WO2021059847A1 - Information processing system, information processing method, and information processing program - Google Patents
Information processing system, information processing method, and information processing program Download PDFInfo
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- This disclosure relates to an information processing system, an information processing method, and an information processing program that perform machine learning to generate a specific trained model and perform data analysis using the trained model.
- Patent Document 1 discloses a sorting system capable of efficiently sorting even rubble in which a wide variety of materials are mixed and waste obtained by dismantling a building.
- image processing is performed from image data obtained by capturing images of waste, and the types of waste materials such as wood, plastic, glass, and gypsum board are sorted.
- Patent Document 2 discloses a system for evaluating a machine learning model using a training data set.
- machine learning required to use such an AI engine requires, for example, machine learning using a large amount of teacher data, which requires a great deal of time and effort.
- machine learning model information can also be used by other AI engines.
- a system that provides the model information used in one AI engine as a learned model to another AI engine has not been provided.
- SaaS Software as a Service
- a cloud server via the Internet
- SaaS Software as a Service
- a learning center for acquiring target data to be analyzed and performing machine learning is provided by a server, a learned model used by an AI engine is acquired from the learning center, and a self-AI engine is used.
- the information processing system, information processing method, and information processing program that can be used to perform analysis processing in.
- the information processing system is an information processing system including a learning server that performs machine learning to generate a specific trained model and an analysis device that performs data analysis using the trained model.
- the learning server generates a trained model which is model information for performing machine learning based on the target data and the target data acquisition unit for acquiring the target data to be data-analyzed, or performing data analysis.
- a learning unit that updates the trained model and a providing unit that provides the trained model to the analysis device are provided, and the analysis device is based on the model acquisition unit that acquires the trained model from the training server and the trained model. It includes an analysis unit that analyzes the data of the target data, and a result output unit that outputs the trained model according to the purpose of the trained model based on the result of the data analysis.
- the information processing method in one aspect of the present disclosure is an information processing method that performs machine learning to generate a specific trained model and provides the trained model to an analyzer that performs data analysis using the trained model. Therefore, the target data acquisition step for acquiring the target data to be the target of data analysis performed by the target data acquisition unit, and the model information for performing machine learning based on the target data and performing data analysis performed by the learning department.
- a training step of generating a trained model or updating a trained model a providing step of providing a trained model to an analysis device performed by the providing unit, and a training model performed by the analysis device performed by the model acquisition unit.
- the purpose of the trained model is determined by the model acquisition step of acquiring the model, the analysis step of analyzing the target data based on the trained model performed by the analysis unit, and the result of the data analysis performed by the result output unit. It includes a result output step for outputting.
- the information processing program in one aspect of the present disclosure performs machine learning to generate a specific trained model, and provides the trained model to an analyzer that performs data analysis using the trained model.
- the target data acquisition step to acquire the target data to be the target of data analysis and the trained model which is the model information for performing machine learning based on the target data and performing the data analysis are generated, or Data analysis of target data is performed based on the training step of updating the trained model, the provision step of providing the trained model to the analysis device, the model acquisition step of acquiring the trained model by the analysis device, and the trained model.
- the computer is made to execute the analysis step and the result output step that outputs according to the purpose of the trained model based on the result of the data analysis.
- a learning server that performs machine learning to generate a specific trained model acquires target data to be analyzed and performs machine learning to generate a trained model.
- the analyzer Provided to the analyzer (AI engine).
- the analysis device analyzes the data based on the trained model and outputs the data according to the purpose of the trained model. Therefore, the result of machine learning performed by the learning server can be used by a plurality of analysis devices. As a result, the labor of machine learning can be reduced, and the trained model, which is the result of machine learning of others, can be used.
- the analysis device can be used in a local environment separated from the learning server.
- FIG. 1 is a block configuration diagram showing an information processing system 1 according to the first embodiment of the present disclosure.
- the information processing system 1 is not limited to, but as an example, is a system that performs machine learning to generate a specific trained model and analyzes data using the generated trained model.
- This trained model is, for example, model information obtained by machine learning from target data to be analyzed for a certain data, and model information that can be used not only by the analysis device that provided the target data but also by other analysis devices.
- the information processing system 1 is a system that enables horizontal expansion of a trained model generated by performing machine learning.
- the information processing system 1 has a learning server 100, analysis devices 201, 202, ..., And a network NW.
- the learning server 100 and the analysis devices 201, 202, ... are connected to each other via the network NW.
- the network NW is a communication network for communication, and is not limited to the Internet, an intranet, a LAN (Local Area Network), a WAN (Wide Area Network), a wireless LAN (Wireless LAN: WAN), and a wireless WAN (Wireless LAN). It is configured to be connected via a communication network including a wireless WAN (WAN), a virtual private network (VPN), and the like.
- WAN Local Area Network
- VPN virtual private network
- analysis devices 201, 202, ... Each have the same configuration, and when the configurations of the analysis devices 201, 202, ... Are described, they are collectively referred to as the analysis device 200.
- the learning server 100 generates a specific trained model by performing machine learning from the target data to be analyzed, and uses the generated trained model as one or a plurality of analysis devices 200 (analysis devices 201, 202, ... ⁇ ) Is a device that provides a function as a learning center by providing it to. As an example, not limited to, model information for reading character information by OCR for handwritten characters and sorting of industrial waste are performed. It is configured as a device that generates model information for controlling the device by machine learning.
- the learning server 100 is not limited, but is composed of, for example, a computer (desktop, laptop, tablet, etc.) that provides various Web services, a server device, and the like.
- the learning server 100 is not limited to a device that operates independently, and may be a distributed server system or a cloud server in which a plurality of devices are connected to each other via a communication network and cooperate with each other to perform communication.
- the analysis device 200 acquires a trained model from the learning server 100, analyzes the target data to be analyzed as an AI engine based on the trained model, and outputs the target data according to the purpose of the trained model. It is a device, and is not limited to an example, and is configured as a device for reading character information by OCR for handwritten characters and a device for controlling a device for separating industrial waste.
- the analysis device 200 is not limited, but is composed of, for example, a computer (desktop, laptop, tablet, etc.) that controls various devices as described above, a server device, and the like. Further, the analysis device 200 is composed of analysis devices 201, 202, ...
- the external device 200 is not limited to a device that operates independently, and may be a distributed server system or a cloud server in which a plurality of devices are connected to each other via a communication network and cooperate with each other to perform communication.
- a sorting device 400 which will be described later, is connected, or the analysis device 200 is built in the sorting device 400.
- FIG. 2 is a block configuration diagram showing an information processing system 1A which is a modification of the information processing system 1 of FIG.
- the information processing system 1A provides a learning center by performing machine learning from target data to be analyzed to generate a specific trained model and providing the generated trained model to the analysis device 200. It is similar to the information processing system 1 in that it is different from the information processing system 1 according to the first embodiment in that it is provided with the communication means T instead of the network NW.
- the analysis device 200 does not need to be always connected to the learning server 100, and when the trained model is acquired, for example, a predetermined timing set or a user of the analysis device 200 operates the learning server. An example of the case of being connected when accessing 100 is shown.
- the analysis device 200 can be operated stand-alone except for the timing of acquiring the trained model from the learning server 100, thereby reducing the labor of machine learning and the machine of another person (other company).
- a trained model that is the result of learning can be used.
- Other configurations are the same as those in the first embodiment.
- the communication means T is a line facility that communicates directly or via a communication network, and is not limited, but is configured to be directly connected by a USB (Universal Serial Bus) cable, a LAN (Local Area Network) cable, or the like. Or, Internet, Intranet, LAN (Local Area Network), WAN (Wide Area Network), Wireless LAN (Wireless LAN: WAN), Wireless WAN (Wireless WAN: WAN), Virtual Private Network (VPN) It is configured to be connected via a communication network including the above.
- USB Universal Serial Bus
- LAN Local Area Network
- WAN Wide Area Network
- Wireless LAN Wireless LAN
- Wireless WAN Wireless WAN
- VPN Virtual Private Network
- FIG. 3 is a functional block configuration diagram showing the learning server 100 of FIG.
- the learning server 100 includes a communication unit 110, a storage unit 120, and a control unit 130.
- the communication unit 110 is a communication interface for communicating with the analysis device 200 by wire or wirelessly via the network NW or the communication means T, and any communication protocol may be used as long as mutual communication can be executed. Good.
- the communication unit 110 is not limited, and for example, communication is performed by a communication protocol such as TCP / IP (Transmission Control Protocol / Internet Protocol).
- the storage unit 120 stores programs, input data, and the like for executing various control processes and each function in the control unit 130.
- the storage unit 120 is not limited, but as an example, a RAM (RandomAccessMemory) and a ROM (ReadOnly). It is composed of a memory including a memory) and a storage including an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory and the like. Further, the storage unit 120 stores the target data DB 121A, the target data DB 121B, and the trained model DB 122. Further, the storage unit 120 temporarily stores the data when communicating with the analysis device 200 and the data generated by each process described later.
- the target data DBs 121A and 121B store target data for machine learning by the learning server 100.
- This target data is data provided by the analysis device 200 by the user's selection of the analysis device 200, which will be described later, and is not limited but, as an example, data according to the device on which the analysis device 200 is mounted, and is OCR. It is image data for reading text information and image data for separating industrial waste.
- the target data DB 121A and the target data DB 121B store the target data provided by different analysis devices 200.
- the target data DB 121A stores the target data provided by the analysis device 201.
- the target data provided by the analysis device 202 is stored in the data DB 121B.
- the users of the analysis devices 201, 202, ... Are configured so that they cannot refer to data other than the target data provided by themselves. The reason for this configuration is that if there is a risk that other companies may refer to your own data, you may hesitate to provide the data, and such data is for the individual customer of the user. This is because information may be included. Therefore, the target data stored in the target data DBs 121A and 121B is used only for machine learning and is not used for any other purpose.
- the trained model DB 122 stores model information generated by machine learning by the learning server 100.
- This model information is model information for the learning server 100 to perform data analysis as an AI engine, not limited to, as an example, model information for reading character information by OCR, and model information for separating industrial waste. Is stored.
- the control unit 130 controls the overall operation of the learning server 100 by executing a program stored in the storage unit 120.
- the control unit 130 is not limited to the CPU (Central Processing Unit) and the MPU (Micro). Devices including ProcessingUnit), GPU (GraphicsProcessingUnit), microprocessor (Microprocessor), processor core (Processorcore), multiprocessor (Multiprocessor), ASIC (Application-Specific IntegratedCircuit), FPGA (Field ProgrammableGateArray), etc. Consists of.
- the target data acquisition unit 131, the learning unit 132, the provision unit 133, and the billing amount determination unit 134 are provided as the functions of the control unit 130.
- the target data acquisition unit 131, the learning unit 132, the provision unit 133, and the charge amount determination unit 134 are started by the program stored in the storage unit 120 and executed by the learning server 100.
- the target data acquisition unit 131 uses the analysis target data provided by the analysis device 200 by the user of the analysis device 200 as the target data for the learning server 100 to perform machine learning. Acquired via the communication unit 110.
- the image data of the handwritten character when reading the character information by OCR for the handwritten character and the image data of the industrial waste for separating the industrial waste according to the implementation example of the analyzer 200.
- the target data acquisition unit 131 transfers the acquired target data to the target data DB 121A from the target data provided by the analysis device 201 and the target data provided from the analysis device 202 to the target data DB 121B as described above. Store each in.
- the learning unit 132 performs machine learning based on the target data acquired by the target data acquisition unit 131, generates a trained model and stores it in the trained model DB 122, or has trained stored in the trained model DB 122. Update the model.
- This target data is data including the data to be analyzed by the analysis device 200 and the result of the data analysis.
- the trained model is updated, for example, by an aggregation process that merges the updated information based on the result of machine learning with the trained model stored in the trained model DB 122.
- the learning unit 132 has learned a model that is the result of machine learning based on the target data stored in the target data DB 121A and a trained model that is the result of machine learning based on the target data stored in the target data DB 121B. You may perform aggregation processing to merge with the model.
- the machine learning by the learning unit 132 is not limited, but as an example, it may be performed by supervised machine learning, it may be performed by unsupervised machine learning, or it may be performed by deep learning.
- the providing unit 133 provides the learned model generated by the learning unit 132 by the operation of the user of the analysis device 200 by transmitting the learned model to the analysis device 200 via the communication unit 110.
- the analysis device 200 is configured to be provided by downloading the trained model from the learning server 100, but is provided as a software service of the AI engine in a state of being stored in the learning server 100. , So-called SaaS may be provided.
- the billing amount determination unit 134 determines the billing amount, which is the consideration for the provision of the learned model.
- This billing amount may be set by, for example, the amount of data of the trained model or the amount of money per unit time using the trained model. Further, for example, when the target data acquired by the target data acquisition unit 252, which will be described later, is provided to the learning server 100, the billing amount is set low, and when the target data is not provided to the learning server 100, the billing amount is high. It may be set. By setting in this way, the user is motivated to provide the target data, and the trained model can be made more accurate by performing more learning.
- FIG. 4 is a functional block configuration diagram showing the analysis device 200 of FIG.
- the analysis device 200 includes a communication unit 210, a display unit 220, an operation unit 230, a storage unit 240, and a control unit 250.
- the communication unit 210 is a communication interface for communicating with the learning server 100 by wire or wirelessly via the network NW or the communication means T, and any communication protocol can be used as long as mutual communication can be executed. Good.
- the communication unit 210 is not limited, and for example, communication is performed by a communication protocol such as TCP / IP. Further, in the case of the information processing system 1A shown in FIG. 2, the communication unit 210 operates when the communication means T is connected between the learning server 100 and the analysis device 200.
- the display unit 220 is a user interface used to display the operation content input by the user and the transmission content from the learning server 100, and is composed of a liquid crystal display or the like.
- the display unit 220 displays, for example, the analysis result by the analysis unit 253.
- the operation unit 230 is a user interface used for the user to input operation instructions, and is composed of a keyboard, a mouse, a touch panel, and the like.
- the operation unit 230 is used, for example, for inputting an instruction for acquiring a learned model provided by the learning server 100 and for responding to an instruction from the learning server 100.
- the storage unit 240 stores programs for executing various control processes and each function in the control unit 250, input data, and the like.
- the storage unit 240 is not limited, and as an example, a memory including a RAM, a ROM, and the like, an HDD, and the like. It is composed of storage including SSD, flash memory and the like.
- the storage unit 240 stores the trained model DB 241 and the target data DB 242. Further, the storage unit 240 temporarily stores the data communicated with the learning server 100 and the data generated by each process described later.
- the trained model DB 241 stores model information generated by the learning server 100 by performing machine learning and provided by the learning server 100.
- This model information is the same model information as the trained model DB 122 of the learning server 100, and is not limited to model information for performing data analysis as an AI engine, but as an example, model information for reading character information by OCR. , Model information for sorting industrial waste is stored.
- the analysis device 200 is provided as a software service of the AI engine, the trained model DB 241 does not have to be provided.
- the target data DB 242 stores the target data to be analyzed by the analysis device 200 as an AI engine.
- This target data is data acquired from a device for reading character information by OCR for handwritten characters and a device for controlling a device for separating industrial waste in the above-mentioned example. In this embodiment, it is the data acquired from the sorting device 400 controlled by the analysis device 200.
- the control unit 250 controls the overall operation of the analyzer 200 by executing the program stored in the storage unit 240, and is not limited to, but as an example, a CPU, an MPU, a GPU, a microprocessor, and a processor. It is composed of a core, a multiprocessor, an ASIC, a device including an FPGA, and the like.
- the functions of the control unit 250 include a model acquisition unit 251, a target data acquisition unit 252, an analysis unit 253, a result output unit 254, and a target data providing unit 255.
- the model acquisition unit 251, the target data acquisition unit 252, the analysis unit 253, the result output unit 254, and the target data provision unit 255 are started by the program stored in the storage unit 240 and executed by the analysis device 200. ..
- the model acquisition unit 251 acquires a learned model provided by the learning server 100 as a learning center. For example, when the trained model is transmitted to the learning server 100 by the operation of the analysis device 200 of the user, the learning server 100 transmits the trained model, and the trained model is accepted via the communication unit 110.
- This trained model is model information for the analysis device 200 to perform data analysis as an AI engine, and may be a single trained model, in order to reflect the latest training results on the already acquired trained model. It may be the update information of. Further, the model acquisition unit 251 stores the acquired trained model in the trained model DB 241.
- the target data acquisition unit 252 acquires the target data to be analyzed by the analysis device 200 as an AI engine.
- the data acquired from the sorting device 400 controlled by the analysis device 200 is acquired.
- the target data acquisition unit 252 stores the acquired target data in the target data DB 2421A.
- the analysis unit 253 refers to the target data acquired by the target data acquisition unit 252 and stored in the target data DB 242 based on the trained model acquired by the model acquisition unit 251 and stored in the trained model DB 241. Performs predetermined data analysis as an engine. Specifically, in the above-mentioned example, in the case of reading the character information for the handwritten character by OCR, the image data of the handwritten character is analyzed, and in the case of separating the industrial waste, the image data of the industrial waste is used. This is an analysis, and in the present embodiment, the image data acquired from the sorting device 400 is analyzed.
- the result output unit 254 outputs the result data and various control signals according to the purpose of the trained model, that is, the purpose of the AI engine, based on the result of data analysis on the data to be analyzed by the analysis unit 253.
- the purpose of the trained model that is, the purpose of the AI engine
- the operation control of the sorting device 400 is performed.
- the target data providing unit 255 When the target data providing unit 255 provides the target data acquired by the target data acquisition unit 252 to the learning server 100 at the user's choice, the target data providing unit 255 transmits the target data to the learning server 100 via the communication unit 210.
- the trained model of the learning server 100 can perform more learning, so that the target data can be provided to the learning server 100. desirable.
- some users may not want to provide the target data to the learning server 100, which is a learning center that is a service provided by another company. Therefore, the target data is low-deficient only when the user desires.
- FIG. 5 is a perspective view showing the appearance of the sorting device 400 controlled by the analysis device 200 of FIG.
- the sorting device 400 is a device that sorts industrial waste, which is an example of various devices controlled by the analysis device 200, and includes a fixed portion 410, a mounting movable portion 420, an upper arm portion 430, and a joint portion 440. It is composed of a forearm portion 450, a wrist portion 460, a support portion 470, and a holding upper portion 480.
- the sorting device 400 captures the industrial waste X placed on the lane L of the belt conveyor shown in FIG. 5 with a camera (not shown), analyzes the image data, and analyzes the material. It is a robot arm for sorting by material.
- the cameras are arranged, for example, on lane L or on the wrist 460.
- the fixing portion 410 is a place where the sorting device 400 is fixed to the mounting table D.
- the mounting table D is fixedly arranged at a predetermined position in the vicinity of the lane L, for example.
- the mounting movable portion 420 is a movable portion to which one end of the upper arm portion 430 is connected and the angle with the upper surface of the mounting base D can be changed.
- a servomotor is built in the mounting movable portion 420, and the servomotor is driven by a control signal to rotate the upper arm portion 430.
- the upper arm portion 430 is a rod-shaped member on the mounting table D side in the sorting device 400.
- the joint portion 440 is a portion that rotatably connects the other end of the upper arm portion 430 and one end of the forearm portion 450.
- a servomotor is built in the joint portion 440, and the servomotor is driven by a control signal in the same manner as the mounted movable portion 420 to rotate the upper arm portion 430.
- the forearm portion 450 is a rod-shaped member on the distal end side of the sorting device 400.
- the wrist portion 460 is a portion that rotatably connects the other end of the forearm portion 450 and one end of the support portion 470.
- a servomotor is built in the wrist portion 460, and the servomotor is driven by a control signal in the same manner as the mounted movable portion 420 to rotate the support portion 470.
- the support portion 470 is a portion that supports the holding upper portion 480 at the tip end portion of the sorting device 400.
- the upper part 480 is a place where the industrial waste X is sandwiched and lifted.
- a servomotor is built in the holding upper part 480, and the servomotor is driven by a control signal in the same manner as the mounted movable portion 420, so that the holding upper part 480 is driven.
- machine learning is performed by image data obtained by capturing the industrial waste X with a camera, and the relationship between the feature amount and the material of the industrial waste X by data analysis of the image data is generated as a learned model. ..
- the learning unit 132 By providing this image data to the learning server 100, the learning unit 132 generates a more accurate trained model by performing machine learning of the industrial waste X actually placed on the lane L. However, it is possible to build an AI engine.
- FIG. 6 is a flowchart showing the operation of the machine learning process in the information processing system 1 of FIG.
- the learning server 100 and the analysis device 200 do not have to be connected by the communication means T while the machine learning process is performed.
- the target data providing unit 255 of the analysis device 200 transmits the analysis target data to be the target of the predetermined data analysis by the user's selection, so that the target data acquisition unit 131 of the learning server 100 communicates. Obtained via unit 110.
- the industrial waste X placed on the lane L is imaged by a camera and image data is acquired.
- the acquired target data is stored in, for example, the target data provided by the analysis device 201 in the target data DB 121A, and the target data provided by the analysis device 202 in the target data DB 121B.
- the learning unit 132 performs machine learning based on the target data acquired in step S101 and stored in the target data DBs 121A and 121B.
- the learning unit 132 generates a trained model based on the result of machine learning performed in step S102 and stores it in the trained model DB 122, or has trained stored in the trained model DB 122.
- the model is updated.
- the trained model is updated, for example, by an aggregation process that merges the updated information based on the result of machine learning with the trained model stored in the trained model DB 122.
- FIG. 7 is a flowchart showing the operation of the data analysis process in the information processing system 1 of FIG.
- the learning server 100 and the analysis device 200 are connected by the communication means T so that communication is possible.
- step S201 in the providing unit 133 of the learning server 100, the learned model generated in step S103 by the user's operation is transmitted to the analysis device 200, so that the model acquisition unit 251 of the analysis device 200 learns.
- the learned model provided by the server 100 as a learning center is acquired via the communication unit 210.
- the acquired trained model is stored in the trained model DB 241.
- the target data acquisition unit 252 acquires the target data to be analyzed by the analysis device 200 as the AI engine.
- the industrial waste X placed on the lane L is imaged by a camera and image data is acquired.
- the acquired analysis target data is stored in the analysis target data DB 142.
- the analysis unit 253 refers to the target data acquired in step S201 and stored in the target data DB 242 based on the trained model acquired in step S201 and stored in the trained model DB 241.
- a predetermined data analysis as an AI engine is performed.
- the image data of the industrial waste X is analyzed, and the type of the material of the industrial waste X is determined.
- the result output unit 254 outputs the result data and various control signals based on the result of the data analysis on the target data performed in step S203.
- a control signal for operating the sorting device 400 is output to operate the mounting movable portion 420, the joint portion 440, the wrist portion 460, and the holding upper portion 480, and the industrial waste X is carried. It is lifted by the upper part 480 and moved to an appropriate place according to the type of material of industrial waste X.
- FIG. 8 is a flowchart showing the operation of the billing amount determination process in the information processing system 1 of FIG.
- the operation unit 230 of the analysis device 200 accepts whether or not the target data is provided by the target data providing unit 255 by the user's operation. If it is instructed to provide the target data, the target data will be provided, and if it is instructed not to provide the target data, the target data will not be provided.
- the billing amount determination unit 134 of the learning server 100 determines the billing amount, which is the consideration for the provision of the learned model. For example, in step S301, if the acquired target data is provided to the learning server 100, the billing amount is set at a low price, and if the target data is not provided to the learning server 100, the billing amount is expensive. Set.
- the information processing system and the information processing method according to the present embodiment acquire the target data to be analyzed, perform machine learning to generate a trained model, and provide the learning to the analysis device. It is equipped with a server (learning center) and an analysis device (AI engine) that analyzes data based on the trained model and outputs data according to the purpose of the trained model. Therefore, the result of machine learning performed by the learning server can be used by a plurality of analysis devices. As a result, the labor of machine learning using a large amount of teacher data can be reduced, and a trained model that is the result of machine learning performed by another person can be used.
- the analysis device is separated from the learning server and can be used in the local environment. This makes it possible to use the AI engine in a local environment even if there is resistance to providing various data of the company on the cloud server.
- the target data provided by oneself is configured so that it cannot be referred to by others (other companies). Therefore, since there is no possibility that the data owned by the other company will be referred to by other companies, it is considered that the number of people who accept the provision of the data will increase, and it will be possible to make the trained model perform more learning. This makes it possible to build a more accurate AI engine.
- FIG. 9 is a functional block configuration diagram showing an analysis device 200B of the information processing system 1 according to the second embodiment of the present disclosure.
- the analysis device 200B acquires a trained model from the training server 100, analyzes the target data to be analyzed as an AI engine based on the trained model, and outputs an output according to the purpose of the trained model. It is the same as the analysis device 200 according to the first embodiment in that it is an apparatus for performing the data, but is different from the information processing system 1 according to the first embodiment in that the input reception unit 256 is provided as a function of the control unit 250. ..
- the learning server 100 when the learning server 100 performs machine learning, it is possible to input tag information related to the analysis target data which is the target of machine learning.
- the input reception unit 256 receives the input of tag information related to the analysis target data, which is the target of machine learning, by the operation of the operation unit 230 of the user at the time of machine learning by the learning unit 132 of the learning server 100.
- the tag information is, for example, information on the date and time and conditions when the analysis target data is acquired.
- the learning unit 132 of the learning server 100 processes the annotation that associates the tag information related to the analysis target data received by the input reception unit 256 with the analysis target data and updates the trained model. Good.
- Other configurations and processing flows are the same as those in the first embodiment.
- an input receiving unit for receiving input of tag information related to analysis target data is provided, and the input tag information is associated with the analysis target data. Processing is done. Therefore, machine learning by the learning unit is performed more appropriately, and more accurate model information can be generated.
- FIG. 10 is a functional block configuration diagram showing an analysis device 200C of the information processing system 1 according to the third embodiment of the present disclosure.
- the analysis device 200C acquires a trained model from the training server 100, analyzes the target data to be analyzed as an AI engine based on the trained model, and outputs an output according to the purpose of the trained model. It is the same as the analysis device 200 according to the first embodiment in that it is an apparatus for performing the data, but is different from the information processing system 1 according to the first embodiment in that the evaluation unit 257 is provided as a function of the control unit 250.
- the result of data analysis is evaluated, and machine learning is performed based on the evaluation result.
- the evaluation unit 257 evaluates the result of data analysis and generates evaluation result data for the evaluation result. Further, the evaluation result data is transmitted to the learning server 100.
- the evaluation result data may be, for example, data showing the relationship between the target data and the data of the analysis result by the trained model, or the data obtained by evaluating the trained model.
- the learning unit 132 of the learning server 100 may select the target data for machine learning based on the evaluation result data. For example, when an abnormal value is detected as a result of data analysis, if the target data is provided to the learning server 100, it becomes a target of machine learning and machine learning based on the abnormal value is performed. Therefore, by showing that it is noise, it is possible to prevent it from affecting machine learning.
- Other configurations and processing flows are the same as those in the first embodiment.
- the provision of the evaluation unit makes it possible to select the target data for machine learning based on the evaluation result data. Therefore, for example, when an abnormal value is detected as a result of data analysis, if the target data is provided to the learning server, it becomes a target of machine learning and machine learning based on the abnormal value is performed. It is also possible to prevent it from affecting machine learning. This enables more accurate machine learning.
- FIG. 10 is a functional block configuration diagram showing an example of the configuration of the computer (electronic computer) 700.
- the computer 700 includes a CPU 701, a main storage device 702, an auxiliary storage device 703, and an interface 704.
- the target data acquisition unit 131, the learning unit 132, the provision unit 133, the charge amount determination unit 134, the model acquisition unit 251 and the target data acquisition unit 252, and the analysis unit 253 according to the first to third embodiments.
- the details of the control program (information processing program) for realizing each function constituting the result output unit 254, the target data providing unit 255, the input receiving unit 256, and the evaluation unit 257 will be described.
- These functional blocks are implemented in the computer 700.
- the operation of each of these components is stored in the auxiliary storage device 703 in the form of a program.
- the CPU 701 reads the program from the auxiliary storage device 703, expands it to the main storage device 702, and executes the above processing according to the program. Further, the CPU 701 secures a storage area corresponding to the above-mentioned storage unit in the main storage device 702 according to the program.
- the program is a target data acquisition step for acquiring the target data to be analyzed in the computer 700, and model information for performing machine learning based on the target data and performing data analysis.
- the providing step of providing the trained model to the analyzer the model acquisition step of acquiring the trained model with the analyzer, and the trained model.
- This is a control program that realizes an analysis step for analyzing the data of the target data and a result output step for outputting the trained model according to the purpose of the trained model based on the result of the data analysis.
- the auxiliary storage device 703 is an example of a tangible medium that is not temporary.
- Other examples of non-temporary tangible media include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, semiconductor memories, etc. connected via interface 704.
- the distributed computer 700 may expand the program to the main storage device 702 and execute the above processing.
- the program may be for realizing a part of the above-mentioned functions. Further, the program may be a so-called difference file (difference program) that realizes the above-mentioned function in combination with another program already stored in the auxiliary storage device 703.
- difference file difference program
- 1,1A Information system 100 learning server, 110 communication unit, 120 storage unit, 121A, 121B target data DB, 122 learned model DB, 130 control unit, 131 target data acquisition unit, 132 learning unit, 133 providing unit, 134 Billing amount determination unit, 200, 200B, 200C analyzer, 210 communication unit, 220 display unit, 230 operation unit, 240 storage unit, 241 trained model DB, 242 target data DB, 250 control unit, 251 model acquisition unit, 252 Target data acquisition unit, 253 analysis unit, 254 result output unit, 255 target data provision unit, 256 input reception unit, 257 evaluation unit, NW network, T communication means
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Abstract
An information processing system 1 comprising: a learning server 100 that obtains target data for data analysis, performs machine learning, generates a trained model, and provides the trained model to an analysis device; and the analysis device 200 that obtains the trained model, obtains the target data, performs data analysis on the basis of the trained model, and performs outputs in accordance with the purpose of the trained model.
Description
本開示は、機械学習を行って特定の学習済モデルを生成し、学習済モデルを用いてデータ解析を行う情報処理システム、情報処理方法及び情報処理プログラムに関する。
This disclosure relates to an information processing system, an information processing method, and an information processing program that perform machine learning to generate a specific trained model and perform data analysis using the trained model.
近年、人工知能(Artificial Intelligence:AI)の機能を有するAIエンジンが組み込まれ、機械学習が行われて動作制御される各種装置が開発されている。例えば、産業廃棄物の処理場では、各種の廃棄物が混在した状態から分別を行い、分別された廃棄物をそれぞれ適切に処理する工程において、AIエンジンが搭載されたロボットにより廃棄物について機械学習が行われ、AIエンジンにより機械的に分別が行われている。
In recent years, various devices have been developed in which an AI engine having an artificial intelligence (AI) function is incorporated and machine learning is performed to control the operation. For example, in an industrial waste treatment plant, in the process of sorting from a mixture of various types of waste and appropriately treating each sorted waste, machine learning about waste is performed by a robot equipped with an AI engine. Is performed, and the sorting is performed mechanically by the AI engine.
例えば、特許文献1には、多種多様な素材が混在する瓦礫や、建築物を解体処理した廃棄物であっても効率よく選別することが可能な選別システムが開示されている。この選別システムでは、廃棄物を撮像した画像データから画像処理を行い、廃棄物の素材、例えば木材、プラスチック、ガラス、石膏ボード等の素材の種類を選別している。
For example, Patent Document 1 discloses a sorting system capable of efficiently sorting even rubble in which a wide variety of materials are mixed and waste obtained by dismantling a building. In this sorting system, image processing is performed from image data obtained by capturing images of waste, and the types of waste materials such as wood, plastic, glass, and gypsum board are sorted.
このようなAIエンジンを利用するためには、適切な機械学習が行われることが必要である。例えば、機械学習の一類型である教師あり機械学習では、大量の教師データによる機械学習を行い、モデル情報を生成する必要があり、このモデル情報によって出力結果が左右される。そのため、AIエンジンでは、適切なモデル情報を使用する必要がある。例えば、特許文献2には、トレーニング用データセットを用いて機械学習モデルを評価するシステムが開示されている。
In order to use such an AI engine, it is necessary to perform appropriate machine learning. For example, in supervised machine learning, which is a type of machine learning, it is necessary to perform machine learning with a large amount of teacher data to generate model information, and the output result depends on this model information. Therefore, it is necessary for the AI engine to use appropriate model information. For example, Patent Document 2 discloses a system for evaluating a machine learning model using a training data set.
ところで、このようなAIエンジンを利用するために必要な機械学習は、例えば大量の教師データによる機械学習が必要であり、大変な手間が必要である。また、このような機械学習によるモデル情報は、他のAIエンジンでも利用することが可能である。しかしながら、あるAIエンジンで利用されているモデル情報を学習済モデルとして、他のAIエンジンに提供するようなシステムは、提供されていなかった。
By the way, the machine learning required to use such an AI engine requires, for example, machine learning using a large amount of teacher data, which requires a great deal of time and effort. In addition, such machine learning model information can also be used by other AI engines. However, a system that provides the model information used in one AI engine as a learned model to another AI engine has not been provided.
また、このようなAIエンジンをクラウドサーバからインターネット経由で提供する、いわゆるSaaS(Software as a Service)が知られている。このようなサービスを利用することにより、機械学習によるモデル情報を他のAIエンジンでも利用することが可能であるが、企業等では、自社の各種データをクラウドサーバ上に提供するのは、セキュリティ上問題となる。そのため、このようなAIエンジンをローカル環境で利用可能なシステムが望まれていた。
Also, so-called SaaS (Software as a Service), which provides such an AI engine from a cloud server via the Internet, is known. By using such a service, it is possible to use model information by machine learning with other AI engines, but in companies, etc., providing various data of their own company on a cloud server is for security reasons. It becomes a problem. Therefore, a system that can use such an AI engine in a local environment has been desired.
そこで、本開示では、データ解析の対象となる対象データを取得して機械学習を行うラーニングセンターをサーバにより提供し、ラーニングセンターからAIエンジンで利用される学習済モデルを取得し、自己のAIエンジンで解析処理を行うために利用可能である情報処理システム、情報処理方法及び情報処理プログラムについて説明する。
Therefore, in the present disclosure, a learning center for acquiring target data to be analyzed and performing machine learning is provided by a server, a learned model used by an AI engine is acquired from the learning center, and a self-AI engine is used. The information processing system, information processing method, and information processing program that can be used to perform analysis processing in.
本開示の一態様における情報処理システムは、機械学習を行って特定の学習済モデルを生成する学習サーバと、学習済モデルを用いてデータ解析を行う解析装置と、を備える情報処理システムであって、学習サーバは、データ解析の対象となる対象データを取得する対象データ取得部と、対象データに基づいて機械学習を行い、データ解析を行うためのモデル情報である学習済モデルを生成し、または学習済モデルを更新する学習部と、学習済モデルを解析装置へ提供する提供部と、を備え、解析装置は、学習サーバから学習済モデルを取得するモデル取得部と、学習済モデルに基づき、対象データのデータ解析を行う解析部と、データ解析の結果により、学習済モデルの目的に応じた出力を行う結果出力部と、を備える。
The information processing system according to one aspect of the present disclosure is an information processing system including a learning server that performs machine learning to generate a specific trained model and an analysis device that performs data analysis using the trained model. , The learning server generates a trained model which is model information for performing machine learning based on the target data and the target data acquisition unit for acquiring the target data to be data-analyzed, or performing data analysis. A learning unit that updates the trained model and a providing unit that provides the trained model to the analysis device are provided, and the analysis device is based on the model acquisition unit that acquires the trained model from the training server and the trained model. It includes an analysis unit that analyzes the data of the target data, and a result output unit that outputs the trained model according to the purpose of the trained model based on the result of the data analysis.
本開示の一態様における情報処理方法は、機械学習を行って特定の学習済モデルを生成し、学習済モデルを用いてデータ解析を行う解析装置に対して学習済モデルを提供する情報処理方法であって、対象データ取得部が行う、データ解析の対象となる対象データを取得する対象データ取得ステップと、学習部が行う、対象データに基づいて機械学習を行い、データ解析を行うためのモデル情報である学習済モデルを生成し、または学習済モデルを更新する学習ステップと、提供部が行う、学習済モデルを解析装置へ提供する提供ステップと、モデル取得部が行う、解析装置で学習済モデルを取得するモデル取得ステップと、解析部が行う、学習済モデルに基づき、対象データのデータ解析を行う解析ステップと、結果出力部が行う、データ解析の結果により、学習済モデルの目的に応じた出力を行う結果出力ステップと、を備える。
The information processing method in one aspect of the present disclosure is an information processing method that performs machine learning to generate a specific trained model and provides the trained model to an analyzer that performs data analysis using the trained model. Therefore, the target data acquisition step for acquiring the target data to be the target of data analysis performed by the target data acquisition unit, and the model information for performing machine learning based on the target data and performing data analysis performed by the learning department. A training step of generating a trained model or updating a trained model, a providing step of providing a trained model to an analysis device performed by the providing unit, and a training model performed by the analysis device performed by the model acquisition unit. The purpose of the trained model is determined by the model acquisition step of acquiring the model, the analysis step of analyzing the target data based on the trained model performed by the analysis unit, and the result of the data analysis performed by the result output unit. It includes a result output step for outputting.
また、本開示の一態様における情報処理プログラムは、機械学習を行って特定の学習済モデルを生成し、学習済モデルを用いてデータ解析を行う解析装置に対して学習済モデルを提供する情報処理プログラムであって、データ解析の対象となる対象データを取得する対象データ取得ステップと、対象データに基づいて機械学習を行い、データ解析を行うためのモデル情報である学習済モデルを生成し、または学習済モデルを更新する学習ステップと、学習済モデルを解析装置へ提供する提供ステップと、解析装置で学習済モデルを取得するモデル取得ステップと、学習済モデルに基づき、対象データのデータ解析を行う解析ステップと、データ解析の結果により、学習済モデルの目的に応じた出力を行う結果出力ステップと、を電子計算機に実行させる。
Further, the information processing program in one aspect of the present disclosure performs machine learning to generate a specific trained model, and provides the trained model to an analyzer that performs data analysis using the trained model. In the program, the target data acquisition step to acquire the target data to be the target of data analysis and the trained model which is the model information for performing machine learning based on the target data and performing the data analysis are generated, or Data analysis of target data is performed based on the training step of updating the trained model, the provision step of providing the trained model to the analysis device, the model acquisition step of acquiring the trained model by the analysis device, and the trained model. The computer is made to execute the analysis step and the result output step that outputs according to the purpose of the trained model based on the result of the data analysis.
本開示によれば、機械学習を行って特定の学習済モデルを生成する学習サーバ(ラーニングセンター)では、データ解析の対象となる対象データを取得し、機械学習を行って学習済モデルを生成し、解析装置(AIエンジン)へ提供する。解析装置では、学習済モデルに基づきデータ解析を行い、学習済モデルの目的に応じた出力を行う。そのため、学習サーバで行った機械学習の結果を複数の解析装置で利用可能になる。これにより、機械学習の手間を削減し、他者の機械学習の成果である学習済モデルを利用することができる。また、解析装置は学習サーバから切り離されたローカル環境でも利用可能になる。
According to the present disclosure, a learning server (learning center) that performs machine learning to generate a specific trained model acquires target data to be analyzed and performs machine learning to generate a trained model. , Provided to the analyzer (AI engine). The analysis device analyzes the data based on the trained model and outputs the data according to the purpose of the trained model. Therefore, the result of machine learning performed by the learning server can be used by a plurality of analysis devices. As a result, the labor of machine learning can be reduced, and the trained model, which is the result of machine learning of others, can be used. In addition, the analysis device can be used in a local environment separated from the learning server.
以下、本開示の実施形態について図面を参照して説明する。なお、以下に説明する実施形態は、特許請求の範囲に記載された本開示の内容を不当に限定するものではない。また、実施形態に示される構成要素のすべてが、本開示の必須の構成要素であるとは限らない。
Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. The embodiments described below do not unreasonably limit the contents of the present disclosure described in the claims. Also, not all of the components shown in the embodiments are essential components of the present disclosure.
(実施形態1)
<構成>
図1は、本開示の実施形態1に係る情報処理システム1を示すブロック構成図である。この情報処理システム1は、限定ではなく例として、機械学習を行って特定の学習済モデルを生成し、生成された学習済モデルを用いてデータ解析を行うシステムである。この学習済モデルは、例えばあるデータ解析の対象となる対象データから機械学習が行われたモデル情報であり、当該対象データを提供した解析装置だけではなく、他の解析装置でも利用可能なモデル情報である。すなわち、情報処理システム1は、機械学習を行って生成された学習済モデルの横展開を可能にするシステムである。 (Embodiment 1)
<Structure>
FIG. 1 is a block configuration diagram showing aninformation processing system 1 according to the first embodiment of the present disclosure. The information processing system 1 is not limited to, but as an example, is a system that performs machine learning to generate a specific trained model and analyzes data using the generated trained model. This trained model is, for example, model information obtained by machine learning from target data to be analyzed for a certain data, and model information that can be used not only by the analysis device that provided the target data but also by other analysis devices. Is. That is, the information processing system 1 is a system that enables horizontal expansion of a trained model generated by performing machine learning.
<構成>
図1は、本開示の実施形態1に係る情報処理システム1を示すブロック構成図である。この情報処理システム1は、限定ではなく例として、機械学習を行って特定の学習済モデルを生成し、生成された学習済モデルを用いてデータ解析を行うシステムである。この学習済モデルは、例えばあるデータ解析の対象となる対象データから機械学習が行われたモデル情報であり、当該対象データを提供した解析装置だけではなく、他の解析装置でも利用可能なモデル情報である。すなわち、情報処理システム1は、機械学習を行って生成された学習済モデルの横展開を可能にするシステムである。 (Embodiment 1)
<Structure>
FIG. 1 is a block configuration diagram showing an
情報処理システム1は、学習サーバ100と、解析装置201,202,・・・と、ネットワークNWとを有している。学習サーバ100と、解析装置201,202,・・・とは、ネットワークNWを介して相互に接続される。ネットワークNWは、通信を行うための通信網であり、限定ではなく例として、インターネット、イントラネット、LAN(Local Area Network)、WAN(Wide Area Network)、ワイヤレスLAN(Wireless LAN:WLAN)、ワイヤレスWAN(Wireless WAN:WWAN)、仮想プライベートネットワーク(Virtual Private Network:VPN)等を含む通信網を介して接続されるように構成されている。
The information processing system 1 has a learning server 100, analysis devices 201, 202, ..., And a network NW. The learning server 100 and the analysis devices 201, 202, ... Are connected to each other via the network NW. The network NW is a communication network for communication, and is not limited to the Internet, an intranet, a LAN (Local Area Network), a WAN (Wide Area Network), a wireless LAN (Wireless LAN: WAN), and a wireless WAN (Wireless LAN). It is configured to be connected via a communication network including a wireless WAN (WAN), a virtual private network (VPN), and the like.
なお、解析装置201,202,・・・は、それぞれ同様の構成を備えるものであり、解析装置201,202,・・・の構成を説明する際、代表して解析装置200と表記する。
Note that the analysis devices 201, 202, ... Each have the same configuration, and when the configurations of the analysis devices 201, 202, ... Are described, they are collectively referred to as the analysis device 200.
学習サーバ100は、データ解析の対象となる対象データから機械学習を行って特定の学習済モデルを生成し、生成した学習済モデルを1または複数の解析装置200(解析装置201,202,・・・)へ提供することにより、ラーニングセンターとしての機能を提供する装置であり、限定ではなく例として、手書き文字に対してOCRにより文字情報を読み取るためのモデル情報や、産業廃棄物の分別を行う装置を制御するためのモデル情報を機械学習により生成する装置として構成されている。この学習サーバ100は、限定ではなく例として、各種Webサービスを提供するコンピュータ(デスクトップ、ラップトップ、タブレット等)や、サーバ装置等により構成されている。なお、学習サーバ100は、単体で動作する装置に限られず、複数の装置が通信網を介して相互に接続され、通信を行うことで協調動作する分散型サーバシステムや、クラウドサーバでもよい。
The learning server 100 generates a specific trained model by performing machine learning from the target data to be analyzed, and uses the generated trained model as one or a plurality of analysis devices 200 ( analysis devices 201, 202, ...・) Is a device that provides a function as a learning center by providing it to. As an example, not limited to, model information for reading character information by OCR for handwritten characters and sorting of industrial waste are performed. It is configured as a device that generates model information for controlling the device by machine learning. The learning server 100 is not limited, but is composed of, for example, a computer (desktop, laptop, tablet, etc.) that provides various Web services, a server device, and the like. The learning server 100 is not limited to a device that operates independently, and may be a distributed server system or a cloud server in which a plurality of devices are connected to each other via a communication network and cooperate with each other to perform communication.
解析装置200は、学習サーバ100から学習済モデルを取得し、AIエンジンとしてデータ解析の対象となる対象データを学習済モデルに基づいてデータ解析を行い、学習済モデルの目的に応じた出力を行う装置であり、限定ではなく例として、手書き文字に対してOCRにより文字情報を読み取るための装置や、産業廃棄物の分別を行う装置を制御する装置として構成されている。この解析装置200は、限定ではなく例として、前述のような各種装置を制御するコンピュータ(デスクトップ、ラップトップ、タブレット等)や、サーバ装置等により構成されている。また、解析装置200は1または複数の同様の構成を備える解析装置201,202,・・・から構成されており、それぞれ異なる企業等により使用され、学習サーバ100が備える同一の学習済モデルを利用可能になっている。なお、外部装置200は、単体で動作する装置に限られず、複数の装置が通信網を介して相互に接続され、通信を行うことで協調動作する分散型サーバシステムや、クラウドサーバでもよい。本実施形態では、解析装置200によって制御される各種装置の例として、後述する分別装置400が接続され、または分別装置400に解析装置200が内蔵されている。
The analysis device 200 acquires a trained model from the learning server 100, analyzes the target data to be analyzed as an AI engine based on the trained model, and outputs the target data according to the purpose of the trained model. It is a device, and is not limited to an example, and is configured as a device for reading character information by OCR for handwritten characters and a device for controlling a device for separating industrial waste. The analysis device 200 is not limited, but is composed of, for example, a computer (desktop, laptop, tablet, etc.) that controls various devices as described above, a server device, and the like. Further, the analysis device 200 is composed of analysis devices 201, 202, ... Having one or a plurality of similar configurations, which are used by different companies and the like, and use the same trained model provided in the learning server 100. It is possible. The external device 200 is not limited to a device that operates independently, and may be a distributed server system or a cloud server in which a plurality of devices are connected to each other via a communication network and cooperate with each other to perform communication. In the present embodiment, as an example of various devices controlled by the analysis device 200, a sorting device 400, which will be described later, is connected, or the analysis device 200 is built in the sorting device 400.
図2は、図1の情報処理システム1の変形例である情報処理システム1Aを示すブロック構成図である。この情報処理システム1Aは、データ解析の対象となる対象データから機械学習を行って特定の学習済モデルを生成し、生成した学習済モデルを解析装置200へ提供することにより、ラーニングセンターを提供する点において、情報処理システム1と同様であるが、ネットワークNWに代えて通信手段Tを備えている点において、実施形態1に係る情報処理システム1と異なる。この変形例は、解析装置200が学習サーバ100と常時接続されている必要はなく、学習済モデルを取得するとき、例えば設定された所定のタイミングや、解析装置200のユーザが操作して学習サーバ100にアクセスしたときに接続される場合の例を示している。すなわち、解析装置200は、学習サーバ100から学習済モデルを取得するタイミング以外は、スタンドアローンで稼働することが可能であり、これにより、機械学習の手間を削減し、他者(他社)の機械学習の成果である学習済モデルを利用することができる。その他の構成については実施形態1と同様である。
FIG. 2 is a block configuration diagram showing an information processing system 1A which is a modification of the information processing system 1 of FIG. The information processing system 1A provides a learning center by performing machine learning from target data to be analyzed to generate a specific trained model and providing the generated trained model to the analysis device 200. It is similar to the information processing system 1 in that it is different from the information processing system 1 according to the first embodiment in that it is provided with the communication means T instead of the network NW. In this modification, the analysis device 200 does not need to be always connected to the learning server 100, and when the trained model is acquired, for example, a predetermined timing set or a user of the analysis device 200 operates the learning server. An example of the case of being connected when accessing 100 is shown. That is, the analysis device 200 can be operated stand-alone except for the timing of acquiring the trained model from the learning server 100, thereby reducing the labor of machine learning and the machine of another person (other company). A trained model that is the result of learning can be used. Other configurations are the same as those in the first embodiment.
通信手段Tは、直接または通信網を介して通信を行う回線設備であり、限定ではなく例として、USB(Universal Serial Bus)ケーブルやLAN(Local Area Network)ケーブル等により直接接続されるように構成され、または、インターネット、イントラネット、LAN(Local Area Network)、WAN(Wide Area Network)、ワイヤレスLAN(Wireless LAN:WLAN)、ワイヤレスWAN(Wireless WAN:WWAN)、仮想プライベートネットワーク(Virtual Private Network:VPN)等を含む通信網を介して接続されるように構成されている。
The communication means T is a line facility that communicates directly or via a communication network, and is not limited, but is configured to be directly connected by a USB (Universal Serial Bus) cable, a LAN (Local Area Network) cable, or the like. Or, Internet, Intranet, LAN (Local Area Network), WAN (Wide Area Network), Wireless LAN (Wireless LAN: WAN), Wireless WAN (Wireless WAN: WAN), Virtual Private Network (VPN) It is configured to be connected via a communication network including the above.
図3は、図1の学習サーバ100を示す機能ブロック構成図である。学習サーバ100は、通信部110と、記憶部120と、制御部130とを備える。
FIG. 3 is a functional block configuration diagram showing the learning server 100 of FIG. The learning server 100 includes a communication unit 110, a storage unit 120, and a control unit 130.
通信部110は、ネットワークNWまたは通信手段Tを介して解析装置200と有線または無線で通信を行うための通信インタフェースであり、互いの通信が実行出来るのであればどのような通信プロトコルを用いてもよい。この通信部110は、限定ではなく例として、TCP/IP(Transmission Control Protocol/Internet Protocol)等の通信プロトコルにより通信が行われる。
The communication unit 110 is a communication interface for communicating with the analysis device 200 by wire or wirelessly via the network NW or the communication means T, and any communication protocol may be used as long as mutual communication can be executed. Good. The communication unit 110 is not limited, and for example, communication is performed by a communication protocol such as TCP / IP (Transmission Control Protocol / Internet Protocol).
記憶部120は、各種制御処理や制御部130内の各機能を実行するためのプログラムや入力データ等を記憶するものであり、限定ではなく例として、RAM(Random Access Memory)、ROM(Read Only Memory)等を含むメモリや、HDD(Hard Disk Drive)、SSD(Solid State Drive)、フラッシュメモリ等を含むストレージから構成される。また、記憶部120は、対象データDB121Aと、対象データDB121Bと、学習済モデルDB122とを記憶する。さらに、記憶部120は、解析装置200との間で通信を行った際のデータや、後述する各処理にて生成されたデータを一時的に記憶する。
The storage unit 120 stores programs, input data, and the like for executing various control processes and each function in the control unit 130. The storage unit 120 is not limited, but as an example, a RAM (RandomAccessMemory) and a ROM (ReadOnly). It is composed of a memory including a memory) and a storage including an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory and the like. Further, the storage unit 120 stores the target data DB 121A, the target data DB 121B, and the trained model DB 122. Further, the storage unit 120 temporarily stores the data when communicating with the analysis device 200 and the data generated by each process described later.
対象データDB121A,121Bには、学習サーバ100が機械学習を行う対象データが格納されている。この対象データは、後述する解析装置200のユーザの選択により解析装置200から提供を受けたデータであり、限定ではなく例として、解析装置200が実装されている装置に応じたデータであり、OCRにより文字情報を読み取るための画像データや、産業廃棄物の分別を行うための画像データである。
The target data DBs 121A and 121B store target data for machine learning by the learning server 100. This target data is data provided by the analysis device 200 by the user's selection of the analysis device 200, which will be described later, and is not limited but, as an example, data according to the device on which the analysis device 200 is mounted, and is OCR. It is image data for reading text information and image data for separating industrial waste.
また、対象データDB121Aと、対象データDB121Bとでは、異なる解析装置200から提供を受けた対象データが格納されており、例えば、対象データDB121Aには解析装置201から提供を受けた対象データが、対象データDB121Bには解析装置202から提供を受けた対象データが、それぞれ格納されている。そして、解析装置201,202,・・・のユーザは、自己が提供した対象データ以外のデータは参照できないように構成されている。このように構成しているのは、自己の保有するデータを他社に参照されるおそれがある場合、データの提供をためらうことが考えられるためであり、このようなデータにはユーザの顧客の個人情報が含まれるおそれもあるからである。そのため、対象データDB121A,121Bに格納されている対象データは、機械学習のためにだけ利用され、それ以外の目的で利用されることはない。
Further, the target data DB 121A and the target data DB 121B store the target data provided by different analysis devices 200. For example, the target data DB 121A stores the target data provided by the analysis device 201. The target data provided by the analysis device 202 is stored in the data DB 121B. Then, the users of the analysis devices 201, 202, ... Are configured so that they cannot refer to data other than the target data provided by themselves. The reason for this configuration is that if there is a risk that other companies may refer to your own data, you may hesitate to provide the data, and such data is for the individual customer of the user. This is because information may be included. Therefore, the target data stored in the target data DBs 121A and 121B is used only for machine learning and is not used for any other purpose.
学習済モデルDB122には、学習サーバ100が機械学習を行って生成したモデル情報が格納されている。このモデル情報は、学習サーバ100がAIエンジンとしてデータ解析を行うためのモデル情報、限定ではなく例として、OCRにより文字情報を読み取るためのモデル情報や、産業廃棄物の分別を行うためのモデル情報が格納されている。
The trained model DB 122 stores model information generated by machine learning by the learning server 100. This model information is model information for the learning server 100 to perform data analysis as an AI engine, not limited to, as an example, model information for reading character information by OCR, and model information for separating industrial waste. Is stored.
制御部130は、記憶部120に記憶されているプログラムを実行することにより、学習サーバ100の全体の動作を制御するものであり、限定ではなく例として、CPU(Central Processing Unit)、MPU(Micro Processing Unit)、GPU(Graphics Processing Unit)、マイクロプロセッサ(Microprocessor)、プロセッサコア(Processor core)、マルチプロセッサ(Multiprocessor)、ASIC(Application-Specific Integrated Circuit)、FPGA(Field Programmable Gate Array)を含む装置等から構成される。制御部130の機能として、対象データ取得部131と、学習部132と、提供部133と、課金額決定部134とを備えている。この対象データ取得部131、学習部132、提供部133、及び課金額決定部134は、記憶部120に記憶されているプログラムにより起動されて学習サーバ100にて実行される。
The control unit 130 controls the overall operation of the learning server 100 by executing a program stored in the storage unit 120. The control unit 130 is not limited to the CPU (Central Processing Unit) and the MPU (Micro). Devices including ProcessingUnit), GPU (GraphicsProcessingUnit), microprocessor (Microprocessor), processor core (Processorcore), multiprocessor (Multiprocessor), ASIC (Application-Specific IntegratedCircuit), FPGA (Field ProgrammableGateArray), etc. Consists of. As the functions of the control unit 130, the target data acquisition unit 131, the learning unit 132, the provision unit 133, and the billing amount determination unit 134 are provided. The target data acquisition unit 131, the learning unit 132, the provision unit 133, and the charge amount determination unit 134 are started by the program stored in the storage unit 120 and executed by the learning server 100.
対象データ取得部131は、解析装置200のユーザの選択により解析装置200から提供された、所定のデータ解析の対象となる解析対象データを、学習サーバ100が機械学習を行うための対象データとして、通信部110を介して取得する。前述の例では、解析装置200の実装例に応じて、手書き文字に対してOCRにより文字情報を読み取る場合の手書き文字の画像データや、産業廃棄物の分別を行うための産業廃棄物の画像データを取得する。また、対象データ取得部131は、取得した対象データを、前述のように、解析装置201から提供を受けた対象データを対象データDB121Aへ、解析装置202から提供を受けた対象データを対象データDB121Bへ、それぞれ格納する。
The target data acquisition unit 131 uses the analysis target data provided by the analysis device 200 by the user of the analysis device 200 as the target data for the learning server 100 to perform machine learning. Acquired via the communication unit 110. In the above example, the image data of the handwritten character when reading the character information by OCR for the handwritten character and the image data of the industrial waste for separating the industrial waste according to the implementation example of the analyzer 200. To get. Further, the target data acquisition unit 131 transfers the acquired target data to the target data DB 121A from the target data provided by the analysis device 201 and the target data provided from the analysis device 202 to the target data DB 121B as described above. Store each in.
学習部132は、対象データ取得部131により取得された対象データに基づいて機械学習を行い、学習済モデルを生成し学習済モデルDB122へ格納し、または学習済モデルDB122に格納されている学習済モデルの更新を行う。この対象データは、解析装置200で解析対象となるデータと、そのデータ解析の結果が含まれるデータである。学習済モデルの更新は、例えば、機械学習の結果による更新情報と、学習済モデルDB122に格納されている学習済モデルとをマージするアグリゲーションの処理により行われる。また、学習部132は、対象データDB121Aに格納されている対象データに基づく機械学習の結果である学習済モデルと、対象データDB121Bに格納されている対象データに基づく機械学習の結果である学習済モデルとをマージするアグリゲーションの処理を行ってもよい。
The learning unit 132 performs machine learning based on the target data acquired by the target data acquisition unit 131, generates a trained model and stores it in the trained model DB 122, or has trained stored in the trained model DB 122. Update the model. This target data is data including the data to be analyzed by the analysis device 200 and the result of the data analysis. The trained model is updated, for example, by an aggregation process that merges the updated information based on the result of machine learning with the trained model stored in the trained model DB 122. Further, the learning unit 132 has learned a model that is the result of machine learning based on the target data stored in the target data DB 121A and a trained model that is the result of machine learning based on the target data stored in the target data DB 121B. You may perform aggregation processing to merge with the model.
学習部132による機械学習は、限定ではなく例として、教師あり機械学習により行われてもよく、教師なし機械学習により行われてもよく、ディープラーニングにより行われてもよい。
The machine learning by the learning unit 132 is not limited, but as an example, it may be performed by supervised machine learning, it may be performed by unsupervised machine learning, or it may be performed by deep learning.
提供部133は、解析装置200のユーザの操作により、学習部132により生成された学習済モデルを、解析装置200へ通信部110を介して送信することで提供する。なお、本実施形態では、解析装置200は学習済モデルを学習サーバ100からダウンロードすることで提供を受ける構成としているが、学習サーバ100に記憶された状態でAIエンジンのソフトウェアサービスとして提供を受ける形式、いわゆるSaaSとして提供されてもよい。
The providing unit 133 provides the learned model generated by the learning unit 132 by the operation of the user of the analysis device 200 by transmitting the learned model to the analysis device 200 via the communication unit 110. In the present embodiment, the analysis device 200 is configured to be provided by downloading the trained model from the learning server 100, but is provided as a software service of the AI engine in a state of being stored in the learning server 100. , So-called SaaS may be provided.
課金額決定部134は、学習済モデルの提供に対する対価である課金額を決定する。この課金額は、例えば学習済モデルのデータ量や、学習済モデルを使用する単位時間当たりの金額で設定してもよい。また、例えば、後述する対象データ取得部252により取得された対象データを学習サーバ100へ提供する場合、課金額を安価に設定し、対象データを学習サーバ100へ提供しない場合、課金額を高価に設定してもよい。このように設定することで、ユーザが対象データを提供するモチベーションになり、学習済モデルがより多くの学習を行うことで、学習済モデルをより精度の高いものにすることができる。
The billing amount determination unit 134 determines the billing amount, which is the consideration for the provision of the learned model. This billing amount may be set by, for example, the amount of data of the trained model or the amount of money per unit time using the trained model. Further, for example, when the target data acquired by the target data acquisition unit 252, which will be described later, is provided to the learning server 100, the billing amount is set low, and when the target data is not provided to the learning server 100, the billing amount is high. It may be set. By setting in this way, the user is motivated to provide the target data, and the trained model can be made more accurate by performing more learning.
図4は、図1の解析装置200を示す機能ブロック構成図である。解析装置200は、通信部210と、表示部220と、操作部230と、記憶部240と、制御部250とを備える。
FIG. 4 is a functional block configuration diagram showing the analysis device 200 of FIG. The analysis device 200 includes a communication unit 210, a display unit 220, an operation unit 230, a storage unit 240, and a control unit 250.
通信部210は、ネットワークNWまたは通信手段Tを介して学習サーバ100と有線または無線で通信を行うための通信インタフェースであり、互いの通信が実行出来るのであればどのような通信プロトコルを用いてもよい。この通信部210は、限定ではなく例として、TCP/IP等の通信プロトコルにより通信が行われる。また、図2に示す情報処理システム1Aの場合、この通信部210は、通信手段Tが学習サーバ100と解析装置200との間で接続されているときに稼働する。
The communication unit 210 is a communication interface for communicating with the learning server 100 by wire or wirelessly via the network NW or the communication means T, and any communication protocol can be used as long as mutual communication can be executed. Good. The communication unit 210 is not limited, and for example, communication is performed by a communication protocol such as TCP / IP. Further, in the case of the information processing system 1A shown in FIG. 2, the communication unit 210 operates when the communication means T is connected between the learning server 100 and the analysis device 200.
表示部220は、ユーザから入力された操作内容や、学習サーバ100からの送信内容を表示するために用いられるユーザインタフェースであり、液晶ディスプレイ等から構成される。表示部220では、例えば解析部253による解析結果等を表示する。
The display unit 220 is a user interface used to display the operation content input by the user and the transmission content from the learning server 100, and is composed of a liquid crystal display or the like. The display unit 220 displays, for example, the analysis result by the analysis unit 253.
操作部230は、ユーザが操作指示を入力するために用いられるユーザインタフェースであり、キーボードやマウス、タッチパネル等から構成される。操作部230は、例えば学習サーバ100から提供を受ける学習済モデルを取得するための指示の入力や、学習サーバ100からの指示の応答に使用される。
The operation unit 230 is a user interface used for the user to input operation instructions, and is composed of a keyboard, a mouse, a touch panel, and the like. The operation unit 230 is used, for example, for inputting an instruction for acquiring a learned model provided by the learning server 100 and for responding to an instruction from the learning server 100.
記憶部240は、各種制御処理や制御部250内の各機能を実行するためのプログラム、入力データ等を記憶するものであり、限定ではなく例として、RAM、ROM等を含むメモリや、HDD、SSD、フラッシュメモリ等を含むストレージから構成される。また、記憶部240は、学習済モデルDB241と、対象データDB242とを記憶する。さらに、記憶部240は、学習サーバ100との間で通信を行ったデータや、後述する各処理にて生成されたデータを一時的に記憶する。
The storage unit 240 stores programs for executing various control processes and each function in the control unit 250, input data, and the like. The storage unit 240 is not limited, and as an example, a memory including a RAM, a ROM, and the like, an HDD, and the like. It is composed of storage including SSD, flash memory and the like. Further, the storage unit 240 stores the trained model DB 241 and the target data DB 242. Further, the storage unit 240 temporarily stores the data communicated with the learning server 100 and the data generated by each process described later.
学習済モデルDB241には、学習サーバ100が機械学習を行って生成し、学習サーバ100から提供を受けたモデル情報が格納されている。このモデル情報は、学習サーバ100の学習済モデルDB122と同様のモデル情報であり、AIエンジンとしてデータ解析を行うためのモデル情報、限定ではなく例として、OCRにより文字情報を読み取るためのモデル情報や、産業廃棄物の分別を行うためのモデル情報が格納されている。なお、前述のように、解析装置200がAIエンジンのソフトウェアサービスとして提供を受ける場合、学習済モデルDB241は備えなくてもよい。
The trained model DB 241 stores model information generated by the learning server 100 by performing machine learning and provided by the learning server 100. This model information is the same model information as the trained model DB 122 of the learning server 100, and is not limited to model information for performing data analysis as an AI engine, but as an example, model information for reading character information by OCR. , Model information for sorting industrial waste is stored. As described above, when the analysis device 200 is provided as a software service of the AI engine, the trained model DB 241 does not have to be provided.
対象データDB242には、解析装置200がAIエンジンとしてデータ解析の対象とする対象データが格納されている。この対象データは、前述の例における、手書き文字に対してOCRにより文字情報を読み取るための装置や、産業廃棄物の分別を行う装置を制御する装置から取得されたデータである。本実施形態では、解析装置200によって制御される分別装置400から取得されたデータである。
The target data DB 242 stores the target data to be analyzed by the analysis device 200 as an AI engine. This target data is data acquired from a device for reading character information by OCR for handwritten characters and a device for controlling a device for separating industrial waste in the above-mentioned example. In this embodiment, it is the data acquired from the sorting device 400 controlled by the analysis device 200.
制御部250は、記憶部240に記憶されているプログラムを実行することにより、解析装置200の全体の動作を制御するものであり、限定ではなく例として、CPU、MPU、GPU、マイクロプロセッサ、プロセッサコア、マルチプロセッサ、ASIC、FPGAを含む装置等から構成される。制御部250の機能として、モデル取得部251と、対象データ取得部252と、解析部253と、結果出力部254と、対象データ提供部255とを備えている。このモデル取得部251、対象データ取得部252、解析部253、結果出力部254、及び対象データ提供部255は、記憶部240に記憶されているプログラムにより起動されて解析装置200にて実行される。
The control unit 250 controls the overall operation of the analyzer 200 by executing the program stored in the storage unit 240, and is not limited to, but as an example, a CPU, an MPU, a GPU, a microprocessor, and a processor. It is composed of a core, a multiprocessor, an ASIC, a device including an FPGA, and the like. The functions of the control unit 250 include a model acquisition unit 251, a target data acquisition unit 252, an analysis unit 253, a result output unit 254, and a target data providing unit 255. The model acquisition unit 251, the target data acquisition unit 252, the analysis unit 253, the result output unit 254, and the target data provision unit 255 are started by the program stored in the storage unit 240 and executed by the analysis device 200. ..
モデル取得部251は、学習サーバ100がラーニングセンターとして提供する学習済モデルを取得する。例えば、ユーザの解析装置200の操作により、学習サーバ100に対して学習済モデルを送信すると、学習サーバ100が学習済モデルを送信するので、その学習済モデルを、通信部110を介して受け付ける。この学習済モデルは、解析装置200がAIエンジンとしてデータ解析を行うためのモデル情報であり、単独の学習済モデルでもよく、既に取得済の学習済モデルに対して最新の学習結果を反映させるための更新情報でもよい。また、モデル取得部251は、取得した学習済モデルを学習済モデルDB241へ格納する。
The model acquisition unit 251 acquires a learned model provided by the learning server 100 as a learning center. For example, when the trained model is transmitted to the learning server 100 by the operation of the analysis device 200 of the user, the learning server 100 transmits the trained model, and the trained model is accepted via the communication unit 110. This trained model is model information for the analysis device 200 to perform data analysis as an AI engine, and may be a single trained model, in order to reflect the latest training results on the already acquired trained model. It may be the update information of. Further, the model acquisition unit 251 stores the acquired trained model in the trained model DB 241.
対象データ取得部252は、解析装置200がAIエンジンとしてデータ解析の対象とする対象データを取得する。前述の例では、解析装置200の実装例に応じて、手書き文字に対してOCRにより文字情報を読み取る場合の手書き文字の画像データや、産業廃棄物の分別を行うための産業廃棄物の画像データを取得する。本実施形態では、解析装置200によって制御される分別装置400から取得されたデータを取得する。また、対象データ取得部252は、取得した対象データを対象データDB2421Aへ格納する。
The target data acquisition unit 252 acquires the target data to be analyzed by the analysis device 200 as an AI engine. In the above example, the image data of the handwritten character when reading the character information by OCR for the handwritten character and the image data of the industrial waste for separating the industrial waste according to the implementation example of the analyzer 200. To get. In the present embodiment, the data acquired from the sorting device 400 controlled by the analysis device 200 is acquired. Further, the target data acquisition unit 252 stores the acquired target data in the target data DB 2421A.
解析部253は、モデル取得部251により取得され、学習済モデルDB241へ格納された学習済モデルに基づき、対象データ取得部252により取得され、対象データDB242へ格納された対象データに対して、AIエンジンとしての所定のデータ解析を行う。具体的には、前述の例では、手書き文字に対してOCRにより文字情報を読み取る場合における、手書き文字の画像データの解析や、産業廃棄物の分別を行う場合における、産業廃棄物の画像データの解析であり、本実施形態では、分別装置400から取得された画像データの解析を行う。
The analysis unit 253 refers to the target data acquired by the target data acquisition unit 252 and stored in the target data DB 242 based on the trained model acquired by the model acquisition unit 251 and stored in the trained model DB 241. Performs predetermined data analysis as an engine. Specifically, in the above-mentioned example, in the case of reading the character information for the handwritten character by OCR, the image data of the handwritten character is analyzed, and in the case of separating the industrial waste, the image data of the industrial waste is used. This is an analysis, and in the present embodiment, the image data acquired from the sorting device 400 is analyzed.
結果出力部254は、解析部253による解析対象データに対するデータ解析の結果により、学習済モデルの目的、すなわちAIエンジンの目的に応じて、結果データや各種制御信号の出力を行う。前述の例では、手書き文字に対してOCRにより文字情報を読み取る場合における、文字情報の判定結果の出力や、産業廃棄物の分別を行う場合における、対象の産業廃棄物に対して移動させるための制御信号の出力であり、本実施形態では、分別装置400の操作制御を行う。
The result output unit 254 outputs the result data and various control signals according to the purpose of the trained model, that is, the purpose of the AI engine, based on the result of data analysis on the data to be analyzed by the analysis unit 253. In the above example, in order to output the judgment result of the character information when reading the character information by OCR for the handwritten character and to move the handwritten character to the target industrial waste when separating the industrial waste. It is an output of a control signal, and in the present embodiment, the operation control of the sorting device 400 is performed.
対象データ提供部255は、ユーザの選択により対象データ取得部252により取得された対象データを学習サーバ100へ提供する場合、当該対象データを学習サーバ100へ、通信部210を介して送信する。AIエンジンとしてのデータ解析の対象データを用いて機械学習を行うことにより、学習サーバ100の学習済モデルはより多くの学習を行うことができるので、対象データは学習サーバ100へ提供されることが望ましい。しかし、ユーザによっては対象データを他社が提供するサービスであるラーニングセンターである学習サーバ100へ提供することを希望しない場合もある。そのため、ユーザが希望する場合にのみ対象データが低虚位される。
When the target data providing unit 255 provides the target data acquired by the target data acquisition unit 252 to the learning server 100 at the user's choice, the target data providing unit 255 transmits the target data to the learning server 100 via the communication unit 210. By performing machine learning using the target data of the data analysis as the AI engine, the trained model of the learning server 100 can perform more learning, so that the target data can be provided to the learning server 100. desirable. However, some users may not want to provide the target data to the learning server 100, which is a learning center that is a service provided by another company. Therefore, the target data is low-deficient only when the user desires.
図5は、図1の解析装置200により制御される分別装置400の外観を示す斜視図である。分別装置400は、解析装置200によって制御される各種装置の一例である産業廃棄物の分別を行う装置であり、固定部410と、載置可動部420と、上腕部430と、継手部440と、前腕部450と、手首部460と、支持部470と、持上部480とから構成されている。この分別装置400は、図5に示すベルトコンベアのレーンL上に載置される産業廃棄物Xをカメラ(図示は省略)で撮像し、画像データのデータ解析を行ってその素材を分析し、素材ごとに分別するためのロボットアームである。カメラは、例えばレーンL上や手首部460に配置されている。
FIG. 5 is a perspective view showing the appearance of the sorting device 400 controlled by the analysis device 200 of FIG. The sorting device 400 is a device that sorts industrial waste, which is an example of various devices controlled by the analysis device 200, and includes a fixed portion 410, a mounting movable portion 420, an upper arm portion 430, and a joint portion 440. It is composed of a forearm portion 450, a wrist portion 460, a support portion 470, and a holding upper portion 480. The sorting device 400 captures the industrial waste X placed on the lane L of the belt conveyor shown in FIG. 5 with a camera (not shown), analyzes the image data, and analyzes the material. It is a robot arm for sorting by material. The cameras are arranged, for example, on lane L or on the wrist 460.
固定部410は、分別装置400が載置台Dに固定されている箇所である。載置台Dは例えば、レーンLの近傍の所定の箇所に固定されて配置されている。載置可動部420は、上腕部430の一端が接続され、載置台Dの上面との角度が変更自在な可動箇所である。載置可動部420には、サーボモータが内蔵されており、制御信号によりサーボモータが駆動して、上腕部430を回動させるように構成されている。上腕部430は、分別装置400における載置台D側の棒状部材である。
The fixing portion 410 is a place where the sorting device 400 is fixed to the mounting table D. The mounting table D is fixedly arranged at a predetermined position in the vicinity of the lane L, for example. The mounting movable portion 420 is a movable portion to which one end of the upper arm portion 430 is connected and the angle with the upper surface of the mounting base D can be changed. A servomotor is built in the mounting movable portion 420, and the servomotor is driven by a control signal to rotate the upper arm portion 430. The upper arm portion 430 is a rod-shaped member on the mounting table D side in the sorting device 400.
継手部440は、上腕部430の他端と前腕部450の一端とを回動自在に接続する箇所である。継手部440には、サーボモータが内蔵されており、載置可動部420と同様に制御信号によりサーボモータが駆動して、上腕部430を回動させるように構成されている。前腕部450は、分別装置400における先端側の棒状部材である。
The joint portion 440 is a portion that rotatably connects the other end of the upper arm portion 430 and one end of the forearm portion 450. A servomotor is built in the joint portion 440, and the servomotor is driven by a control signal in the same manner as the mounted movable portion 420 to rotate the upper arm portion 430. The forearm portion 450 is a rod-shaped member on the distal end side of the sorting device 400.
手首部460は、前腕部450の他端と支持部470の一端とを回動自在に接続する箇所である。手首部460には、サーボモータが内蔵されており、載置可動部420と同様に制御信号によりサーボモータが駆動して、支持部470を回動させるように構成されている。支持部470は、分別装置400の先端部において持上部480を支持する箇所である。持上部480は、産業廃棄物Xを挟みこんで持ち上げる箇所である。この持上部480には、サーボモータが内蔵されており、載置可動部420と同様に制御信号によりサーボモータが駆動して、持上部480が駆動させるように構成されている。
The wrist portion 460 is a portion that rotatably connects the other end of the forearm portion 450 and one end of the support portion 470. A servomotor is built in the wrist portion 460, and the servomotor is driven by a control signal in the same manner as the mounted movable portion 420 to rotate the support portion 470. The support portion 470 is a portion that supports the holding upper portion 480 at the tip end portion of the sorting device 400. The upper part 480 is a place where the industrial waste X is sandwiched and lifted. A servomotor is built in the holding upper part 480, and the servomotor is driven by a control signal in the same manner as the mounted movable portion 420, so that the holding upper part 480 is driven.
分別装置400では、産業廃棄物Xをカメラで撮像した画像データにより機械学習が行われ、画像データのデータ解析による特徴量と産業廃棄物Xの素材との関係が学習済モデルとして生成されている。この画像データが学習サーバ100に提供されることで、学習部132では、実際にレーンL上に載置される産業廃棄物Xの機械学習を行うことで、より精度の高い学習済モデルを生成し、AIエンジンを構築することを可能にしている。
In the sorting device 400, machine learning is performed by image data obtained by capturing the industrial waste X with a camera, and the relationship between the feature amount and the material of the industrial waste X by data analysis of the image data is generated as a learned model. .. By providing this image data to the learning server 100, the learning unit 132 generates a more accurate trained model by performing machine learning of the industrial waste X actually placed on the lane L. However, it is possible to build an AI engine.
<処理の流れ>
情報処理システム1の学習サーバ100及び解析装置200が実行する、情報処理方法の一例の処理の流れについて説明する。まず、図6を参照しながら、学習サーバ100が実行する、情報処理方法の一部である機械学習処理の流れについて説明する。図6は、図1の情報処理システム1における機械学習処理の動作を示すフローチャートである。 <Processing flow>
The processing flow of an example of the information processing method executed by the learningserver 100 and the analysis device 200 of the information processing system 1 will be described. First, with reference to FIG. 6, the flow of machine learning processing, which is a part of the information processing method, executed by the learning server 100 will be described. FIG. 6 is a flowchart showing the operation of the machine learning process in the information processing system 1 of FIG.
情報処理システム1の学習サーバ100及び解析装置200が実行する、情報処理方法の一例の処理の流れについて説明する。まず、図6を参照しながら、学習サーバ100が実行する、情報処理方法の一部である機械学習処理の流れについて説明する。図6は、図1の情報処理システム1における機械学習処理の動作を示すフローチャートである。 <Processing flow>
The processing flow of an example of the information processing method executed by the learning
なお、図2に示す情報処理システム1Aの場合、この機械学習処理が行われる間は、学習サーバ100と解析装置200との間は通信手段Tにより接続されなくてもよい。
In the case of the information processing system 1A shown in FIG. 2, the learning server 100 and the analysis device 200 do not have to be connected by the communication means T while the machine learning process is performed.
ステップS101の処理として、解析装置200の対象データ提供部255では、ユーザの選択により所定のデータ解析の対象となる解析対象データが送信されるので、学習サーバ100の対象データ取得部131では、通信部110を介して取得される。図5に示す分別装置400の例では、レーンL上に載置される産業廃棄物Xがカメラで撮像され、画像データが取得される。取得された対象データは、例えば解析装置201から提供を受けた対象データは対象データDB121Aへ、解析装置202から提供を受けた対象データは対象データDB121Bへ、それぞれ格納される。
As the process of step S101, the target data providing unit 255 of the analysis device 200 transmits the analysis target data to be the target of the predetermined data analysis by the user's selection, so that the target data acquisition unit 131 of the learning server 100 communicates. Obtained via unit 110. In the example of the sorting device 400 shown in FIG. 5, the industrial waste X placed on the lane L is imaged by a camera and image data is acquired. The acquired target data is stored in, for example, the target data provided by the analysis device 201 in the target data DB 121A, and the target data provided by the analysis device 202 in the target data DB 121B.
ステップS102の処理として、学習部132では、ステップS101で取得され、対象データDB121A,121Bへ格納された対象データに基づいて、機械学習が行われる。
As the process of step S102, the learning unit 132 performs machine learning based on the target data acquired in step S101 and stored in the target data DBs 121A and 121B.
ステップS103の処理として、学習部132では、ステップS102で行われた機械学習の結果により、学習済モデルが生成されて学習済モデルDB122へ格納され、または学習済モデルDB122に格納されている学習済モデルが更新される。学習済モデルの更新は、例えば、機械学習の結果による更新情報と、学習済モデルDB122に格納されている学習済モデルとをマージするアグリゲーションの処理により行われる。
As the process of step S103, the learning unit 132 generates a trained model based on the result of machine learning performed in step S102 and stores it in the trained model DB 122, or has trained stored in the trained model DB 122. The model is updated. The trained model is updated, for example, by an aggregation process that merges the updated information based on the result of machine learning with the trained model stored in the trained model DB 122.
次に、図7を参照しながら、解析装置200が実行する、情報処理方法の一部であるデータ解析処理の流れについて説明する。図7は、図1の情報処理システム1におけるデータ解析処理の動作を示すフローチャートである。
Next, with reference to FIG. 7, the flow of data analysis processing, which is a part of the information processing method, executed by the analysis device 200 will be described. FIG. 7 is a flowchart showing the operation of the data analysis process in the information processing system 1 of FIG.
なお、図2に示す情報処理システム1Aの場合、このデータ解析処理が行われる間は、学習サーバ100と解析装置200との間は通信手段Tにより接続され、通信可能になっている。
In the case of the information processing system 1A shown in FIG. 2, while this data analysis process is being performed, the learning server 100 and the analysis device 200 are connected by the communication means T so that communication is possible.
ステップS201の処理として、学習サーバ100の提供部133では、ユーザの操作によりステップS103で生成された学習済モデルが、解析装置200へ送信されるので、解析装置200のモデル取得部251では、学習サーバ100がラーニングセンターとして提供する学習済モデルが通信部210を介して取得される。取得された学習済モデルは、学習済モデルDB241へ格納される。
As the process of step S201, in the providing unit 133 of the learning server 100, the learned model generated in step S103 by the user's operation is transmitted to the analysis device 200, so that the model acquisition unit 251 of the analysis device 200 learns. The learned model provided by the server 100 as a learning center is acquired via the communication unit 210. The acquired trained model is stored in the trained model DB 241.
ステップS202の処理として、対象データ取得部252では、解析装置200がAIエンジンとしてデータ解析の対象とする対象データが取得される。図4に示す分別装置400の例では、レーンL上に載置される産業廃棄物Xがカメラで撮像され、画像データが取得される。取得された解析対象データは、解析対象データDB142へ格納される。
As the process of step S202, the target data acquisition unit 252 acquires the target data to be analyzed by the analysis device 200 as the AI engine. In the example of the sorting device 400 shown in FIG. 4, the industrial waste X placed on the lane L is imaged by a camera and image data is acquired. The acquired analysis target data is stored in the analysis target data DB 142.
ステップS203の処理として、解析部253では、ステップS201で取得されて学習済モデルDB241へ格納された学習済モデルに基づき、ステップS201で取得されて対象データDB242へ格納された対象データに対して、AIエンジンとしての所定のデータ解析が行われる。図4に示す分別装置400の例では、産業廃棄物Xの画像データが解析され、産業廃棄物Xの素材の種類が判定される。
As the process of step S203, the analysis unit 253 refers to the target data acquired in step S201 and stored in the target data DB 242 based on the trained model acquired in step S201 and stored in the trained model DB 241. A predetermined data analysis as an AI engine is performed. In the example of the sorting device 400 shown in FIG. 4, the image data of the industrial waste X is analyzed, and the type of the material of the industrial waste X is determined.
ステップS204の処理として、結果出力部254では、ステップS203で行われた対象データに対するデータ解析の結果により、結果データや各種制御信号の出力が行われる。図4に示す分別装置400の例では、分別装置400を動作させる制御信号が出力されて載置可動部420、継手部440、手首部460及び持上部480が動作され、産業廃棄物Xが持上部480により持ち上げられ、産業廃棄物Xの素材の種類に応じて適切な箇所に移動させられる。
As the process of step S204, the result output unit 254 outputs the result data and various control signals based on the result of the data analysis on the target data performed in step S203. In the example of the sorting device 400 shown in FIG. 4, a control signal for operating the sorting device 400 is output to operate the mounting movable portion 420, the joint portion 440, the wrist portion 460, and the holding upper portion 480, and the industrial waste X is carried. It is lifted by the upper part 480 and moved to an appropriate place according to the type of material of industrial waste X.
次に、図8を参照しながら、学習サーバ100が実行する、情報処理方法の一部である課金額決定処理の流れについて説明する。図8は、図1の情報処理システム1における課金額決定処理の動作を示すフローチャートである。
Next, with reference to FIG. 8, the flow of the billing amount determination process, which is a part of the information processing method, executed by the learning server 100 will be described. FIG. 8 is a flowchart showing the operation of the billing amount determination process in the information processing system 1 of FIG.
ステップS301の処理として、解析装置200の操作部230では、対象データ提供部255による対象データの提供を行うか否かを、ユーザの操作により受け付ける。対象データの提供を行うと指示された場合、対象データの提供を行い、対象データの提供を行わないと指示された場合、対象データの提供は行われない。
As the process of step S301, the operation unit 230 of the analysis device 200 accepts whether or not the target data is provided by the target data providing unit 255 by the user's operation. If it is instructed to provide the target data, the target data will be provided, and if it is instructed not to provide the target data, the target data will not be provided.
ステップS302の処理として、学習サーバ100の課金額決定部134では、学習済モデルの提供に対する対価である課金額が決定される。例えば、ステップS301において、取得された対象データを学習サーバ100へ提供することとした場合、課金額が安価に設定され、対象データを学習サーバ100へ提供しないこととした場合、課金額が高価に設定される。
As the process of step S302, the billing amount determination unit 134 of the learning server 100 determines the billing amount, which is the consideration for the provision of the learned model. For example, in step S301, if the acquired target data is provided to the learning server 100, the billing amount is set at a low price, and if the target data is not provided to the learning server 100, the billing amount is expensive. Set.
<効果>
以上のように、本実施形態に係る情報処理システム、及び情報処理方法は、データ解析の対象となる対象データを取得し、機械学習を行って学習済モデルを生成し、解析装置へ提供する学習サーバ(ラーニングセンター)と、学習済モデルに基づきデータ解析を行い、学習済モデルの目的に応じた出力を行う解析装置(AIエンジン)とを備えている。そのため、学習サーバで行った機械学習の結果を複数の解析装置で利用可能になる。これにより、大量の教師データによる機械学習の手間を削減し、他者が行った機械学習の成果である学習済モデルを利用することができる。 <Effect>
As described above, the information processing system and the information processing method according to the present embodiment acquire the target data to be analyzed, perform machine learning to generate a trained model, and provide the learning to the analysis device. It is equipped with a server (learning center) and an analysis device (AI engine) that analyzes data based on the trained model and outputs data according to the purpose of the trained model. Therefore, the result of machine learning performed by the learning server can be used by a plurality of analysis devices. As a result, the labor of machine learning using a large amount of teacher data can be reduced, and a trained model that is the result of machine learning performed by another person can be used.
以上のように、本実施形態に係る情報処理システム、及び情報処理方法は、データ解析の対象となる対象データを取得し、機械学習を行って学習済モデルを生成し、解析装置へ提供する学習サーバ(ラーニングセンター)と、学習済モデルに基づきデータ解析を行い、学習済モデルの目的に応じた出力を行う解析装置(AIエンジン)とを備えている。そのため、学習サーバで行った機械学習の結果を複数の解析装置で利用可能になる。これにより、大量の教師データによる機械学習の手間を削減し、他者が行った機械学習の成果である学習済モデルを利用することができる。 <Effect>
As described above, the information processing system and the information processing method according to the present embodiment acquire the target data to be analyzed, perform machine learning to generate a trained model, and provide the learning to the analysis device. It is equipped with a server (learning center) and an analysis device (AI engine) that analyzes data based on the trained model and outputs data according to the purpose of the trained model. Therefore, the result of machine learning performed by the learning server can be used by a plurality of analysis devices. As a result, the labor of machine learning using a large amount of teacher data can be reduced, and a trained model that is the result of machine learning performed by another person can be used.
また、学習サーバと解析装置とに切り分けを行ったことにより、解析装置が学習サーバから切り離され、ローカル環境でも利用可能である。これにより、自社の各種データをクラウドサーバ上に提供することに抵抗がある場合であっても、AIエンジンをローカル環境で利用することが可能になる。
Also, by separating the learning server and the analysis device, the analysis device is separated from the learning server and can be used in the local environment. This makes it possible to use the AI engine in a local environment even if there is resistance to providing various data of the company on the cloud server.
さらに、自己が提供した対象データは、他者(他社)から参照できないように構成されている。そのため、自己の保有するデータを他社に参照されるおそれがなくなるので、データの提供を受け入れる者が増えると考えられ、学習済モデルに対してより多くの学習を行わせることが可能になる。これにより、より精度の高いAIエンジンを構築することが可能である。
Furthermore, the target data provided by oneself is configured so that it cannot be referred to by others (other companies). Therefore, since there is no possibility that the data owned by the other company will be referred to by other companies, it is considered that the number of people who accept the provision of the data will increase, and it will be possible to make the trained model perform more learning. This makes it possible to build a more accurate AI engine.
(実施形態2)
図9は、本開示の実施形態2に係る情報処理システム1の解析装置200Bを示す機能ブロック構成図である。この解析装置200Bは、学習サーバ100から学習済モデルを取得し、AIエンジンとしてデータ解析の対象となる対象データを学習済モデルに基づいてデータ解析を行い、学習済モデルの目的に応じた出力を行う装置である点において、実施形態1に係る解析装置200と同様であるが、制御部250の機能として、入力受付部256を備えている点において、実施形態1に係る情報処理システム1と異なる。 (Embodiment 2)
FIG. 9 is a functional block configuration diagram showing ananalysis device 200B of the information processing system 1 according to the second embodiment of the present disclosure. The analysis device 200B acquires a trained model from the training server 100, analyzes the target data to be analyzed as an AI engine based on the trained model, and outputs an output according to the purpose of the trained model. It is the same as the analysis device 200 according to the first embodiment in that it is an apparatus for performing the data, but is different from the information processing system 1 according to the first embodiment in that the input reception unit 256 is provided as a function of the control unit 250. ..
図9は、本開示の実施形態2に係る情報処理システム1の解析装置200Bを示す機能ブロック構成図である。この解析装置200Bは、学習サーバ100から学習済モデルを取得し、AIエンジンとしてデータ解析の対象となる対象データを学習済モデルに基づいてデータ解析を行い、学習済モデルの目的に応じた出力を行う装置である点において、実施形態1に係る解析装置200と同様であるが、制御部250の機能として、入力受付部256を備えている点において、実施形態1に係る情報処理システム1と異なる。 (Embodiment 2)
FIG. 9 is a functional block configuration diagram showing an
本実施形態では、学習サーバ100が機械学習を行う際に、機械学習の対象である解析対象データに関連するタグ情報の入力を可能にするものである。
In the present embodiment, when the learning server 100 performs machine learning, it is possible to input tag information related to the analysis target data which is the target of machine learning.
入力受付部256は、学習サーバ100の学習部132による機械学習に際して、機械学習の対象である解析対象データに関連するタグ情報の入力を、ユーザの操作部230の操作により受け付ける。タグ情報は、例えば解析対象データを取得したときの日時や条件の情報である。
The input reception unit 256 receives the input of tag information related to the analysis target data, which is the target of machine learning, by the operation of the operation unit 230 of the user at the time of machine learning by the learning unit 132 of the learning server 100. The tag information is, for example, information on the date and time and conditions when the analysis target data is acquired.
また、学習サーバ100の学習部132は、入力受付部256によって受け付けられた解析対象データに関連するタグ情報を、その解析対象データに関連付けるアノテーションの処理を行い、学習済モデルの更新を行ってもよい。その他の構成及び処理の流れについては、実施形態1と同様である。
Further, even if the learning unit 132 of the learning server 100 processes the annotation that associates the tag information related to the analysis target data received by the input reception unit 256 with the analysis target data and updates the trained model. Good. Other configurations and processing flows are the same as those in the first embodiment.
本実施形態によれば、上記実施形態1の効果に加え、解析対象データに関連するタグ情報の入力が受け付けられる入力受付部を備え、入力されたタグ情報が、その解析対象データに関連付けられるアノテーション処理が行われる。そのため、学習部による機械学習がより適切に行われるので、より精度の高いモデル情報を生成することができる。
According to the present embodiment, in addition to the effect of the first embodiment, an input receiving unit for receiving input of tag information related to analysis target data is provided, and the input tag information is associated with the analysis target data. Processing is done. Therefore, machine learning by the learning unit is performed more appropriately, and more accurate model information can be generated.
(実施形態3)
図10は、本開示の実施形態3に係る情報処理システム1の解析装置200Cを示す機能ブロック構成図である。この解析装置200Cは、学習サーバ100から学習済モデルを取得し、AIエンジンとしてデータ解析の対象となる対象データを学習済モデルに基づいてデータ解析を行い、学習済モデルの目的に応じた出力を行う装置である点において、実施形態1に係る解析装置200と同様であるが、制御部250の機能として、評価部257を備えている点において、実施形態1に係る情報処理システム1と異なる。 (Embodiment 3)
FIG. 10 is a functional block configuration diagram showing ananalysis device 200C of the information processing system 1 according to the third embodiment of the present disclosure. The analysis device 200C acquires a trained model from the training server 100, analyzes the target data to be analyzed as an AI engine based on the trained model, and outputs an output according to the purpose of the trained model. It is the same as the analysis device 200 according to the first embodiment in that it is an apparatus for performing the data, but is different from the information processing system 1 according to the first embodiment in that the evaluation unit 257 is provided as a function of the control unit 250.
図10は、本開示の実施形態3に係る情報処理システム1の解析装置200Cを示す機能ブロック構成図である。この解析装置200Cは、学習サーバ100から学習済モデルを取得し、AIエンジンとしてデータ解析の対象となる対象データを学習済モデルに基づいてデータ解析を行い、学習済モデルの目的に応じた出力を行う装置である点において、実施形態1に係る解析装置200と同様であるが、制御部250の機能として、評価部257を備えている点において、実施形態1に係る情報処理システム1と異なる。 (Embodiment 3)
FIG. 10 is a functional block configuration diagram showing an
本実施形態では、データ解析の結果について評価を行い、その評価結果に基づいて機械学習を行うものである。
In this embodiment, the result of data analysis is evaluated, and machine learning is performed based on the evaluation result.
評価部257は、データ解析の結果について評価を行い、その評価結果についての評価結果データを生成する。また、この評価結果データを、学習サーバ100へ送信する。評価結果データは、例えば対象データと、学習済モデルによる解析結果のデータとの関係を示したデータでもよく、学習済モデルを評価したデータでもよい。
The evaluation unit 257 evaluates the result of data analysis and generates evaluation result data for the evaluation result. Further, the evaluation result data is transmitted to the learning server 100. The evaluation result data may be, for example, data showing the relationship between the target data and the data of the analysis result by the trained model, or the data obtained by evaluating the trained model.
また、学習サーバ100の学習部132は、評価結果データに基づいて機械学習の対象データを選択してもよい。例えば、データ解析の結果として異常値が検出された場合、その対象データを学習サーバ100へ提供すると、機械学習の対象となって異常値に基づく機械学習が行われることになる。そのため、ノイズであることを示すことで、機械学習に影響を及ぼさないようにすることが可能になる。その他の構成及び処理の流れについては、実施形態1と同様である。
Further, the learning unit 132 of the learning server 100 may select the target data for machine learning based on the evaluation result data. For example, when an abnormal value is detected as a result of data analysis, if the target data is provided to the learning server 100, it becomes a target of machine learning and machine learning based on the abnormal value is performed. Therefore, by showing that it is noise, it is possible to prevent it from affecting machine learning. Other configurations and processing flows are the same as those in the first embodiment.
本実施形態によれば、上記実施形態1の効果に加え、評価部を備えたことにより、評価結果データに基づいて機械学習の対象データを選択することが可能になる。そのため、例えば、データ解析の結果として異常値が検出された場合、その対象データを学習サーバへ提供すると、機械学習の対象となって異常値に基づく機械学習が行われることになるが、評価結果により機械学習に影響を及ぼさないようにすることも可能になる。これにより、より精度の高い機械学習が可能になる。
According to the present embodiment, in addition to the effect of the first embodiment, the provision of the evaluation unit makes it possible to select the target data for machine learning based on the evaluation result data. Therefore, for example, when an abnormal value is detected as a result of data analysis, if the target data is provided to the learning server, it becomes a target of machine learning and machine learning based on the abnormal value is performed. It is also possible to prevent it from affecting machine learning. This enables more accurate machine learning.
(実施形態4(プログラム))
図10は、コンピュータ(電子計算機)700の構成の例を示す機能ブロック構成図である。コンピュータ700は、CPU701、主記憶装置702、補助記憶装置703、インタフェース704を備える。 (Embodiment 4 (Program))
FIG. 10 is a functional block configuration diagram showing an example of the configuration of the computer (electronic computer) 700. Thecomputer 700 includes a CPU 701, a main storage device 702, an auxiliary storage device 703, and an interface 704.
図10は、コンピュータ(電子計算機)700の構成の例を示す機能ブロック構成図である。コンピュータ700は、CPU701、主記憶装置702、補助記憶装置703、インタフェース704を備える。 (Embodiment 4 (Program))
FIG. 10 is a functional block configuration diagram showing an example of the configuration of the computer (electronic computer) 700. The
ここで、実施形態1ないし3に係る対象データ取得部131と、学習部132と、提供部133と、課金額決定部134と、モデル取得部251と、対象データ取得部252と、解析部253と、結果出力部254と、対象データ提供部255と、入力受付部256と、評価部257とを構成する各機能を実現するための制御プログラム(情報処理プログラム)の詳細について説明する。これらの機能ブロックは、コンピュータ700に実装される。そして、これらの各構成要素の動作は、プログラムの形式で補助記憶装置703に記憶されている。CPU701は、プログラムを補助記憶装置703から読み出して主記憶装置702に展開し、当該プログラムに従って上記処理を実行する。また、CPU701は、プログラムに従って、上述した記憶部に対応する記憶領域を主記憶装置702に確保する。
Here, the target data acquisition unit 131, the learning unit 132, the provision unit 133, the charge amount determination unit 134, the model acquisition unit 251 and the target data acquisition unit 252, and the analysis unit 253 according to the first to third embodiments. The details of the control program (information processing program) for realizing each function constituting the result output unit 254, the target data providing unit 255, the input receiving unit 256, and the evaluation unit 257 will be described. These functional blocks are implemented in the computer 700. The operation of each of these components is stored in the auxiliary storage device 703 in the form of a program. The CPU 701 reads the program from the auxiliary storage device 703, expands it to the main storage device 702, and executes the above processing according to the program. Further, the CPU 701 secures a storage area corresponding to the above-mentioned storage unit in the main storage device 702 according to the program.
当該プログラムは、具体的には、コンピュータ700において、データ解析の対象となる対象データを取得する対象データ取得ステップと、対象データに基づいて機械学習を行い、データ解析を行うためのモデル情報である学習済モデルを生成し、または学習済モデルを更新する学習ステップと、学習済モデルを解析装置へ提供する提供ステップと、解析装置で学習済モデルを取得するモデル取得ステップと、学習済モデルに基づき、対象データのデータ解析を行う解析ステップと、データ解析の結果により、学習済モデルの目的に応じた出力を行う結果出力ステップと、をコンピュータによって実現する制御プログラムである。
Specifically, the program is a target data acquisition step for acquiring the target data to be analyzed in the computer 700, and model information for performing machine learning based on the target data and performing data analysis. Based on the training step of generating a trained model or updating the trained model, the providing step of providing the trained model to the analyzer, the model acquisition step of acquiring the trained model with the analyzer, and the trained model. This is a control program that realizes an analysis step for analyzing the data of the target data and a result output step for outputting the trained model according to the purpose of the trained model based on the result of the data analysis.
なお、補助記憶装置703は、一時的でない有形の媒体の一例である。一時的でない有形の媒体の他の例としては、インタフェース704を介して接続される磁気ディスク、光磁気ディスク、CD-ROM、DVD-ROM、半導体メモリ等が挙げられる。また、このプログラムがネットワークを介してコンピュータ700に配信される場合、配信を受けたコンピュータ700が当該プログラムを主記憶装置702に展開し、上記処理を実行してもよい。
The auxiliary storage device 703 is an example of a tangible medium that is not temporary. Other examples of non-temporary tangible media include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, semiconductor memories, etc. connected via interface 704. When this program is distributed to the computer 700 via the network, the distributed computer 700 may expand the program to the main storage device 702 and execute the above processing.
また、当該プログラムは、前述した機能の一部を実現するためのものであってもよい。さらに、当該プログラムは、前述した機能を補助記憶装置703に既に記憶されている他のプログラムとの組み合わせで実現するもの、いわゆる差分ファイル(差分プログラム)であってもよい。
Further, the program may be for realizing a part of the above-mentioned functions. Further, the program may be a so-called difference file (difference program) that realizes the above-mentioned function in combination with another program already stored in the auxiliary storage device 703.
以上、開示に係る実施形態について説明したが、これらはその他の様々な形態で実施することが可能であり、種々の省略、置換および変更を行なって実施することが出来る。これらの実施形態および変形例ならびに省略、置換および変更を行なったものは、特許請求の範囲の技術的範囲とその均等の範囲に含まれる。
Although the embodiments related to the disclosure have been described above, these can be implemented in various other embodiments, and can be implemented by making various omissions, replacements, and changes. These embodiments and modifications, as well as those omitted, replaced and modified, are included in the technical scope of the claims and the equivalent scope thereof.
1,1A 情報処理システム、100 学習サーバ、110 通信部、120 記憶部、121A,121B 対象データDB、122 学習済モデルDB、130 制御部、131 対象データ取得部、132 学習部、133 提供部、134 課金額決定部、200,200B,200C 解析装置、210 通信部、220 表示部、230 操作部、240 記憶部、241 学習済モデルDB、242 対象データDB、250 制御部、251 モデル取得部、252 対象データ取得部、253 解析部、254 結果出力部、255 対象データ提供部、256 入力受付部、257 評価部、NW ネットワーク、T 通信手段
1,1A Information system, 100 learning server, 110 communication unit, 120 storage unit, 121A, 121B target data DB, 122 learned model DB, 130 control unit, 131 target data acquisition unit, 132 learning unit, 133 providing unit, 134 Billing amount determination unit, 200, 200B, 200C analyzer, 210 communication unit, 220 display unit, 230 operation unit, 240 storage unit, 241 trained model DB, 242 target data DB, 250 control unit, 251 model acquisition unit, 252 Target data acquisition unit, 253 analysis unit, 254 result output unit, 255 target data provision unit, 256 input reception unit, 257 evaluation unit, NW network, T communication means
1,1A Information system, 100 learning server, 110 communication unit, 120 storage unit, 121A, 121B target data DB, 122 learned model DB, 130 control unit, 131 target data acquisition unit, 132 learning unit, 133 providing unit, 134 Billing amount determination unit, 200, 200B, 200C analyzer, 210 communication unit, 220 display unit, 230 operation unit, 240 storage unit, 241 trained model DB, 242 target data DB, 250 control unit, 251 model acquisition unit, 252 Target data acquisition unit, 253 analysis unit, 254 result output unit, 255 target data provision unit, 256 input reception unit, 257 evaluation unit, NW network, T communication means
Claims (8)
- 機械学習を行って特定の学習済モデルを生成する学習サーバと、前記学習済モデルを用いてデータ解析を行う解析装置と、を備える情報処理システムであって、
前記学習サーバは、
前記データ解析の対象となる対象データを取得する対象データ取得部と、
前記対象データに基づいて機械学習を行い、前記データ解析を行うためのモデル情報である前記学習済モデルを生成し、または前記学習済モデルを更新する学習部と、
前記学習済モデルを前記解析装置へ提供する提供部と、を備え、
前記解析装置は、
前記学習サーバから前記学習済モデルを取得するモデル取得部と、
前記学習済モデルに基づき、前記対象データのデータ解析を行う解析部と、
前記データ解析の結果により、前記学習済モデルの目的に応じた出力を行う結果出力部と、を備える、情報処理システム。 An information processing system including a learning server that performs machine learning to generate a specific trained model and an analysis device that analyzes data using the trained model.
The learning server
The target data acquisition unit that acquires the target data to be the target of the data analysis,
A learning unit that performs machine learning based on the target data, generates the trained model that is model information for performing the data analysis, or updates the trained model.
A providing unit that provides the trained model to the analysis device is provided.
The analyzer is
A model acquisition unit that acquires the trained model from the learning server,
An analysis unit that analyzes the target data based on the trained model, and
An information processing system including a result output unit that outputs a result according to the purpose of the trained model based on the result of the data analysis. - 前記解析装置は、前記学習サーバに対して前記対象データを提供する対象データ提供部を備え、
前記学習サーバは、前記対象データ取得部により、前記解析装置から提供された前記対象データを取得する、請求項1に記載の情報処理システム。 The analysis device includes a target data providing unit that provides the target data to the learning server.
The information processing system according to claim 1, wherein the learning server acquires the target data provided by the analysis device by the target data acquisition unit. - 前記対象データ取得部は、前記解析装置から提供された前記対象データを、他の前記解析装置から参照できないように取得して記憶し、
前記学習部は、前記解析装置から提供された前記対象データを、他の前記解析装置から参照できないように前記学習済モデルを生成し、または前記学習済モデルを更新する、請求項2に記載の情報処理システム。 The target data acquisition unit acquires and stores the target data provided by the analysis device so that it cannot be referred to by other analysis devices.
The learning unit according to claim 2, wherein the learning unit generates the trained model or updates the trained model so that the target data provided by the analysis device cannot be referred to by another analysis device. Information processing system. - 前記学習サーバは、前記学習済モデルを前記解析装置へ提供する際の課金額を決定する課金額決定部を備え、前記解析装置から前記対象データの提供を受ける場合と、前記対象データの提供を受けない場合とで異なる前記課金額を決定する、請求項2または請求項3に記載の情報処理システム。 The learning server includes a billing amount determining unit that determines a billing amount when the trained model is provided to the analysis device, and receives the provision of the target data from the analysis device and provides the target data. The information processing system according to claim 2 or 3, wherein the billing amount differs depending on whether or not the data is received.
- 前記解析装置は、前記対象データに関連するタグデータの入力を受け付ける入力受付部を備え、
前記対象データ提供部は、前記対象データと共に前記タグデータを前記学習サーバに対して提供し、
前記対象データ取得部は、前記解析装置から前記タグデータを取得し、前記タグデータを前記対象データに関連付けるアノテーションを行い、
前記学習部は、前記対象データと、前記タグデータとに基づく機械学習を行う、請求項2から請求項4のいずれか1項に記載の情報処理システム。 The analysis device includes an input receiving unit that receives input of tag data related to the target data.
The target data providing unit provides the tag data together with the target data to the learning server.
The target data acquisition unit acquires the tag data from the analysis device, performs annotations relating the tag data to the target data, and then performs annotations.
The information processing system according to any one of claims 2 to 4, wherein the learning unit performs machine learning based on the target data and the tag data. - 前記解析装置は、前記学習済モデルに基づく前記データ解析の結果について評価を行い、評価結果データを生成する評価部を備え、
前記対象データ提供部は、前記対象データと共に前記評価結果データを前記学習サーバに対して提供し、
前記対象データ取得部は、前記解析装置から前記評価結果データを取得し、
前記学習部は、前記評価結果データに基づいて前記対象データを選択し、選択した前記対象データに基づく機械学習を行う、請求項2から請求項5のいずれか1項に記載の情報処理システム。 The analysis device includes an evaluation unit that evaluates the result of the data analysis based on the trained model and generates evaluation result data.
The target data providing unit provides the evaluation result data together with the target data to the learning server.
The target data acquisition unit acquires the evaluation result data from the analysis device, and obtains the evaluation result data.
The information processing system according to any one of claims 2 to 5, wherein the learning unit selects the target data based on the evaluation result data and performs machine learning based on the selected target data. - 機械学習を行って特定の学習済モデルを生成し、前記学習済モデルを用いてデータ解析を行う解析装置に対して前記学習済モデルを提供する情報処理方法であって、
対象データ取得部が行う、前記データ解析の対象となる対象データを取得する対象データ取得ステップと、
学習部が行う、前記対象データに基づいて機械学習を行い、前記データ解析を行うためのモデル情報である前記学習済モデルを生成し、または前記学習済モデルを更新する学習ステップと、
提供部が行う、前記学習済モデルを前記解析装置へ提供する提供ステップと、
モデル取得部が行う、前記解析装置で前記学習済モデルを取得するモデル取得ステップと、
解析部が行う、前記学習済モデルに基づき、前記対象データのデータ解析を行う解析ステップと、
結果出力部が行う、前記データ解析の結果により、前記学習済モデルの目的に応じた出力を行う結果出力ステップと、を備える情報処理方法。 An information processing method that performs machine learning to generate a specific trained model and provides the trained model to an analysis device that performs data analysis using the trained model.
The target data acquisition step of acquiring the target data to be the target of the data analysis performed by the target data acquisition unit, and the target data acquisition step.
A learning step performed by the learning unit to perform machine learning based on the target data, generate the trained model which is model information for performing the data analysis, or update the trained model.
The providing step of providing the trained model to the analysis device performed by the providing unit, and
A model acquisition step of acquiring the trained model by the analysis device performed by the model acquisition unit, and
An analysis step performed by the analysis unit to analyze the target data based on the trained model, and
An information processing method including a result output step of performing output according to the purpose of the trained model based on the result of the data analysis performed by the result output unit. - 機械学習を行って特定の学習済モデルを生成し、前記学習済モデルを用いてデータ解析を行う解析装置に対して前記学習済モデルを提供する情報処理プログラムであって、
前記データ解析の対象となる対象データを取得する対象データ取得ステップと、
前記対象データに基づいて機械学習を行い、前記データ解析を行うためのモデル情報である前記学習済モデルを生成し、または前記学習済モデルを更新する学習ステップと、
前記学習済モデルを前記解析装置へ提供する提供ステップと、
前記解析装置で前記学習済モデルを取得するモデル取得ステップと、
前記学習済モデルに基づき、前記対象データのデータ解析を行う解析ステップと、
前記データ解析の結果により、前記学習済モデルの目的に応じた出力を行う結果出力ステップと、を電子計算機に実行させるための、情報処理プログラム。
An information processing program that performs machine learning to generate a specific trained model and provides the trained model to an analysis device that performs data analysis using the trained model.
The target data acquisition step for acquiring the target data to be the target of the data analysis, and
A learning step of performing machine learning based on the target data, generating the trained model which is model information for performing the data analysis, or updating the trained model.
A provision step of providing the trained model to the analysis device, and
A model acquisition step of acquiring the trained model with the analysis device, and
An analysis step for analyzing the target data based on the trained model, and
An information processing program for causing a computer to execute a result output step that outputs a result according to the purpose of the trained model based on the result of the data analysis.
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