Salt damage occurrence determination system, worker terminal, and salt damage occurrence determination method

Abstract

To easily and highly accurately determine the presence or absence of salt damage in power transmission and distribution equipment, thereby reducing the burden on workers.SOLUTION: Provided is a system for determining the presence or absence of salt damage that stores an analysis model that is a machine learning model constructed with information based on sound data obtained at a site where power transmission and distribution equipment is installed as an explanatory variable, and with information about leak noise emitted from the power transmission and distribution equipment due to salt damage as an objective variable, and that outputs information based on the objective variable obtained by entering sound data to be analyzed acquired in the site into the analysis model as an explanatory variable. The system for determining the presence or absence of salt damage also uses, as the analysis model, a machine learning model constructed with information obtained in the site and based on the sound data and environment measurement data as an explanatory variable, and with information about leak noise emitted from the power transmission and distribution equipment due to salt damage as an objective variable.SELECTED DRAWING: Figure 3A

Description

[Technical Field]

[0001] The present invention relates to a salt damage occurrence determination system, an operator terminal, a model learning device, and a salt damage occurrence determination method. [Background technology]

[0002] Patent Document 1 describes an insulator contamination detection device designed to automatically detect insulator contamination and determine the optimal time to clean the insulators. The insulator contamination detection device includes a waveform recorder attached to a transmission line tower, which has a sensor that measures the waveform of insulator noise, a waveform comparison circuit that compares the waveform with that of a non-contaminated insulator, a CPU that outputs a transmission signal when the waveform is abnormal, and a transmitter that transmits the waveform; and an analyzer installed in a monitoring station, which has a receiver that receives the waveform, a noise comparison circuit that compares noise at a specific frequency within the waveform with that of a non-contaminated insulator, a CPU that outputs a display signal when the comparison difference is equal to or greater than a predetermined value, and a display that displays the comparison result. The insulator contamination detection device measures the waveform of insulator noise, compares the noise at a specific frequency with that of a non-contaminated insulator, and automatically reports insulator contamination when the comparison difference is equal to or greater than a predetermined value. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 10-197326 Summary of the Invention [Problem to be solved by the invention]

[0004] Electric power transmission and distribution equipment such as insulators installed on utility poles and transmission line towers can experience partial discharge due to salt damage (deterioration of insulation), which can cause noise (corona noise) and radio interference in the vicinity. Furthermore, as salt damage progresses, it can cause widespread power outages. For this reason, those who manage electric power equipment, such as power transmission companies, regularly patrol areas where salt damage is likely to occur and perform inspections and maintenance (cleaning, etc.) of electric power transmission and distribution equipment.

[0005] However, the identification of areas and facilities affected by salt damage currently relies on the intuition and experience of workers, and if there is even the slightest uncertainty, workers go to the site to carry out inspections, maintenance, etc. In particular, when a major natural disaster such as a typhoon, strong winds, or heavy snowfall occurs, the scope of inspections and maintenance must be expanded, and workers spend a great deal of time and effort on taking measures and responding to salt damage.

[0006] The present invention has been made in view of the above background, and provides a salt damage occurrence determination system and an operator terminal that can easily and accurately determine the presence or absence of salt damage in power transmission and distribution facilities. 、 Another object of the present invention is to provide a method for determining whether salt damage has occurred. [Means for solving the problem]

[0007] One of the present inventions for achieving the above-mentioned object is an information processing system (salt damage occurrence determination system) that determines whether salt damage has occurred in power transmission and distribution equipment, which stores an analytical model that is a machine learning model trained using learning data configured with information based on sound data acquired at the site where the power transmission and distribution equipment is installed as an explanatory variable and information regarding leak sounds emitted from the power transmission and distribution equipment due to salt damage as a target variable, and outputs information based on the target variable obtained by inputting information based on the sound data acquired at the site as the explanatory variable into the analytical model as the analysis target.

[0008] According to the salt damage occurrence determination system of the present invention, information about leak sounds emitted from power transmission and distribution equipment located near the site is automatically output based on sound data acquired at the site. Therefore, workers performing work such as inspection and maintenance of power transmission and distribution equipment at the site can easily determine whether salt damage has occurred in the power transmission and distribution equipment located near the site, thereby enabling the inspection and maintenance work to be carried out efficiently. Furthermore, by using an analytical model trained using sound data acquired in various environments, workers can accurately determine whether salt damage has occurred.

[0009] Another aspect of the present invention for achieving the above-mentioned object is an information processing system (salt damage occurrence determination system) that determines whether salt damage has occurred in power transmission and distribution equipment, which stores an analytical model that is a machine learning model trained using learning data configured with information based on sound data and environmental measurement information acquired at the site where the power transmission and distribution equipment is installed as explanatory variables and information on leak sounds emitted from the power transmission and distribution equipment due to salt damage as a target variable, and outputs information based on the target variable obtained by inputting information based on sound data and environmental measurement information acquired at the site as the explanatory variables into the analytical model.

[0010] According to the salt damage occurrence determination system of the present invention, information regarding leak sounds emitted from power transmission and distribution equipment located near the site is automatically output based on sound data and environmental measurement information (temperature, humidity, air pressure, etc.) acquired at the site. Therefore, workers performing work such as inspecting and maintaining power transmission and distribution equipment at the site can easily determine whether salt damage has occurred in the power transmission and distribution equipment located near the site, allowing for efficient inspection and maintenance work. Furthermore, by using an analytical model trained using sound data and environmental measurement information as explanatory variables, information regarding leak sounds is generated taking into account the effects of differences in the on-site environment (differences in temperature, humidity, air pressure, etc.) on the sound data, allowing workers to accurately determine whether salt damage has occurred.

[0011] Another aspect of the present invention for achieving the above-mentioned object is the above-mentioned salt damage occurrence determination system, which stores equipment information, which is information about the power transmission and distribution equipment that is the object of management, and reflects the contents of the target variable obtained by inputting information based on the sound data acquired on-site as the object of analysis into the analysis model, in the equipment information.

[0012] According to the salt damage occurrence determination system of the present invention, the contents of the objective variable obtained by inputting explanatory variables based on information on the analysis target obtained on-site into the analytical model are automatically reflected in the facility information managed regarding the power transmission and distribution facilities. Therefore, those who manage the power transmission and distribution facilities at organizations such as electric power utilities can manage the facilities efficiently.

[0013] Another aspect of the present invention for achieving the above-mentioned object is an information processing device (worker terminal) that constitutes the above-mentioned salt damage occurrence determination system, which is used by a worker performing work at a site where power transmission and distribution facilities are present, stores the analytical model, and outputs information based on the objective variable obtained by inputting information based on sound data acquired at the site as an analysis target into the analytical model as an explanatory variable.

[0014] In this way, the worker terminal of the present invention outputs information based on the objective variables obtained by inputting sound data acquired on-site into the analysis model, so that the worker can check the contents of the objective variables on-site and proceed with work efficiently.

[0015] Another aspect of the present invention for achieving the above-mentioned object is an information processing device (model learning device) that constitutes the above-mentioned salt damage occurrence determination system, which is communicatively connected to a worker terminal used by a worker performing work in the vicinity of a site where power transmission and distribution equipment is present, generates the analytical model as needed, and transmits the generated analytical model to the worker terminal.

[0016] In this way, the model learning device of the present invention generates trained analytical models at any time and transmits them to the worker terminals, thereby efficiently managing the analytical models on the worker terminals used by each worker to keep them up to date.

[0017] Other problems and solutions disclosed in the present application will be made clear in the detailed description and drawings. [Effects of the Invention]

[0018] According to the present invention, it is possible to easily and accurately determine whether or not salt damage has occurred in power transmission and distribution facilities, thereby supporting the work of workers who perform inspections and maintenance, for example. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a diagram showing a schematic configuration of a salt damage occurrence determination system. [Figure 2] FIG. 1 is a diagram showing how a worker at a site acquires information (sound data (environmental sound), measurement information) used to generate explanatory variables. [Figure 3A] FIG. 2 is a diagram illustrating main functions of the model learning device. [Figure 3B] FIG. 2 is a diagram illustrating a hardware configuration of a model learning device. [Figure 4] FIG. 2 is a diagram illustrating an example of sound data. [Figure 5A] FIG. 2 is a diagram illustrating main functions of a worker terminal. [Figure 5B] FIG. 2 is a diagram illustrating a hardware configuration of a worker terminal. [Figure 6] 1 is an example of a data structure of learning data. [Figure 7] 10 is an example of a data structure of facility information. [Figure 8] 10 is an example of a data structure of worker terminal information. [Figure 9] 10 is an example of a data structure of an analysis result. [Figure 10] 10 is a flowchart illustrating a learning process. [Figure 11] 10 is a flowchart illustrating an analysis process. [Figure 12] 10 is a flowchart illustrating an analysis model receiving process. [Figure 13] 10 is a flowchart illustrating a facility information update process. DETAILED DESCRIPTION OF THE INVENTION

[0020] Hereinafter, an embodiment of the present invention will be described with reference to the drawings as appropriate. Note that the following description and drawings are examples for explaining the present invention, and some omissions and simplifications have been made as appropriate for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural.

[0021] In the following description, the same or similar components may be designated by the same reference numerals, and redundant description may be omitted. Also, in the following description, the letter "S" before a reference numeral indicates a processing step.

[0022] FIG. 1 shows a schematic configuration of an information processing system (hereinafter referred to as "salt damage occurrence determination system 1") that will be described as one embodiment of the present invention. As shown in the figure, salt damage occurrence determination system 1 includes a model learning device 100, which is an information processing device (computer) installed in a facility management department, data center, or the like operated by an electric utility, and one or more worker terminals 200, which are information processing devices used by workers 2 who perform work such as inspection and maintenance of power transmission and distribution facilities on-site. The worker terminals 200 are, for example, smartphones, tablets, notebook computers, dedicated terminals, etc.

[0023] The model learning device 100 and the worker terminal 200 are communicatively connected via a communication network 5. The communication network 5 is, for example, a local area network (LAN), a wide area network (WAN), the Internet, various wireless communication networks (3G communication network, 4G communication network, 5G communication network, etc.), and various dedicated lines.

[0024] The salt damage occurrence determination system 1 uses a machine learning model (hereinafter referred to as the "analysis model") to analyze sound data (environmental sounds) acquired around the site where various types of power transmission and distribution equipment (insulators, power transmission and distribution lines, various types of substation equipment, various types of power distribution equipment, etc.) are installed on utility poles, power transmission line towers, substations, etc., to determine the presence or absence of sounds (hereinafter referred to as "leak sounds") emitted from the power transmission and distribution equipment due to salt damage, and provides information based on the determination results as analysis results to users such as workers 2 working on site and managers of the power transmission and distribution equipment.

[0025] During normal inspection / maintenance, or when there is a high possibility that the transmission and distribution equipment has suffered salt damage due to a natural disaster (typhoon, strong wind, rainfall, snowfall, fog, etc.), worker 2 goes to the site where the transmission and distribution equipment is located, refers to the analysis results output by worker terminal 200, determines whether or not salt damage has occurred in the transmission and distribution equipment around the site, and takes measures such as cleaning if necessary.

[0026] In this way, salt damage occurrence determination system 1 automatically generates analysis results based on information acquired on-site and provides them to the user, so the user can easily and accurately determine whether salt damage has occurred based on the analysis results provided, without relying on intuition or experience. Also, it becomes possible to accurately detect subtle leak sounds that would be undetectable to the human ear or leak sounds buried in noise in power transmission and distribution equipment, allowing the user to accurately determine whether salt damage has occurred and efficiently perform inspection and maintenance work on the power transmission and distribution equipment.

[0027] The model learning device 100 learns an analytical model using learning data (teacher data, training data) in which explanatory variables (features) based on information (sound data (environmental sounds), measurement information) acquired at the site are associated with objective variables (labels) received from a user (presence or absence of leak sounds, type of leak sounds, etc.). The information used to generate the explanatory variables is acquired under various on-site environments. The objective variables (labels) may be, for example, the results of determining whether or not the sound data contains components derived from salt damage using frequency analysis based on a Fourier transform or environmental sound identification technology (acoustic event analysis (classification) technology, acoustic scene classification (identification) technology, abnormal sound detection technology, etc.). Note that, for example, a machine learning model used to identify environmental sounds may be used as the analytical model. Furthermore, for example, an existing environmental sound corpus may be used as sound data (explanatory variables) for normal cases (not including leak sounds).

[0028] FIG. 2 shows a situation in which a worker 2 acquires information (sound data (environmental sound) and measurement information) to be used for generating explanatory variables at a site where power transmission and distribution equipment 3 is installed. The worker 2 sets up a tripod at the site to which a microphone 27 and a measurement device 28 (environmental sensors (temperature sensor, humidity sensor, barometric pressure sensor, etc.)) are attached, and acquires (collects) the sound data and measurement information. In this example, the worker 2 points the microphone 27 toward an insulator attached to the top of a utility pole. The information acquired by the microphone 27 and the measurement device 28 is input into, for example, a worker terminal 200 (for example, a notebook personal computer, a smartphone, a tablet, etc.) that the worker 2 has brought to the site.

[0029] Note that by using a highly sensitive microphone 27, sound data can be acquired from a wider range of power transmission and distribution equipment 3. In this example, the microphone 27 and measuring device 28 are connected to a tripod installed on the ground to acquire information (sound data, measurement information), but these may also be attached to a vehicle used by the worker 2 for inspection and maintenance, for example. In this way, the worker 2 can save the trouble of setting up and removing the device, and can proceed with the work more efficiently.

[0030] 3A shows the main functions of model learning device 100. As shown in the figure, model learning device 100 has the following functions: storage unit 110, information acquisition management unit 120, learning data generation unit 130, analytical model learning unit 135, analytical model distribution unit 140, analysis result receiving unit 145, and equipment information update unit 150.

[0031] Of the above functions, the storage unit 110 stores sound data 111, measurement information 112, learning data 113, analysis model 114, equipment information 115, and worker terminal information 116.

[0032] Of these, the sound data 111 includes a large amount of sound data (sound waveform data) acquired at various sites. Note that, since the leak sound emitted from the power transmission and distribution equipment when salt damage occurs is usually continuous, one piece of sound data is acquired as data of the length of time required to accurately determine whether or not there is a leak sound (hereinafter referred to as a "sound clip" or "audio clip").

[0033] An example of sound data is shown in Figure 4. The sound data is, for example, waveform data for a predetermined period (such as several tens of milliseconds to several tens of seconds) that serves as a unit of analysis.

[0034] Returning to FIG. 3A, the measurement information 112 is data measured by various sensors (environmental sensors (temperature, humidity, atmospheric pressure, etc.), position sensors (GPS sensors (GPS: Global Positioning System), etc.) using measuring equipment such as a measuring device 28 described later at the site where the sound data 111 is acquired. The measurement information 112 is used, for example, to make the analysis model 114 learn the influence on the waveform of the sound data of differences in the environment (differences in temperature, humidity, atmospheric pressure, etc.) of the site where the sound data is acquired.

[0035] The training data 113 is training data used to train the analysis model 114. The training data is data in which explanatory variables generated based on information (sound data, measurement information) acquired on-site are associated with objective variables (labels) (presence or absence of leak sound, type of leak sound, etc.). The explanatory variables are, for example, feature quantities extracted from sound clips. Note that the training data 113 is sufficient as long as it includes at least sound data as explanatory variables, and whether or not measurement information is used as an explanatory variable, and if so, what type of measurement information to use, may be determined according to the need and the situation.

[0036] The analytical model 114 is a machine learning model that is trained using the training data 113. Examples of features of the sound data 111 used in the analytical model 114 include Mel frequency cepstrum coefficients (MFCCs), cepstrums, MPEG-7 acoustic features, and other acoustic features. The analytical model 114 is embodied as, for example, a polynomial, a determinant, a mathematical expression, a vector, or the like, including adjustable parameters. The type of the analytical model 114 is not necessarily limited, and examples include various deep learning models (CNNs: convolutional neural networks), RNNs (recurrent neural networks), Gaussian mixture models (GMMs), hidden Markov models (HMMs), and support vector machines (SVMs). As the analysis model 114, a so-called anomaly detection model (such as a machine learning model trained by an autoencoder (such as a VAE (Variational Autoencoder)) that learns an abnormal state by learning a steady state may be used.

[0037] Equipment information 115 is information used by electric power companies and the like to manage power transmission and distribution equipment, and includes information about the power transmission and distribution equipment installed in the area (type and model of equipment, installation location of the equipment, installation date, dates and details of past inspections / maintenance work, whether or not there is current salt damage, etc.).

[0038] The worker terminal information 116 includes information about the worker terminal 200 to which the analytical model 114 is delivered from the model learning device 100.

[0039] Among the functions shown in the figure, an information acquisition management unit 120 manages sound data 111 and measurement information 112 acquired in the field, in a test environment, or the like.

[0040] The training data generation unit 130 generates the training data 113. The training data generation unit 130 generates the training data 113 by, for example, presenting explanatory variables and receiving, from a user via a user interface, a target variable to be set for the explanatory variables.

[0041] The analytical model learning unit 135 uses the learning data 113 to learn the analytical model 114 .

[0042] The analytical model distribution unit 140 distributes (transmits) the latest analytical model 114 trained by the analytical model training unit 135 to the worker terminal 200 via the communication network 5.

[0043] The analysis result receiving unit 145 receives the analysis results sent from the worker terminal 200 via the communication network 5 .

[0044] The equipment information update unit 150 receives the analysis results sent from the operator terminal 200 and updates the equipment information 115 with the details of the received analysis results.

[0045] 3B is a diagram showing the hardware configuration of model learning device 100. As shown in the figure, model learning device 100 includes processor 11, main memory device 12, auxiliary memory device 13, input device 14, output device 15, and communication device 16. Model learning device 100 may be configured from multiple information processing devices connected to each other so that they can communicate with each other.

[0046] All or part of the configuration shown in the figure may be realized using virtual information processing resources provided using virtualization technology, process space separation technology, or the like, such as a virtual server provided by a cloud system. All or part of the functions of model learning device 100 may be realized, for example, by a service provided by a cloud system via an API (Application Programming Interface). All or part of the functions of model learning device 100 may be realized, for example, using Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), or the like.

[0047] In the same figure, the processor 11 is configured using, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), an AI (Artificial Intelligence) chip, etc.

[0048] Main memory device 12 is a device used by processor 11 when executing a program, and is, for example, a read-only memory (ROM), a random access memory (RAM), or a non-volatile memory (NVRAM (Non-Volatile RAM)). The various functions of model learning device 100 are realized by processor 11 reading programs and data stored in auxiliary memory device 13 into main memory device 12 and executing them.

[0049] The auxiliary storage device 13 is, for example, an SSD (Solid State Drive), a hard disk drive, an optical storage device (CD (Compact Disc), DVD (Digital Versatile Disc), etc.), a storage system, a reading / writing device for a recording medium such as an IC card, an SD card, or an optical recording medium, a storage area of a cloud server, etc. Programs and data can be read into the auxiliary storage device 13 via a recording medium reading device or a communication device 16. The programs and data stored (memorized) in the auxiliary storage device 13 are read into the main storage device 12 as needed.

[0050] The input device 14 is an interface that accepts input from the outside, and is, for example, a touch panel, a keyboard, a mouse, a card reader, a pen-input tablet, a voice input device, or the like.

[0051] Output device 15 is an interface that outputs various information such as processing progress and processing results. Output device 15 is, for example, a display device (liquid crystal monitor, LCD (Liquid Crystal Display), graphic card, etc.) that visualizes the various information described above, a device that converts the various information described above into audio (audio output device (speaker, etc.)), or a device that converts the various information described above into text (printer, etc.). Note that, for example, model learning device 100 may be configured to input and output information to and from other devices via communication device 16.

[0052] The input device 14 and the output device 15 constitute a user interface that receives information from the user and presents information to the user.

[0053] The communication device 16 is a device that realizes communication with other devices. The communication device 16 is a wired or wireless communication interface that realizes communication with other devices via a communication network 5 such as the Internet, and is, for example, a NIC (Network Interface Card), a wireless communication module, a USB module, or the like.

[0054] The model learning device 100 may be equipped with, for example, an operating system, a file system, a DBMS (DataBase Management System) (relational database, NoSQL, etc.), a KVS (Key-Value Store), etc.

[0055] Each of the above-described functions of model learning device 100 is realized by processor 11 reading and executing a program stored in main memory device 12, or by hardware (FPGA, ASIC, AI chip, etc.) that constitutes model learning device 100. Model learning device 100 stores the above-described various pieces of information (data), for example, as a database table or a file managed by a file system.

[0056] 5A shows the main functions of the worker terminal 200. As shown in the figure, the worker terminal 200 has the following functions: a storage unit 210, an analysis target information acquisition unit 220, an explanatory variable generation unit 225, an analysis processing unit 230, an analysis result presentation unit 235, an analysis result transmission unit 240, and an analysis model update unit 245.

[0057] Of the above functions, the storage unit 210 stores the following information: analysis target information 211, explanatory variables 212, analysis model 114, and analysis results 213.

[0058] Of the above information, the analysis target information 211 is information (sound data, measurement information) that is input as an analysis target at the site.

[0059] The explanatory variables 212 are explanatory variables (feature amounts) generated based on information input as an analysis target (sound data input from the microphone 27, measurement information input from the measurement device 28).

[0060] The analytical model 114 is distributed from the model learning device 100. The analytical model 114 is the same as that described above, so a description thereof will be omitted.

[0061] The analysis result 213 includes information about the analysis result performed by the analysis processing unit 230 using the analysis model 114 (presence or absence of leak sound, type of leak sound, presence or absence of salt damage, etc.).

[0062] Of the above functions, the explanatory variable generation unit 225 extracts feature amounts from the analysis target information 211 and generates explanatory variables 212 .

[0063] The analysis processing unit 230 inputs the explanatory variables 212 into the analysis model 114, and thereby obtains the response variables output by the analysis model 114.

[0064] The analysis result presentation unit 235 generates the analysis result 213 based on the objective variable acquired by the analysis processing unit 230, and presents the generated analysis result 213 to the user.

[0065] The analysis result transmission unit 240 transmits the analysis result 213 to the model learning device 100 via the communication network 5.

[0066] The analytical model update unit 245 receives the analytical model 114 sent from the model learning device 100 via the communication network 5, and updates the analytical model 114 stored in the storage unit 210 to the received analytical model 114. Note that the analytical model update unit 245 may receive only the update difference of the analytical model 114 from the model learning device 100, and update the analytical model 114 using the received update difference.

[0067] 5B shows an example of the hardware configuration of the worker terminal 200. As shown in the figure, the worker terminal 200 includes a processor 21, a main memory device 22, an auxiliary memory device 23, an input device 24, an output device 25, a communication device 26, a microphone 27, and a measurement device 28. Of these, the processor 21, the main memory device 22, the auxiliary memory device 23, the input device 24, the output device 25, and the communication device 26 are similar to the processor 11, the main memory device 12, the auxiliary memory device 13, the input device 14, the output device 15, and the communication device 16 of the model learning device 100, respectively, and therefore description thereof will be omitted. The microphone 27 and the measurement device 28 may be integrated into the worker terminal 200 main body or may be external.

[0068] The microphone 27 is a device that acquires sound data (sound waveform data), and may be, for example, a dynamic microphone or a condenser microphone. The microphone 27 is equipped with an amplifier circuit, and the acquired sound data is amplified by the amplifier circuit to a predetermined required power.

[0069] The measuring device 28 is configured using various sensors (temperature sensor, humidity sensor, air pressure sensor, etc.) and amplifier circuits for the sensor output values, and outputs measured values of information related to the on-site environment (temperature, humidity, air pressure, etc.).

[0070] 6 shows an example of the data structure of the training data 113. As shown in the figure, the training data 113 shown in the figure is composed of one or more records each having items such as a data ID 611, sound data 612, measurement information 613, presence / absence of leak sound 614, and type of leak sound 615. One record of the training data 113 corresponds to one piece of training data.

[0071] Of the above items, data ID 611 stores a data ID that is an identifier for the learning data. Sound data 612 and measurement information 613 correspond to explanatory variables. Furthermore, presence / absence of leak sound 614 and type of leak sound 615 correspond to objective variables. For example, the user checks the contents of the explanatory variables presented on the screen via a user interface provided by model learning device 100, and sets the contents of the objective variables corresponding to the explanatory variables.

[0072] 7 shows an example of the data structure of the facility information 115. As shown in the figure, the facility information 115 shown in the figure is made up of one or more records each having items such as a facility ID 711, a type 712, a model 713, an installation location 714, an installation date 715, a date of previous work 716, work content 717, and whether or not there is salt damage 718. One record in the facility information 115 corresponds to one power transmission and distribution facility.

[0073] Of the above items, equipment ID 711 stores an identifier of the power transmission and distribution equipment (hereinafter referred to as "equipment ID"). Type 712 stores information indicating the type of power transmission and distribution equipment 3. Model 713 stores information indicating the model (model number) of the power transmission and distribution equipment 3. Installation location 714 stores information indicating the location (position) where the power transmission and distribution equipment is installed. Installation date 715 stores the date on which the power transmission and distribution equipment was installed. Last work date 716 stores the date on which the worker 2 last (most recently) performed work (inspection, maintenance, etc.) on the power transmission and distribution equipment. Work content 717 stores information indicating the content of the work last performed by worker 2 on the power transmission and distribution equipment. Salt damage presence / absence 718 stores information indicating whether salt damage has occurred on the power transmission and distribution equipment ("yes" if salt damage has occurred, and "no" if no salt damage has occurred).

[0074] 8 shows an example of the data structure of worker terminal information 116. As shown in the figure, the illustrated worker terminal information 116 is made up of one or more records each having items such as a terminal ID 811, a network address 812, a current version 813, and a last update date and time 814. One record of the worker terminal information 116 corresponds to one worker terminal 200.

[0075] The terminal ID 811 stores an identifier of the worker terminal 200 (hereinafter referred to as "terminal ID"). The NW address 812 stores a network address (such as an IP address) assigned to the worker terminal 200. The current version 813 stores information indicating the current version of the analytical model 114 stored in the worker terminal 200. The last update date and time 814 stores the date and time of the most recent update of the analytical model 114 of the worker terminal 200.

[0076] 9 is an example of the data structure of the analysis result 213. As shown in the figure, the illustrated analysis result 213 is made up of one or more records having items such as sound data acquisition date 911, sound data acquisition location 912, presence or absence of leak sound 913, leak sound type 914, and presence or absence of salt damage 915. One record of the analysis result 213 corresponds to the analysis result performed on one piece of sound data acquired at a certain site.

[0077] The sound data acquisition date 911 stores the date on which the sound data (sound data to be analyzed) was acquired. The sound data acquisition position 912 stores information indicating the position where the sound data was acquired. The presence or absence of leak sound 913 stores information indicating the presence or absence of leak sound among the objective variables output by the analysis model 114. The leak sound type 914 stores information indicating the type of leak sound among the objective variables output by the analysis model 114. The presence or absence of salt damage 915 stores information indicating whether salt damage is currently occurring in the power transmission and distribution equipment at the site ("yes" if it has occurred, and "no" if it has not occurred).

[0078] Furthermore, the analysis result presentation unit 235 of the worker terminal 200 determines that salt damage has occurred, for example, if a leak sound is occurring at the site or if the intensity of the leak sound is equal to or greater than a preset value, and sets the salt damage occurrence status 915 in the analysis result 213 to "yes."

[0079] Next, the main processes performed in the salt damage occurrence determination system 1 will be described.

[0080] 10 is a flowchart illustrating the process (hereinafter referred to as "learning process S1000") that analytical model learning unit 135 of model learning device 100 performs when learning analytical model 114. The learning process S900 will be described below with reference to this figure.

[0081] First, the analytical model learning unit 135 reads the learning data 113 from the auxiliary storage device 13 into the main storage device 12 (S1011).

[0082] Next, the analytical model learning unit 135 uses the learning data 113 to learn the analytical model 114 (S1012).

[0083] Next, the analytical model distribution unit 140 distributes (transmits) the latest trained analytical model 114 to each worker terminal 200 via the communication network 5 (S1013).

[0084] Since the learning of the analytical model 114 requires a large amount of information processing resources, the learning may be performed using, for example, an API (Application Programming Interface) for machine learning provided by a cloud system.

[0085] Furthermore, the analytical model learning unit 135 may, for example, verify the prediction accuracy of the learned analytical model 114. In this case, for example, the learning data is classified in advance into data for learning and data for verification, and learning is performed using the data for learning, and verification is performed using the data for verification.

[0086] FIG. 11 is a flowchart illustrating a process (hereinafter referred to as "analysis process S1100") performed by the worker terminal 200 when analyzing information to be analyzed (sound data, measurement information) obtained on-site. The analysis process S1100 may be performed by the worker terminal 200 alone, or may be performed by the worker terminal 200 in cooperation with resources such as a cloud service via the communication network 5, for example. The analysis process S1100 is started, for example, when the worker 2 performs a predetermined start operation on the worker terminal 200. The analysis process S1100 will be described below with reference to FIG.

[0087] First, the analysis target information acquisition unit 220 of the worker terminal 200 acquires the analysis target information 211 from the microphone 27 and the measuring device 28 (S1111 to S1112). At this time, the analysis target information acquisition unit 220 may present (display, etc.) the acquired analysis target information 211 to the worker 2.

[0088] Next, the explanatory variable generation unit 225 of the worker terminal 200 extracts feature amounts from the acquired analysis target information 211 and generates explanatory variables 212 (S1113).

[0089] Next, the analysis processing unit 230 of the operator terminal 200 inputs the explanatory variables 212 into the analysis model 114, and acquires the objective variables output by the analysis model 114 (S1114).

[0090] Next, the analysis result presenting unit 235 of the worker terminal 200 presents information based on the acquired objective variables as the analysis results (presence or absence of leak noise, type of leak noise, presence or absence of salt damage, etc.) (S1115).

[0091] Next, the analysis result transmission unit 240 of the operator terminal 200 transmits the analysis result to the model learning device 100 via the communication network 5 (S1116).

[0092] In this way, the worker 2 can accurately determine whether salt damage has occurred in the power transmission and distribution equipment at the site, even if he or she does not have knowledge or experience, by using the worker terminal 200. This allows the inspection work of the power transmission and distribution equipment to be carried out efficiently and reliably, and the workload of the worker 2 can be significantly reduced.

[0093] 12 is a flowchart for explaining the process (hereinafter referred to as "analysis model reception process S1200") that is performed when the operator terminal 200 updates the analysis model 114. The analysis model reception process S1200 will be explained below with reference to this figure.

[0094] The analytical model update unit 245 of the operator terminal 200 constantly monitors whether a new analytical model 114 (the latest analytical model 114 learned using the latest learning data 113) or an update difference has been received from the model learning device 100 (S1211). The analytical model distribution unit 140 of the model learning device 100 distributes the analytical model to the operator terminal 200 at an appropriate timing, such as when the analytical model 114 has been updated with new learning data 113.

[0095] When the analytical model update unit 245 receives a new analytical model 114 or an update difference from the model learning device 100, it updates the analytical model 114 to the content of the received analytical model 114 (or reflects the update difference in the analytical model 114) (S1212).

[0096] In this way, the analytical model 114 of the worker terminal 200 is always kept up to date by learning using the latest learning data 113, so the worker 2 can determine whether salt damage is occurring continuously and stably at the site.

[0097] 13 is a flowchart illustrating the process (hereinafter referred to as "facility information update process S1300") performed by the model learning device 100 when updating the facility information 115. The facility information update process S1300 will be described below with reference to this figure.

[0098] The equipment information update unit 150 of the model learning device 100 constantly monitors whether or not the analysis result has been received from the operator terminal 200 (S1311).

[0099] When the facility information update unit 150 receives new analysis results from the operator terminal 200, it reflects the content of the received analysis results in the content (such as salt damage presence / absence 718) of the corresponding power transmission and distribution facility in the facility information 115 (S1312).

[0100] In this way, the analysis results of the power transmission and distribution facilities installed at various locations are managed in a unified manner as the facility information 115, so that, for example, a person who manages the power transmission and distribution facilities in an organization such as an electric utility can efficiently manage the power transmission and distribution facilities. Note that the analysis results received from the operator terminal 200 may be used, for example, as a target variable (label) when generating the learning data 113 for the analysis model 114.

[0101] Although the embodiments of the present invention have been described in detail above, the above description is intended to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and the present invention naturally includes equivalents thereof. For example, the above embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to add, delete, or replace part of the configuration of the above embodiments with other configurations. [Explanation of symbols]

[0102] 1 salt damage occurrence determination system, 2 worker, 3 power transmission and distribution equipment, 27 microphone, 28 measuring device, 5 communication network, 100 model learning device, 110 memory unit, 111 sound data, 112 measurement information, 113 learning data, 114 analysis model, 115 equipment information, 116 worker terminal information, 120 information acquisition management unit, 130 learning data generation unit, 135 analysis model learning unit, 140 analysis model distribution unit, 145 analysis result receiving unit, 150 equipment information update unit, 200 worker terminal, 210 memory unit, 211 analysis target information, 212 explanatory variable, 213 analysis result, 220 analysis target information acquisition unit, 225 explanatory variable generation unit, 230 analysis processing unit, 235 analysis result presentation unit, 240 analysis result transmission unit, 245 analysis model update unit, S1000 Learning process, S1100 analysis process, S1200 analysis model reception process, S1300 facility information update process

Claims

1. An information processing system for determining whether salt damage has occurred in power transmission and distribution equipment, An analytical model is stored, which is a machine learning model trained using learning data configured using information based on sound data, which is environmental sound acquired at the site where the power transmission and distribution equipment is installed, as explanatory variables, and information indicating the presence or absence of leak sounds emitted from the power transmission and distribution equipment due to salt damage and information indicating the type of leak sounds if the leak sounds are present, and outputting information based on the objective variables obtained by inputting information based on sound data acquired at the site as an analysis target into the analysis model as the explanatory variables. A system for determining whether salt damage has occurred.

2. An information processing system for determining whether salt damage has occurred in power transmission and distribution equipment, an analytical model that is a machine learning model trained using learning data configured using information based on sound data, which is environmental sound acquired at a site where the power transmission and distribution equipment is installed, and measurement information, which is information acquired by an environmental sensor at the site, as explanatory variables, and information indicating the presence or absence of leak sounds emitted from the power transmission and distribution equipment due to salt damage and information indicating the type of leak sounds if the leak sounds are present, as objective variables; outputting information based on the objective variables obtained by inputting information based on sound data and environmental measurement information acquired at the site as the analysis target into the analysis model as the explanatory variables; A system for determining whether salt damage has occurred.

3. The salt damage occurrence determination system according to claim 1, storing facility information that is information about the power transmission and distribution facility to be managed; reflecting the content of the objective variable obtained by inputting information based on the sound data acquired at the site as an analysis target into the analysis model in the facility information; A system for determining whether salt damage has occurred.

4. An information processing device constituting the salt damage occurrence determination system according to claim 1, Used by workers working at sites where power transmission and distribution facilities exist, storing the analytical model; and outputting information based on the objective variables obtained by inputting information based on sound data acquired at the site as an analysis target into the analysis model as the explanatory variables. Worker terminal.

5. A method for determining whether salt damage has occurred in power transmission and distribution equipment, comprising: The information processing device a step of storing an analytical model, which is a machine learning model trained using learning data configured using information based on sound data, which is environmental sound acquired at the site where the power transmission and distribution equipment is installed, as explanatory variables, and information indicating the presence or absence of leak sounds emitted from the power transmission and distribution equipment due to salt damage and information indicating the type of leak sounds if the leak sounds are present, as objective variables; and a step of outputting information based on the objective variables obtained by inputting information based on sound data acquired at the site as an analysis target into the analysis model as the explanatory variables; A method for determining whether salt damage has occurred.

6. A method for determining whether salt damage has occurred in power transmission and distribution equipment, comprising: The information processing device a step of storing an analytical model, which is a machine learning model trained using learning data configured using information based on sound data, which is environmental sound acquired at the site where the power transmission and distribution equipment is installed, and measurement information, which is information acquired by an environmental sensor at the site, as explanatory variables, and information indicating the presence or absence of leak sounds emitted from the power transmission and distribution equipment due to salt damage and information indicating the type of leak sounds if the leak sounds are present, as objective variables; and a step of outputting information based on the objective variables obtained by inputting information based on sound data and environmental measurement information acquired at the site as the analysis target into the analysis model as the explanatory variables; A method for determining whether salt damage has occurred.

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