent systems includes sufficient remaining power

Rate or data storage to meet the possibility of an associated smart systems energy or data storage request; the transmitter is also used to transmit the energy or data storage request to a subset of the external NE, and/ Or a receiver coupled to the processor for receiving energy or data storage requirements from an external NE as an external EIB within the data and energy storage Internet at a specific time; a credit scheme based on points determines the allocation to The eligibility of the external intelligent system of the external NE, and the credit scheme based on points compares the amount of energy or data sent to the external intelligent system allocated to the external NE with the received energy or data received from the external intelligent system Wherein the points-based credit scheme includes a weight function including a time distance based on the specific time and the peak time of energy or data consumption in the smart system allocated to the NE Determined integral value; send a command to the power generation equipment in the smart system so that the power generation equipment determines that the external smart system is eligible to receive energy or data, and provides satisfaction to the external smart system at the specific time The amount of energy or data generated by the energy or data storage request. In some embodiments, the present invention further includes: further generating the control command set according to a human-centered optimization goal, The human-centered optimization goal includes the reduction of energy costs in the smart system and the optimization of the users productivity in the smart system, and/or the human activity data includes the user and each of the smart systems. The time of the day and several hours of interaction between each device, and/or update the dynamic cost function by weighting the cost function according to user preferences received from the user interface in the intelligent system coupled to the NE A human-centered cost function, and/or based on the NE receiving a request to establish the friend connection from an external NE through the receiver and the NE sending a confirmation that the friend connection has been established through the transmitter To establish the friend connection for one of the external NEs, and/or the transmitter is also used to transmit the human presence data to the external NE when the external NE is established as a friend connection.

[0007] In yet another embodiment, the present invention is implemented in a network element (NE) used as an ensemble information broker (EIB) in a distributed data and energy storage Internet architecture. Methods. The method includes collecting energy data about energy flow through a plurality of devices in a smart system associated with the NE in a specific time period, the amount of energy consumed by the device in the specific time period, and The amount of energy generated by the device in the specific time period; collect the human presence data in the smart system about the user appearing in the smart system during the specific time period; collect the smart system in the specific time period Human activity data about the users input to the device and the users interaction with the device in the system; the intelligence is predicted by applying predictive algorithms to and analyzing the collected energy data, human presence data, and human activity data The future energy consumption demand and energy generation of the system; based on the future energy consumption demand and energy generation applied to the dynamic human-centered cost function associated with the intelligent system, a set of control commands for the devices in the intelligent system is generated ; Transmit the set of control commands to the corresponding devices in the intelligent system; transmit a broadcast message to determine the distribution environment or network And the external NE as an external EIB in the data and energy storage Internet architecture establishes a friend connection based on user preferences; transmits a request to establish a friend connection with the determined NE, wherein after receiving the request from the external NE The friend connection is established when the friend connection is established; when the friend connection is established, the human presence data is transmitted to the external NE. In some embodiments, the method further includes: when the buddy connection is established, receiving an external NE external human indicating that multiple external users interact with multiple external devices in an external intelligent system associated with multiple external NEs Store data, and/or determine to include the external NE in the external NE set based on the analysis of the external human presence data, wherein the external human presence data indicates that the external intelligent system includes sufficient remaining power or data storage to satisfy The possibility of an associated intelligent system for energy or data storage requests, when the external NE is included in the external NE set, the energy or data storage request is transmitted to the external NE, and/or in A specific time from the data and energy storage mutual

The external NE of the external EIB in the network receives the energy or data storage request; the credit scheme based on points determines the eligibility of the external intelligent system assigned to the external NE, wherein the credit scheme based on points is compared and sent to the external The amount of energy or data sent from the external intelligent system of the NE and the amount of energy or data received from the external intelligent system. The credit-based credit scheme includes a weight function that includes a weight function based on the specific time and The integrated value determined by the time distance between the peak time of energy or data consumption in the smart system allocated to the NE; send a command to the power generating equipment in the smart system so that the power generating equipment is determined When the external intelligent system is qualified to receive energy or data, it provides the external intelligent system with energy or data generation that meets the energy or data storage request at the specific time. These and other features will be more clearly understood through the following detailed description in conjunction with the accompanying drawings and claims.

Description of the drawings

[0008] For a more thorough understanding of the present invention, now refer to the following brief description described in conjunction with the drawings and specific embodiments, in which the same reference numerals denote the same parts.

[0009] FIG. 1 is a schematic diagram of an intelligent system deployed in the data and energy storage Internet architecture;

[0010] FIG. 2 is a schematic diagram of an embodiment of an NE used to implement EIB in a network;

[0011] FIG. 3 is a schematic diagram of an embodiment of a data and energy storage Internet architecture including multiple intelligent systems;

[0012] FIG. 4 is a schematic diagram of an embodiment of EIB implementation in a network;

[0013] FIG. 5 is a schematic diagram of an embodiment of an energy supply/demand model and predictor (P) component implemented on an EIB;

[0014] FIG. 6 is a flowchart of an embodiment of the implementation of the human behavior analyzer component of the EIB;

[0015] FIG. 7 is a flowchart of an embodiment of the implementation of the data and energy flow analyzer component when the data and energy flow analyzer component receives input;

[0016] FIG. 8 is a flowchart of an embodiment implemented by the predictor component of the EIB when the predictor component receives input;

[0017] FIG. 9 is a flowchart of an embodiment implemented by an external EIB connected to the EIB through an external gateway to receive input data or send input data to the external EIB;

[0018] FIG. 10 is a schematic diagram of an embodiment of a cost-aware workload controller/scheduler (C) component implemented on the EIB;

[0019] FIG. 11 is a flowchart of an embodiment implemented by EIB when input data of the user interface is received;

[0020] FIG. 12 is a flowchart of an embodiment implemented by the EIB in the data and energy storage Internet architecture when the EIB receives a friend request from another EIB implemented in the data and energy storage Internet;

[0021] FIG. 13 is a flowchart of an embodiment implemented by the EIB in the data and energy storage Internet architecture when the EIB initiates a charge or discharge request;

[0022] FIG. 14 is a flowchart of an exemplary embodiment of a method adopted by the EIB. The EIB is implemented in the data and energy storage Internet architecture to determine the configuration when the smart system associated with the EIB requires the device in the smart system In the case of energy, the external NE set of the EIB that can provide energy.

Detailed ways

[0023] First, it should be understood that although illustrative implementations of one or more examples are provided below, the disclosed systems and/or methods can be implemented using any number of technologies, regardless of whether the technologies are currently known or current. some. The present invention should never be limited to the illustrative embodiments, drawings and techniques described below, including the exemplary designs and embodiments illustrated and described herein, but may be within the scope of the appended claims and their equivalents Modify within the full scope.

[0024] One type of feedback control includes an intelligent controller. The task of the intelligent controller can be to control the system under various constraints to achieve the control goal. For example, the intelligent controller can control various environmental parameters of a specific environmental system. The intelligent controller can resolve and manage potential conflicting targets. Therefore, when two or more control objectives conflict, the designer of the intelligent controller can seek a control method and system for effectively controlling the system.

[0025] Smart controllers may only focus on how to reduce energy costs, rather than how to increase productivity, because increasing productivity may consume more resources than reducing energy costs. However, combined with the concept of economic sharing, there are opportunities to reduce energy costs while increasing productivity and efficiency. For example, the control decisions of multiple devices may be related, so individual control of each device may lead to sub-optimal results. In one embodiment, the energy solution can use GooglcKNest to execute machine learning algorithms. However, Google® Nest can only control one device (such as heating, ventilation and air conditioning (HVAC)). In addition, Google®Nest cannot use the communication between smart controllers to achieve better resource sharing and collaboration.

[0026] The intelligent controller can be designed to be based on the control model and sensor feedback of the system, and output the control signal to various dynamic components of the system. Such a system may show predetermined operating behaviors or operating modes. Therefore, the control components of such systems are designed to ensure that predetermined system behaviors occur under normal operating conditions. In some cases, the system may have various operating modes. Therefore, the control component of the system can select the current operating mode of the system and control the system to conform to the selected operating mode.

[0027] This article discloses a data and energy storage Internet architecture, including a distributed ensemble information broker (EIB) (for example, an intelligent controller), which is used to monitor and control an associated intelligent system or an intelligent network. Devices and communication between each other. The Internet of data and energy storage provides a mechanism for users who interact with devices controlled by the Internet of data and energy storage to achieve the dual goals of energy cost efficiency and productivity improvement, while meeting the service levels established for the Internet of data and energy storage Agreement (service level agreement, referred to as SLA) and quality of service (Quality of Service, referred to as QoS) requirements. In one embodiment, the EIB in the data and energy storage Internet architecture communicates with another EIB in a peer-to-peer manner (for example, by adopting a peer-to-peer communication protocol). Peer-to-peer communication between EIBs allows data and energy storage in the Internet. Each EIB sends and receives requests to directly exchange information and energy resources with other EIBs in the data and energy storage Internet through the network.

[0028] The EIB in the data and energy storage Internet architecture uses smart information and resource sharing solutions to achieve the above system goals. Each EIB and controlled consumption, production, and storage (for example, batteries, data storage) devices form an intelligent system or intelligent network. In each embodiment, the EIB serves as the intelligent controller of the controlled device in the associated intelligent system. In an embodiment, the EIB includes an analysis model and predictor and a cost-aware workload controller/scheduler.

[0029] EIB and associated intelligent systems can be deployed for offices, homes, large enterprises, and other applicable environments. In an embodiment, the device can be used to act as an EIB to store energy usage, data usage, and user personalized data about the device in the associated smart system, the interaction with the device, and the information in the associated smart system. exist. The user personalization data may include human presence data about the time period during which the user appears in the intelligent system. User personalization data may include human activity data, about the user's use or interaction with devices in the associated smart system (the time of a day and/or several hours during which the user interacts with the devices in the smart system), or user input or selection Or configure the device/intelligent system

Time period. The EIB uses machine learning and model-based predictive control technology to analyze and control the devices in the associated intelligent system. For example, the EIB can control the power of the associated device (for example, whether the device is turned on or off) and/or the charging (for example, receiving) and/or discharging (for example, releasing) stored in the device (for example, an energy generation or storage device) . In each embodiment, the EIB learns the habits of users interacting with devices in the smart system, and can exchange the human presence data and human activity data with other EIBs in the data and energy storage Internet. The EIB can use human presence data and human activity data to predict behavior and perform performance and activity scheduling for devices in the associated intelligent system. In addition, EIB can detect anomalies (for example, energy theft) by monitoring the energy and data flow in the smart system. In various embodiments, EIB can be used to contribute to and/or as part of software-defined commodity hardware, including smart homes or smart cities. In an embodiment, EIB may be implemented as a virtual information agent for smart homes and/or smart offices.

[0030] The EIB in the data and energy storage Internet can establish a "friend" relationship between each other. The EIB can exchange resources with each other based on a rule set and/or a credit scheme based on points, and established or unestablished friend relationships. In one embodiment, rule sets and points-based credit schemes enforce policies that require each EIB to contribute to energy transactions within the Internet of data and energy storage before issuing a discharge request. In one embodiment, the points-based credit scheme adopted by the EIB assigns weighted values to the external EIB within the Internet of Data and Energy Storage, and the external EIB has sent energy to and obtained energy from the intelligent system associated with the EIB. The weighted value takes into account the energy taken or received during the day and the amount of energy taken or received. The points-based credit scheme determines whether the external EIB is eligible to receive the energy of the intelligent system associated with the EIB at the determined request time based on the weighted value and the requested energy amount. In an embodiment, the credit scheme based on points compares the amount of energy or data sent to the external intelligent system and the amount of energy or data received from the external intelligent system, and includes a weight function, where the weight function includes The weight is determined based on the time distance between the specific time and the peak time of energy or data consumption in the associated intelligent system allocated to the EIB.

[0031] FIG. 1 is a schematic diagram of an embodiment of a data and energy storage Internet architecture 100 including smart systems 110, 120, and 130. The smart systems 110, 120, and 130 respectively include distributed EIB 115, 125, and 135 and multiple display devices, electronic devices, energy generation devices, energy storage devices, smart devices, and Wi-Fi adapters (not shown). The smart systems 110, 120, and 130 are basically similar to each other. EIB 115, 125, and 135 are basically similar to each other. EIB 115, 125, and 135 communicate in a peer-to-peer manner. Figure 1 describes how EIBs 115, 125, and 135 in the data and energy storage Internet architecture 100 communicate with each other in a distributed environment. Although three intelligent systems are shown in FIG. 1, it can be understood that the data and energy storage Internet architecture can include any number of intelligent systems.

[0032] In the disclosed embodiment, EIB 115, 125, and 135 each maintain control and data collection of devices within the corresponding associated intelligent systems 110, 120, and 130. The EBs 110, 120, and 130 communicate with other EBs (external) in the data and energy storage Internet architecture 100 to obtain/send energy. For example, EIB 115, 125, and 135 may send or request energy for devices in the associated smart system, but not send or request energy for devices outside the associated smart system. In various embodiments, the EIB 115, 125, and 135 may not share the control information or consumption patterns of the devices in the associated smart system with other external EIBs.

[0033] In various embodiments, EIB 115, 125, and 135 can establish a friend connection between each other. In one embodiment, the friend connection represents a state of mutual recognition between EIBs (such as EIB 115, 125, and/or 135) in an energy storage Internet architecture (such as the energy storage Internet architecture 100). The EBs 115, 125, and 135 may each adopt different rule sets and/or points-based credit schemes to manage the energy exchange with the EBs that are connected to friends. In various embodiments, if another EIB grants a friend to connect, the EIB is not controlled by a device in another intelligent system associated with the other EIB.

Any eib can communicate with any other EIB in the data and energy storage Internet architecture 100 through a network connection and/or through the cloud in a peer-to-peer manner. In various embodiments, EIB 115, 125, and 135 may be assigned unique identifiers.

[0034] An EIB (such as EIB 115, 125, and 135) can request any other EIB in the data and energy storage Internet architecture 100 to become a buddy connection. Once an EIB (such as EIB 115, 125, and 135) sends a request to establish a buddy connection and receives a request confirmation from the requested EIB, the buddy connection is established. In an embodiment, two EIBs that have established a friend connection can exchange general information and send messages with each other, and use different rule sets and/or points-based credit schemes to manage the energy exchange between the two EIBs. In addition, multiple EIBs (such as EIB 115, 125, and 135) that have established friend connections can share energy mode data with each other. In various embodiments, EIBs (such as EIB 115, 125, and 135) may not share energy mode data with other EIBs that are not connected as friends. In various embodiments, the energy pattern data is a usage pattern of energy consumption in a smart system or environment (for example, a home or office) served by a device in the smart system by energy and/or data usage. The EB (such as EB 115, 125, and 135) can store energy mode data received from the EB that has established a friend connection locally, and can analyze the shared energy mode data to determine whether the EB may satisfy the charge/discharge request. In various embodiments, the EIB that has not yet established a friend connection with the second EIB can still exchange energy with the second EIB through the energy storage Internet architecture 100 based on the credit scheme based on points.

[0035] In various embodiments, the peer-to-peer communication between EIB 115, 125, and 135 is a decentralized communication model, where all parties/nodes (for example, each EIB) have the same capabilities, and either party can initiate communication Conversation. Unlike the client/server model in which the client sends a service request and the server completes the request, the peer-to-peer model allows each node to act as a client and a server at the same time. In various embodiments, EIB 115, 125, and 135 can exchange resource information with each other and request and send energy. In a disclosed embodiment, the EIB 115, 125, and 135 send and receive requests to exchange information and energy resources with each other, and can establish a friend connection between each other. In one embodiment, the buddy connection represents a state of mutual recognition between the EIB (such as the EIB 115, 125, and 135) and another EIB in the energy storage Internet architecture (such as the energy storage Internet architecture 100). The EIB 115, 125, and 135 may exchange resources (for example, energy) with each other based on a rule set and/or a credit scheme based on points. In various embodiments, when another EIB has provided enough contribution to the network and obtained enough points to exchange the requested resources, the EIB 115, 125, and 135 can request energy from another EIB (such as EIB 115 and 125) . in In one embodiment, the global settings of the energy storage Internet architecture 100 are used to determine a sufficient amount of contribution to the network and/or sufficient points for exchanging the requested resources. In another embodiment, the local setting of each EIB (such as EIB 115, 125, and 135) in the energy storage Internet architecture 100 is used to determine the sufficient amount of contribution to the network and/or the sufficient amount for exchanging the requested resources. integral. In one embodiment, the rule set and/or points-based system used by the EIB (such as EIB 115.125 and 135) to exchange resources with other EIBs is determined based on whether the EIB has been established as a friend connection. In other words, for EIBs that have established connections as friends, EIBs (such as EIB 115, 125, and 135) can use a rule set and/or points-based system. For the second EIB that has not yet established a connection as a friend, the EIB can use another rule set and/or points-based system. In one embodiment, the rule set and/or point-based credit scheme enforcement requires each EIB (for example, energy request) and/or discharge (for example, announcing the remaining energy available for transfer) request Such as EIB 115, 125, and 135) contribute to the strategy of energy trading within the energy storage Internet architecture 100. In one embodiment, EIB (such as The points-based credit system adopted by ΕΙΒ115.125 and 135) assigns weighted values to each EB (such as ΕΙΒ 115, 125, and 135) within the energy storage Internet architecture 200 that it has assigned to the associated intelligent system (such as the intelligent system 110). , 120 and 130) send energy and obtain energy from it. The weighted value takes into account the energy taken or received during the day and the amount of energy taken or received. In one embodiment, the points-based credit system adopted by the EIB 115, 125, and 135 determines whether the EIB associated with another smart system (such as the smart system 110, 120, and 130) is eligible to be based on the weight at the determined request time. value

And the amount of energy requested, receive energy from devices in the associated smart system (such as smart systems 110.120 and 130). In an embodiment, the credit scheme based on points compares the amount of energy or data sent to the external intelligent system and the amount of energy or data received from the external intelligent system, and includes a weight function, where the weight function includes The weight is determined based on the time distance between a specific time and the peak time of energy or data consumption in intelligent systems allocated to EIB (such as EIB 115, 125, and 135).

[0036] FIG. 2 is a schematic diagram of a data and energy storage Internet architecture 200 including a smart system 205 and an external EIB 280. The smart system 205 includes an EIB 210, a plurality of display devices 240, an electronic device 250, an energy generation device 252, an energy storage device 254, a smart device 256 (for example, a smart phone/tablet), and a Wi-Fi adapter 258. The energy storage Internet architecture 200 is basically similar to the energy storage Internet architecture 100. The intelligent system 205 is basically similar to the intelligent systems 110, 120, and 130. EIB 210 and external EIB 280 are basically similar to EIB 115, 125 and 135. In a disclosed embodiment, EIB 210 is related to smart system 205 and multiple electronic devices 250, energy generating device 252, energy storage device 254, smart device 256 (eg, smart phone/tablet), and Wi-Fi adapter 258 To control the associated devices and adapters 250, 252, 254, 256, and 258 and monitor the behavior of the devices and adapters 250, 252, 254, 256, and 258, and the users interaction with the devices and adapters 250, 252, 254, 256, and 258. For example, EIB 210 collects information about energy consumption and generation of devices and adapters 250, 252, 254, 256, and 258. In another example, ΕΙΒ 210 Collect information about energy flow. The EIB 210 may collect information about energy consumption and generation and/or energy flow continuously or within a specific time period. In a disclosed embodiment, the EIB 210 controls the associated devices and adapters 250, 252, 254, 256, and 258 in order to provide energy cost efficiency and efficiency for users interacting with the devices and adapters 250, 252, 254, 256, and 258. Improve productivity while meeting the SLA and QoS requirements established for the energy storage Internet architecture 200 and/or the intelligent system 205. In a disclosed embodiment, the EIB 210 communicates with the associated devices and adapters 250, 252, 254, 256, and 258 through the internal network gateway 220. In one embodiment, EIB 210 is implemented on a hardware computing device. In other embodiments, the EIB 210 may be implemented as a virtual layer (for example, a virtual machine) on the NE configured in the network, or implemented by commodity software integrated on the NE.

[0037] In various embodiments, the internal network gateway 220 is a gateway through which a device in the intelligent system 205 associated with the EIB 210 communicates with the EIB 210. In one embodiment, the internal network gateway 220 is connected to devices in a single network. In one embodiment, the internal network gateway 220 is connected to devices in multiple networks. In various embodiments, the external network gateway 230 is a gateway through which a device external to the smart system 205 (such as the external EIB 280) is connected to the EIB 210.

[0038] In various embodiments, the electronic device 250 is any device that consumes energy (for example, a personal computer, a notebook computer). In a disclosed embodiment, the electronic device 250 can be configured to have real-time control requirements or no real-time control requirements. In various embodiments, the energy generating device 252 is any device (eg, a generator) that has the potential to generate energy. The energy generating device 252 can be configured to have constant data flow requirements. In various embodiments, the energy storage device 254 is any device (eg, a battery) that has the potential to store energy. The energy generating device 254 can be configured to have real-time control requirements. In various embodiments, the smart device 256 is connected to other devices or networks via different wireless protocols, and these smart devices can operate interactively and autonomously to a certain extent (for example, a smart phone/tablet). In various embodiments, the Wi-Fi adapter 258 is a device that adds wireless connectivity to another device. In various embodiments, the devices and adapters 250, 252, 254, 256, and 258 are coupled to the internal network. In other embodiments, the devices and adapters 250, 252, 254, 256, and 258 are coupled to multiple internal networks.

[0039] In various embodiments, EIB 210 collects information about energy consumption (for example, energy data consumption) and/or data consumption from multiple devices and adapters (such as devices and adapters 250, 252, 254, 256, and 258). For example, Internet data consumption) data of any device, and use the collected data to provide a set of control commands to the device. ΕΙΒ 210 receive

And store the energy usage and user personalized data of the devices and adapters 250, 252, 254, 256, and 258 in the associated smart system 205. The EIB 210 analyzes the collected data and controls the devices and adapters 250, 252, 254, 256, and 258 by applying machine learning and model-based predictive control technology to the collected data. For example, the EIB 210 may control the power supply of the electronic device 250 (for example, whether the device is turned on or off) and/or the charging or discharging of the energy stored on the energy storage device 254. In one embodiment, EIB 210 is used to optimize the energy costs of controlled devices and adapters 250, 252, 254, 256, and 258. In various embodiments, the user personalization data includes the settings of controlled devices and adapters 250, 252, 254, 256, and 258 in the smart system 205 that the user can define. User personalization data may also include recorded human presence data about the user's presence in the smart system and about participation when the user interacts with devices in the smart system 205 (such as devices and adapters 250, 252, 254, 256, and 258). The time period and length of human activity data.

[0040] In various embodiments, EIB 210 is used to increase the productivity of users interacting with controlled devices and adapters 250, 252, 254, 256, and 258. The EIB 210 can learn the habits of users interacting with the devices and adapters 250, 252, 254, 256, and 258 in the associated intelligent system 205, and can exchange human presence data and human activity data with other external EIB 280 in the energy storage Internet architecture 200. The EIB 210 can use locally collected human presence data and human activity data (that is, using the assigned smart system 205 and/or received from the external EIB 280) to predict behavior and schedule the devices and devices in the associated smart system 205. Performance and activity of adapters 250, 252, 254, 256, and 258. In addition, the EIB 210 can detect anomalies (for example, energy theft) by monitoring the energy and data flow in the smart system 205.

[0041] In a disclosed embodiment, the EIB 210 communicates with other external EIB 280 in the energy storage Internet architecture 200 through the external network gateway 230 in a peer-to-peer manner. In each embodiment, the EIB 210 is configured on the internal network, and the external EIB 280 is configured on the external network. The external EIB 280 is basically similar to the EIB 210 and the EIB 115, 125, and 135.

[0042] In various embodiments, the EIB 210 may be coupled to a plurality of display devices 240. The display device 240 may be associated with a subnet including devices within the intelligent system 205 controlled by the EIB 210. In an embodiment, the display device 240 may be an application view on a mobile device or various other display type devices. In various embodiments, the display device 240 provides a user interface that can access and monitor various devices and adapters 250, 252, 254, 256, and 258 that are associated with and controlled by the EIB 210. In an embodiment, one display device 240 may be used to display the read information from the EIB 210, and another display device 240 may be used to display the read information from each sub-network to the smart system 205. In various embodiments, the read information includes the status of the managed device received from the external EIB 280, scheduled tasks, request status, and history. The sub-network may include a subset of devices and adapters (such as devices and adapters 250, 252, 254, 256, and 258) within the intelligent system 205.

[0043] FIG. 3 is a schematic diagram of an embodiment of a NE 300 for implementing EIB (such as EIB 115, 125, 135, 210, and 280) in a network including smart systems (such as smart systems 110, 120, 130, and 205). The NE 300 may be implemented in a single node, or the functions of the NE 300 may be implemented in multiple nodes. Those skilled in the art should be aware that the term NE includes equipment in a broad sense, and NE 300 is only one example among them. The included NE 300 is for clarity of presentation, and is not intended to limit the application of the present invention to a specific NE embodiment or a certain type of NE embodiment. At least some of the features/methods of the present invention are implemented in a network device or component (such as NE 300). For example, the features/methods of the present invention can be implemented by hardware, firmware, and/or software installed and running on hardware. The NE 300 is any device that transmits messages through the network, such as switches, routers, bridges, servers, and clients.

[0044] As shown in FIG. 3, the NE 300 may include a transceiver (Tx/Rx) 310, which may be a transmitter, a receiver, or a combination of both. The Tx/Rx 310 is coupled to multiple downstream ports 320 (for example, Downstream interface) to transmit messages and/or receive messages from other nodes, Tx/Rx 310 is coupled to multiple upstream ports 350 (for example, upstream interfaces) to transmit messages and/or from other nodes

The node receives the message. The processor 330 is coupled to the Tx/Rx 310 to process the message and/or determine which nodes to send the message to. The processor 330 may include one or more multi-core processors, and/or a memory 332, used as data storage, buffer, random access memory (Random Access Memory, RAM for short), read only memory (ROM for short) Wait. The processor 330 may be implemented as a general-purpose processor, or as a part of one or more application specific integrated circuits (ASIC for short) and/or digital signal processing (DSP for short). The processor 330 includes an EIB module 334, and the EIB module 334 implements at least some of the methods discussed herein, such as the methods 500 and 600 described below. In an alternative embodiment, the EIB module 334 is implemented as instructions stored in the memory 332, executed by the processor 330, or partially implemented in the processor 330 and partially implemented in the memory 332, for example, A computer program product in non-transitory memory that implements instructions. In another alternative embodiment, the EIB module 334 is implemented on a separate NE. Downstream port 320 and/or upstream port 350 may contain electronic and/or optical transmission and/or reception components.

[0045] It can be understood that by programming and/or loading executable instructions to the NE 300, at least one of the processor 330, the EIB module 334, the Tx/Rx 310, the memory 332, the downstream port 320, and/or the upstream port 350 will Changes occur, thereby partially transforming the NE 300 into a specific machine or device with the novel functions described in the present invention, such as a multi-core forwarding architecture. It is very important for the fields of electrical engineering and software engineering to convert the functions that can be realized by loading executable software to the computer into hardware according to well-known design rules. Whether to implement a certain concept in software or hardware usually depends on the consideration of the stability and quantity of the design of the units to be produced, rather than considering any issues involving the conversion from software domain to hardware domain. Generally, designs that change frequently can be implemented in software first, because reprogramming hardware implementation is more expensive than reprogramming software design. Generally, a design with good stability and mass production is more suitable for implementation in hardware such as ASICs, because mass production operation makes hardware implementations cheaper than software implementations. Generally, a design can be developed and tested in the form of software, and then transformed into an equivalent hardware implementation in an application specific integrated circuit according to recognized design rules, in which the hardware is used to control the instructions of the software. In the same way, the new ASIC-controlled machine A device is a specific machine or device. Similarly, a computer that has been programmed and/or loaded with executable instructions can also be regarded as a specific machine or device.

[0046] FIG. 4 is a schematic diagram of an embodiment of a data and energy storage Internet architecture 400 including a smart system 405 and an external EIB 410. Intelligent system 405 includes EIB 420 and controlled device 430_oThe EIB 420 includes a P component 422 and a C component 424. The data and energy storage Internet architecture 400 is basically similar to the data and energy storage Internet architectures 100 and 200. The intelligent system 405 is basically similar to the intelligent systems 110, 120, 130, and 205. External EIB 410 and EIB 420 are basically similar to EIB 115, 125, 135, 210 and 280 and NE 300. The controlled device 430 is basically similar to the devices and adapters 250, 252, 254, 256, and 258.

[0047] In an embodiment, the P component 422 reads the internal data input from the controlled device 430 and the external data input from the external EIB 410, updates the internal system model, and sends the updated model data to the C component 424. The internal and external inputs may include energy usage and user personalization data obtained from various sources such as the controlled device 430 and external EIB 410. The C component 424 receives the updated model data and determines the control function (ie, control command) of the controlled device 430 in the associated intelligent system 405 according to the optimization goal through a dynamic human-centered cost function. The optimization goal is the predetermined expected result to be achieved by the intelligent system, such as reducing cost and/or optimizing productivity.

[0048] In various embodiments, the EIB 420 may define and divide a certain time period (for example, 24 hours) into multiple time slots, and schedule a command function for the controlled device 430. In an embodiment, the C component 424 can send the determined control function to the controlled device 430 through the internal network gateway (for example, the internal network gateway 220), or can adjust the device for a specific time slot.

The degree command function is updated. The P component 422 and C component 424 will be described in more detail in other figures below.

[0049] In various embodiments, the P component 422 and the C component 424 may be in a network device or module such as NE 300, personal computer (PC) and/or mobile equipment (such as smart phones, tablet computers, etc.) In the realization. The features and/or methods implemented by the EIB 420 may be implemented using hardware, firmware, and/or software installed on a network device or module.

[0050] In an embodiment, the dynamic human-centered cost calculation formula is expressed as:

[0051] Cost_function = cl*Cost_Function (Device_Status) +c2*Productivity_Function

[0052] (Device_Status).

[0053] The productivity formula is expressed as:

[0054] Productivity_Function=Function{wl*sl*Noise_Function (Device_Status); +

[0055] w2*s2*Cool_Function (Device_Status) +

[0056] w3*s3*Warm_Function (Device_Status) +

[0057] w4*s4*Fresh_Function (Device_Status) +

[0058] w5*s5*Humidity_Function (Device_Status)},

[0059] Among them, w1 to w5 are based on user input. If no input is provided, they are set as default values; si to s5 are based on human behavior analysis dynamically updated according to human behavior; Noise_Function, Cool_Function, Warm_Function, Fresh_Function, and Humidity_Function use existing The function of the regression method training. In an embodiment, the input of each function (Female nNoise_Function>Cool_Function>Warm_Function>Fresh_Function^n/ii!<Humidity_Function) is the device state, and the output of each function is the intelligent system associated with the EIB using the cost function The comfort of the person interacting with the device. Comfort can be measured from the following aspects: noise, coolness, warmth, air freshness and humidity. In one embodiment, W is the weight of user input, and S is the weight based on analysis. In an embodiment, the cost function may be recursive, as shown above, depending on the real-time energy price and the state of the devices in the intelligent system associated with the EIB using the cost function.

[0060] In various embodiments, the dynamic human-centered cost function includes a human-centered multimodal representation of the weighted sum of the energy cost function and the user productivity function. In one embodiment, the human-centered multimodal representation is a measurable human-centered effect (e.g., human comfort and/or user productivity) and a measurable causal effect (e.g., noise, coolness, warmth, freshness) Degree, humidity, brightness and color preference). In one embodiment, the causal effect may have such an effect on the human-centered effect in this multimodal representation. In one embodiment, the energy cost function represents the operating status of the equipment (such as the controlled equipment 420) in the associated intelligent system (such as the intelligent systems 110, 120, 130, 205, and 405). In an embodiment, the user productivity function represents human comfort and human productivity level, and human comfort and human productivity level are determined by the users appearing in the associated intelligent systems (such as intelligent systems 110, 120, 130, 205, and 405). And/or determined by user measurements that interact with devices in the associated smart system (such as the controlled device 420). The user productivity function may include a noise function, a cooling function, a heating function, a freshness function, and a humidity function. These functions (noise, cooling, heating, freshness, and humidity) provide the corresponding output of the device (for example, noise, cooling, heating, freshness, and humidity). Control and monitoring of operating status. In one embodiment, the dynamic human-centered cost function integrates multiple representations and functions with corresponding causal relationships to link the energy cost function with the human-centered multimodal representation/function. Therefore, it provides a mechanism to increase human productivity using less energy/energy costs.

[0061] The EIB 420 is derived from an internal source such as the controlled device 430 (ie, a source within the intelligent system 405) and an external EIB 410, etc.

External sources (ie, sources not within the smart system 405) receive various data inputs. Examples of data input include data sources and system requirements including renewable energy supply, energy storage status, data stream SLA, energy consumption data, renewable energy, weather information, and owner QoS requirements. Data input may also include commands from a user interface displayed on, for example, a display (such as display 240) or a smart device (such as smart device 256). The data input may also include measured human activity for the controlled device within the associated smart system and/or calendar data associated with the controlled device. The data input may include data received from Internet data consuming devices and energy consuming devices. The data entry can also include updated price information.

[0062] The data input received from the controlled device 430 and the external EIB 410 is fed back to the P component 422 as data points. The P component 422 processes and models the various data points provided, and feeds the results back to the C component 424°C The component 424 determines the control function of the controlled device in the associated intelligent system based on the modeled data, and feeds back the decision (for example, control parameter) to the various controlled devices 430 controlled by the EIB 420, such as the electronic device 250, Energy generation equipment 252 and energy storage equipment 254. For example, the EIB 420 may receive command internal input (for example, from a user interface), and may forward the received command to the P component 422, and then the P component 422 may forward the result of the command and model data to the C component 424. In other embodiments, the EIB 420 can directly forward the command to the C component 424° C. The component 424 processes the received data (for example, the command or the modeling result), and then can send a control signal to the corresponding device to change or change the device. The act of directing energy from a specific device to a distance or from a distance to a specific device. In various embodiments, the controlled device feeds back the status update to the C component 424. When the C component 424 determines that various command functions are to be sent to the controlled device 430, the status update can be used to use the data.

[0063] In an example, the EIB 420 may receive a command from the user interface for the electronic device M (eg, the electronic device 250) that does not require real-time control to activate the control decision variable associated with the electronic device M. For example, the specific control may be to turn on or turn off the device, send the stored or generated energy to the destination, or schedule the increased power consumption on the device for a period of time. This command is forwarded by the EIB 420 to the C component 424, where the control decision variable associated with the electronic device M is activated. Then, the C component 424 sends a corresponding control command to the electronic device M corresponding to the activated control decision variable. In various embodiments, control decisions for various devices (such as those included in the controlled device 430) are mapped to specific controls on the device. A control decision variable can be established for each controlled device 430 in the intelligent system 405. Therefore, the EIB 420 can control various mapping functions on the controlled device by manipulating the mapped control decision variables.

[0064] In another example, the EIB 420 may receive a command from the user interface for the electronic device N that requires real-time control (eg, the electronic device 250) to update the model associated with the electronic device N and activate the electronic device N Associated control decision variables. The command is forwarded by the EIB 420 to the P component 422, where the model associated with the electronic device N is updated, and the result is forwarded to the C component 424. Then, the C component 424 activates the control decision variable associated with the electronic device N. Then, the C component 424 sends a corresponding control command to the electronic device N corresponding to the activated control decision variable. The commands in the above examples can also be used for energy generation devices (such as energy generation device 252) or energy storage devices (such as energy storage device 254). In yet another example, the EIB 420 may receive a command from the user interface to update the monitored human activity. The command is forwarded by the EIB 420 to the C component 424, where the cost function and constraints associated with the monitored human activity are updated. The C component 424 can then send control commands to any devices affected by the updated cost function and/or constraints.

[0065] FIG. 5 is a schematic diagram of an embodiment 500 of a P component 520 implemented on an EIB (such as EIB 115, 125, 135, 210, 280 and 420 and NE 300). The P component 520 constantly checks for commands or data from the input 510. The input 510 may be received from an internal source (such as the controlled device 430) and/or from an external source (such as external EIB 280 and 410). The P component 520 is basically similar to the P component 422. The P 520 includes an online calculation component 530 and a batch analysis component 540. The batch analysis component 540 includes humans

Behavior analyzer component 542, data and energy flow analyzer component 544, and predictor component 546. In an embodiment, the online computing component 530 updates in real time the control parameters associated with the models for the devices controlled by the EIB (such as devices and adapters 250, 252, 254, 256, and 258), and sends the results to the C component 550 . The C component 550 is basically similar to the C component 424.

[0066] The batch analysis component 540, the human behavior analyzer component 542, the data and energy flow analyzer component 544, and the predictor component 546 collect data received from the input 510 and perform calculations on various models at a defined time The analysis is applied to various models in the paragraph. The calculation result is sent to the C component 550 at a determined time interval.

[0067] In an embodiment, the human behavior analyzer component 542 collects data about human interactions with various devices controlled by the EIB. Such interaction may include a user's interaction with a specific device for a day and a few hours and/or a combination of interactions with the device within a specific time interval.

[0068] In an embodiment, the data and energy flow analyzer component 544 collects data about the energy and data flow of various devices controlled by EIB, and the amount of energy consumed and/or generated by the device in a specific time period . In an embodiment, the predictor component 546 uses existing predictive algorithms to estimate future transactions of different types of information, and can analyze the energy consumption and generation of various inputs. Predictive algorithms can be applied to collected and received personalized data and energy pattern data. Further, the predictor component 546 updates the charging model of the devices in the associated smart system. In one example, the charging model can be set to "x'(t+1) = xt+ut-qt", where χ is the inventory level of the energy storage device (such as batteries), t is the specific time, and xt is The inventory level of energy storage devices (such as batteries) at time t, ut is the amount of energy charged to the energy storage device at time t, and qt is the amount of energy discharged from the energy storage device at time t. The predictor can update the charging model based on the input of SLA, QoS, energy storage status, and renewable energy supply.

[0069] FIG. 6 is when the human behavior analyzer component receives input, the human behavior analyzer component (such as the human behavior analyzer component 542) of an EIB (such as EIB 115, 125, 135, 210, 280 and 420 and NE 300) A flowchart of an embodiment of the method 600 implemented in ). In step 610, the human behavior analyzer component checks information input from various internal sources (such as the controlled device 430) and external sources (such as external EIB 280 and 410). In the decision step 620, if there is a direct input from a user interface displayed through a display (such as the display 240) or a smart device (such as the smart device 256), the human behavior analyzer component goes to step 635. In the decision step 620, if there is no direct input from the user interface, the human behavior analyzer component goes to step 630. In the decision step 630, if there is an input from the calendar associated with the user who can interact with the controlled device in the associated intelligent system, the human behavior analyzer component goes to step 635. In the decision step 630, if there is no input from the calendar, the human behavior analyzer component goes to step 640. In step 635, the human behavior analyzer component schedules the task to compare the time input received through the user interface or the time input read from the calendar. At the appropriate time point, update the cost function managed by the C component (such as C components 550 and 424), and go to step 690. In step 640, the human behavior analyzer component connects and interacts with the Wi-Fi router, and reads the current traffic. In step 650, the human behavior analyzer component estimates the number of occupants using the associated smart system (such as smart systems 110, 120, 130, 205, and 405) based on the number of Wi-Fi connections, and based on the Wi-Fi router Traffic inference activities. In the decision step 660, if the inferred activity needs to be updated to the behavior model associated with the human behavior observed in the intelligent system, the human behavior analyzer component proceeds to step 670. In step 670, the human behavior analyzer component sends a request to the C component to update the cost function based on the update of the human behavior model, and moves to step 690. In step 690, the human behavior analyzer component integrates the received information into the behavior model associated with the intelligent system, and sends an update to the C component.

[0070] FIG. 7 shows the data and energy flow analyzer components (such as data and energy flow analyzers) in an EIB (such as EIB 115, 125, 135, 210, 280 and 420 and NE 300) when the data and energy flow analyzer components receive input. A flowchart of an embodiment of the method 700 implemented in component 544). In step 710, the data and energy flow analyzer components check to

Information input from various internal sources (such as controlled device 430) and external sources (such as external EIB 280 and 410). In decision step 720, if an input is received from Internet data consuming devices (such as devices 250 and 252), the data and energy flow analyzer component proceeds to step 725. In the decision step 720, if there is no input from the Internet data consuming device, the data and energy flow analyzer component proceeds to step 730. In step 725, the data and energy flow analyzer updates the Internet data consumption pattern with the received input, and proceeds to step 740. The Internet data consumption pattern is an internal model that contains historical data about Internet data consumed by devices in the associated smart system. In the decision step 730, if there is no input from the energy consuming device, the data and energy flow analyzer ends the process. In the decision step 730, if there is an input from the energy consuming device, the data and energy flow analyzer proceeds to step 735. In step 735, the data and energy flow analyzer updates the energy consumption pattern with the received input, and proceeds to step 740. The energy data consumption pattern is an internal model that contains historical data about the energy consumed by the devices in the associated smart system. In step 740, the data and energy flow analyzer checks the maximum energy to data consumption ratio based on the time theoretical data model within the determined time period. In decision step 750, such as If the energy to data consumption ratio exceeds a certain threshold, the data and energy flow analyzer proceeds to step 760. In one embodiment, the ratio is set and changed by a command received by a user interface displayed on a display (such as the display 240) and/or a smart device (such as the smart device 256). In the decision step 750, if the energy to data consumption ratio does not exceed the threshold, the data and energy flow analyzer ends the process. In step 760, the data and energy flow analyzer sends an alarm that the ratio of data to energy consumption exceeds the ratio of the data to the energy consumption ratio to the user interface displayed on the display (such as the display 240) and/or the smart device (such as the smart device 256). In decision step 770, if the user interface returns an error, the data and energy flow analyzer proceeds to step 780. In decision step 770, if the user interface returns an error, the data and energy flow analyzer ends the process. In step 780, if the user interface returns an error, the data and energy flow analyzer updates the threshold and proceeds to step 740.

[0071] FIG. 8 is a method implemented in the predictor component (such as the predictor component 546) of an EIB (such as EIB 115, 125, 135, 210, 280 and 420 and NE 300) when the predictor component receives input A flowchart of an embodiment of 800. In step 810, the predictor component checks information input from various internal sources (such as the controlled device 430) and external sources (such as the external EIB 410), and proceeds to decision step 820, decision step 830, and decision step 840. In decision step 820, if the input data is not energy consumption data, the process ends. In the decision step 820, if the input data is energy consumption data, the predictor component proceeds to step 822. In step 822, the predictor component executes the prediction algorithm to estimate the energy consumption of the next time slot, and proceeds to step 825. In one embodiment, the prediction algorithm includes time theoretical data. In step 825, the predictor component sends the update determined by the prediction algorithm to the external EIB (such as the external EIB 280) connected to the EIB through the external gateway (such as the external network gateway 230), and proceeds to step 860. In decision step 830, if the data input is not the amount of renewable energy, Then the process ends. In decision step 830, if the input data is the amount of renewable energy, the predictor component proceeds to step 835. In step 835, the predictor component sends a request to the C component (such as the C components 424 and 550) to update the cost function, and proceeds to step 860. In decision step 840, if the input data does not include weather information, the process ends. In decision step 840, if the input data includes weather information, the predictor component proceeds to step 845. In step 845, the predictor component compares the historical data of the resources in the associated intelligent system, and based on the historical data of the past few days and current weather data, predicts the associated intelligent system for various devices in the associated intelligent system The future value of energy consumption and/or generation within. The predictor component then proceeds to step 860. In step 860, the predictor component integrates the received information, sends updated data model data to the C component, and ends the process.

[0072] FIG. 9 is a flowchart of an embodiment of a method 900 implemented on an EIB (such as EIB 110 and 320 and NE 200) when input data is received or sent to an external EIB (such as external EIB 280 and 410) , In the energy storage Internet architecture (such as the energy storage Internet architecture 100, 200 and 400), the external EIB passes through the external gateway (such as the external network gateway

230) Connect with EIB. In step 910, the EIB checks information input from various internal sources (such as the controlled device 330) and external sources (such as the external EIB 410), and proceeds to decision step 920, decision step 930, and decision step 940. In the decision step 920, if the data input is not the estimated energy consumption data received from the predictor component (such as the predictor component 546), the process ends. In the decision step 920, if the data input is the estimated energy consumption data received from the predictor component (such as the predictor component 546), the EIB proceeds to step 930. In decision step 930, if the estimated energy consumption data received from the predictor component is negative, the EIB proceeds to step 932. In decision step 930, if the estimated energy consumption data received from the predictor component is positive, the EIB proceeds to step 934. In step 932, since the energy consumption of the associated device is negative (for example, the power consumed by the device exceeds the power it is receiving and/or generating), the EIB sends a charging request to the external EIB. In decision step 934, if the total charging request of the associated smart system is greater than the total discharging request (for example, the smart system has remaining energy), the EIB proceeds to step 936. In step 936, the EIB sends a discharge request to the external EIB. Decision Step 950 If the data input is not charging or discharging confirmation, the process ends. In decision step 950, if the data input is charging or discharging confirmation, EIB proceeds to step 970. In decision step 960, if the data input is not for new price information, the process ends. In decision step 960, if the data input is for new price information, the EIB proceeds to step 970. In step 970, EIB integrates the charging or discharging confirmation and/or new price information, and updates it to the C component (such as C components 424 and 550).

[0073] FIG. 10 is a schematic diagram of an embodiment 1000 of a C component 1020 implemented on an EIB (such as EIB 115, 125, 135, 210, 280 and 420 and NE 300). The C component 1020 is basically similar to the C components 424 and 550. The C component 1020 includes an online monitoring component 1032, a variable filter 1034, and an update algorithm component 1036, which is used to recursively update the costs associated with EIB-controlled intelligent systems (such as intelligent systems 110, 120, 130, 205, and 405) And state functions. In various embodiments, the online monitoring component 1032 monitors system dynamics and user input updates, the variable filter 1034 selects coefficients for the adaptive filter, and the update algorithm component 1036 updates the adaptive filter when new data arrives. The C component 1020 receives input from the P component 1010 and sends output to the electronic equipment, energy storage and energy generation 1040 controlled by the EIB. The P component 1010 is basically similar to the P component 422. The electronic device, energy storage and energy generation 1040 are basically similar to the electronic device 250, the energy storage device 254, and the energy generation device 252. In various embodiments, the C component 1020 generates output based on an online learning algorithm and a pattern detection method. Online learning algorithms can recursively find the filter that minimizes the weighted linear square cost function coefficient. Pattern detection methods monitor energy and data flow patterns, and report alarms to assigned recipients when abnormalities occur. In one example, each cost function can be expressed as a linear or non-linear function of a decision variable (eg, control output). However, online calculation algorithms can be used to estimate the parameters in the cost function.

[0074] FIG. 11 is a flowchart of an embodiment of a method 1100 implemented in an EIB (such as EIB 115, 125, 135, 210, 280 and 420 and NE 300) when input data is received from the user interface. In step 1110, the EIB checks information input from various internal sources (such as the controlled device 430) and external sources (such as external EIB 280 and 410). In the decision step 1120, if the input is a direct input from a user interface displayed on a display (such as the display 240) or a smart device (such as the smart device 256), the EIB proceeds to step 1130. In step 1130, the EIB updates the cost function associated with the associated intelligent system with the input. In an embodiment, the update to the cost function may include user preferences, where the user preferences are coefficients or implicit coefficients in the cost function.

[0075] FIG. 12 is when an EIB (such as EIB 115, 125, 135, 210, 280, and 420, and NE 300) from a remote EIB (such as the energy storage Internet architecture 100, 200 and 400) within the data and energy storage Internet (such as the energy storage Internet architecture 100, 200 and 400) The EIB A in the disclosed embodiment is a flowchart of an embodiment of the method 1200 implemented by the EIB in the data and energy storage Internet architecture when a friend request is received. In step 1210, the local EIB receives a friend request from the remote EIB. In decision step 1220, if the local EIB refutes

Reply/reject the friend request, then the process ends. In decision step 1220, if the local EIB confirms/accepts the friend request, the local EIB proceeds to step 1230. In step 1230, the local EIB sends a response confirming the friend request to the remote EIB. In decision step 1240, if the local EIB receives human presence data from the remote EIB, the local EIB proceeds to step 1250. In decision step 1240, if the local EIB does not receive human presence data from the remote EIB, the local EIB proceeds to step 1260. In various embodiments, the human presence data is information collected by EIB (such as EIB 115, 125, 135, 210, 280, and 420, and NE 300) about the interaction of one or more people with devices in the associated intelligent system . In step 1250, the local EIB stores the received human presence data. In decision step 1260, if the local EIB determines not to send local human presence data to the remote EIB, the process ends. In decision step 1260, if the local EIB determines to send human presence data (ie, local human presence data) related to the interaction between the user and the assigned intelligent system device to the remote EIB, the local EIB proceeds to step 1270. In step 1270, the local EIB sends local human presence data to the remote EIB.

[0076] Figure 13 is when EIB (such as EIB 115, 125, 135, 210, 280 and 420 and NE 300) initiates a charge or discharge request, data and energy storage Internet (such as energy storage Internet architecture 100, 200 and 400) A flowchart of an embodiment of a method 1300 implemented by the EIB within. In step 1310, EIB initiates a charge or discharge request. In various embodiments, a charging request is a request for energy from a device in an associated smart system (for example, smart systems 110, 120, 130, 205, and 405); a discharging request is a request from a device in the associated smart system Notification of the remaining energy transferred. In step 1320, the ELB determines the friend list based on the analysis of the stored human presence data received from other ELBs that have been accepted by the ELB as a friend connection. In an embodiment, the buddy list is sorted based on the established EIB buddy connections that are most likely to satisfy the request, which is based on an analysis of the received human presence data. For example, the EIB may determine that the friend EIB may have additional energy capacity to satisfy the charge based on the possibility that the user of the smart system associated with the friend EIB does not use the energy provided to/provided by the associated smart system. request. In one embodiment, the value is set to satisfy the request Find the determined possibility of the established friend connection EIB, and then sort the elements in the friend list according to the value. In the decision step 1330, if the determined friend list is empty, the EIB proceeds to step 1335. In decision step 1330, if the determined friend list is not empty, EIB proceeds to step 1340. In step 1335, the ELB broadcasts a charging or discharging request to all ELBs in the data and energy storage Internet, and proceed to the decision step 1350. In an alternative embodiment, in step 1335, the EIB only broadcasts a charging or discharging request to the EIB that has established a friend connection in the data and energy storage Internet, and proceeds to the decision step 1350. In step 1340, the EIB sends charging or discharging to the EIB in the ordered list one at a time, until the confirmation is received or a certain period of time has elapsed after the message. In decision step 1350, if a confirmation is received, EIB proceeds to step 1360. In decision step 1350, if no confirmation is received, the process ends. In one embodiment, if the charge or discharge request is broadcast, when the first confirmation is received, the EIB proceeds to step 1360, and the subsequent confirmation received by the EIB is ignored. In step 1360, the EIB schedules the charging or discharging corresponding to the requested type for the EIB sending the confirmation.

[0077] In an embodiment, the EIB (such as EIB 115, 125, 135, 210, 280, and 420 and NE 300) implemented in the data and energy storage Internet architecture (such as the energy storage Internet architecture 100, 200, and 400) can be determined A buddy list to start a charge or discharge through the buddy index algorithm. The buddy index algorithm sorts all EIBs that have established buddy connections according to the owner's presence information of the next time slot T received from each agent. In an embodiment, the buddy index algorithm determines which of the EIBs that have been established as a buddy connection potentially has the most available resources and ranks the EIBs accordingly. In an embodiment, the friend index algorithm can be expressed as:

[0078]

Frio nd Indexing (present during lime slot)

Fricnd_Indcxing_Algorilhm(Fric nd _Sc() if sizc(FricndScl) == 1 rcturn F"icnd_Sc( else read prose nee dal a for FricndSci in the next T time slot and save to Presence matrix;

ίbr i = 1: sizc(Fricnd_Set)

Friend index = T-num(zeros in Prcscnccii, :));

end sori FricndSci in descending order of Friend J ndex;

[0079] where_?Friend_Set includes all EIBs that have been established as friend connections; Presence Matrix is a 0, 1 matrix indicating the existence of each time slot; T and the length of the time slot are parameters that can be configured according to the system settings; Friend_Set sorting checks each The presence information of each row of the friends presence matrix is calculated and the index number is calculated.

[0080] In an embodiment, the EIB (such as EIB 115.125.135.210.280 and 420 and NE 300) implemented in the data and energy storage Internet architecture (such as the energy storage Internet architecture 100.200 and 400) may be a credit scheme based on points Determine whether to exchange energy in an EIB that is not connected to a friend. In one embodiment, the points-based credit scheme used by EIB (such as EIB 115, 125.135.210.280 and 420 and NE 300) is expressed as the following algorithm:

[0081]

Encrgy_Trading_Algorithm(Ch_Broker., Dis Brokcr., Ch Amounl., Dis_Amount) //Checks ifhavc enough points il'Poinl (Dis_Brokcr)+Point_Algorilhm_Ncgalivc(Dis_Amouni, I) <0 to check the eligibility requests from EIB, which discharge . report "ineligible^: /7 sort the discharge requesting brokers in descending order of poims

SDisBrokcr = Sorl_Dcsccnl_Ordcr(Point (Dis_ Broker));

loop i =1: size (S Dis Brokcr) // assign charge requesting brokers to discharge requesting brokers // update points inibnnation of requesting fu! filled brokers find min(Ch AmounlQ)) where min(C?h_Amounl(j))> = Dis Amounl(i);

assign Ch Brokcr(j) to S_Dis_Brokcr(i);

Point (ChBrokcr(j)) = PointAIgoriLhmPositvc (ChAinounl(j), L) +

Poini(Ch_ Broker(j));

Point (Dis_Broker(i)) = PointAlgorithinNcgativc (Dis_Amount(i), t) ten

Point(Dis_Brokcr(i));

delete Ch Brokcrij);

exit loop ilCh Brokcr is empty

Point Algorithm Posilivc (Ch Amount, l) return weight (l)startCh_Amount waterPoinl Unil;

Point_Algorilhm_Ncgalivc(Dis_AmounL t) return wcighl (l)waterDis Amounl Poinl Unit:

[0082] where_?Ch_Broker is a list of EIBs that require energy injection into other EIBs; Ch_Amount is a list of the value of the corresponding charge; Dis_Broker is a list of EIBs that require energy to be released from other EIBs; Dis_Amount is a list of the values of the corresponding release; the parameter (Ch_Broker>Dis_Broker> Ch_Amount and Dis_Amount) is a matrix containing the list of EIB requesting energy exchange within a period of time; Point_Algorithm_Negative calculates the points that need to be deducted from the existing points of the discharge request EIB; Point_Algorithm_Positive calculates the points that need to be issued to the charging request EIB; weight (t) Is the weight function, and its value is between 0.5 and 1]1.5; when t is the peak of power consumption.weight (t) is close to 1.5; when t is the trough (ie, the lowest point) of power consumption.weight (t) Close to 0.5ο

[0083] FIG. 14 is a method 1400 adopted by EIB (such as EIB 115.125.135.210.280 and 420 and NE 300)

A flowchart of an exemplary embodiment of the EIB, which is implemented in data and energy storage Internet architectures (such as energy storage Internet architectures 100, 200, and 400) to collect energy data, human presence data, and human activity data, which are associated with The future energy consumption requirements and energy of the equipment in the intelligent system are used to generate and transmit the control command set of the equipment in the associated intelligent system EIB, and to determine and establish a friend connection with an external NE in the energy storage Internet architecture. In step 1410, the EIB collects energy data about energy flow through multiple devices in the smart system associated with the NE in a specific time period, and the amount of energy consumed by the equipment in the specific time period. And the amount of data and the amount of energy and data generated by the device in the specific time period; in step 1420, the EIB collects human presence data about the user in the intelligent system during the specific time period. In step 1430, the EIB collects human activity data on the user's input to the device and the user's interaction with the device in the intelligent system within the specific time period. In step 1440, the EIB predicts the future energy consumption demand and energy generation of the smart system by applying a prediction algorithm to and analyzing the collected energy data, human presence data, and human activity data. In step 1450, The EIB generates a set of control commands for devices in the intelligent system based on predicted future energy consumption demand and energy generation applied to a dynamic human-centered cost function associated with the intelligent system. In step 1460, the EIB transmits the control command set to the corresponding device in the intelligent system. In step 1470, the EIB transmits the control command set to the corresponding device in the intelligent system. In step 1480, the EIB transmits the control command set to the corresponding device in the intelligent system. In step 1490, the EIB transmits the control command set to the corresponding device in the intelligent system.

[0084] Although there are multiple specific embodiments of the present invention, it should be understood that the disclosed system and method may also be embodied in other specific forms without departing from the spirit or scope of the present invention. The examples of the present invention should be regarded as illustrative rather than restrictive, and the present invention is not limited to the details given herein. For example, various elements or components may be combined or integrated in another system, or certain features may be omitted or not implemented.

[0085] In addition, without departing from the scope of the present invention, the discrete or separate technologies, systems, subsystems and methods described and illustrated in the embodiments can be combined or combined with other systems, modules, technologies or methods. integrated. Other items shown or discussed as being coupled or directly coupled or communicating with each other may also be indirectly coupled or communicating via a certain interface, device, or intermediate component in an electrical, mechanical, or other manner. Other changes, substitutions, and replacement examples are obvious to those skilled in the art, and they do not depart from the spirit and scope disclosed herein.