Analysis processing device and analysis processing method

Abstract

To provide a technique of grasping the ability of a worker who performs work for sorting wastes by item.SOLUTION: An analysis processor includes: an acquisition unit for acquiring pre-sorting waste information including a weight of a pre-sorting waste as a waste before sorting and items included in the pre-sorting waste, acquiring sorting work information including worker information regarding a worker engaged in sorting work of the pre-sorting waste and information regarding the execution of the sorting work, and acquiring post-sorting waste information showing a waste amount of each item after sorting the pre-sorting waste; and an analysis unit for deriving a sorting work parameter showing the workability of the worker on the basis of the pre-sorting waste information, sorting work information, and post-sorting waste information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technique for analyzing the ability of a worker to perform a task of sorting waste. [Background technology]

[0002] Patent Document 1 discloses a waste sorting method in which brought-in waste is separated into oversized waste and undersized waste using a large sieve, and each is transported by a large oversized conveyor and an undersized conveyor, and large items are manually sorted from the oversized waste by type by workers waiting on the conveyor's transport path, and then the items are dumped into a discharge chute that opens next to the workers. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 11-19593 Summary of the Invention [Problem to be solved by the invention]

[0004] When workers sort waste by item, it is desirable to be able to grasp the workers' capabilities, as this allows the workers to be appropriately assigned to one of multiple sorting tasks.

[0005] An object of the present invention is to provide a technique for understanding the ability of a worker who sorts waste by item. [Means for solving the problem]

[0006] In order to solve the above problems, an analysis processing device according to one aspect of the present invention comprises: An analytical processing device that analyzes the work ability of a worker who performs a sorting operation in a sorting operation in which pre-sorted waste, which is a mixture of multiple items, is sorted into sorted waste, which is sorted for each item, Acquire pre-sorted waste information including the weight of the pre-sorted waste before sorting and the items contained in the pre-sorted waste, and acquire information on workers who have engaged in sorting work for the pre-sorted waste; Sorting time spent performing sorting workand an acquisition unit that acquires sorting work information including the above and acquires post-sorting waste information that indicates the amount of waste for each item after the pre-sorting waste has been sorted; and an analysis unit that derives sorting work parameters that indicate the work ability of each worker based on the pre-sorting waste information, sorting work information, and post-sorting waste information. The analysis unit derives the processing speed for each waste item as a sorting work parameter for the worker.

[0007] Another aspect of the present invention is an analysis processing method, the method comprising the steps of: In a sorting operation for sorting pre-sorted waste containing a mixture of multiple items into sorted waste sorted for each item, the work ability of a worker performing the sorting operation is analyzed; An analytical processing method in which each step is executed by a computer, the method comprising the steps of: acquiring pre-sorted waste information including the weight of pre-sorted waste before sorting and items contained in the pre-sorted waste; and acquiring information on workers who have engaged in sorting work for the pre-sorted waste. Sorting time spent performing sorting work The method includes a step of acquiring sorting work information including the above; a step of acquiring post-sorting waste information indicating the amount of waste for each item after sorting the pre-sorting waste; and a step of deriving sorting work parameters indicating the work ability of the worker for each worker based on the pre-sorting waste information, sorting work information, and post-sorting waste information. In the deriving step, the processing speed for each waste item is derived as the sorting work parameter of the worker. [Effects of the Invention]

[0008] According to the present invention, a technique can be provided for understanding the ability of a worker who performs the task of sorting waste by item. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram for explaining an overview of waste disposal. [Figure 2] FIG. 2 is a diagram illustrating a functional configuration of the analysis processing system. [Figure 3] FIG. 10 is a diagram illustrating an example of sorting task parameters of a worker. [Figure 4] 10 is a flowchart of a process for deriving sorting task parameters for each worker. DETAILED DESCRIPTION OF THE INVENTION

[0010] FIG. 1 is a diagram for explaining an overview of waste disposal. Each vehicle 10 delivers pre-sorted waste 12a, pre-sorted waste 12b, and pre-sorted waste 12c (referred to as "pre-sorted waste 12" when no distinction is made between them) to a disposal facility 14. The pre-sorted waste 12 is a mixture of multiple items, and the items may vary depending on the supplier. For example, pre-sorted waste 12a includes glass scraps, wood chips, and rubble, pre-sorted waste 12b includes rubble and scrap metal, and pre-sorted waste 12c includes waste paper and plastic. The supplier declares to the facility staff at the disposal facility 14 what the waste contains.

[0011] At the treatment facility 14, multiple workers receive the pre-sorted waste 12 and manually sort the pre-sorted waste 12 that has been conveyed down the conveyor by item. The pre-sorted waste 12 is converted into post-sorted waste through the sorting process and delivered to each facility. For example, rubble is delivered to a landfill 16, wood waste and paper waste are delivered to an incineration plant 18, and glass scraps, metal scraps, and plastics are delivered to a recycling facility 20. In this way, pre-sorted waste containing a mixture of multiple wastes is brought to the treatment facility 14, sorted by item, and delivered to a disposal site appropriate for the item. Plastics can be delivered not only to the recycling facility 20 but also to the incineration plant 18.

[0012] The abilities of workers engaged in sorting work vary. For example, some workers are not good at sorting large items such as rubble but are good at sorting cardboard and paper waste, while others have fast processing speeds but low sorting accuracy. It is preferable to assign workers to appropriate sorting tasks based on their abilities. Therefore, the analysis processing device of the embodiment collects waste information and sorting work information before and after sorting, and derives the sorting work ability of each worker, i.e., the sorting work parameters for each worker.

[0013] 2 is a diagram showing the functional configuration of the analysis processing system 1. In terms of hardware, each function of the analysis processing system 1 can be configured using circuit blocks, memory, and other LSIs, and in terms of software, it is realized by system software, application programs, etc. loaded into memory. Therefore, it will be understood by those skilled in the art that each function of the analysis processing system 1 can be realized in various forms using only hardware, only software, or a combination of these, and is not limited to any one of them.

[0014] The analysis processing system 1 comprises an input unit 22, an external terminal device 24, an analysis processing device 26, and an output unit 28. The input unit 22 may be, for example, a touch panel, a keyboard, a microphone, etc., and receives input of pre-sorting waste information, post-sorting waste information, or sorting work information, and sends this information to the analysis processing device 26. The external terminal device 24 is also connected to the analysis processing device 26 via a network, and sends pre-sorting waste information, post-sorting waste information, or sorting work information. In other words, the pre-sorting waste information, post-sorting waste information, and sorting work information are sent from the input unit 22 or the external terminal device 24.

[0015] The pre-sorted waste information includes a delivery destination ID, a pre-sorted waste ID, the weight of the pre-sorted waste, the waste items contained in the pre-sorted waste, and the proportion of each item in the pre-sorted waste. The waste items contained in the pre-sorted waste and the proportion of each item in the pre-sorted waste may be determined by a declaration by a supplier. For example, the supplier may declare that the pre-sorted waste 12a shown in Figure 1 is composed of 30% glass waste, 20% wood chips, and 50% rubble.

[0016] The sorted waste information includes the delivery destination ID, the type of sorted waste, and the weight of each type of waste. The sorted waste information is obtained by measuring the weight at the processing facility 14 after sorting and inputting it by the worker.

[0017] The sorting work information includes the sorting work ID, the ID of the worker engaged in the sorting work, the ID of the unsorted waste to be sorted, the sorting work time, the work area of ​​the area where the sorting work is performed, the sorting accuracy, the recycling rate, and the waste layer thickness. Information such as the sorting work time, the work area of ​​the area where the sorting work is performed, the sorting accuracy, the recycling rate, or the waste layer thickness is information related to the execution of the sorting work. The sorting accuracy is calculated by an inspector checking the sorted waste and removing impurities, and may be calculated from the weight of the impurities and the sorted waste. The waste layer thickness is determined by the height of the gates installed on the belt conveyor. Because unsorted waste passes through gates installed on the belt conveyor, the waste layer thickness can also be said to be determined by the thickness of the unsorted waste. The sorting work time may be measured using camera images monitoring the sorting work. The sorting work information may be obtained by input from a worker or inspector.

[0018] The output unit 28 is a display, a speaker, or the like, and outputs the information generated by the analysis processing device 26. The analysis processing device 26 includes an acquisition unit 30, an analysis unit 32, a calculation unit 34, a reception unit 36, a storage unit 38, and a determination unit 40.

[0019] The acquisition unit 30 acquires pre-sorting waste 12 information, which includes the weight of the pre-sorting waste 12 before sorting and the items contained in the pre-sorting waste 12. The acquisition unit 30 acquires sorting work information, which includes information about the workers who were engaged in sorting the pre-sorting waste 12 and information about the execution of the sorting work. The acquisition unit 30 acquires post-sorting waste information, which indicates the amount of waste for each item after sorting the pre-sorting waste 12. In other words, the acquisition unit 30 acquires pre-sorting waste information, sorting work information, and post-sorting waste information.

[0020] The analysis unit 32 derives sorting work parameters for each worker based on the pre-sorting waste information, the sorting work information, and the sorted waste information. The analysis unit 32 may derive the sorting work parameters using a multiple regression analysis method, or may derive the sorting work parameters using a machine learning method. The sorting work parameters indicate the worker's skill when performing the sorting work, and are derived from past work results.

[0021] However, because sorting work is performed by a team of multiple workers, it is difficult to measure the ability of each individual worker. The analysis unit 32 collects multiple results of sorting work performed by the team and derives the ability of each worker based on the differences between them. For example, the analysis unit 32 compares the sorting work performed by "Worker A" and "Worker B" with the sorting work performed by "Worker A," "Worker B," and "Worker C" to measure the ability of "Worker C." In this way, the analysis unit 32 derives sorting work parameters for each worker based on the differences between the workers engaged in the sorting work. However, because the content of the sorting work varies, the analysis unit 32 cannot derive sorting work parameters based on the differences alone, but must use a method such as multiple regression analysis.

[0022] The sorting parameters for each worker include the processing speed (kg / hour) for each item and the recycling rate for each item. The recycling rate refers to the sorting accuracy for each item. The sorting accuracy is determined by an inspector's survey, who determines the proportion of mixed items in the sorted waste. Plastic-only waste is delivered to a recycling facility 20, while mixed waste of wood chips and plastic is delivered to an incineration plant 18. Therefore, the higher the sorting accuracy, the higher the recycling rate. Simply put, the sorting processing speed is calculated from the weight of the waste before sorting, the working time, and the number of workers. However, because workers and items are mixed, the processing speed for each item of a specific worker is calculated using multivariate analysis.

[0023] The sorting task parameters of the worker may further include the overall sorting accuracy, the processing speed according to the work area, etc. As the work area becomes larger, the amount of movement during work increases, and therefore the processing speed may vary greatly depending on the worker.

[0024] The analysis unit 32 may use a program that receives pre-sorting waste information, sorting work information, and post-sorting waste information as input and outputs the processing speed for each item by worker. This program may be created using a multiple regression analysis technique, or may be created using machine learning or deep learning techniques.

[0025] Figure 3 shows an example of the sorting work parameters for workers. The vertical axis of Figure 3 is skill value, which is a coefficient of processing speed, and multiplying the skill value by a predetermined processing speed gives the processing speed of the worker. Figure 3 shows the skill values ​​of "Worker A," "Worker B," and "Worker C."

[0026] For example, worker A's cardboard skill value of 42 is higher than worker B's cardboard skill value of 44, but lower than worker C's cardboard skill value of 46. Furthermore, worker A's metal skill value of 48 is higher than worker B's metal skill value of 50, and higher than worker C's metal skill value of 52. This shows that worker A has few items that he is not good at, while worker C has an extremely wide range of items that he is good at and items that he is not good at.

[0027] In this way, the sorting work parameters of each worker specifically indicate which worker is suitable for which sorting work. The analysis unit 32 may use machine learning techniques to learn and update the sorting work parameters of each worker based on the pre-sorting waste information and post-sorting waste information. The analysis unit 32 associates the sorting work parameters of each worker with the worker ID and stores them in the memory unit 38.

[0028] Returning to FIG. 2, the calculation unit 34 creates a plurality of patterns of worker teams each made up of a plurality of workers. That is, the calculation unit 34 creates a plurality of combinations of workers that make up a worker team. The number of people in a worker team is two or more.

[0029] The calculation unit 34 calculates the correlation between the worker team and the objective of the sorting work based on the pre-sorting waste information and the sorting work parameters for each worker. The objective of the sorting work is, for example, processing speed, work completion time, recycling rate, sorting accuracy, and weight of waste after sorting, and is an indicator of the results of the sorting work. The objective of the sorting work may be the pre-sorting waste information of the requested sorting work. The correlation is a predicted value of the work results when the sorting work is performed. In other words, the calculation unit 34 calculates, for each worker team, a predicted performance value of the sorting work according to the objective of the sorting work based on the pre-sorting waste information and the sorting work parameters for each worker. The predicted performance value is the processing speed, work completion time, recycling rate, sorting accuracy, and the like. As a result, by having the calculation unit 34 calculate in advance the predicted performance values ​​of various worker teams composed of combinations of workers, i.e., the capacity values ​​for the sorting work, personnel allocation and work plans according to the sorting work can be easily developed.

[0030] The calculation unit 34 may calculate sorting work parameters for each worker team based on the pre-sorting waste information and the sorting work parameters for each worker. The sorting work parameters for the worker team calculated here may be the processing speed for each item and the recycling rate for each item. The sorting work parameters for each worker team calculated by the calculation unit 34 are stored in the memory unit 38. The memory unit 38 stores the worker IDs included in the worker team in association with the sorting work parameters.

[0031] The determination unit 40 determines the worker teams to be engaged in the sorting work based on the predicted execution value of the sorting work for each worker team. This allows the determination unit 40 to appropriately select the worker teams to perform the sorting work. The determination unit 40 determines the worker teams according to objectives such as improving the processing speed, task completion time, recycling rate, and sorting accuracy. For example, the determination unit 40 may determine the worker teams with the objectives of high processing speed and limited number of workers, or may determine the worker teams with the objectives of high recycling rate and limited number of workers. Furthermore, the determination unit 40 may determine the worker teams with the objectives of increasing the recycling rate while efficiently improving the processing speed and number of workers.

[0032] The analysis unit 32 executes a process for associating the pre-sorted waste information with the post-sorted waste information, and the memory unit 38 stores the pre-sorted waste information in association with the post-sorted waste information. Post-sorted waste is often a mixture of multiple pre-sorted wastes, and the post-sorted waste and pre-sorted waste cannot be directly associated. The pre-sorted waste information includes the weight of the pre-sorted waste and the proportion of each item contained in the pre-sorted waste. Therefore, the weight of each item of pre-sorted waste is simply calculated by multiplying the weight of the pre-sorted waste by the proportion of the item. The weight of each item of pre-sorted waste calculated in this way does not match the weight of each item of post-sorted waste. Therefore, the analysis unit 32 determines the weight of each item of post-sorted waste to be the correct value, and executes a process for back-calculating the weight of each item of post-sorted waste and applying it to multiple pieces of pre-sorted waste information to estimate the weight of each item of pre-sorted waste. This allows the supplier who delivered the unsorted waste to understand the actual sorting results.

[0033] The memory unit 38 stores the sorted waste information in association with delivery destination information for the sorted waste. Since the sorted waste information is associated with the pre-sorted waste information, it becomes possible for the supplier that delivered the pre-sorted waste to know where the sorted waste has been delivered. The information stored in the memory unit 38 can be output from the output unit 28 in response to instructions from the input unit 22 and the external terminal device 24.

[0034] 4 is a flowchart of a process for deriving sorting work parameters for each worker. The acquisition unit 30 acquires pre-sorted waste information from the input unit 22 or external terminal device 24 (S10). Workers work in teams to perform sorting work on the pre-sorted waste, and a camera and / or an inspector monitors the sorting work (S12) and generates sorting work information. The inspector inputs the sorting work information, and the acquisition unit 30 acquires the sorting work information from the input unit 22 or external terminal device 24 (S14).

[0035] The inspector measures the weight of the post-sorting waste for each item, measures the degree of sorting for each item, and generates post-sorting waste information (S16). The acquisition unit 30 acquires the post-sorting information from the input unit 22 or the external terminal device 24 (S18).

[0036] The analysis unit 32 receives the pre-sorting waste information, sorting work information, and post-sorting waste information and executes an analysis process to output sorting work parameters for each worker (S20). The storage unit 38 stores the sorting work parameters for each worker in association with the worker ID (S22).

[0037] It should be understood by those skilled in the art that the embodiments are merely illustrative and that various modifications are possible in the combination of the components, and that such modifications are also within the scope of the present invention. [Explanation of symbols]

[0038] 1 Analysis processing system, 10 Vehicle, 12 Pre-sorted waste, 14 Treatment facility, 16 Landfill disposal site, 18 Combustion plant, 20 Recycling facility, 22 Input unit, 24 External terminal device, 26 Analysis processing device, 28 Output unit, 30 Acquisition unit, 32 Analysis unit, 34 Calculation unit, 36 Reception unit, 38 Memory unit, 40 Determination unit.

Claims

1. An analytical processing device that analyzes the work ability of a worker performing a sorting operation in a sorting operation in which pre-sorted waste, which is a mixture of multiple items, is converted into sorted waste, which is sorted by item, comprising: an acquisition unit that acquires pre-sorting waste information including the weight of the pre-sorting waste before sorting and the items contained in the pre-sorting waste, acquires sorting work information including information on workers who engaged in sorting the pre-sorting waste and the sorting work time spent performing the sorting work, and acquires post-sorting waste information that indicates the amount of waste for each item after sorting the pre-sorting waste; an analysis unit that derives sorting work parameters that indicate the work ability of each worker based on the pre-sorting waste information, the sorting work information, and the post-sorting waste information, The analysis processing device is characterized in that the analysis unit derives a processing speed for each waste item as the sorting work parameter of the worker.

2. The analysis processing device described in Claim 1, characterized in that it is provided with a memory unit that stores the pre-sorted waste information and the post-sorted waste information in association with each other, and that stores the post-sorted waste information in association with delivery destination information for the post-sorted waste.

3. A method for analyzing the work ability of a worker performing a sorting operation to convert pre-sorted waste, which is a mixture of multiple items, into sorted waste, which is sorted by item, and for each step to be executed by a computer, comprising: A step of acquiring pre-sorted waste information including a weight of the pre-sorted waste before sorting and items contained in the pre-sorted waste; A step of acquiring sorting work information including information on workers who have engaged in sorting work of unsorted waste and the time spent on sorting work; A step of acquiring post-sorting waste information indicating the amount of waste for each item after sorting the pre-sorting waste; and deriving a sorting work parameter indicating the work ability of each worker based on the pre-sorting waste information, the sorting work information, and the post-sorting waste information, The analytical processing method, wherein in the deriving step, a processing speed for each waste item is derived as the sorting work parameter of the worker.

Images