Learning device, inference device, disconnection location identification system, learning method, inference method, and program

Abstract

The learning device (30) is a learning device (30) that generates a trained model for inferring the location and state of a disconnection in a sensor network (10) to be diagnosed, and includes a training data acquisition unit that acquires training data including: an actual measured waveform of a signal flowing through a transmission path of the sensor network (10); scenario information for each of a plurality of disconnection scenarios in which the resistance value of a resistor and the insertion position of the resistor are changed in a sensor network model in which the sensor network (10) is modeled and a resistor is inserted at a specific position in the transmission path; and a model generation unit (32) that generates a trained model for inferring the location and state of a disconnection in the sensor network (10) from the actual measured waveform by machine learning using the training data acquired by the training data acquisition unit.

Description

[Technical field]

[0001] The present disclosure relates to a learning device, an inference device, a disconnection location specifying system, a learning method, an inference method, and a program. [Background technology]

[0002] In order to identify the location and state of a break in a transmission line including a cable, an electric wire, etc., physical inspections such as visual inspections, electrical measurements, etc. are performed.

[0003] Physical inspection can quickly pinpoint the location of a break if it is in a place that is easily visible or if the damage is obvious from the outside, but if this is not the case, it is necessary to check a wide range of transmission lines one by one, which is a huge burden in time and cost. Also, electrical measurements can only obtain limited data, making it difficult to pinpoint the exact location. Therefore, with these methods, it takes time to pinpoint the location and state of the break, and there is a possibility that the system will be stopped during that time.

[0004] Patent Document 1 discloses an abnormality inspection device that inspects a wire harness incorporated in an electromechanical device for abnormalities. This abnormality inspection device includes a reflected wave detector that detects a reflected wave caused by an input wave transmitted to the wire harness, and judges an abnormality in the wire harness based on an abnormality judgment model generated by a feature amount obtained from the frequency characteristics of the reflected wave when the wire harness has an abnormality and when there is no abnormality.

[0005] Patent Document 2 discloses a cable status management device that manages the progression of a disconnection in a cable used as wiring for a device to be managed. This cable status management device estimates the progression of a disconnection in a cable based on resistance value data, which is a time-series change in the resistance value of the cable when the cable is operated repeatedly and cyclically. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2022-120857 A [Patent Document 2] JP 2023-044616 A Summary of the Invention [Problem to be solved by the invention]

[0007] The devices described in Patent Documents 1 and 2 use only measured waveforms such as reflected waves and time-series changes in resistance values ​​as learning data. Since waveform data changes depending on the measurement environment, the devices described in Patent Documents 1 and 2 take a long time to learn. Therefore, there is room for improvement in terms of more efficiently identifying the location and state of a disconnection.

[0008] The present disclosure has been made in consideration of the above-mentioned problems, and aims to propose a learning device, an inference device, a break location identification system, a learning method, an inference method, and a program that can more efficiently identify the break location and break state. [Means for solving the problem]

[0009] In order to achieve the above object, the learning device of the present disclosure is a learning device that generates a learned model for inferring the location and state of a disconnection in a sensor network to be diagnosed, and includes a learning data acquisition unit that acquires learning data including an actual waveform of a signal flowing through a transmission path of the sensor network, scenario information for each of a plurality of disconnection scenarios in which the resistance value of a resistor and the insertion position of the resistor are changed in a sensor network model in which the sensor network is modeled and a resistor is inserted at a specific position in the transmission path, and a simulation waveform obtained by performing a simulation for each disconnection scenario, and a model generation unit that generates a learned model for inferring the location and state of the disconnection in the sensor network from the actual waveform by machine learning using the learning data acquired by the learning data acquisition unit. Effect of the Invention

[0010] According to the present disclosure, a trained model for inferring the location and state of a disconnection in a sensor network from the measured waveform is generated using learning data including an actual measured waveform, scenario information for each of a plurality of disconnection scenarios, and a simulation waveform for each disconnection scenario. Therefore, data acquisition, learning, and the like can be performed more efficiently than when only the measured waveform is used. Therefore, by performing an inference process using the trained model generated by this learning device, the location and state of a disconnection can be efficiently identified. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing a configuration of a disconnection location identification system according to a first embodiment of the present disclosure. [Diagram 2] FIG. 1 is a block diagram showing an example of a physical configuration of an information processing device. [Diagram 3] FIG. 2 is a block diagram showing the functional configuration of the learning device shown in FIG. 1. [Figure 4] 1 is a flowchart showing a learning process performed by a learning device. [Diagram 5] FIG. 2 is a block diagram showing the functional configuration of the inference device shown in FIG. [Figure 6] 1 is a flowchart showing an inference process performed by an inference device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, a learning device, an inference device, a disconnection location identification system, a learning method, an inference method, and a program according to embodiments of the present disclosure will be described with reference to the drawings. Note that the same reference numerals are used to denote the same or corresponding parts in the drawings.

[0013] FIG. 1 is a schematic diagram showing the entirety of a disconnection position identification system 100. The disconnection position identification system 100 is a system for identifying a disconnection position and a disconnection state, i.e., a disconnection state such as a disconnection state, such as a disconnection state, of a sensor network 10 to be diagnosed. The disconnection state is not limited to a state in which the cable is completely disconnected, and includes, for example, a cable deterioration state such as a broken or damaged part of a core wire or an element wire included in the cable, and a deterioration of the electrical characteristics of the cable due to aging. As shown in the figure, the disconnection position identification system 100 includes a sensor network 10 to be diagnosed, a simulation device 20 that generates a disconnection scenario based on a sensor network model that simulates the configuration of the sensor network 10 and executes a transmission line simulation of the generated disconnection scenario, a learning device 30 that generates a trained model for identifying the disconnection position and the disconnection state of the sensor network 10, a trained model storage unit 40 that stores the trained model generated by the learning device 30, and an inference device 50 that infers the disconnection position and the disconnection state of the sensor network 10.

[0014] The sensor network 10 includes a unit 11 which is a master unit that manages the sensor network 10, and sensors 12a, 12b, 12c, ... which acquire various measurement data, and the unit 11 and the sensors 12a, 12b, 12c, ... are connected via a transmission path 13. In the following description, the sensors 12a, 12b, 12c, ... are also collectively referred to as sensor 12. The unit 11 and the sensor 12 have a function of logging waveform data of signals flowing through the transmission path.

[0015] The simulation device 20 generates a disconnection scenario 21 and includes a simulation unit 22 .

[0016] The simulation device 20 inserts resistors at specific positions in the transmission path in a sensor network model that simulates the configuration of the sensor network 10 to be diagnosed, and generates a plurality of disconnection scenarios 21 by changing the resistance value and the insertion position of the inserted resistor. In the disconnection scenarios 21, the position of the resistor indicates the disconnection position, and the resistance value indicates the disconnection state.

[0017] The simulation unit 22 executes a transmission path simulation for each disconnection scenario 21, and outputs a simulation waveform of a signal flowing through the transmission path for each disconnection scenario 21. The transmission path simulation refers to, for example, simulating a signal at an observation point of the sensor network 10 when a signal with a specific waveform is input from the input end of the sensor network 10, expressing the sensor network 10 as a distributed parameter system, and calculating the signal waveform at the observation point from the reflection at the impedance change point and the group velocity of the transmission path.

[0018] The learning device 30 generates a learned model for inferring the break position and break state from the measured waveform, using measured waveforms, which are waveform data logged by the units 11 and sensors 12 of the sensor network 10, scenario information including the resistance values ​​and resistance positions of the break scenarios 21 generated by the simulation device 20, and learning data including the simulation waveforms of each break scenario 21.

[0019] The measured waveforms used as learning data may be data acquired during demonstration operation of the sensor network 10 before commercial operation, data acquired by the sensor network 10 in the past and stored in advance, or data acquired from the sensor network 10 currently in operation.

[0020] The trained model storage unit 40 stores the trained model generated by the learning device 30.

[0021] The inference device 50 acquires an actual measured waveform from the sensor network 10, and uses the learned model generated by the learning device 30 to infer the location and state of the disconnection from the acquired actual measured waveform.

[0022] In the broken line location identification system 100, the information processing device 60 in which the simulation device 20, the learning device 30, the learned model storage unit 40, and the inference device 50 are realized physically comprises, as illustrated in FIG. 2, a CPU (Central Processing Unit) 61 that executes processing according to a program, a RAM (Random Access Memory) 62 which is a volatile memory, a ROM (Read Only Memory) 63 which is a non-volatile memory, a storage unit 64 that stores data, an input unit 65 that accepts input of information, a display unit 66 that visualizes and displays information, and a communication unit 67 that transmits and receives information, which are connected via an internal bus 99.

[0023] The CPU 61 executes various processes by reading out the programs stored in the storage unit 64 into the RAM 62 and executing them. As main functions provided by the programs, the CPU 61 executes processes by the simulation unit 22 of the simulation device 20, the model generation unit 32, the reward calculation unit 33, and the function update unit 34 of the learning device 30 illustrated in Fig. 3, and the inference unit 52 of the inference device 50 illustrated in Fig. 5.

[0024] The RAM 62 is used as a work area for the CPU 61. The ROM 63 stores the control programs executed by the CPU 61, a BIOS (Basic Input Output System), and the like.

[0025] The storage unit 64 includes a hard disk drive, stores the programs executed by the CPU 61, and stores various data used when executing the programs. The storage unit 64 functions as the trained model storage unit 40.

[0026] The input unit 65 is a user interface that includes a keyboard, a mouse, a communication device, and the like.

[0027] The display unit 66 is a display device such as a liquid crystal display or an organic EL (Electro Luminescence) display that visualizes and displays information.

[0028] The communication unit 67 is a network termination device or a wireless communication device that connects to the network, and a serial interface or a LAN (Local Area Network) interface that connects to them. The communication unit 67 functions as the data acquisition unit 31 of the learning device 30 illustrated in FIG. 3 and the data acquisition unit 51 of the inference device 50 illustrated in FIG.

[0029] <Learning Phase> 3 is a configuration diagram of the learning device 30. The learning device 30 includes a data acquisition unit 31 that acquires learning data, and a model generation unit 32 that generates a trained model based on the learning data acquired by the data acquisition unit 31.

[0030] The data acquiring unit 31 acquires, from the simulation device 20, scenario information including the resistance value and resistance position of each of the multiple disconnection scenarios 21 generated by the simulation device 20, and a simulation waveform obtained by performing a simulation for each disconnection scenario 21. The data acquiring unit 31 also acquires, from the sensor network 10, actual measurement waveforms logged by the units 11 and sensors 12 of the sensor network 10. The data acquiring unit 31 is an example of a learning data acquiring unit.

[0031] The model generation unit 32 generates a trained model for inferring the location and state of a disconnection in the sensor network 10 based on the learning data including the scenario information, simulation waveforms, and measured waveforms acquired by the data acquisition unit 31. That is, a trained model for inferring the location and state of a disconnection from the measured waveforms of the sensor network 10 is generated.

[0032] The learning algorithm used by the model generation unit 32 may be a known algorithm such as supervised learning, unsupervised learning, or reinforcement learning. As an example, a case where reinforcement learning is applied will be described. In reinforcement learning, an agent, which is an acting subject in a certain environment, observes environmental parameters that indicate the current state and determines the action to be taken. The environment changes dynamically depending on the actions of the agent. The agent is given a reward according to the change in the environment. The agent repeats this process and learns the course of action that will obtain the most reward through a series of actions.

[0033] Representative methods of reinforcement learning include Q-learning, TD-learning, etc. For example, in the case of Q-learning, a general update formula for the action value function Q(s, a) is expressed by the following formula 1.

[0034]

number

[0035] In the formula 1, s t represents the state of the environment at time t, and a t represents the action at time t. Action a t Therefore, the state is s t+1 Changes to r t+1 represents the reward that can be obtained depending on the change in state, γ represents the discount rate, and α represents the learning coefficient. Note that γ is in the range of 0<γ≦1, and α is in the range of 0<α≦1. The scenario information including the resistance value and the resistance position of each disconnection scenario 21 generated by the simulation device 20, and the simulation waveform output by each disconnection scenario 21 are used as the action a. t The measured waveform of the sensor network 10 is in state s t At time t, the state s t Best Practices in a t Learn.

[0036] The update formula expressed by Equation 1 increases the action value Q if the action value Q of the action a with the highest Q value at time t+1 is greater than the action value Q of the action a executed at time t, and decreases the action value Q in the opposite case. In other words, the action value function Q(s, a) is updated so that the action value Q of the action a at time t approaches the best action value at time t+1. As a result, the best action value in a certain environment is propagated sequentially to the action value in the previous environment.

[0037] As described above, when generating a trained model by reinforcement learning, the model generation unit 32 includes a reward calculation unit 33 and a function update unit 34.

[0038] The reward calculation unit 33 calculates a reward based on scenario information including the resistance value and the resistance position of each disconnection scenario 21, a simulation waveform output by simulating each disconnection scenario 21, and an actual measured waveform logged by the unit 11 and the sensor 12 of the sensor network 10. The reward calculation unit 33 calculates a reward r based on a potential difference between the simulation waveform and the actual measured waveform. When the potential difference between the simulation waveform and the actual measured waveform decreases, the reward calculation unit 33 increases the reward r (for example, gives a reward of "1"), and when the potential difference between the simulation waveform and the actual measured waveform increases, the reward calculation unit 33 reduces the reward r (for example, gives a reward of "-1"). The reward calculation unit 33 may calculate the reward r based on the similarity of the long-term or short-term trend, the waveform length, the frequency, etc., between the actual measured waveform and the simulation waveform.

[0039] The function update unit 34 updates the function for determining the disconnection position and the disconnection state according to the reward r calculated by the reward calculation unit 33, and outputs the updated function to the trained model storage unit 40. For example, in the case of Q-learning, the action value function Q(s t ,a t ) is used as a function for calculating the disconnection location and disconnection state.

[0040] The function update unit 34 repeatedly executes the above learning to generate a trained model. The trained model storage unit 40 stores the action value function Q(s t ,a t ), i.e., memorize the trained model.

[0041] Next, the operation of the learning device 30 to generate and output a trained model will be described with reference to the flowchart illustrated in Fig. 4. When the learning device 30 is powered on, it starts executing a learning process. The learning process is executed before performing a diagnostic process on the sensor network 10 for the first time, when a change in the configuration of the sensor network 10 requires updating the trained model, when it is desired to retrain the trained model using more training data and improve the accuracy of inference, etc.

[0042] The data acquiring unit 31 of the learning device 30 acquires learning data from the sensor network 10 and the simulation device 20 (step S11). Specifically, the data acquiring unit 31 acquires, as learning data, scenario information including the resistance value and the resistance position of each disconnection scenario 21 generated by the simulation device 20, simulation waveforms output by simulating each disconnection scenario 21, and actual measurement waveforms logged by the unit 11 of the sensor network 10 and the sensor 12.

[0043] Next, the reward calculation unit 33 calculates reward based on the scenario information of the disconnection scenario 21 acquired in step S11, the simulation waveform, and the measured waveform (step S12). Specifically, the reward calculation unit 33 acquires the scenario information of the multiple disconnection scenarios 21 and the simulation waveform and the measured waveform for each disconnection scenario 21, and determines whether to increase or decrease the reward based on a potential difference between the signals of the predetermined simulation waveform and the measured waveform.

[0044] The reward calculation unit 33 increases the reward when the potential difference between the signals of the simulation waveform and the actual measured waveform decreases (step S13). On the other hand, the reward calculation unit 33 decreases the reward when the potential difference between the signals of the simulation waveform and the actual measured waveform increases (step S14).

[0045] Next, the function update unit 34 updates the action value function Q(s t ,a t ) is updated (step S15).

[0046] The learning device 30 repeatedly executes the above steps S11 to S15 to generate the action-value function Q(s t ,a t ) is stored as a trained model.

[0047] The learning device 30 is configured to store the learned model in a learned model storage unit 40 provided outside the learning device 30, but the learned model storage unit 40 may be provided inside the learning device 30.

[0048] <Utilization phase> 5 is a configuration diagram of the inference device 50. The inference device 50 includes a data acquisition unit 51 that acquires measured waveforms from the sensor network 10, and an inference unit 52 that infers the location and state of a disconnection in the sensor network 10.

[0049] The data acquisition unit 51 acquires measured waveforms logged by the units 11 and sensors 12 of the sensor network 10 .

[0050] The inference unit 52 infers the disconnection location and state by using the trained model generated by the learning device 30. That is, by inputting the measured waveform acquired by the data acquisition unit 51 to this trained model, it is possible to infer the disconnection location and state based on the measured waveform.

[0051] In this embodiment, the disconnection location and disconnection state are output using a trained model trained by the model generation unit 32 of the disconnection location identification system 100. However, it is also possible to obtain a trained model from another disconnection location identification system and output the disconnection location and disconnection state based on this trained model.

[0052] Next, with reference to Figure 6, we will explain the inference process for obtaining inference results using the trained model.

[0053] The data acquiring unit 51 of the inference device 50 acquires the measured waveforms from the sensor network 10 (step S21). Specifically, the data acquiring unit 51 acquires the measured waveforms logged by the units 11 and sensors 12 of the sensor network 10.

[0054] Next, the inference unit 52 inputs the measured waveform acquired in step S21 to the trained model stored in the trained model storage unit 40 (step S22), and obtains an inference result including the disconnection position and the disconnection state. The inference unit 52 outputs the obtained inference result including the disconnection position and the disconnection state to the disconnection position identification system 100 (step S23).

[0055] Next, the disconnection position identification system 100 uses the disconnection position output by the inference unit 52 to display the disconnection position on a management terminal (not shown) or notify a facility manager of the disconnection position. This makes it possible to identify the disconnection position at an early stage.

[0056] As described above, the disconnection position identification system 100 performs learning using the measured waveform of the sensor network 10 to be diagnosed, scenario information including the resistance value and resistance position of each of the multiple disconnection scenarios 21, and learning data including the simulation waveform of each disconnection scenario 21, and infers the disconnection position and disconnection state using the learned model. Therefore, it becomes possible to more efficiently identify the disconnection position and infer the disconnection state including the degree of cable deterioration due to aging.

[0057] In the present embodiment, a case has been described in which reinforcement learning is applied to the learning algorithm used by the inference unit 52, but the present invention is not limited to this. As for the learning algorithm, it is also possible to apply supervised learning, unsupervised learning, semi-supervised learning, or the like, in addition to reinforcement learning.

[0058] In addition, the learning algorithm used in the model generation unit 32 may be deep learning, which learns to extract the features themselves, or machine learning may be performed according to other known methods, such as neural networks, genetic programming, functional logic programming, support vector machines, etc.

[0059] The learning device 30 and the inference device 50 may be connected to the disconnection location identification system 100 via a network, and may be separate devices from the disconnection location identification system 100. The learning device 30 and the inference device 50 may be built into the disconnection location identification system 100. Furthermore, the simulation device 20, the learning device 30, and the inference device 50 may exist on a cloud server.

[0060] The model generating unit 32 may learn the wire breakage position by using learning data acquired from a plurality of wire breakage position identification systems 100. The model generating unit 32 may acquire learning data from a plurality of wire breakage position identification systems 100 used in the same area, or may learn the wire breakage position by using learning data collected from a plurality of wire breakage position identification systems 100 operating independently in different areas. It is also possible to add or remove a wire breakage position identification system 100 that collects learning data to or from the target midway. Furthermore, the learning device 30 that has learned the wire breakage position for a certain wire breakage position identification system 100 may be applied to another wire breakage position identification system 100, and the wire breakage position for the other wire breakage position identification system 100 may be re-learned and updated.

[0061] In the above embodiment, the functions of the disconnection location identification system 100 have been described as being executed by individual devices, namely, the simulation device 20, the learning device 30, the trained model storage unit 40, and the inference device 50, but the present invention is not limited to this, and the functions of each device may be executed by an information processing device that is a single computer. Also, each process of the disconnection location identification system 100 may be executed by multiple computers that execute the functions of several devices, such as by executing the functions of some devices by one computer and the functions of other devices by another computer.

[0062] Furthermore, data used in the learning process and inference process, such as the disconnection scenario 21, the simulation waveform, the measured waveform, the trained model, etc., may be collectively managed by a cloud server on the network, and the learning device 30 and the inference device 50 may access the cloud server as necessary to read and write information. In this case, the disconnection location identification system 100 does not need to include the trained model storage unit 40.

[0063] Furthermore, the simulation device 20, the learning device 30, the trained model storage unit 40, and the inference device 50 can be realized by using a normal computer system, not by a dedicated device. For example, a program for realizing each function may be stored and distributed on a computer-readable recording medium such as a CD-ROM (Compact Disc Read Only Memory) or a DVD-ROM (Digital Versatile Disc Read Only Memory), and the program may be installed in a computer to configure a computer capable of realizing each of the above-mentioned functions.

[0064] Furthermore, when each function is realized by sharing the functions between an OS (Operating System) and an application, or by cooperation between the OS and the application, only the application may be stored on the recording medium.

[0065] Various embodiments and modifications of the present disclosure are possible without departing from the broad spirit and scope of the present disclosure. The above-described embodiments are for explaining the present disclosure and do not limit the scope of the present disclosure. In other words, the scope of the present disclosure is indicated by the claims, not the embodiments. Various modifications made within the scope of the claims and within the scope of the disclosure equivalent thereto are considered to be within the scope of the present disclosure. [Explanation of symbols]

[0066] 100 Disconnection location identification system, 10 sensor network, 11 unit, 12, 12a, 12b, 12c sensors, 13 transmission path, 20 simulation device, 21 disconnection scenario, 22 simulation unit, 30 learning device, 31 data acquisition unit, 32 trained model generation unit, 33 reward calculation unit, 34 function update unit, 40 trained model memory unit, 50 inference device, 51 data acquisition unit, 52 inference unit, 60 information processing device, 61 CPU, 62 RAM, 63 ROM, 64 memory unit, 65 input unit, 66 display unit, 67 communication unit, 99 internal bus.

Claims

1. A learning device that generates a trained model for inferring a disconnection location and a disconnection state of a sensor network to be diagnosed, a learning data acquisition unit that acquires learning data including an actual waveform of a signal flowing through a transmission path of the sensor network, scenario information for each of a plurality of disconnection scenarios in which a resistance value of a resistor and an insertion position of the resistor are changed in a sensor network model in which the sensor network is modeled and a resistor is inserted at a specific position of the transmission path, and a simulation waveform obtained by executing a simulation for each of the disconnection scenarios; a model generation unit that generates a trained model for inferring a disconnection position and a disconnection state of the sensor network from the measured waveform by machine learning using the learning data acquired by the learning data acquisition unit; A learning device comprising:

2. The model generation unit a reward calculation unit that calculates a reward based on a potential difference between the actual measured waveform and a simulation waveform for each of the disconnection scenarios; a function update unit that updates a function for inferring a disconnection location and a disconnection state of the sensor network based on the reward calculated by the reward calculation unit; The learning device according to claim 1 .

3. The model generation unit re-learns the trained model by using an actual waveform acquired from the sensor network after generation of the trained model as the training data. The learning device according to claim 1 or 2.

4. the model generation unit acquires the actual waveforms that are measured during operation of the sensor network and stored in advance, and uses the acquired waveforms as the learning data. The learning device according to claim 1 or 2.

5. An inference device that infers a disconnection location and a disconnection state of a sensor network to be diagnosed, a data acquisition unit for acquiring an actual waveform of a signal passing through a transmission path of the sensor network; an inference unit that outputs an inference result including a disconnection position and a disconnection state of the sensor network by inputting the measured waveform acquired by the data acquisition unit into a trained model generated by machine learning using training data including: the measured waveform; scenario information for each of a plurality of disconnection scenarios in which a resistance value of a resistor and an insertion position of the resistor are changed in a sensor network model in which the sensor network is modeled and a resistor is inserted at a specific position of a transmission line; and a simulation waveform obtained by executing a simulation for each of the disconnection scenarios; An inference device comprising:

6. A disconnection location identification system for identifying a disconnection location and a disconnection state of a sensor network to be diagnosed, comprising: A learning device according to claim 1 or 2; The inference device according to claim 5 ; a simulation device that models the sensor network, inserts a resistor at a specific position in a transmission line in the sensor network model, generates a plurality of disconnection scenarios by changing a resistance value of the resistor and an insertion position of the resistor, and outputs a simulation waveform by executing a simulation for each of the disconnection scenarios; A wire break location identifying system comprising:

7. a computer that generates a trained model for inferring a disconnection location and a disconnection state of a sensor network to be diagnosed, acquiring learning data including an actual waveform of a signal flowing through a transmission path of the sensor network, scenario information for each of a plurality of disconnection scenarios in which the resistance value of a resistor and the insertion position of the resistor are changed in a sensor network model in which the sensor network is modeled and a resistor is inserted at a specific position of the transmission path, and a simulation waveform obtained by executing a simulation for each of the disconnection scenarios; generating a trained model for inferring a disconnection location and a disconnection state of the sensor network from the measured waveform by machine learning using the acquired learning data; Learn how to do it.

8. a computer that infers a disconnection location and a disconnection state of a sensor network to be diagnosed, acquiring an actual waveform of a signal passing through a transmission path of the sensor network; a step of inputting the acquired measured waveform into a trained model generated by machine learning using training data including the measured waveform, scenario information of a plurality of disconnection scenarios in which the resistance value of a resistor and the insertion position of the resistor are changed in a sensor network model in which the sensor network is modeled and a resistor is inserted at a specific position of a transmission line, and a simulation waveform obtained by executing a simulation for each of the disconnection scenarios, thereby outputting an inference result including a disconnection position and a disconnection state of the sensor network; An inference method to perform.

9. A computer that generates a trained model for inferring a disconnection location and a disconnection state of a sensor network to be diagnosed, A process of acquiring learning data including an actual waveform of a signal flowing through a transmission path of the sensor network, scenario information for each of a plurality of disconnection scenarios in which the resistance value of a resistor and the insertion position of the resistor are changed in a sensor network model in which the sensor network is modeled and a resistor is inserted at a specific position of the transmission path, and a simulation waveform obtained by executing a simulation for each of the disconnection scenarios; A process of generating a trained model for inferring a disconnection location and a disconnection state of the sensor network from the measured waveform by machine learning using the acquired learning data; A program that executes the following.

10. A computer that infers a disconnection location and a disconnection state of a sensor network to be diagnosed, A process of acquiring an actual waveform of a signal flowing through a transmission path of the sensor network; a process of inputting the acquired measured waveform into a trained model generated by machine learning using learning data including the measured waveform, scenario information of a plurality of disconnection scenarios in which the resistance value of a resistor and the insertion position of the resistor are changed in a sensor network model in which the sensor network is modeled and a resistor is inserted at a specific position of a transmission line, and a simulation waveform obtained by executing a simulation for each of the disconnection scenarios, and outputting an inference result including the disconnection position and the disconnection state of the sensor network; A program that executes the following.

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