Switch state identification and diagnosis method, device, equipment and medium

Abstract

The invention discloses a switch state identification and diagnosis method and device, equipment and a medium. The switch state identification and diagnosis method comprises the following steps: acquiring a to-be-analyzed high-frequency signal of a target electrical switch; extracting a target characteristic parameter of the to-be-analyzed high-frequency signal, and inputting the target characteristic parameter into the switch state intelligent identification model to obtain electrical switch state identification data; and determining the current state of the target electrical switch according to the electrical switch state identification data, performing fault diagnosis based on the current state of the target electrical switch, and generating fault diagnosis data. According to the technical scheme of the embodiment of the invention, automatic diagnosis of the electrical switch can be realized, the diagnosis result is high in timeliness, and the accuracy is higher.

Description

Technical Field

[0001] The present invention relates to the field of electrical technology, and in particular to a switch state identification and diagnosis method, device, equipment and medium. Background Art

[0002] Electrical switches are common electrical equipment in power distribution systems, and they are responsible for breaking circuits and protecting lines and equipment. With the construction of smart grids, the number of electrical switches in power distribution systems is increasing, and the maintenance and operation management of electrical switches are becoming more complicated. If the status of electrical switches can be quickly identified and diagnosed, the maintenance efficiency of electrical switches can be improved, and the impact of electrical switch failures on the power grid can be reduced.

[0003] However, traditional switch status identification and diagnosis methods mainly rely on the experience and on-site inspection of maintenance personnel. That is, on-site staff need to carry multiple tools and use multiple methods to judge the switch status and perform diagnosis based on the switch status. This method is time-consuming and error-prone, and it is difficult to meet the accuracy requirements. Summary of the invention

[0004] The present invention provides a switch state identification and diagnosis method, device, equipment and medium to solve the problems of high labor cost and poor accuracy in current electrical switch state identification and diagnosis.

[0005] According to one aspect of the present invention, a switch state identification and diagnosis method is provided, comprising:

[0006] Collect the high-frequency signal to be analyzed from the target electrical switch;

[0007] Extracting target characteristic parameters of the high-frequency signal to be analyzed, and inputting the target characteristic parameters into the switch state intelligent recognition model to obtain electrical switch state recognition data;

[0008] According to the electrical switch state identification data, the current state of the target electrical switch is determined, and a fault diagnosis is performed based on the current state of the target electrical switch to generate fault diagnosis data.

[0009] According to another aspect of the present invention, there is provided a switch state identification and diagnosis device, comprising:

[0010] A signal acquisition module, used to collect the high-frequency signal to be analyzed from the target electrical switch;

[0011] An electrical switch state identification data determination module is used to extract target characteristic parameters of the high-frequency signal to be analyzed, and input the target characteristic parameters into the switch state intelligent identification model to obtain electrical switch state identification data;

[0012] The switch state recognition and diagnosis module is used to determine the current state of the target electrical switch according to the electrical switch state recognition data, and perform fault diagnosis based on the current state of the target electrical switch to generate fault diagnosis data.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the switch state identification and diagnosis method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the switch state identification and diagnosis method described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention collects the high-frequency signal to be analyzed of the target electrical switch, thereby extracting the target characteristic parameters of the high-frequency signal to be analyzed, and inputting the target characteristic parameters into the switch state intelligent recognition model to obtain electrical switch state recognition data, and then determining the current state of the target electrical switch according to the electrical switch state recognition data, and performing fault diagnosis based on the current state of the target electrical switch to generate fault diagnosis data. In this solution, the target characteristic parameters of the high-frequency signal to be analyzed can accurately reflect the operation of the electrical switch, and the state of the electrical switch can be automatically and quickly obtained with high accuracy by analyzing the target characteristic parameters with the switch state intelligent recognition model, so as to further perform fault diagnosis on the correctly identified electrical switch state, solving the problem of high labor cost and poor accuracy in the current electrical switch state recognition and diagnosis, and realizing automatic diagnosis of electrical switches, with strong timeliness and higher accuracy in the diagnosis results.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flow chart of a switch state identification and diagnosis method provided in Embodiment 1 of the present invention;

[0022] Figure 2 A flow chart of a switch state identification and diagnosis method provided in Embodiment 2 of the present invention;

[0023] Figure 3 A schematic diagram of the structure of a switch state identification and diagnosis device provided in Embodiment 3 of the present invention;

[0024] Figure 4 A schematic diagram of the structure of an electronic device that can be used to implement an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the term "target" and the like in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1This is a flow chart of a switch state identification and diagnosis method provided in the first embodiment of the present invention. This embodiment is applicable to the situation of accurately identifying and diagnosing the state of an electrical switch. The method can be performed by a switch state identification and diagnosis device. The switch state identification and diagnosis device can be implemented in the form of hardware and / or software. The switch state identification and diagnosis device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0029] Step 110: Collect the high-frequency signal to be analyzed of the target electrical switch.

[0030] The target electrical switch may be an electrical switch in the power system that needs to be monitored for status. The high-frequency signal to be analyzed may be a high-frequency electrical signal of the collected electrical switch.

[0031] In the embodiment of the present invention, after the target electrical switch to be monitored is determined, the high-frequency signal to be analyzed of the target electrical switch may be collected based on a high-frequency signal collection device.

[0032] Step 120: extract target characteristic parameters of the high-frequency signal to be analyzed, and input the target characteristic parameters into the switch state intelligent recognition model to obtain electrical switch state recognition data.

[0033] The target characteristic parameters may be time domain characteristics and frequency domain characteristics for determining the target electrical switch state. The switch state intelligent recognition model may be a model for identifying the electrical switch state based on the target characteristic parameters. The electrical switch state recognition data may be data describing the working state of the electrical switch. The working state of the electrical switch may include closed, open or other fault states.

[0034] In an embodiment of the present invention, feature extraction can be performed on the high-frequency signal to be analyzed from two dimensions, time domain and frequency domain, to obtain target feature parameters, which are then input into a switch state intelligent recognition model, so that the state of the target electrical switch can be identified according to the target feature parameters through the switch state intelligent recognition model, and electrical switch state recognition data corresponding to the recognition result can be output.

[0035] Step 130 : Determine the current state of the target electrical switch according to the electrical switch state identification data, perform fault diagnosis based on the current state of the target electrical switch, and generate fault diagnosis data.

[0036] The current state of the target electrical switch may be the current working state of the target electrical switch. The fault diagnosis data may be used to describe the fault diagnosis result of the target electrical switch. Exemplarily, the fault type of the electrical switch may include but is not limited to poor contact and mechanical damage.

[0037] In an embodiment of the present invention, the current state of the target electrical switch can be determined based on the electrical switch state identification data, and then when the current state of the target electrical switch is a fault state, a fault analysis is performed on the target electrical switch to determine the fault type of the target electrical switch, and fault diagnosis data is generated based on the fault type and the equipment information of the target electrical switch.

[0038] In a specific example, a high-frequency electrical signal of a faulty electrical switch can be collected, and a multi-layer perception model can be trained using the voltage and current characteristics of the high-frequency electrical signal, so that the trained model (multi-layer perception fault diagnosis model) has the ability to infer the specific fault type of the electrical switch. That is, when the current state of the target electrical switch is determined to be a fault state, the multi-layer perception fault diagnosis model is used to perform fault diagnosis.

[0039] Optionally, after receiving the detailed diagnostic report query command sent by the user, the fault diagnosis data and maintenance suggestions are displayed through the human-computer interaction interface, and an alarm (such as an audible and visual type alarm) can be issued when a fault is detected in the target electrical switch, and the fault information (such as a fault code, etc.) and the diagnosis time are recorded in a log file. The human-computer interaction interface can be a computer terminal, such as a desktop computer, a laptop computer, a tablet computer, etc., or a mobile device, such as a mobile phone, a tablet, etc.

[0040] Exemplarily, the maintenance data matching the fault type of the fault diagnosis data can be used as maintenance suggestions in the fault and maintenance database (pre-stored with known fault types and maintenance related data). For example, when the fault type is poor contact, the maintenance suggestion can be to disconnect the power supply, check the cleanliness of the contacts, and determine whether to use a detergent to clean the contacts.

[0041] The technical solution of the embodiment of the present invention collects the high-frequency signal to be analyzed of the target electrical switch, thereby extracting the target characteristic parameters of the high-frequency signal to be analyzed, and inputting the target characteristic parameters into the switch state intelligent recognition model to obtain electrical switch state recognition data, and then determining the current state of the target electrical switch according to the electrical switch state recognition data, and performing fault diagnosis based on the current state of the target electrical switch to generate fault diagnosis data. In this solution, the target characteristic parameters of the high-frequency signal to be analyzed can accurately reflect the operation of the electrical switch, and the state of the electrical switch can be automatically and quickly obtained with high accuracy by analyzing the target characteristic parameters with the switch state intelligent recognition model, so as to further perform fault diagnosis on the correctly identified electrical switch state, solving the problems of high labor cost and poor accuracy in the current electrical switch state recognition and diagnosis, and realizing automatic diagnosis of electrical switches, with strong timeliness and higher accuracy of the diagnosis results.

[0042] Embodiment 2

[0043] Figure 2 This is a flow chart of a switch state identification and diagnosis method provided in the second embodiment of the present invention. This embodiment is specific based on the above embodiment and provides a specific optional implementation method for extracting the target characteristic parameters of the high-frequency signal to be analyzed. Figure 2 As shown, the method includes:

[0044] Step 210: Collect the high-frequency signal to be analyzed of the target electrical switch.

[0045] In an optional embodiment of the present invention, collecting the high-frequency signal to be analyzed of the target electrical switch may include: collecting the high-frequency voltage signal of the target electrical switch based on a high-frequency voltage collection module, and collecting the high-frequency current signal of the target electrical switch based on a high-frequency current collection module; using the high-frequency voltage signal and the high-frequency current signal as the high-frequency signals to be analyzed.

[0046] Among them, the high-frequency voltage acquisition module can be a device for collecting high-frequency voltage signals. The high-frequency current acquisition module can be a device for collecting high-frequency current signals. Exemplarily, the high-frequency voltage acquisition module can be a high-frequency voltage sensor. The high-frequency current acquisition module can be a high-frequency current sensor. The sampling frequency of the high-frequency voltage sensor can be set to 1MHz, and the range is 0-1000V. The sampling frequency of the high-frequency current sensor can be set to 1MHz, and the range is 0-100A. In order to ensure the sampling synchronization of the voltage and current signals, the sampling rate is set to 1MS / s.

[0047] In an embodiment of the present invention, a high-frequency voltage acquisition module can be used to collect high-frequency voltage signals of the target electrical switch, and a high-frequency current acquisition module can be used to collect high-frequency current signals of the target electrical switch, thereby using the obtained two types of high-frequency signals as high-frequency signals to be analyzed.

[0048] In an optional embodiment of the present invention, the target electrical switch may include a relay or a contactor; the high-frequency voltage acquisition module is installed at the voltage input terminal or the voltage output terminal of the target electrical switch; and the high-frequency current acquisition module is installed in the current circuit of the target electrical switch.

[0049] In an embodiment of the present invention, by installing a high-frequency voltage acquisition module at the voltage input terminal or the voltage output terminal of the target electrical switch, and installing a high-frequency current acquisition module at the current circuit of the target electrical switch, it can be ensured that the acquisition of voltage and current signals is synchronized so that they can accurately correspond during subsequent signal processing.

[0050] Step 220: perform signal preprocessing on the high-frequency signal to be analyzed to obtain a preprocessed high-frequency signal.

[0051] The signal preprocessing may be a preset signal processing operation, and the signal preprocessing may include but is not limited to signal filtering and wavelet transform.

[0052] In the embodiment of the present invention, the high-frequency signal to be analyzed may be preprocessed based on digital signal processing technology, and the processing result may be used as the preprocessed high-frequency signal.

[0053] In an optional embodiment of the present invention, signal preprocessing is performed on the high-frequency signal to be analyzed to obtain a preprocessed high-frequency signal, which may include: filtering the high-frequency signal to be analyzed to obtain a high-frequency signal in a target frequency band; amplifying the high-frequency signal in the target frequency band according to a preset signal amplification factor to obtain a high-frequency signal to be denoised; and denoising the high-frequency signal to be denoised based on a wavelet transform to obtain a preprocessed high-frequency signal.

[0054] The target frequency band high-frequency signal may be a high-frequency signal to be analyzed in a preset signal frequency band. The preset signal frequency band may be 100kHz-500kHz. The preset signal amplification factor may be a preset signal amplification factor. The high-frequency signal to be denoised may be a signal of the target frequency band high-frequency signal amplified by the preset signal amplification factor.

[0055] In an embodiment of the present invention, a bandpass filter can be used to filter the high-frequency signal to be analyzed, retain the signal of the required analysis frequency band, obtain the high-frequency signal of the target frequency band, and then amplify the high-frequency signal of the target frequency band according to a preset signal amplification factor to improve the signal-to-noise ratio and generate a high-frequency signal to be denoised. The wavelet basis function and the decomposition level can be used to perform wavelet transformation on the high-frequency signal to be denoised, further eliminate the noise component in the signal, complete the denoising of the high-frequency signal to be denoised, and obtain a preprocessed high-frequency signal.

[0056] Step 230 : Determine target characteristic parameters based on the time domain signal of the preprocessed high-frequency signal, and input the target characteristic parameters into the switch state intelligent recognition model to obtain electrical switch state recognition data.

[0057] In an embodiment of the present invention, the time domain signal in the preprocessed high-frequency signal can be parsed, and then the frequency domain signal corresponding to the time domain signal can be determined based on the time domain signal of the preprocessed high-frequency signal, so that feature extraction is performed on the time domain signal and the frequency domain signal of the preprocessed high-frequency signal to obtain target feature parameters, and then the target feature parameters are input into the switch state intelligent recognition model to obtain electrical switch state recognition data.

[0058] In an optional embodiment of the present invention, before inputting the target characteristic parameters into the switch state intelligent recognition model, it may also include: acquiring a switch state data set; and training the target model to be trained using the switch state data set to obtain the switch state intelligent recognition model.

[0059] The switch state data set may be sample data for electrical switch identification training. Exemplarily, each sample in the switch state data set includes characteristic parameters of two dimensions, voltage and current, and specifically includes the type of the parameter being the same as the type of the target characteristic parameter. The target model to be trained may be a model that can recognize a specific type of data after training. Exemplarily, the target model to be trained may perform data analysis based on a machine learning algorithm or a deep learning network. For example, the target model to be trained may be an MLP (Multi Layer Perceptron) model.

[0060] In an embodiment of the present invention, a switch state data set may be first obtained, and then the switch state data set may be further input into a target model to be trained, so as to train the target model to be trained based on the switch state data set, and the trained model that meets the recognition accuracy may be used as a switch state intelligent recognition model.

[0061] In an optional embodiment of the present invention, determining the target characteristic parameters based on the time domain signal of the preprocessed high-frequency signal may include: calculating the peak value, rise time and fall time of the electrical signal based on the time domain signal of the preprocessed high-frequency signal; using fast Fourier transform to convert the time domain signal of the preprocessed high-frequency signal into a frequency domain signal, and extracting the main frequency component and spectral energy of the frequency domain signal; using the peak value, rise time, fall time, main frequency component and spectral energy of the electrical signal as the target characteristic parameters.

[0062] The peak value of the electrical signal can be used to describe the maximum instantaneous value of the time domain signal of the preprocessed high-frequency signal. The peak value of the electrical signal can include the voltage peak value and the current peak value of the preprocessed high-frequency signal.

[0063] In an embodiment of the present invention, the time domain signal of the preprocessed high-frequency signal can be calculated based on the existing algorithm for processing time domain signals to obtain the peak value, rise time and fall time of the electrical signal, and the time domain signal of the preprocessed high-frequency signal can be further subjected to a fast Fourier transform to obtain the frequency domain signal of the preprocessed high-frequency signal, and then the frequency domain characteristics of the frequency domain signal of the preprocessed high-frequency signal can be extracted to obtain the main frequency component and spectral energy, thereby taking the peak value, rise time, fall time, main frequency component and spectral energy of the electrical signal as target feature parameters.

[0064] Step 240: Determine the current state of the target electrical switch according to the electrical switch state identification data, perform fault diagnosis based on the current state of the target electrical switch, and generate fault diagnosis data.

[0065] The technical solution of the embodiment of the present invention collects the high-frequency signal to be analyzed of the target electrical switch, thereby performing signal preprocessing on the high-frequency signal to be analyzed to obtain a preprocessed high-frequency signal, and then determines the target characteristic parameters based on the time domain signal of the preprocessed high-frequency signal, and inputs the target characteristic parameters into the switch state intelligent recognition model to obtain electrical switch state recognition data, and further determines the current state of the target electrical switch based on the electrical switch state recognition data, and performs fault diagnosis based on the current state of the target electrical switch to generate fault diagnosis data. In this solution, the target characteristic parameters of the high-frequency signal to be analyzed can accurately reflect the operation of the electrical switch, and the state of the electrical switch can be automatically and quickly obtained with high accuracy by analyzing the target characteristic parameters through the switch state intelligent recognition model, so as to further perform fault diagnosis on the correctly identified electrical switch state, solve the problems of high labor cost and poor accuracy in the current electrical switch state recognition and diagnosis, and can realize automatic diagnosis of electrical switches, and the diagnosis results are highly timely and accurate.

[0066] Embodiment 3

[0067] Figure 3 This is a schematic diagram of the structure of a switch state identification and diagnosis device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device comprises:

[0068] A signal acquisition module 310 is used to acquire a high-frequency signal to be analyzed from a target electrical switch;

[0069] The electrical switch state identification data determination module 320 is used to extract the target characteristic parameters of the high-frequency signal to be analyzed, and input the target characteristic parameters into the switch state intelligent identification model to obtain the electrical switch state identification data;

[0070] The switch state identification and diagnosis module 330 is used to determine the current state of the target electrical switch according to the electrical switch state identification data, and perform fault diagnosis based on the current state of the target electrical switch to generate fault diagnosis data.

[0071] The technical solution of the embodiment of the present invention collects the high-frequency signal to be analyzed of the target electrical switch, thereby extracting the target characteristic parameters of the high-frequency signal to be analyzed, and inputting the target characteristic parameters into the switch state intelligent recognition model to obtain electrical switch state recognition data, and then determining the current state of the target electrical switch according to the electrical switch state recognition data, and performing fault diagnosis based on the current state of the target electrical switch to generate fault diagnosis data. In this solution, the target characteristic parameters of the high-frequency signal to be analyzed can accurately reflect the operation of the electrical switch, and the state of the electrical switch can be automatically and quickly obtained with high accuracy by analyzing the target characteristic parameters with the switch state intelligent recognition model, so as to further perform fault diagnosis on the correctly identified electrical switch state, solving the problems of high labor cost and poor accuracy in the current electrical switch state recognition and diagnosis, and realizing automatic diagnosis of electrical switches, with strong timeliness and higher accuracy of the diagnosis results.

[0072] Optionally, the signal acquisition module 310 is used to collect the high-frequency voltage signal of the target electrical switch based on the high-frequency voltage acquisition module, and to collect the high-frequency current signal of the target electrical switch based on the high-frequency current acquisition module; and use the high-frequency voltage signal and the high-frequency current signal as the high-frequency signals to be analyzed.

[0073] Optionally, the electrical switch state identification data determination module 320 includes a signal preprocessing unit and a target characteristic parameter determination unit. The signal preprocessing unit is used to perform signal preprocessing on the high-frequency signal to be analyzed to obtain a preprocessed high-frequency signal. The target characteristic parameter determination unit is used to determine the target characteristic parameter based on the time domain signal of the preprocessed high-frequency signal.

[0074] Optionally, the switch state identification and diagnosis device further includes a model training module for acquiring a switch state data set; and using the switch state data set to train a target model to be trained to obtain the switch state intelligent identification model.

[0075] Optionally, a signal preprocessing unit is used to filter the high-frequency signal to be analyzed to obtain a high-frequency signal in a target frequency band; amplify the high-frequency signal in the target frequency band according to a preset signal amplification factor to obtain a high-frequency signal to be denoised; and denoise the high-frequency signal to be denoised based on a wavelet transform to obtain the preprocessed high-frequency signal.

[0076] Optionally, a target characteristic parameter determination unit is used to calculate the peak value, rise time and fall time of the electrical signal based on the time domain signal of the preprocessed high-frequency signal; use fast Fourier transform to convert the time domain signal of the preprocessed high-frequency signal into a frequency domain signal, and extract the main frequency component and spectral energy of the frequency domain signal; use the peak value, rise time, fall time, main frequency component and spectral energy of the electrical signal as the target characteristic parameters.

[0077] Optionally, the target electrical switch includes a relay or a contactor; the high-frequency voltage acquisition module is installed at the voltage input terminal or the voltage output terminal of the target electrical switch; and the high-frequency current acquisition module is installed at the current circuit of the target electrical switch.

[0078] The switch state identification and diagnosis device provided in the embodiment of the present invention can execute the switch state identification and diagnosis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0079] Embodiment 4

[0080] Figure 4 A schematic diagram of an electronic device that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0081] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0082] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0083] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the switch state identification and diagnosis method.

[0084] In some embodiments, the switch state identification and diagnosis method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the switch state identification and diagnosis method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the switch state identification and diagnosis method in any other appropriate manner (for example, by means of firmware).

[0085] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0086] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0087] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0088] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0089] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0090] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0091] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0092] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A switch state identification and diagnosis method, characterized in that: include: Collect the high-frequency signal to be analyzed from the target electrical switch; Extracting target characteristic parameters of the high-frequency signal to be analyzed, and inputting the target characteristic parameters into a switch state intelligent recognition model to obtain electrical switch state recognition data; According to the electrical switch state identification data, the current state of the target electrical switch is determined, and fault diagnosis is performed based on the current state of the target electrical switch to generate fault diagnosis data.

2. The method according to claim 1, characterized in that The collecting of the high-frequency signal to be analyzed of the target electrical switch comprises: Based on the high-frequency voltage acquisition module, the high-frequency voltage signal of the target electrical switch is acquired, and based on the high-frequency current acquisition module, the high-frequency current signal of the target electrical switch is acquired; The high-frequency voltage signal and the high-frequency current signal are used as the high-frequency signals to be analyzed.

3. The method according to claim 2, characterized in that The step of extracting target characteristic parameters of the high-frequency signal to be analyzed includes: Performing signal preprocessing on the high-frequency signal to be analyzed to obtain a preprocessed high-frequency signal; The target characteristic parameter is determined based on the time domain signal of the preprocessed high-frequency signal.

4. The method according to claim 1, characterized in that: Before inputting the target characteristic parameters into the switch state intelligent recognition model, the method further includes: Get the switch status data set; The switch state data set is used to train the target model to be trained to obtain the switch state intelligent recognition model.

5. The method according to claim 3, characterized in that: The performing signal preprocessing on the high-frequency signal to be analyzed to obtain a preprocessed high-frequency signal includes: Filtering the high-frequency signal to be analyzed to obtain a high-frequency signal in a target frequency band; amplifying the high-frequency signal of the target frequency band according to a preset signal amplification factor to obtain a high-frequency signal to be denoised; Based on wavelet transform, the high-frequency signal to be denoised is denoised to obtain the preprocessed high-frequency signal.

6. The method according to claim 3, characterized in that The determining the target characteristic parameter based on the time domain signal of the preprocessed high-frequency signal comprises: Calculate the peak value, rise time and fall time of the electrical signal based on the time domain signal of the preprocessed high-frequency signal; Using fast Fourier transform, the time domain signal of the preprocessed high-frequency signal is converted into a frequency domain signal, and the main frequency component and spectrum energy of the frequency domain signal are extracted; The peak value, rise time, fall time, main frequency component and spectrum energy of the electrical signal are used as the target characteristic parameters.

7. The method according to claim 2, characterized in that The target electrical switch includes a relay or a contactor; the high-frequency voltage acquisition module is installed at the voltage input end or the voltage output end of the target electrical switch; and the high-frequency current acquisition module is installed at the current circuit of the target electrical switch.

8. A switch state identification and diagnosis device, characterized in that: include: A signal acquisition module, used to collect the high-frequency signal to be analyzed from the target electrical switch; An electrical switch state identification data determination module is used to extract target characteristic parameters of the high-frequency signal to be analyzed, and input the target characteristic parameters into a switch state intelligent identification model to obtain electrical switch state identification data; The switch state identification and diagnosis module is used to determine the current state of the target electrical switch according to the electrical switch state identification data, and perform fault diagnosis based on the current state of the target electrical switch to generate fault diagnosis data.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the switch state identification and diagnosis method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the switch state identification and diagnosis method according to any one of claims 1 to 7 when executed.

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