• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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  • 法律状态
  • 优先权
    • 专利摘要:
    The problem is to provide a new combination measuring device where it is possible to improve both the average accuracy and the rate of operation in a long-term opera-tion. A plurality of evaluation functions, which evaluate for each of the combinations the effect on the accuracy and the effect on the rate of operation respectively when each of the combinations is selected, are prepared. Evaluation values based on the evaluation functions are calculated for each of the combinations when combining each of the measured values, and the combination which results in the maximum sum of the calculated evaluation values is selected as the optimal combination.
    公开号:DK201570021A1
    申请号:DK201570021
    申请日:2015-01-15
    公开日:2015-02-02
    发明作者:Hitoshi Iba；Yoshito Inazumi；Takuyu Kubo
    申请人:Hitoshi Iba；Ishida Seisakusho；
    IPC主号:
    专利说明:

    DESCRIPTION
    COMBINATION MEASURING DEVICE
    TECHNICAL FIELD
    The present invention relates to a combination measuring device where an optimal combination of measuring units is selected by combining a plurality of measured values which are input from a plurality of the measuring units and articles are discharged from the selected measuring units.
    BACKGROUND ART A high rate of operation as well as constant high accuracy is desired for this type of combination measuring device. The loss of articles in combination measurement is lowered when accuracy is high and productivity is improved when rate of operation is high. For this reason, inventions have been proposed up until now as disclosed in the patent documents described below with the aim of improving both the accuracy (yield) and the rate of operation. SUMMARY OF THE INVENTION <Problems to be Solved by the Invention>
    However, since a combination of the measuring units closest to a target value is selected as the optimal combination in each measuring cycle in these devices, it is not always the best combination when considering the average accuracy and the rate of operation in a long time. -term operation even if the combination is the best for accuracy in each cycle.
    Typically, individual measuring hoppers used in the combination measuring device discharge measured articles when selected in a combination and subsequently receive new articles being supplied, and the next measuring cycle is started. While these cycles are being repeated on the individual hoppers, the rate of operation of the device is improved. However, when a portion of the measuring hoppers is passed over from such a cycle, articles tend to adhere to these measuring hoppers and this causes effects on the measuring accuracy even if these measuring hoppers are selected in a combination. In addition, the possibility that the articles in the hoppers which are passed over in this manner are selected is relatively low and this also causes an effect on the measurement accuracy.
    On the other hand, in a case where there are a plurality of combinations in which combination total values fall between set upper and lower limits (referred to below as being within a permissible range), leaving more combinations which are within the permissible range in the next measuring cycle is linked with improving the rate of operation.
    Furthermore, there are cases where selecting a second or third optimal combination within the permissible range is preferable in terms of improving the accuracy and rate of operation.
    However, there is a problem in that the average accuracy and the rate of operation in a long-term operation is not always the best since the conventional devices do not make selections in consideration of the future prospect.
    The present invention attempts to solve this problem and has an object to provide a new combination measuring device where it is possible to improve both the average accuracy and the rate of operation in a long-term operation. <Means to Solve the Problems> A combination measuring device according to the present invention selects an optimal combination of measuring units by combining measured values which are obtained using a plurality of the measuring units which measure articles, where a plurality of evaluation functions, which evaluate for each of the combinations has an effect on the accuracy and an effect on the rate of operation respectively when each of the combinations is selected, evaluation values based on the evaluation functions are calculated for each of the combinations when combining each of the measured values, and the combination which results in a maximum sum of the calculated evaluation values is selected as the optimal combination.
    In addition, each of the evaluation functions includes a weight coefficient which represents a degree of relevance of each of the evaluation functions and the weight coefficient is determined with a computer that executes a genetic algorithm.
    In addition, an operation unit is provided in the combination measuring device. It is possible to arbitrarily set from the operation unit whether to perform an operation which gives priority to the accuracy or to perform an operation which gives priority to the rate of operation.
    In the combination measuring device, it is typical that the rate of operation deteriorates when the measuring accuracy is improved and the measuring accuracy deteriorates when the rate of operation is improved. In this manner, there is a conflicting relationship between the accuracy and the rate of operation. A multiple objective optimization method is a method for simultaneously improving the matters in this relationship (tradeoff relationship).
    In the present invention, the effect on the accuracy and the effect on the rate of operation when each of the combinations is selected are respectively expressed in equations (evaluation functions) in order to simultaneously improve the accuracy and the rate of operation which are in a tradeoff relationship using the multiple objective optimization method. <Effects of the Invention>
    According to the present invention, it is possible to improve both the average accuracy and the rate of operation which are in a tradeoff relationship after long-term surgery. In addition, it is possible to perform the operation which gives priority to the accuracy or the operation which gives priority to the rate of operation with an operation from the operation unit.
    LETTER DESCRIPTION OF THE DRAWINGS
    [Fig. 1] FIG. 1 is a diagram for explaining Pareto optimal solutions.
    [Fig. 2] FIG. 2 is a schematic diagram of the main parts of a combination measuring device according to an embodiment of the present invention.
    [Fig. 3] FIG. 3 is a configuration block diagram of a combination measuring device according to an embodiment of the present invention.
    DESCRIPTION OF THE EMBODIMENT
    The following six evaluation functions are defined as examples in the present invention.
    One of them, which evaluates the effects on accuracy with regard to each combination, is defined using equation (1) below. The evaluation function of equation (1) is a linear function which is equal to one if the combination total value is equal to a target value and gradually if the combination total value gets farther away from the target value. Here, the deviation from the target value is ew, the upper limit value of the deviation is eu, bi = -l / eu, andb2 = 1. <Equation 1>
    (1)
    Using equation (1), the deviations of the respective combination total values are standardized in a permissible range.
    Next, the following four evaluation functions are defined in order to evaluate respectively the effect on the accuracy or the effect on the rate of operation for each of the combinations. 1. Evaluation function relating to passed-over count 2. Evaluation function relating to number of selected measuring units 3. Evaluation function relating to degree of contribution 4. Evaluation function relating to degree of dispersion
    Below, each of these evaluation functions will be described in order. 1: Evaluation Function Relating to Passed-over Count A combination measuring device supplies articles to a measuring hopper of each measuring unit, measures them, and select an optimal combination by combining the obtained measured values. In addition, articles are discharged from the measuring hopper of each of the selected measuring units and articles are supplied again to the measuring hoppers where discharge is carried out. Such a series of cycle is repeated. Each of the measuring units is respectively provided with a counter. The counter increases the count value by one in a case where articles are not discharged and returns the count value to zero in a case where articles are discharged. When the count value in the counter is high, the articles in the measuring units have been passed over for a number of cycles. When the article is left in this manner, the number of combinations that are within the permissible range is reduced and the measurement accuracy deteriorates. In addition, when there is an increase in the number of measuring units where the articles are passed, the number of combinations that are within the permissible range is reduced relatively and combination failures tend to be caused. This affects the accuracy and rate of operation. Therefore, an evaluation function of equation (2) is defined so as to proactively discharge articles from the measuring units where the count value is high. Here, the maximum count value out of the measuring units belonging to one of the combinations is Cm and the upper limit value of the count value of being passed is Cl. <Equation 2>
    (2) 2: Evaluation Function Relating to Number of Selected Measuring Units
    The combination measuring device is provided with a plurality of the measuring units. The number of measuring units that can be used in the next cycle increases as the number of measuring units that fit within the combination permissible range is lower in each of the combinations. This mainly has an effect on the rate of operation. Therefore, an evaluation function of equation (3) is defined so that the combination with a lower number of the measuring units tends to be selected. Here, the number of measuring units that fit within the permissible range is Ns and the total number of measuring units is Nl. <Equation 3>
    (3) 3: Evaluation Function Relating to Degree of Contribution
    In a case where the optimal combination is selected from among a plurality of combinations that are within the permissible range, leaving more combinations that are within the permissible range in the next cycle improves the rate of operation. For example, it is presumed that the measured values of ten measuring units are combined and six groups of combinations which are within the permissible range are found as shown in Table 1. Here, the symbol "o" in Table 1 represents the measuring units which is selected as the measuring units that are within the permissible range. <Table 1>
    Then, the number of belonging to any of the groups is counted for each of the measuring units and a degree of contribution of each of the measuring units is defined by equation 4 based on the counted values. In doing this, the degree of contribution of each of the measuring units is shown in Table 1 in this applied example. Here, the degree of contribution of the hth measuring unit is Ch, the number of belonging to groups is nh, and the total number of the measuring units is Nl. <Equation 4>
    (4)
    Then, when the combination with the lowest total value of the degree of contribution is selected, the total value of the degree of contribution of the remaining measuring units is maximized. By selecting the combination in this way, the rate of operation has improved consistently since more combinations that are within the permissible range remain in the next measurement cycle. Therefore, equation (5) is defined as an evaluation function which determines the total value of the degree of contribution of the remaining measuring units. <Equation 5>
    (5)
    Table 2 shows the total value of the degree of contribution of each group and the total value of the degree of contribution of the remaining measuring units with respect to Table 1. Accordingly, Group 4 is the optimal combination if the cases in Table 1 are considered only and the other evaluation functions are not considered. By doing this, the rate of operation improves since the remaining measuring units that belong to Group 6 fit within the permissible range in the next cycle. <Table 2>

    4: Evaluation Function Relating to Degree of Dispersion
    It is understood that greater variation in the measured values of each of the measuring units in the combination measuring device generates a larger number of the combinations which are within the permissible range. Therefore, the degree of dispersion of each of the remaining measuring units is defined by equation (6) so that the measuring units where the degree of dispersion of the measured values is large has remained proactively. Here, the number of the remaining measuring units is n, the measured values of the remaining measuring units is hi, and the average value of each of the measured values hi is μ. <Equation 6>
    (6)
    Then, equation (7) is defined as an evaluation function where the evaluation value increases as V increases. Here, Wt is the combination target value. <Equation 7>
    (7)
    The evaluation functions above are used for combining the measured values (weights), but combinations in unit numbers may also be performed in combination measuring devices. For example, in a case where frankfurters being packaged are the weighing target, articles where the combination total value is within a permissible range and the unit numbers are the target unit number are discharged. The evaluation of such unit number combinations will be described next.
    For example, there are cases where none of the combinations is established in a case of selecting articles with a target unit number depending on the unit number which is fed into each of the measuring units. For example, combination failures occur in a case where the target unit number is set to be an odd number and an even number of units are supplied to each of the measuring units. This state is referred to here as a taboo state and the rate of operation is reduced when this state occurs. Therefore, a degree of margin until such a taboo state occurs is calculated for each of the combinations. For example, a case is assumed in table 3 where the unit numbers shown in table 3 are respectively fed to eight of the measuring units and the target unit number is seven. <Table 3>
    When the units with numbers 3,4 and 5 are initially selected as the combination in table 3, the measuring units which satisfy the target unit number from among the remaining measuring units is combination 1 and the measuring units which satisfy the target unit number excluding these measuring units is combination 2. However, since there are no more combinations of the measuring units that satisfy the target unit number, it is possible to guarantee the combinations with the target unit number up to two cycles in the case of this selection route. Therefore, the degree of margin of the selection route in this case is two. Subsequently, the measuring units which can be selected from among the measuring units excluding the units with numbers 3, 4, and 5 is combination 3 and it is possible to subsequently select combination 4. Since two cycles are guaranteed in this case, the degree of margin in this case is also two.
    In this manner, the degree of margin of each of the selection routes is determined for all of the combinations which satisfy the target unit number and the maximum of the degree of margin is defined as a degree of margin T for the combination. For example, the degree of margin T of the combination of the units with numbers 3, 4, and 5 is two in the example of Table 3. Equation (8) is defined as an evaluation function in a case of unit number combinations using the degree of margin T. <Equation 8>
    (8)
    By defining the six evaluation functions in this manner, the effect on the accuracy and the effect on the rate of operation are respectively evaluated for each of the combinations. However, since it is not understood how much extent these functions should have relevance to maximize the accuracy or rate of operation, an evaluation value Vc is determined using equation (9) for each of the combinations and the combination which has the maximum evaluation value Vc is selected as the optimal combination <Equation 9>
    (9)
    Here, a; are weight coefficients which are set as arbitrary values under the limiting conditions of equation (10) using <Equation 10>
    (1 0)
    The coefficients a; are indicators of the extent to which the evaluation functions are given priority and it is possible to perform an operation which gives priority to the accuracy and an operation which gives priority to the rate of operation by increasing or reducing these coefficients. However, since it is difficult for humans to determine respective coefficients, Pareto optimal solutions are determined using a computer which executes a genetic algorithm A collection of solutions representing the boundary of a tradeoff relationship is typically referred to as "Pareto optimal solutions". The Pareto optimal solutions are solutions where, if the value of any of the target functions (for example, the accuracy) is improved, a value of another of the target functions (for example, the rate of operation) deteriorates.
    FIG. 1 is a diagram for explaining Pareto optimal solutions. Each of the solutions "a", "b", "c", and "d" is a Pareto optimal solution and the solutions "e" and "f" which are out of these solutions are inferior solutions. formed by the collection of the solutions "a", "b", "c" and "d" is referred to a Pareto front. There are a plurality of Pareto optimal solutions and it is possible to give priority to the accuracy or give priority for the rate of operation depending on which of the solutions is selected. For example, if solution "a" is selected, the accuracy improves but the rate of operation falls. If solution "d" is selected, the accuracy falls but the rate or operation improves.
    Therefore, a genetic algorithm is used in order to determine the Pareto optimal solutions. The genetic algorithm is an algorithm that was proposed by J. Holland and developed with a hint of processes that are seen in natural evolution (selecting chromosomes, crossover, or mutation). When using this algorithm, the measured values of each of the measuring units acquired in, for example, 10,000 measuring cycles are recorded as sample data. Then, individuals (the coefficients a;) are randomly generated and the degree of fitness of each individual is evaluated. That is, 10,000 of the optimal combinations are determined using equation (9) and 10,000 of the average accuracies and the rates of operation are determined. Then, individuals having the higher degree of fitness remain by increasing more the possibility that individuals are selected as the degree of fitness becomes higher. That is, the individual (the coefficients a;) where the average accuracy and the rate of operation are higher remain when the individual (the coefficients a;) change. Furthermore, new individuals (the coefficients a,) are generated by crossover and mutation, and the degree of fitness of each individual (the coefficient a;) is evaluated. In this manner, Pareto optimal solutions are determined using the screened weight coefficients a; obtained by repeating changes of generations with evaluation, selection, crossover, and mutation.
    As the genetic algorithm, there are MOGA (Multiobjective Genetic Algorithm) by Fonseca et al., NSGA-II (Non-Dominated Sorting Genetic Algorithm-II) by Deb et al., SPEA2 (Strength Pareto Evolutionary Algorithm 2) by Zitzler et al. ., and the like. In the present invention, NSGA-II is used but the genetic algorithm is not limited to this. A combination measuring device according to an embodiment of the present invention will be described based on the diagrams.
    FIG. 2 is a schematic diagram of the main parts of a combination measuring device according to an embodiment of the present invention. In the diagram, a combination measuring device 100 is provided with a dispersing feeder DF, a plurality of radiating RF feeders, a plurality of pool hoppers PH, a plurality of measuring hoppers WH, and a collection chute CS. The dispersing feeder DF is arranged at a central upper portion of the device. The plurality of radiating RF feeders are aligned in a radial formation around the dispersing feeder DF so as to surround the dispersing feeder DF. The plurality of pool hoppers PH are aligned at a lower level of each of the radiating feeders RF. The plurality of measuring hoppers WH are aligned below the plurality of pool hoppers PH. The numbers of the measuring hoppers WH and the pool hoppers PH are the same. The collection chute CS is arranged below the plurality of measuring hoppers WH.
    The dispersing feeder DF disperses articles which are fed in from above in a circumferential direction with vibrations of an electromagnetic feeder DV.
    The radiating feeder RF transports the articles being transported from the dispersing feeder DF to the tip edge of a trough TR with vibrations of an electromagnetic feeder RV and discharges the articles to the pool hoppers PH which are at the lower level.
    The pool hopper PH temporarily retains the articles which are discharged from the radiating feeder RF. The pool hopper PH opens and closes a gate g based on a command from a control section CU when the measuring hopper WH which is at a lower level is opened and closed and discharges the articles which are retained in the pool hopper PH to the measuring hopper WH which is at a lower level.
    In addition, weight sensors WS are attached to the measuring hoppers WH. The weight detected by the weight sensor WH is input into the control section CU and is used in combination computations. Since each of the hoppers PH and WH are known configurations, description of the gate opening / closing mechanism, the support mechanisms of the hoppers PH and WH, and the like are omitted.
    The control section CU is configured using a computer and is equipped with a CPU 10 along with a ROM 11, a RAM 12, a hard disk 13 which are controlled by the CPU 10 as shown in FIG. 3. The CPU 10, the ROM 11, the RAM 12, the hard disk 13, and the like are connected to each other via a bus line such as an address bus or a data bus. In addition, via interface 14, the control section CU is connected to the dispersing feeder DF, the radiating feeders RF, the pool hoppers PH, the measuring hoppers WH, and an operation unit RU which is provided with a touch panel function. In addition, the RU operation unit is connected to a computer C which executes a genetic algorithm and updates the weight coefficients a; described above is performed.
    Various types of programs are stored in the ROM 11. The CPU 10 performs computation of each of equations (1) to (8), management of the passed-over count values, and control for opening and closing gates of the pool hoppers PH and the measuring hoppers WH by reading out and executing the various types of programs stored in the ROM 11. The evaluation functions of each equation (1) to (8) are stored in the hard disk 13. The weight coefficients a, which are used are periodically updated using the computer C which executes the genetic algorithm.
    The CPU 10 inputs measured values from the weight sensors WS in respective measuring units Ml to Mn and stores the measured values in the RAM 12 when a discharge request signal is received from a packaging unit which is not shown in the diagrams or when started by a cycle timer. Next, computation of each equation (1) to (9) is performed based on each of the measured values which are stored. At this time, if there is leeway in terms of computation time, computation of each equation (1) to (9) is executed for all of the combinations. On the other hand, if operation speed is increased and there is no leeway in terms of computation time, combinations where the combination total values are within the permissible range are extracted beforehand and computation of each equation (1) to (9) is executed for each of the extracted combinations. When combination of weight is performed, computation of equation (9) is executed as the linear sum of the equations excluding equation (8) which is used in selecting combinations of unit numbers. When combination of unit numbers is performed, computation of equation (9) is executed as the linear sum of equations including equation (8).
    When the CPU 10 selects the combination where the evaluation values are maximized as the optimal combination using these computations, a discharge command is transmitted to each of the selected measuring units Ml to Mn and then, with a slight delay, discharge commands are transmitted to the corresponding pool hoppers PH corresponding to the selected measuring units Ml to Mn. By doing this, the measuring units Ml to Mn which receive the discharge commands open and close their measuring hoppers WH and discharge articles and then the pool hoppers PH is opened and closed and articles are supplied to the empty measuring hoppers WH. Next, when the pool hoppers PH are empty, drive commands are transmitted to the radiating feeders RF and the dispersing feeder DF and new articles are supplied to the empty pool hoppers PH from the radiating feeders RF which correspond to the empty pool hoppers PH. In this manner, one cycle of combination weighing is complete and the next measuring cycle is started with the next discharge request signal or a start signal from the cycle timer.
    The measured values of each of the measuring units Ml to Mn and the total weight of the optimal combination are recorded in the hard disk 13 for each of the cycles. The computer C periodically accesses the hard disk 13, takes the recorded data, and updates the weight coefficient a; while calculating the average accuracy and rate of operation by executing the genetic algorithm based on the recorded data.
    When the weight coefficient a; are updated and the accuracy and rate of operation given in the Pareto optimal solutions are specified in this lesser, the operation unit RU displays, for example, the Pareto front as shown in Fig. 1 on an operation screen. When the desired accuracy and rate of operation are designated by an operator touching the Pareto front, the weight coefficients a; which provide the specified accuracy and the specified rate of operation are specified and stored in the RAM 12. The CPU 10 executes each equation (1) to (9) based on the weight coefficients a; which are stored in the RAM 12. In this manner, it is possible to change the accuracy and rate of operation to desired values. It is confirmed that the rate of operation is improved by 3% with a 0.2% sacrifice of accuracy in an experiment using snack products as articles, although each of the weight coefficients a; is different depending on the type and transport characteristics of the articles being the measuring target, operating conditions, and the like.
    Above, the embodiment of the present invention is described, but the present invention is not limited to this and it is possible to adopt other aspects. For example, the computer C which executes the genetic algorithm is externally attached to the embodiment, but it is possible to update the weight coefficients a; during operation and to display the accuracy and rate of operation up until then by including the computer C in the operation unit RU.
    The computer C which is connected externally may be connected in a wired or wireless manner. Furthermore, the computer C which is externally attached may be a computer which is provided in a remote data center. In addition, there may be a configuration where the functions of the computer C which are externally attached are dealt with using cloud computing.
    In addition, there may be a configuration where execution of the genetic algorithm is performed during a time when the combination measuring device is not in operation (for example, at night) in order to reduce the processing burden.
    REFERENCE SIGNS LIST
    100 COMBINATION MEASURING DEVICE
    Ml - Mn MEASURING UNITS
    RU OPERATION UNIT
    C COMPUTER WHICH EXECUTES GENETIC ALGORITHM
    CITATION LIST PATENT LITERATURE
    [PTL 1] Japanese Patent No. 3360895 [PTL 2] Japanese Unexamined Patent Application Publication No. 2009-47519
    权利要求:
    Claims (3)
    [1] 1. A combination measuring device which selects an optimal combination of measuring units by combining measured values which are obtained using a plurality of the measuring units which measure articles, wherein a plurality of evaluation functions, which evaluate for each of combinations an effect on the accuracy and an effect on the rate of operation respectively when each of the combinations is selected, are prepared, evaluation values based on the evaluation functions are calculated for each of the combinations when combining each of the measured values, and the combination which results in a maximum sum of the calculated evaluation values is selected as the optimal combination.
    [2] 2. The combination measuring device according to claim 1, wherein each of the evaluation functions includes a weight coefficient which represents a degree of relevance of each of the evaluation functions, and the weight coefficient is determined with a computer which executes a genetic algorithm.
    [3] 3. The combination measuring device according to claim 1 or 2, wherein an operation unit is provided, and it is possible to arbitrary set from the operation unit whether to perform an operation which gives priority to the accuracy or to perform an operation which gives priority to the rate of operation.
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    同族专利:
    公开号 | 公开日
    JP6053782B2|2016-12-27|
    WO2013191234A1|2013-12-27|
    DE112013003115T5|2015-04-02|
    JPWO2013191234A1|2016-05-26|
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    引用文献:
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    法律状态:
    2019-01-17| PBP| Patent lapsed|Effective date: 20180620 |
    优先权:
    申请号 | 申请日 | 专利标题
    JP2012140266|2012-06-21|
    JP2012140266|2012-06-21|
    JP2013066916|2013-06-20|
    PCT/JP2013/066916|WO2013191234A1|2012-06-21|2013-06-20|Combination weighing device|
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