Information processing apparatus and information processing method

Abstract

To easily optimize the state of the whole equipment of a factory or the like.SOLUTION: The information processing apparatus (30) includes a first acquirer (32) configured to acquire first information relating to a state of each of the facilities (10), a second acquirer (33) configured to acquire second information relating to a skill of an operator involved in each of the facilities (10), and a determiner (34) configured to determine a countermeasure for optimizing a state of the entire facilities (10) including each of the facilities (10) and an operator who should take the countermeasure against each of the facilities (10) by referring to the first information and the second information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] One aspect of the present invention relates to an information processing device and an information processing method. [Background technology]

[0002] Systems that use machine learning to predict abnormalities in equipment on factory lines and the like are known. Systems that can manage the skills of personnel who deal with equipment abnormalities and the like are also known. Patent Document 1 discloses an equipment monitoring system that can easily determine equipment abnormalities. Patent Document 2 discloses a skill map processing device that can easily and appropriately generate a skill map that shows the order in which skills should be acquired. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2019-169021 [Patent Document 2] Patent Publication No. 2020-107210 Summary of the Invention [Problem to be solved by the invention]

[0004] However, even if an equipment manager assigns personnel to deal with equipment abnormalities, etc. as needed based on the abnormality prediction results, there is a problem in that it is difficult to optimize the overall condition of the equipment owned by a factory, etc.

[0005] The present invention has been made in view of the above problems, and aims to make it easier to optimize the overall state of equipment in a factory or the like. [Means for solving the problem]

[0006] In order to solve the above problems, an information processing device according to a first aspect of the present invention includes a first acquisition unit that acquires first information regarding the status of each piece of equipment, a second acquisition unit that acquires second information regarding the skills of workers involved in each piece of equipment, and a determination unit that refers to the first information and the second information to determine measures to optimize the status of the entire equipment including each piece of equipment and workers who should take measures for each piece of equipment. The above configuration makes it easy to optimize the status of the entire equipment in a factory or the like.

[0007] In the information processing device according to Aspect 2 of the present disclosure, in Aspect 1 above, the determination unit receives the first information and the second information, and determines measures to optimize the overall state of the equipment and workers to handle each piece of equipment using a machine learning model that outputs information indicating measures to optimize the overall state of the equipment and information related to skills according to the state of the equipment. According to the above configuration, for example, by updating the machine learning model in accordance with updates to the first information or the second information, it is possible to more easily optimize the overall state of equipment in a factory or the like.

[0008] In the information processing device according to Aspect 3 of the present disclosure, in Aspect 2, the AI ​​having the machine learning model accepts instructions from a user in natural language. This configuration makes it easier to give instructions to the information processing device, contributing to improved user convenience.

[0009] In the information processing device according to aspect 4 of the present disclosure, in aspect 2 or 3, each piece of equipment is equipment for producing an item, and production plan information regarding the item is input to the machine learning model in addition to the first information and the second information. With the above configuration, it is possible to determine measures to optimize the overall state of the equipment including each piece of equipment and the workers who should handle each piece of equipment, based on the production plan information.

[0010] The information processing device according to aspect 5 of the present disclosure further includes a notification unit that sends a notification requesting each of the workers determined to be the workers who will handle each piece of equipment in any of aspects 1 to 4 to handle each piece of equipment, the notification including information indicating the handling procedure. According to the above configuration, it is possible to reduce the effort of the manager contacting the workers who will handle the equipment and the effort of the workers understanding the handling procedure.

[0011] The information processing device according to aspect 6 of the present disclosure is configured in any one of aspects 1 to 5 above to update the first information and the second information so that the results of the action taken by the worker are reflected when the worker takes action on the equipment. With the above configuration, the first information and the second information can be quickly updated to the latest state.

[0012] The information processing device according to aspect 7 of the present disclosure is any one of aspects 1 to 6, wherein the first acquisition unit acquires, as the first information, information indicating feature amounts extracted from sensing results of a sensor installed in the facility. According to the above configuration, extracting feature amounts to reduce the amount of data contributes to saving storage capacity and reducing communication traffic.

[0013] An information processing device according to an eighth aspect of the present disclosure is any one of the above-mentioned first to seventh aspects, wherein the second acquisition unit acquires at least one of a skill map, a training plan, and a skill analysis result of a worker involved in the equipment as the second information. The above configuration has the effect of making it easier for a manager to understand, for example, what measures are being taken to maintain each piece of equipment.

[0014] In order to solve the above problems, an information processing method according to a ninth aspect of the present invention is an information processing method executed by an apparatus, and includes: a first acquisition step of acquiring first information regarding the status of each piece of equipment; a second acquisition step of acquiring second information regarding the skills of workers involved in each piece of equipment; and a determination step of determining measures to optimize the status of the entire equipment including each piece of equipment and workers to deal with each piece of equipment by referring to the first information and the second information. The above method makes it easy to optimize the status of the entire equipment in a factory or the like.

[0015] The information processing device according to each aspect of the present invention may be realized by a computer. In this case, the control program of the information processing device that causes the computer to operate as each part (software element) of the information processing device to realize the information processing device on the computer, and the computer-readable recording medium on which the control program is recorded, also fall within the scope of the present invention. [Effects of the Invention]

[0016] According to one aspect of the present invention, it is possible to easily optimize the overall state of equipment in a factory or the like. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is an example of a functional block diagram of a system. [Figure 2] 10 is an example of a table showing first information. [Figure 3] 10 is an example of a table showing second information. [Figure 4] 10 is an example of a flowchart showing a processing flow of an information processing method. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, one embodiment of the present invention will be described in detail.

[0019] [1. System configuration example] The system 100 according to the present disclosure will be described below. The system 100 is a system for optimally maintaining the overall condition of each piece of equipment 10 on a factory line or the like. Here, the entire equipment 10 refers to all of the one or more pieces of equipment 10 that the system 100 targets for equipment maintenance.

[0020] Fig. 1 is an example of a functional block diagram of a system 100. As shown in Fig. 1, the system 100 includes one or more facilities 10, one or more information processing devices (server devices) 30, and one or more terminal devices 40. In normal operation, the system 100 includes two or more facilities 10 and two or more terminal devices 40, but the present disclosure also includes a configuration in which the system 100 includes a single facility 10 or a single terminal device 40. Furthermore, the information processing device 30 may be realized by two or more devices.

[0021] The equipment 10 is, for example, a production facility or processing facility for a product, and includes linear motion parts, rotating parts, tools, etc. In many cases, the linear motion parts, etc. are operated by a motor.

[0022] The sensor 11 is a sensor attached to the equipment 10 that senses vibrations or the current value of the motor when a linear motion part or the like of the equipment 10 operates. In one embodiment, the sensor 11 senses when a moving part such as a linear motion part operates, but is not limited to this. For example, the present disclosure also includes an embodiment in which the equipment 10 includes a heater or a cooler and the sensor 11 senses the temperature of the heater or cooler, and an embodiment in which the sensor 11 is an image sensor.

[0023] The information processing device 30 functions as a server for the terminal device 40, and includes a control unit 31, a storage unit 36, a communication unit 38, and a notification unit 39.

[0024] The control unit 31 is a control device such as a CPU that controls the entire information processing device 30, and includes a first acquisition unit 32, a second acquisition unit 33, a determination unit , and a learning unit .

[0025] The first acquisition unit 32 acquires the first information relating to the state of the equipment 10 from the storage unit 36. However, the first acquisition unit 32 may acquire the first information directly from the equipment 10.

[0026] In one aspect, the first information includes the sensing result of the sensor 11 and information indicating whether the equipment 10 is in a state requiring an action by an operator or a state not requiring an action. Here, a state requiring an action by an operator means, for example, a state in which an abnormality has occurred in the equipment 10, or a state in which repair, part replacement, inspection, or maintenance is required. When the equipment 10 requires an action by an operator, the first information corresponding to the equipment 10 may include information indicating an estimated time for the action based on the proficiency level of the operator, and an overall improvement degree of the equipment 10 upon completion of the action.

[0027] The first acquisition unit 32 may also be configured to acquire, as the first information, information indicating the degree of damage, lubrication, or wear of movable parts or the like of the equipment 10, derived from the sensing result.

[0028] The first acquisition unit 32 may also be configured to acquire, as the first information, information indicating feature quantities extracted from the sensing results of the sensors 11 provided in the equipment 10. Examples of the feature quantities include feature quantities related to the signal level or waveform of vibration or power waveform in a moving part of the equipment 10. The extraction of the information indicating the feature quantities may be performed by a device (not shown) connected to the equipment 10, or by the control unit 31. The same applies to the derivation of the lubrication degree and the like. Extracting feature quantities to reduce the amount of data contributes to saving storage capacity and reducing communication traffic.

[0029] Fig. 2 is an example of a table showing the first information stored in the storage unit 36. In the table of Fig. 2, the first information displayed includes information such as that equipment A needs inspection, that equipment B and D do not need any action, that equipment C needs repair, and that equipment E is broken down and not operational. Here, equipment A to equipment E are each an example of equipment 10.

[0030] 2, the table displays, as first information, numerical values ​​indicating the "sufficiency status" of each facility 10. However, each of the numerical values ​​may change depending on the state of the facility 10 that requires action.

[0031] Here, the "required level (or higher)" in the sufficiency status refers to the minimum proficiency required for a worker to deal with the equipment 10, and the "required number of workers" refers to the number of workers required for equipment maintenance, i.e., the number of workers required as candidates to deal with the equipment 10. Furthermore, the "number of workers on hand" refers to the number of workers who meet the conditions for dealing with the equipment 10, and the "difference" refers to the difference between the required number of workers and the number of workers on hand. In other words, a "difference" of -1 means that the number of workers on hand is one worker short of the required number of workers. Furthermore, the "number of workers to be trained" refers to the number of people currently being trained to meet the conditions for dealing with the equipment 10.

[0032] Furthermore, the "elapsed time" in Fig. 2 refers to the time that has elapsed since the equipment 10 entered a state requiring some kind of action. For example, three hours have passed since equipment A entered a state requiring inspection, and two days have passed since equipment E broke down. Note that no elapsed time is specified for equipment B and D, which are in a state that does not require action, i.e., are in a "normal" state. The same applies to the "estimated time to action" and "degree of improvement upon completion of action" described below.

[0033] The "estimated response time" refers to the estimated time required for a single worker to respond from the start of the response to the completion of the response and return the system to normal. The estimated response time may be determined based on the worker's level of proficiency, and may also vary depending on the number of workers performing the response.

[0034] Furthermore, the "degree of improvement upon completion of measures" refers to a value indicating the degree of improvement of the entire equipment 10 as a result of completing measures on the equipment 10. For example, the value of the degree of improvement upon completing measures on equipment C is 20, and the value of the degree of improvement upon completing measures on equipment A is 10, so completing measures on equipment C contributes more to the degree of improvement of the entire equipment 10 than completing measures on equipment A.

[0035] Furthermore, the values ​​of "estimated response time" and "degree of improvement upon completion of response" may be predetermined values ​​corresponding to each state requiring response, or the control unit 31 may be configured to periodically update them using a machine learning technique.

[0036] The second acquisition unit 33 acquires second information related to the skills of workers involved in the equipment 10 from the storage unit 36. However, the second acquisition unit 33 may acquire the second information directly from the terminal device 40. Examples of the second information will be described later.

[0037] The determination unit 34, for example, compares the sensing result of the sensor 11 with a predetermined threshold value to determine whether the equipment 10 is in a state that requires an action by an operator. Furthermore, the determination unit 34 refers to the first information and the second information to determine an action to optimize the overall state of the equipment 10 including each piece of equipment 10, and an operator to handle each piece of equipment 10. Here, the action to optimize the overall state of the equipment 10 may include information indicating the priority order of the equipment 10 that should be handled.

[0038] In the example of FIG. 2, when equipment C is compared with equipment A, the degree of improvement upon completion of the countermeasure is greater for equipment C for the same expected time for the countermeasure. Furthermore, when equipment A is compared with equipment E, the expected time for the countermeasure is shorter for equipment A for the same degree of improvement upon completion of the countermeasure. Therefore, unless influenced by factors such as the schedule of the workers, the determination unit 34 normally determines the order of priority to be equipment C, equipment A, and equipment E. Furthermore, in one aspect, the countermeasure that optimizes the overall state of the equipment 10 means the countermeasure that can be assigned to each worker in order of highest priority and so that each equipment 10 is in a normal state as much as possible.

[0039] Furthermore, the determination unit 34 may determine measures to optimize the overall state of the equipment 10 and the worker to handle each piece of equipment 10 using a learning model (machine learning model) 37 that receives the first information and the second information and outputs measures to optimize the overall state of the equipment 10 and information related to skills according to the state of the equipment 10. Here, the information related to skills according to the equipment 10 is, for example, information indicating skills according to the state of the equipment 10 or information indicating a worker who has the skills. Note that a manner in which other information is also input to the learning model 37 will be described later.

[0040] In addition, the judgment unit 34 may be configured to use a learning model 37 to which at least the first information is input to judge whether the equipment 10 is in any state that requires action, and to make a judgment about predictive detection that predicts when the equipment 10 will enter the state.

[0041] For example, the determination unit 34 can make the determination using any one of the following machine learning techniques or a combination thereof.

[0042] Support Vector Machine (SVM) Clustering Inductive Logic Programming (ILP) Genetic Algorithm (GP) Bayesian Network (BN) Neural Network (NN) When using a neural network, input data may be preprocessed before being used as input to the neural network. For such processing, techniques such as data augmentation, in addition to one-dimensional or multidimensional arraying of data, can be used.

[0043] Furthermore, when a neural network is used, a convolutional neural network (CNN) including convolution processing may be used. More specifically, a convolutional layer that performs convolutional calculations may be provided as one or more layers included in the neural network, and a filter calculation (product-sum calculation) may be performed on input data input to the layer. When performing the filter calculation, processing such as padding may be used in combination, or an appropriately set stride width may be adopted.

[0044] Furthermore, a multi-layer or ultra-multi-layer neural network having tens to thousands of layers may be used as the neural network. The above-mentioned machine learning may be supervised learning, unsupervised learning, or reinforcement learning.

[0045] The learning unit 35 trains the learning model 37 by updating the values ​​of the parameter set that defines the learning model 37. In one aspect, the learning unit 35 trains the learning model 37, which outputs information indicating measures to optimize the overall state of the equipment 10, by inputting the first information and the second information as explanatory variables and the information indicating measures to optimize the overall state of the equipment 10 and information regarding skills according to the state of the equipment 10 as objective variables.

[0046] Furthermore, the control unit 31 may refer to the first information, second information, etc. stored in the storage unit 36 ​​when performing each process, and the same applies below.

[0047] The storage unit 36 ​​is a storage device that at least temporarily stores various information. For example, the storage unit 36 ​​stores the sensing results of the sensors 11 acquired from each of the facilities 10 as first information related to the state of the facilities 10.

[0048] The storage unit 36 ​​also stores second information related to the skills of workers involved in the facility 10. In one aspect, the second information may include some or all of the worker's qualifications, training history, department, years of service, past history and results of actions taken against the facility 10, and the worker's skill map, development plan, and skill analysis results derived from these.

[0049] Here, the aforementioned skill map visualizes, using numerical values ​​and symbols, the equipment 10 that each worker can handle, their proficiency in handling the equipment, and the skills that each worker possesses, particularly in relation to their work. The training plan is a schedule for training and other activities to improve work skills. In many cases, the training plan includes an estimated time required for completion. The skill analysis result is, for example, an evaluation of the worker's skills and the tasks that the worker is expected to be able to perform based on the skills possessed by the worker. The control unit 31 may also derive other types of second information by referring to one or more types of second information stored in the memory unit 36. For example, the control unit 31 may derive the skill analysis result for a certain worker by referring to past results of handling the equipment 10.

[0050] Fig. 3 is an example of a table showing the second information stored in the memory unit 36. In the table of Fig. 3, the proficiency level (level) of the worker in handling each piece of equipment 10 is displayed as the second information. In the example of Fig. 3 and Fig. 2, Mr. Yamada's proficiency level in handling equipment A is "4", which exceeds the proficiency level of "3" required to handle equipment A, and therefore Mr. Yamada can handle equipment A.

[0051] Furthermore, "Training A Completion Date" in FIG. 3 refers to the date on which the worker completed a certain training A related to equipment 10. Specifically, "Yamada" and "Tanaka" completed training A on February 1, 20YY, and training A was not conducted for "Sato." Furthermore, "Qualification B Acquisition Date" refers to the date on which the worker acquired a certain qualification B related to equipment 10. In the example of FIG. 3, "Yamada" does not hold qualification B.

[0052] The storage unit 36 ​​also stores data such as parameter sets that define one or more learning models 37. The storage unit 36 ​​may be configured to store data that realizes each of the learning models 37 described above, as well as data that realizes a learning model 37 for performing natural language processing. In this configuration, the storage unit 36 ​​and the control unit 31 can be considered to operate as an AI (Artificial Intelligence) having the learning models 37. The AI ​​may also be configured to accept instructions in natural language from a user. This enables, for example, requests to output information and instructions regarding equipment maintenance to be made in natural language, thereby improving user convenience. Instructions from a user may be entered via an input device, such as a keyboard (not shown), connected to the information processing device 30, or via a terminal device 40 via the communication unit 38.

[0053] Additionally, the storage unit 36 ​​may store information indicating procedures for dealing with the equipment 10 and information indicating the schedules of each worker. Note that the schedules of workers may affect their skills, and therefore may be included in the second information.

[0054] The communication unit 38 is an interface that performs communication processing with other devices such as the facility 10 and the terminal device 40 under the control of the control unit 31.

[0055] The notification unit 39 notifies each worker determined to be responsible for handling each facility 10, requesting that the worker handle the facility 10. In one embodiment, the notification unit 39 performs the notification by transmitting information indicating a request for handling the facility 10 to the terminal device 40 carried by each worker via the communication unit 38. The notification may also include information indicating a procedure for handling the facility 10. The information indicating the procedure may include the location of the facility 10, an equipment maintenance manual, and storage locations and instructions for obtaining replacement parts, consumables, or tools. The terminal device 40 may also be configured to store at least a portion of the information indicating the procedure for handling the facility 10 and read and output the procedure in response to the requested action. The information indicating the procedure for handling the facility 10, which is notified to each worker, may also be derived by the control unit 31 through machine learning using the learning model 37, which inputs at least the second information. For example, even when the same action is notified, different information may be notified depending on the worker's level of proficiency, etc. The notification unit 39 may also transmit information indicating that the equipment 10 is in a state requiring action to the terminal device 40 carried by another worker who has not been determined to be the worker who will take action on the equipment 10. Here, the other worker is a worker in the same department as the worker who will take action, or a superior, etc. The control unit 31 may be configured to also function as the notification unit 39.

[0056] Furthermore, the information processing device 30 may be realized as an on-premise type provided in the same facility as the equipment 10, or as a cloud type, or may be realized as a combination of these. For example, the information processing device 30 that stores the first information may be realized as an on-premise type, and the information processing device 30 that stores the second information may be realized as a cloud type. Furthermore, the terminal device 40 may obtain each piece of information of the information processing device 30 and perform a predetermined input operation using, for example, an API (Application Programming Interface).

[0057] The terminal device 40 is a device realized as a smartphone, tablet, personal computer, or the like carried by the manager and worker of the facility 10.

[0058] For example, the terminal device 40 performs a process of outputting a notification received from the notification unit 39 of the information processing device 30, and a process of transmitting information indicating that the handling has been completed to the information processing device 30.

[0059] Furthermore, it is desirable that at least the terminal device 40 possessed by the administrator is configured to be able to acquire the first information or the second information from the information processing device 30 and display a graph, table, or the like based on each piece of information. Note that the graph, etc. may be generated by the information processing device 30 and transmitted to the terminal device 40.

[0060] Furthermore, a configuration may be adopted in which a portion of the information stored in the information processing device 30 and a portion of the information stored in the terminal device 40 are synchronized as needed. For example, when an input operation for updating the second information is performed on the terminal device 40, the updated content of the second information may be automatically reflected in the second information stored in the information processing device 30.

[0061] [2. System processing example] Next, a process flow of an information processing method executed by the information processing device 30 included in the system 100 will be described. FIG. 4 is an example of a flowchart illustrating the process flow. The process illustrated in the flowchart of FIG. 4 is started, for example, when a predetermined timing set in advance arrives, when a certain amount of time has elapsed since the most recent process was performed, or when a predetermined instruction is given to the information processing device 30 via a terminal device 40 held by the manager of the facility 10. As described above, the instruction may be given in natural language. For example, the system 100 may start the process illustrated in the flowchart of FIG. 4 in response to input of the text "Perform facility maintenance of the factory" from the terminal device 40 to the information processing device 30.

[0062] In S101 (step S101), the first acquisition unit 32 of the information processing device 30 acquires first information regarding the state of each facility 10 from the storage unit 36. Here, the acquired first information includes information that indicates the state of the facility 10 that requires an action by an operator and that is determined in advance by the determination unit 34 based on the sensing result.

[0063] In S102, the second acquisition unit 33 acquires second information on the skills of the workers involved in the equipment 10 from the storage unit .

[0064] In S103, the determination unit 34 inputs the first information and the second information to the learning model 37 that outputs measures to optimize the state of the entire equipment 10 and information related to skills according to the state of the equipment 10.

[0065] In S104, the determination unit 34 determines, based on the output of the learning model 37, a measure to optimize the overall state of the facilities 10 including each piece of equipment 10, and a worker to handle each piece of equipment 10. Here, the worker to handle each piece of equipment 10 includes one or more workers who satisfy the conditions for proficiency in handling the piece of equipment 10.

[0066] If all of the facilities 10 targeted for facility maintenance by the system 100 are in a state requiring no action, the subsequent processing is not performed, and the processing shown in the flowchart of FIG. 4 ends.

[0067] In S105, the notification unit 39 transmits a request to take action on the facility 10 to the terminal device 40 carried by the worker who has been determined to be the worker who will take action on the facility 10.

[0068] In S106, after the worker has completed the treatment of the equipment 10, the control unit 31 acquires information indicating that the treatment has been completed.

[0069] Here, the process in which the control unit 31 acquires the information may be performed by an operator inputting information indicating that the response has been completed into the terminal device 40, and the terminal device 40 then transmitting the information to the information processing device 30.

[0070] In S107, the control unit 31 updates the first information and the second information stored in the storage unit 36 ​​so as to reflect the results of the worker's actions on the equipment 10. For example, the control unit 31 updates the action history for each equipment 10 and each worker, and then updates the first information for the equipment 10 for which action has been taken, assuming that the equipment 10 is in a normal state that does not require action. Furthermore, when the number of actions taken reaches a predetermined number or more, the control unit 31 updates the second information so that the worker's proficiency level increases.

[0071] As described above, according to the information processing method described above, it is possible to easily optimize the overall state of the equipment 10 in a factory or the like.

[0072] [3. Modifications] In a configuration in which the determination unit 34 uses the learning model 37 to determine measures to optimize the overall state of the facilities 10 and the workers to handle each facility 10, the information input to the learning model 37 is not limited to the first information and the second information. For example, each facility 10 may be a facility for producing an item, and the learning model 37 may be configured to input production plan information for the item in addition to the first information and the second information. The same applies to the learning of the learning model 37 by the learning unit 35.

[0073] The production plan information may be included in the first information regarding the state of each facility 10. The production plan information may include, for example, at least one of the following information: Power plan information: This may include information indicating the target value for the upper limit of power consumed by the entire equipment 10, the target value for reducing power consumption, the electricity cost per unit amount, and the power consumption of each equipment 10. By referring to the power plan information, for example, priority is given to equipment 10 that consumes less power. CO2 planning information: This may include information indicating the target upper limit of CO2 emitted by the entire equipment 10, the target value for reducing CO2 emissions, and the CO2 emissions of each equipment 10. By referring to the CO2 planning information, for example, priority is given to equipment 10 with low CO2 emissions. Management plan information: This may include target values ​​for sales, profits, and management-related indexes for the products produced at each facility 10, as well as information indicating the degree of contribution of the products to these sales, etc. By referring to the management plan information, for example, priority is given to dealing with facilities 10 that produce products that have a large contribution to profits. Demand forecast information: This may include information indicating the expected number of items in demand for the products produced at each facility 10 and the increase or decrease in that number. By referring to the demand forecast information, for example, priority is given to dealing with facilities 10 that produce items expected to be in high demand. Sales target information: This may include information regarding the sales target number of products produced at each facility 10. By referring to the sales target information, for example, a facility 10 that produces products for which the sales target has not been achieved is given higher priority.

[0074] [4. Additional Notes] The proficiency of a worker in handling equipment 10 can be updated manually or automatically by the control unit 31 by completing a development plan such as training for each piece of equipment 10, as well as by, for example, obtaining a qualification, handling the equipment one or more predetermined number of times, accompanying other highly skilled workers in handling the equipment, or working for a predetermined period of time.

[0075] Furthermore, the workers who deal with the equipment 10 may include outsourced contractors and workers who place orders with the contractors.

[0076] Furthermore, the control unit 31 may update the proficiency level of the worker according to the time required for the worker to deal with the equipment 10. Here, the time required for the worker to deal with the equipment 10 may be the time from when the notification unit 39 requests the worker to deal with the equipment 10 until the completion of the deal, or the time from when the worker starts to deal with the equipment 10 until the completion of the deal. In the latter case, the worker may transmit the time when the worker starts to deal with the equipment 10 or the time required for the deal to the information processing device 30 via the terminal device 40. For example, if the time required for the worker to deal with the equipment 10 is less than a predetermined time, the control unit 31 may increase the proficiency level of the worker, or if the time required for the worker to deal with the equipment 10 is equal to or greater than the predetermined time, the control unit 31 may decrease the proficiency level of the worker. The predetermined times described above may be the same or different from each other. The same applies to the predetermined period described below.

[0077] Furthermore, if a predetermined period of time has passed since the worker completed dealing with the equipment 10 and before the equipment 10 next entered any state requiring attention, the control unit 31 may increase the proficiency level of the worker. Furthermore, if the equipment 10 entered any state requiring attention before a predetermined period of time has passed since the worker completed dealing with the equipment 10, the control unit 31 may decrease the proficiency level of the worker.

[0078] Furthermore, even if the equipment 10 does not currently require any action, the control unit 31 may be configured to detect signs and calculate backwards to determine when inspection or maintenance of the equipment 10 will be required, allowing a manager to assign workers in advance. As described above, the detection of signs may be performed by the determination unit 34 using a machine learning technique.

[0079] In addition, if the control unit 31 knows based on each worker's schedule that the "number of holders" shown in Figure 2 will decrease on a specified date due to the planned retirement of a highly skilled worker, etc., the control unit 31 may set a training plan so that other workers will meet the conditions for taking action on the equipment 10 around the specified date.

[0080] Furthermore, the control unit 31 may automatically set a training plan for workers and an equipment maintenance plan for inspection or maintenance, etc., by referring to the first information, the second information, etc. For example, the control unit 31 may set a training plan so that the "difference" in FIG. 2, i.e., the difference between the number of workers required for equipment maintenance and the number of workers who meet the conditions for dealing with the equipment 10, varies less among the equipment 10. Furthermore, the control unit 31 may set an equipment maintenance plan so that, when a certain equipment 10 needs repair or has broken down and is no longer operational, the frequency of inspection or maintenance of that equipment 10 is increased for a certain period of time from the default frequency.

[0081] In addition, the control unit 31 may be configured to recommend one or more of the above-mentioned development plans or equipment maintenance plans by sending them to a terminal device 40 held by the manager, and the manager may select the development plan or equipment maintenance plan to be implemented via the terminal device 40.

[0082] In addition, the terminal device 40 carried by each worker may store information indicating the worker's schedule, and when a request to take action on the equipment 10 is received from the information processing device 30, the planned action may be reflected in the schedule automatically or by the worker performing an operation to approve the action.

[0083] As described above, the system 100 may include a plurality of members (devices), and a plurality of administrators may each possess a terminal device 40.

[0084] [5. Software implementation example] The functions of the information processing device 30 and the terminal device 40 (hereinafter referred to as "devices") can be realized by a program that causes a computer to function as the device, and a program that causes a computer to function as each control block of the device.

[0085] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program, thereby realizing the functions described in each of the above embodiments.

[0086] The program may be non-transitory and may be recorded on one or more computer-readable recording media. The recording media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.

[0087] Furthermore, some or all of the functions of the control blocks can be realized by logic circuits. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present invention. In addition, the functions of the control blocks can also be realized by, for example, a quantum computer.

[0088] Furthermore, each process described in each of the above embodiments may be executed by AI (Artificial Intelligence). In this case, the AI ​​may run on the control device or on another device (for example, an edge computer or a cloud server).

[0089] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of symbols]

[0090] 10 Equipment 11 Sensors 30 Information processing device (server device) 31 Control Unit 32 First acquisition part 33 Second acquisition part 34 Judgment section 35 Learning Department 36 Memory section 37 Learning model (machine learning model) 38 Communications Department 39 Notification Department 40 Terminal Equipment 100 systems

Claims

1. a first acquisition unit that acquires first information regarding the state of each piece of equipment; a second acquisition unit that acquires second information related to the skills of workers involved in each piece of equipment; a determination unit that refers to the first information and the second information to determine a measure to optimize the overall state of the facilities including each piece of equipment and a worker that should deal with each piece of equipment; An information processing device comprising:

2. The determination unit The first information and the second information are input, and a machine learning model is used to output information indicating measures to optimize the state of the entire equipment and information regarding skills according to the state of the equipment to determine measures to optimize the state of the entire equipment and workers to handle each piece of equipment. The information processing device according to claim 1 .

3. The AI ​​having the machine learning model accepts instructions from a user in natural language. The information processing device according to claim 2 .

4. Each facility is a facility for producing goods, In addition to the first information and the second information, production plan information regarding the item is input to the machine learning model.

4. The information processing device according to claim 2 or 3.

5. The system further includes a notification unit that issues a notification to each worker determined to be a worker who will handle each piece of equipment, the notification including information indicating a handling procedure, requesting the worker to handle each piece of equipment.

3. The information processing device according to claim 1 or 2.

6. When the worker takes action on the equipment, the first information and the second information are updated so that the result of the action is reflected.

3. The information processing device according to claim 1.

7. The first acquisition unit Acquire, as the first information, information indicating a feature extracted from a sensing result of a sensor installed in the facility.

3. The information processing device according to claim 1 or 2.

8. The second acquisition unit At least one of a skill map, a training plan, and a skill analysis result of a worker involved in the equipment is acquired as the second information.

3. The information processing device according to claim 1 or 2.

9. 1. An information processing method performed by an apparatus, comprising: a first acquisition step of acquiring first information regarding the state of each piece of equipment; a second acquisition step of acquiring second information on the skills of workers involved in each piece of equipment; a determination step of determining a measure to optimize the overall state of the facilities including each piece of equipment and a worker to deal with each piece of equipment by referring to the first information and the second information; An information processing method including:

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