Device and process for processing sheet articles such as bank notes

IS 2 171 685 T3

DESCRIPTION

Apparatus and method for processing sheet material, such as banknotes.

The present invention relates to an apparatus and method for processing sheet material such as banknotes.

Document DE 27 60 166 shows such an apparatus built for different units. In a sorter the sheet material present in a stack is sorted sheet by sheet and delivered to a conveying means that transports the sorted sheets through the apparatus.

A series of detector units are mounted along the transport path, each of which detects certain characteristics of the sheet-shaped material and combines them into a measurement result. The structure of the measurement units used is shown in DE-PS 27 60 165. Each of the detector units has a cartridge which detects certain characteristics of the sheet material and converts them into an electrical signal. This signal is transformed into a signal processing stage. The signal, usually analog, is converted at this stage into digital measurement data. The measurement data are finally transformed into information of the type whether or not in an evaluation unit of the detector unit. This information constitutes the measurement result of the unit's detector and is stored in a main memory.

The main memory is used as a connection for data exchange between the units of the device. It can be accessed by all units that write or read the data necessary to process sheet material. In main memory, a data record is stored for several sheets in each case.

From the measurement results of the detector units stored in main memory for each sheet material, an information evaluation is first produced in a central evaluation unit. A decision table stored in the evaluation unit is used to determine, from the evaluation information, the target units for the relevant sheet material.

The target units can be, for example, stackers for stacking sheet material or shredders for shredding the sheet material. The target units for the corresponding sheet material are stored in main memory. With reference to the stored or memorized destination unit, the sheet material is guided accordingly and deposited by the transport unit. After the transport of the laminar material to the destination unit, said destination unit writes positive or negative information regarding the result of the process in the main memory.

The process unit of the apparatus is controlled by a control unit. This unit also has access to the main memory and can control and record the process operation with reference to the deposited information. Furthermore, the control unit serves to start the units of the apparatus according to an operating mode set by the operator. This includes, for example, storing the correct decision table for the selected operating mode in the central evaluation unit.

In the known system, each detector unit can deduce its own measurement result only from the measurement data of the sheet material it has received.

Based on these premises, the invention is based on the problem of providing an apparatus and method for the process of a material in the form of sheets, which allows to improve the quality of the deduction of the measurement result of the detector of the unit.

This problem is solved by the features of claims 1 and 16.

The basic concept of the invention is substantially to deduce a measurement result from the detector unit using data from other detector units with respect to the corresponding sheet material. For this purpose, at least one detector unit is provided with a memory in which data records of a series of sheets of said material can be managed. Each of these data registers is equipped with areas in which data from at least one other detector unit can be stored.

The advantage of the invention is that the detector unit has data from other available detector units which it can take into account when deriving its own measurement result. Knowing these data enables the detector unit to deduce its measurement result from these data faster and more accurately.

The detector unit preferably has a measurement unit and an evaluation unit, the memory of the detector unit being constituted in the evaluation unit. In addition, the measurement results of the detector unit are not restricted to information of the "know or not" type, but are endowed with a higher information content. The measurement results can be, for example, the length or width of the sheet material in millimeters, a dimension figure for dirt, the agreement of the printed image with a reference image, the distance of a metallic thread with With respect to the leading edge of the sheet-like material, an identification number as to the type or position of the sheet material or the like.

Other characteristics and advantages of the invention will be deduced from the dependent claims and from the description of embodiments of the invention with reference to the figures, in which:

Figure 1 shows a schematic diagram of an embodiment of the present invention,

Figure 2 shows a representation of the content in memory in the evaluation unit of a detector,

Figure 3 shows a representation of the content in memory in the central unit of eva2

ES 2 171 685 T3 luacion,

Figure 4 shows a flow chart of a first embodiment of the method of the invention,

Figure 5 shows a representation of the measurement results map in different classes,

Figure 6 shows a representation of the control matrix of the first embodiment,

Figure 7 shows the representation of the alteration conditions for classification classes,

Figure 8 shows a flow chart of a second embodiment of the method of the invention,

Figure 9 shows a representation of the map of the measurement results in overlapping classes with affiliation functions,

Figure 10 shows a representation of the control matrix of the second embodiment,

Figure 11 shows a representation of the affiliation functions of the classification classes,

Figure 12 shows a graphical derivation of an affiliation function resulting from a class of classification,

Figure 13 shows a representation of the resulting affiliation functions,

Figure 1 shows a schematic diagram of an embodiment of the invention. Sheet material is sorted sheet by sheet from a stack in a sorting unit and is supplied to a conveying means that transports the sheets along a certain path by the apparatus and which is controlled by the conveying unit ( 30). The transport path is divided into individual parts, each of them controlled by decentralized sub-units (30.1) - (30.M) of the transport unit (30).

During sorting, each sheet receives an identification (ID) that allows the sheet to be clearly recognized by the units of the apparatus. The data necessary to process a sheet are exchanged using the sheet identification (ID) through connection (100). The connection (100) made the interconnection of both subunits (30.1) - (30.M) and the central evaluation unit (10), a series of detector units (20.1) - (20.N) and the control unit ( 40).

The detector units (20.1) - (20.N) are each made up of the measurement unit (21.1) - (21.N) and the evaluation unit (22.1) - (22.N). Each of the measuring units (21.n) has a transducer that detects certain characteristics of the material in the form of leaves and converts them into electrical signals. These electrical signals are then converted into digital measurement data and can optionally be normalized and / or transformed before further processing. The evaluation unit (22.n) of the detector (20.n) receives the measurement data from the measurement unit (21.n) and uses the measurement data to derive a measurement result.

As a mononym, an evaluation unit (22.n) is provided with a memory whose content is shown in figure 2. The evaluation unit (22.2) has been selected in this case as an example. In the memory of the evaluation unit (22.2) a series of data records can be managed. Each data record is assigned to a sheet with a certain identification (ID). The memory shown is capable of managing a number (L) of data records.

Each data record has an area for external data (ED). Either measurement data (MD) or measurement data (ME) from other detector units are stored there. In the figure

2, for example, the measurement data (MD) from the detector unit (20.3) and the measurement results from the detector unit (20.1) are stored in each data record. For example, the measurement data from the detector unit (20.3) for the sheet with identification (ID) = 2 are designated in this case in the form of (MD.2), the first ondx corresponding to the identification of the banknote. (ID) = 2 and the second ondx to the ondx of the detector unit =

3. The other data are designated anaologously.

The measurement data (MD) provided by the measurement unit (21.2) are preferably also stored in the memory of the evaluation unit (22.2) of each sheet. The evaluation unit (22.2) deduces from its own measurement data (MD) and from the external data (ED) of a data record, the corresponding measurement result (ME) for each sheet, which can optionally be stored in the corresponding data record.

When the measurement result for a sheet has been determined, it is written with the corresponding identification (ID) of the sheet in the data line (100). If necessary, the measurement result can be read by other detector units and can be stored in the memory of the evaluation unit of this detector unit. If the knowledge of certain measurement data from one detector is necessary to deduce the measurement result from another detector unit, the corresponding measurement data must be written in the data line (100), so that the other detector unit can read. Alternatively, the measurement data can be written only after a corresponding signal has been received from the other detector unit.

Furthermore, the apparatus has a central evaluation unit (10) with a memory, the content of which is shown in figure 3. The central evaluation unit (10) reads the measurement results of all detector units (20.1) - (20 .N) from the data line (100) and stores them under the identification (ID) of the corresponding sheet. When the measurement results of all receiving units are known for the identification (ID), the central evaluation unit (10) deduces from the measurement results the classification class (KL) for the corresponding sheet material and writes the Identification (ID) and classification class affiliated (KL) to the data line (100). The classification class (KL) can optionally be stored in memory under the corresponding

ES 2 171 685 T3 sheet identification.

The classification class (KL) is evaluated by the subunits of the transport unit that controls the transport of the sheet to the destination unit. If the corresponding subunit (30.m) is not responsible for the process of the leaf, the latter is passed to the next subunit (30.m + 1). Otherwise, the sheet is guided to the corresponding manipulators of the subunit (30.m) and is processed. After processing the sheet material, the processing unit writes the corresponding positive or negative information about the processing result to the data line (100). This information is read, for example, by the control unit (40) and used to record the process operation.

In addition, each of the subunits (30.m) can write error messages to the data line if, for example, a sheet jam occurs in the transport system of the subunit (30.m). These error messages can be interpreted by other units in the device and appropriate measures can be initiated.

The subunits (30.m) are preferably designed for the purpose of controlling the electrical and mechanical functions of the means of transport. This includes, among other factors, the division of the transport route or path, the actuation of the switches within said transport path, the measurement of the position of the sheet material by means of light barriers, etc. In addition, the subunits (30.m) can also control special electrical or mechanical manipulators within the units of the apparatus. This includes, for example, the control of the sorting components, the stacking wheels and the shredding rollers, etc.

The control unit (40) serves to control and record the process operations on the sheets. It is in a position to send, through the data line (100), a control information that is interpreted in a corresponding way by the individual units. This control information can be used, for example, to put the apparatus in a process situation selected by the operator. Furthermore, the control unit (40) can cause special programs or reference data from the control unit (40) to be stored in the other units of the apparatus through the data line (100). For this purpose, the control unit (40) has mass memories in which this data is managed.

The control unit (40) can monitor and record the process operation on each individual sheet using data from the sub-units (30.1) - (30.M), detector units (20.1) - (20.N) and from the Classification class (KL) of the central evaluation unit (10). During the actual process of the sheet material, the function of the control unit is reduced to monitoring the data line (100).

The data line (100) is realized in the form of a data bus. A CAN bus is the one used preferably. AND^orThis is especially suitable for so-called real-time applications such as those mainly existing in this embodiment. Other data lines 101, 102 may optionally be arranged in parallel to the data line 100 in order to free up the data line 100.

The data line (101) can also be realized by means of a CAN bus and serves to improve the data exchange between the detector units (20.1) - (20.N) and the central evaluation unit (10). This is useful in particular when a lot of measurement data, which frequently has a high data volume, is exchanged between the detector units (20.n).

The data line (102) is used specifically by the control unit (40) for so-called non-real time applications. This may involve, for example, the writing of extensive programs or reference data to the detector units (20) or to the central evaluation unit (10) during the start-up of the apparatus in a given operating state. A connection to the drives (30.m) can also be avoided since the amounts of data transferred to them are generally small.

The class of classification of a sheet can be deduced from the measurement results of the detector units, for example, using freely configurable tables and / or matrices that are managed in a central memory of the evaluation unit (10). During the calculation operation the continuous measurement results are first distributed in classes. Discrete or separate measurement results are assigned directly to a class. The individual classes are combined into one sheet feature with different shapes. A control matrix can be used to associate arbitrary but firmly selected combinations of different ways of a certain volume or quantity of characteristics with a certain class of classification.

Figure 4 shows a flow chart of a first embodiment of the method of the invention for processing sheet material, especially banknotes. The measurement data (MD) of banknotes is collected by detectors (20.n). The measurement data (MD) are used to direct the measurement results (ME) of banknotes that are stored in the evaluation unit (10) according to figure 3.

In the first embodiment, the measurement results (ME) are first classified into separate classes. An example of such classification is shown in figure 5. The measurement result is, in this case, to represent the area of ​​a banknote in square millimeters that is covered by spots. If the measurement result determined in the first measurement (M1) is, for example, 140 mm², this measurement result is entered in the class classification with the class code (4). The number of classes and the position of their limits can be configured optionally. Classes (0) to (5) can be combined in the characteristic "spots". For the sake of clarity, individual classes are often endowed with verbal designations such as "very few," "few," "many," and so on.

Figure 6 shows the control matrix of the

ES 2 171 685 T3 first embodiment. For the individual characteristics “double extraction”, “alteration”, etc., the corresponding classes are indicated for each one with the verbal and class codes. For the sake of clarity, different characteristics are combined in larger groups.

To derive or calculate the banknote classification class, a feature vector is first formed from the classes of all the features. Figure 6 shows by way of example four vectors of classes (V1) to (V4). In each characteristic, the class corresponding to the measurement result of the banknote is precisely marked. For the characteristic “spots”, the measurement result of the sheet material corresponding to the vector (V1) is, for example, the class “few”, while the measurement result of the characteristic “bent corners” is found in the class "very few". The class vector therefore classifies the shape of all the features of a banknote.

The control matrix consists of a number of rules that have been designated in this case with numbers (1) to (5). Each of the norms consists of a norm vector formed from the classes of all characteristics in an analogous way to the class vector. In contrast to the class vector, however, it is possible for a number of classes of a feature to be marked, for example the feature "dirt" in standards (1) to (5). Each of the standards (1) to (5) has a classification class associated with it which has been indicated in this case by the specific classification destinations "stacker 1", "stacker 2", and so on. In general, the same class of classification can be assigned to a series of rules.

Statements of individual standards can be verbally formulated more or less as indicated below. According to the standard (1) the classification class "stacker 1" is assigned to banknotes whose value is $ 50, which are oriented upwards, have all the security characteristics and are clean banknotes with very few defects. . According to the standard (2), the classification class "stacker 2" is assigned to banknotes whose orientation is downwards and which otherwise have the same characteristics as banknotes according to the standard (1 ). The classification class “stacker 3” is assigned to all 1 and 2 banknotes that have at least one correct security thread, are clean and have few defects. The classification class “stacker 4” is assigned to banknotes which, regardless of their value, are clean, have few defects and for which neither the “watermark” characteristic nor the “thread of thread” is correct. safety". The classification class "shredder" is assigned to all banknotes that, regardless of their value and defects, have correct security characteristics and are dirty.

To calculate the classification classes, the marks of the class vector, for example (V1), are compared with the corresponding marks of the norm vectors (1), (2), (3), (4), (5) , successively in this order. The classification class assigned to the first marked norm vector in all classes of the class vector is assigned to the sheet material as the classification class. If there is no standard mark that matches all the marks in the class vector, the sheet material receives an arbitrary but firmly selected sort class.

For the examples in Figure 6, this means that the sheet material for the class vector (V1) is assigned to the classification class "stacker 2". The sheet material for class vector (V2) is assigned to classification class "stacker 4". The sheet material for the class vector (V3) is assigned to the classification class "shredder". Since there is no norm vector mark that corresponds to all the marks in the class vector (V4), this sheet material receives an arbitrary but firmly selected sort class that is designated as "reject."

After the sheet material has received the classification class assignment, it is transported to the corresponding destination unit with reference to the classification class. Sheets with the classification class "reject" are generally stacked in a so-called reject compartment from which they can be removed outside the apparatus for inspection by the operator.

In order to prevent unauthorized alteration of the control matrix rules, each of the classes has a security level (SL) assigned to it. AND^í This can be used to specify which users can make alterations in this class. For example, the value (3) is intended for the "device developer", (2) for the supervisor and (1) for the operator. The operator of the apparatus is allowed to alter the classes of the characteristic "banknote value" while the characteristics of the group "banknote security characteristics" can only be altered by the supervisor.

Furthermore, it is possible to associate the weight (G) at least with some specific classes. The weights (G) can be used, for example, to check the norms in the norms matrix for consistency or to alter the classification class derived from the control matrix if necessary.

As an example, only one possible meaning for the weights (G) of the classes in the group "banknote security features" will be explained. In addition to the two features "watermark" and "security thread" that have been shown, there are generally a number of other features in this group that are omitted for clarity.

To evaluate a banknote, it may be interesting not only to check the individual properties of the security features, but also to carry out the weighting of the individual properties or features against each other in order to distinguish informative features from less informative ones, for example. In this case, for example, the correction of the "security thread" property is qualified with

ES 2 171 685 T3 a higher rating (5) than the “watermark” property correction (3). With a series of said properties, a fine grading of the individual properties or characteristics can be carried out by corresponding weightings.

From the weights of the individual classes in the group "banknote security characteristics", a minimum weighting of each norm can then be determined by adding the weights of the individual phases of each characteristic of the group with the weighting of Smaller or lower mark than the norm. This means, for the example in figure 6, that standards (1), (2) and (5) of the group "security characteristics of a banknote" are assigned a minimum weight of 8. For the standard (3), the minimum weight is 5 and for the norm (4), the minimum weight is 0.

The minimum weight determined in this way for each standard in the group "banknote security characteristics" therefore provides a measure of the security of the banknote. A high minimum weight means high security and a low minimum weight means low security. For a banknote suitable for circulation, the desired security can therefore be defined by a minimum weight determined in the group "security characteristics of the banknote".

The minimum weighting determined for the security of a bank note suitable for circulation will then be determined from the weightings of the characteristic "value". The minimum weighting determined by a standard within the group "security characteristics of a banknote" for banknotes suitable for circulation, is the maximum of the weights of the classes marked by the standard in the property or characteristic "value" . For standards (1), (2), (4) and (5), therefore, a minimum weighting for the security of a banknote suitable for circulation is obtained of 8 and for standard (3) it is 3.

Comparison of the predetermined minimum weight for the security of a banknote suitable for circulation according to the characteristic "value" with the weighting within the group "security characteristics of the banknote" for each standard shows that the minimum weights for the security of a bank note suitable for circulation are higher for the standards (1), (2), (3) and (5) than the minimum weights of the group “security characteristics of a banknote”. The relationship and therefore the criteria for a banknote suitable for circulation are not met only in the standard (4). These criteria can be used, for example, to check the consistency of each standard.

The introduction of a minimum weight for each standard in the group "security characteristics of a banknote" provides a criterion that also allows banknotes with different security characteristics to be compared with each other. If necessary, one can, of course, also use other evaluation algorithms for the individual class weights.

As shown in FIG. 7, the sorting class determined with the aid of the control matrix can optionally be altered later according to a certain condition. This subsequent alteration can be of interest, for example, for servicing the apparatus or for designing the control matrix.

The conditions can be deduced from the control matrix, eg minimum weight (MG) for a norm from the group "security characteristics of a banknote". Furthermore, the conditions may also depend on the measurement results of the detectors or the class code of a certain measurement result. In general, all available data for the testing device can be used in any combination under one condition.

Furthermore, it is possible to separate certain banknotes in statistical distribution by means of a random number generator RND. In the example shown in FIG. 7, 20% of the banknotes are statistically diverted from the "shredder" classification class to the "reject" classification class. This method makes it possible, for example, to check the sorting quality of banknotes continuously if the operator of the apparatus personally inspects the separated banknotes with the sorting class "reject". You will then be able to alter the class limits of certain classes appropriately, based on your inspection, if necessary.

Furthermore, altering the sorting class below makes it possible, in the event of an alteration, to separate the corresponding banknotes without having to make major changes to the control matrix.

A second embodiment of the method of the invention for processing sheet material is shown in figure 8. Also in this case, as explained above in the first embodiment, the measurement data is first collected by the sensors ( 20.n) and measurement data (MD) used to deduce or calculate measurement results (ME).

In contrast to the first embodiment, the medicioan (ME) results are assigned to overlapping classes or a blur or fuzzified effect is introduced. An example of such an assignment is shown in figure 9. For the sake of clarity, only the properties "dirt", "bent corners" and "stains" have been used from the characteristics of the first embodiment. In this example, the measurement results of the sheet material can take values ​​between 0 and 1. Three overlapping classes are assigned to each characteristic. For the characteristic "dirt", these classes are "high" with results of medicioan in the range of 0 to 0.5, "medium" in the range of 0 to 1, and "low" in the range of 0.5 to 1. The classes "high", "medium", "low" are used below as confusing or fuzzy classes.

Each of these “fuzzy” classes is assigned an affiliated function, as shown below.

ES 2 171 685 T3 has shown in Figure 9. The number of overlapping fuzzy or fuzzy classes and the shape of the different forms of affiliation can be set as desired. By choosing the right affiliation functions, the functionality of the method can be optimized for the specific application.

In Figure 9, the measurement results of the two measurements (M1) and (M2) are represented by the affiliation values ​​resulting from the affiliation functions. The measurement (M1) shows a banknote with little dirt, relatively many bent corners and few stains. In the measurement (M2) the dirt is greater than in the measurement (M1) and has a greater number of bent corners. Furthermore, it shows a lower number of spots than in the measurement (M1).

The “fuzzy” classes are used to define a control matrix shown in figure 10. In the columns of the control matrix the possible combinations of the individual classes of the characteristics “dirt”, “bent corners” and “ stains". The last column of the control matrix is ​​the "sort" feature with three "fuzzy" classes designated "stacker," "shredder," and "reject." The rows of the control matrix show the norms (1) to (8) to each of which a possible combination of “fuzzy” classes of three characteristics is associated to a “fuzzy” class of the “classification” characteristic. In these specific designations of the "fuzzy" class the affiliation value determined from Figure 9 for measurements of (M1) and (M2) is indicated. The determination of the values ​​indicated for the “fuzzy” classes of the “classification” characteristic are explained below.

Verbally, the norms of the control matrix can be indicated as follows. The standard (1) says, for example, that a banknote with a low level of dirt, many bent corners and few stains should be assigned to the “fuzzy” class of “rejection” of a “sort” characteristic. According to the standard (2), a banknote, with medium dirt, many folded corners and few stains receives the assignment of the class "shredder" of the characteristic "classification", etc. The control matrix is ​​limited to eight norms since there are no other reasonable combinations with measurements (M1) and (M2). However, it is basically unnecessary for the control matrix to contain rules for all possible combinations. It is sufficient that it contains only rules for relevant combinations.

To deduce a classification class for a banknote, the corresponding affiliation function is first associated with each “fuzzy” class of the “classification” characteristic as shown in figure 11.

From these “fuzzy” classes with their affiliation functions and the control matrix, the resulting classes “stacker”, “shredder”, “rejection” of the classification are first deduced by means of a so-called inference machine.

The “fuzzy” classes of the “classification” characteristic are obtained, resulting from the norms, by first jointly relating the corresponding values ​​of affiliation of the measurement results within a norm and associating the result of the association with the classification, as shown in figure 10. The selection of the lowest affiliation value is selected in this case as a simple case for association (framed in each case).

Figure 12 shows by way of example the "fuzzy" class indicated as "shredder", of the "classification" characteristic resulting from the corresponding standards. The affiliation function of the “fuzzy” class indicated as “crusher” is cut to the corresponding height according to the result of the association or link in the standard. Figures (12a) and (12b) show this process for the measurement results (M2) of standard (4). According to the control matrix shown in figure 10, the standard (4) provides the value 0.2 for measurement (M2), and the class “fuzzy” indicated “crusher” of the characteristic “classification”. As a consequence, the affiliation function of the “fuzzy” class indicated as “shredder” is cut at the value 0.2. The thus obtained parts of the individual standards are linked together. For the sake of simplicity, the maximum area covered by the individual partial areas has been selected in this case as a link or connection. The results of the connection are shown in Figure 12c.

When performing the analogous method for all the “fuzzy” phases of the “classification” characteristic and all the norms, the classes resulting from the “classification” characteristic shown in the figures are obtained for the measurement (M1) and the measurement (M2). 13a and 13b with their membership functions. The result determined by way of example in FIG. 12c is found again in this case in FIG. 13b.

At a last stage, an individual classification class must be deduced among the resulting classes “fuzzy” from the “classification” characteristic or from the “classification” characteristic without “fuzzy” effect. A simple way to carry out this deduction or calculation is to assign the classification class to the sheet material whose “fuzzy” class has the most important area. In the case of measuring results (M1), the laminar material received the assignment of the classification class "rejection" and the material in sheets with the average values ​​(M2) the classification class "crusher".

A method more elaborated to deduce the class of classification of the classes "fuzzy" resulting from the characteristic "classification" consists, for example, in first linking the individual resulting "fuzzy" classes "stacker", "shredder", "reject "Of the characteristic" classification "with each other, for example by combination, and calculate the position of the center of gravity of the resulting area. By rounding this value can be represented in an individual classification class.

It is of course possible to transfer other forms of dealing with the indeterminate logic known from prior art to the problem of processing material in the form of sheets or lamines in the sense described above.

As in the first realization of the method, it is also possible to provide individual standards or rules with security levels. Tam7

It is possible to use combinations for each class, for example, by linking the specific affiliation function of an indeterminate class with the corresponding combination, for example, by multiplication. The security level and the weights can be treated the same as in the first embodiment.