Multi-source collaborative power equipment evaluation system and method and computer equipment

Abstract

The invention relates to a multi-source collaborative power equipment evaluation system and method and computer equipment. The system comprises a distributed intelligent sensing module which comprises a plurality of sensing probes distributed on power equipment and is used for detecting the power equipment to obtain detection data; the collaborative acquisition and edge calculation module is used for controlling a plurality of sensing probes included in the distributed intelligent sensing unit to perform synchronous detection and performing local data processing according to the detection data to obtain a data processing result; and the cloud edge collaborative data analysis module is used for performing equipment state evaluation according to the detection data and the data processing result to obtain an evaluation result of the power equipment. By adopting the method, omnibearing information acquisition and data analysis of the power equipment can be realized, and the state evaluation accuracy of the power equipment is improved.

Description

Technical Field

[0001] This application relates to the field of online monitoring and fault diagnosis technology for power equipment, and in particular to a multi-source collaborative power equipment evaluation system, method and computer equipment. Background Technology

[0002] With the development of electrical technology, gas-insulated switchgear (GIS) power equipment has emerged. Due to its compact structure, high reliability, and minimal susceptibility to environmental influences, GIS is widely used in modern high-voltage and ultra-high-voltage substations. The health status of GIS equipment directly affects the safe and stable operation of the entire power grid. However, the complex internal structure of GIS makes it difficult to detect potential faults (such as insulation degradation and poor contact) through traditional inspection methods, and these faults often occur suddenly and are severe.

[0003] Traditional technologies for GIS monitoring mostly rely on single-parameter detection methods, such as gas density monitoring and temperature detection. However, this results in severe information silos, with data from different systems being out of sync and lacking standardized data, making it difficult to perform correlation analysis and leading to low accuracy in assessing the status of GIS. Therefore, there is an urgent need for a system that can comprehensively and accurately assess the operational status of GIS. Summary of the Invention

[0004] Therefore, it is necessary to provide a multi-source collaborative power equipment assessment system, method, and computer equipment that can achieve comprehensive information collection and data analysis of power equipment and improve the accuracy of power equipment condition assessment, in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a multi-source collaborative power equipment evaluation system, comprising:

[0006] The distributed intelligent sensing module includes multiple sensing probes distributed on the power equipment for detecting the power equipment and obtaining detection data;

[0007] The collaborative acquisition and edge computing module is used to control multiple sensing probes included in the distributed intelligent sensing unit to conduct synchronous detection, and to perform local data processing based on the detection data to obtain data processing results;

[0008] The cloud-edge collaborative data analysis module is used to assess the equipment status based on the detection data and the data processing results, and obtain the assessment results of the power equipment.

[0009] In one embodiment, the collaborative acquisition and edge computing module includes a data processing unit;

[0010] The data processing unit is used to call the diagnostic model to perform local data diagnosis based on the probe data and obtain the data processing results.

[0011] In one embodiment, the cloud-edge collaborative data analysis module further includes a model iteration module and a model distribution module;

[0012] The model iteration module is used to optimize and train the diagnostic model based on the evaluation results, the data processing results, and the detection data to obtain an optimized diagnostic model.

[0013] The model distribution module is used to distribute the optimized diagnostic model to the collaborative acquisition and edge computing module.

[0014] In one embodiment, the collaborative acquisition and edge computing module includes a synchronous acquisition unit;

[0015] The synchronous acquisition unit is used to connect multiple sensing probes included in the distributed intelligent sensing unit, and to provide synchronous trigger clock signals to the multiple sensing probes through a clock source.

[0016] In one embodiment, the collaborative acquisition and edge computing module further includes an encapsulation unit and a communication unit;

[0017] The encapsulation unit is used to encapsulate the detection data and the data processing results according to a target format to obtain encapsulated data;

[0018] The communication unit is used to upload the encapsulated data to the cloud-edge collaborative data analysis unit.

[0019] In one embodiment, the cloud-edge collaborative data analysis module includes a data storage unit and a status assessment unit;

[0020] The storage unit is used to store historical data; the historical data includes at least one of historical detection data, historical data processing results, and historical evaluation results.

[0021] The status assessment unit is used to call the status assessment model to perform equipment assessment based on the historical data, the detection data, and the data processing results, and obtain the assessment result of the power equipment.

[0022] Secondly, this application also provides a multi-source collaborative power equipment evaluation method, which is applied to a multi-source collaborative power equipment diagnostic system. The multi-source collaborative power equipment diagnostic system includes a distributed intelligent sensing unit, a collaborative data acquisition and edge computing unit, and a cloud-edge collaborative data analysis unit, comprising:

[0023] The collaborative acquisition and edge computing module controls multiple sensing probes included in the distributed intelligent sensing unit to synchronously detect the power equipment and obtain detection data.

[0024] The collaborative acquisition and edge computing module performs local data processing based on the detection data to obtain the data processing results.

[0025] The cloud-edge collaborative data analysis module performs equipment evaluation based on the detection data and the data processing results to obtain the evaluation result of the power equipment.

[0026] In one embodiment, the process by which the collaborative acquisition and edge computing module performs local data processing on the probe data to obtain data processing results includes:

[0027] The collaborative acquisition and edge computing module preprocesses the detection data to obtain preprocessed data;

[0028] The collaborative acquisition and edge computing module performs local data diagnosis based on the preprocessed data to obtain the data processing results.

[0029] In one embodiment, the method further includes:

[0030] Based on the evaluation results of the power equipment, a prompt message is generated; the prompt message includes at least one of early warning information and maintenance suggestions.

[0031] The evaluation results of the power equipment are mapped onto the digital twin model of the power equipment to obtain a display model for showing the evaluation results.

[0032] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0033] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0034] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0035] The aforementioned multi-source collaborative power equipment assessment system, method, and computer equipment, through the distributed intelligent sensing module, utilizes multiple sensing probes strategically distributed across the power equipment. This allows for comprehensive detection of the equipment from different angles and parameter dimensions, acquiring rich and accurate detection data to lay a solid foundation for subsequent assessments. Simultaneously, the collaborative acquisition and edge computing module precisely controls the synchronous detection of multiple sensing probes, ensuring consistency in the data collected by different probes over time and avoiding data errors caused by time differences. Furthermore, it processes the detection data locally in a timely manner, effectively reducing the pressure of transmitting massive amounts of data to the cloud, lowering transmission latency, and improving data processing efficiency. Finally, the cloud-edge collaborative data analysis module performs a more comprehensive status assessment based on the detection data and data processing results, thereby obtaining a comprehensive and accurate assessment result reflecting the actual status of the power equipment. This significantly improves the real-time performance, reliability, and accuracy of power equipment status assessments, effectively guaranteeing the stable operation and intelligent maintenance management of power equipment. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of a multi-source collaborative power equipment evaluation system in one embodiment;

[0038] Figure 2 This is a flowchart illustrating a data processing procedure in one embodiment.

[0039] Figure 3 This is an application scenario diagram of a multi-source collaborative power equipment evaluation system in one embodiment;

[0040] Figure 4 Provided in one embodiment Figure 3 A schematic diagram of the framework for the collaborative acquisition and edge computing layer;

[0041] Figure 5 This is a flowchart illustrating a multi-source collaborative power equipment evaluation method in one embodiment;

[0042] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0045] In one exemplary embodiment, such as Figure 1 As shown, Figure 1 This application illustrates a multi-source collaborative power equipment evaluation system provided in an embodiment, including a distributed intelligent sensing module, a collaborative data acquisition and edge computing module, and a cloud-edge collaborative data analysis module. Wherein:

[0046] The distributed intelligent sensing module includes multiple sensing probes distributed on the power equipment for detecting the power equipment and obtaining detection data; the collaborative acquisition and edge computing module is used to control the multiple sensing probes included in the distributed intelligent sensing unit to conduct synchronous detection, and to perform local data processing based on the detection data to obtain data processing results; the cloud-edge collaborative data analysis module is used to evaluate the equipment status based on the detection data and data processing results to obtain the evaluation results of the power equipment.

[0047] Among them, power equipment refers to equipment that can perform functions such as power transmission and distribution, voltage transformation, protection and control in a power system, such as GIS equipment, generators, transformers, etc.

[0048] In some embodiments, the sensing probe can be installed at the detection location of the power equipment according to the detection requirements. For example, for GIS equipment, the sensing probe can be installed near the basin insulator, the circuit breaker gas chamber, the disconnector gas chamber, the cable terminal, etc. Furthermore, multiple sensing probes can be used to detect the same type of data or to detect different types of data.

[0049] For example, for GIS power equipment, the sensing probe may include a gas density sensor, employing a high-precision piezoresistive or resonant sensor to monitor the SF6 (sulfur hexafluoride) gas density value in real time and possessing temperature compensation functionality; a micro-moisture content sensor, employing a polymer capacitive or dew point sensor to accurately measure the moisture content in the SF6 (sulfur hexafluoride) gas; an ultra-high frequency (UHF) partial discharge sensor, with a built-in or external UHF antenna, used to detect electromagnetic wave signals in the 300MHz~3GHz frequency band generated by insulation defects inside the GIS; an ultrasonic partial discharge sensor, employing a piezoelectric ceramic element adsorbed on the GIS shell, to detect acoustic wave signals in the 20kHz~300kHz frequency band generated by partial discharge, forming an electromagnetic-acoustic joint detection with the UHF sensor to enhance anti-interference capability and positioning accuracy; and a temperature sensor, employing a PT100 platinum resistance thermometer or a fiber optic grating. Fiber Bragg grating (FBG) temperature sensors monitor the temperature of conductor connection points and housing surfaces; furthermore, fiber optic grating sensors are preferred due to their inherent insulation and resistance to electromagnetic interference. Characteristic gas sensors monitor SF6 gas decomposition products such as SO2, H2S, and CO, used to assist in determining the nature of discharge and overheating faults. Circuit breaker opening / closing coil current sensors employ high-precision Hall effect current sensors to acquire the current waveform of the coil during circuit breaker operation. Circuit breaker contact travel-time characteristic sensors use linear potentiometers or photoelectric encoders to accurately measure the linear displacement of the moving contact and generate travel-time curves. Circuit breaker vibration sensors, using high-performance MEMS (Micro-Electro-Mechanical System) accelerometers, monitor vibration signals during mechanical operations of disconnecting switches, grounding switches, etc., to assess mechanical condition. Energy storage motor operating status sensors monitor motor current, voltage, and operating time.

[0050] Furthermore, the detection data can include temperature data, gas density data, current data, humidity data, etc.

[0051] The collaborative acquisition and edge computing module can be an embedded hardware unit with signal transmission and edge computing capabilities. The signal transmission capability refers to the ability to control multiple sensing probes included in the distributed intelligent sensing unit to conduct synchronous detection by sending synchronous acquisition signals. The edge computing capability refers to the ability to perform local data processing on the detection data to obtain data processing results.

[0052] Furthermore, the synchronous acquisition signal can be implemented based on a synchronous trigger or based on a software algorithm.

[0053] Local data processing can include data preprocessing, data analysis, data diagnosis, and other data processing methods. Among them, data analysis and data diagnosis can be achieved through preset data analysis algorithms or through pre-deployed data processing models. The specific implementation method is not limited here.

[0054] In some embodiments, the collaborative acquisition and edge computing module can be deployed with multiple diagnostic models, and different diagnostic models are used to realize correlation analysis between different data.

[0055] In some embodiments, the cloud-edge collaborative data analysis module can evaluate the status of the device through an evaluation model or evaluation algorithm to obtain evaluation results.

[0056] Understandably, the cloud-edge collaborative data analysis module can be deployed on a cloud server. Since cloud servers have a large amount of computing resources, the cloud-edge collaborative data analysis module can perform in-depth data analysis to ensure the accuracy of the evaluation results.

[0057] In the aforementioned multi-source collaborative power equipment assessment system, the distributed intelligent sensing module, by rationally distributing multiple sensing probes on the power equipment, can conduct comprehensive detection of the power equipment from different angles and parameter dimensions, acquiring rich and accurate detection data, laying a solid foundation for subsequent assessment. Simultaneously, the collaborative acquisition and edge computing module precisely controls the synchronous detection of multiple sensing probes, ensuring the consistency of data collected by different probes in the time dimension and avoiding data errors caused by time differences. Furthermore, it processes the detection data locally in a timely manner, effectively reducing the pressure of transmitting massive amounts of data to the cloud, lowering transmission latency, and improving data processing efficiency. Finally, the cloud-edge collaborative data analysis module can perform a more comprehensive status assessment based on the detection data and data processing results, thereby obtaining a comprehensive and accurate assessment result reflecting the actual status of the power equipment. This significantly improves the real-time performance, reliability, and accuracy of power equipment status assessment, effectively guaranteeing the stable operation and intelligent operation and maintenance management of power equipment.

[0058] In some embodiments, the collaborative acquisition and edge computing module includes a data processing unit; the data processing unit is used to call the diagnostic model to perform local data diagnosis based on the probe data and obtain the data processing results.

[0059] Among them, the diagnostic model is pre-deployed; furthermore, there can be multiple diagnostic models, and different diagnostic models are used to analyze different data.

[0060] For example, the diagnostic model may include a "partial discharge-gas-temperature" correlation analysis model, used to jointly analyze partial discharge, gas, and temperature monitoring data of GIS to diagnose the insulation status of GIS; a "temperature-load" dynamic calibration model, used to synchronously correlate conductor temperature measurement data with the load current of transmission lines to distinguish between normal temperature rise caused by load changes and abnormal overheating caused by excessive contact resistance; and an "electromechanical correlation" diagnostic model, used to synchronously acquire the current waveform of the trip coil, contact stroke curve, and operating vibration signal, perform feature extraction after signal preprocessing, and finally perform data fusion analysis and collaborative analysis, comparing the characteristic moments with historical baselines to generate diagnostic conclusions and early warning information. The processing flow is as follows: Figure 2 As shown; the "multi-physics field" integrated diagnostic model is used to integrate all parameters to make a comprehensive judgment on the GIS operation status. For example, when diagnosing internal discharge of a circuit breaker, it can be associated with its contact resistance, vibration signal, and partial discharge and gas decomposition product data to make a comprehensive judgment on the GIS operation status.

[0061] In some embodiments, before calling the diagnostic model to perform local data diagnosis on the probe data, the data processing unit may preprocess the probe data, such as filtering, noise reduction, and feature extraction (e.g., extracting the phase-resolved partial discharge spectrum of the partial discharge signal, the time-frequency characteristics of the vibration signal, temperature trends, etc.).

[0062] In the above embodiments, the data processing unit included in the collaborative acquisition and edge computing module can achieve lightweight data processing on edge devices, thereby reducing the data processing burden on the cloud.

[0063] In some embodiments, the cloud-edge collaborative data analysis module further includes a model iteration module and a model distribution module; the model iteration module is used to optimize and train the diagnostic model based on the evaluation results, data processing results and detection data to obtain an optimized diagnostic model; the model distribution module is used to distribute the optimized diagnostic model to the collaborative acquisition and edge computing module.

[0064] In some embodiments, the cloud-edge collaborative data analysis module can perform comprehensive analysis on all probe data with the assistance of data processing results to obtain evaluation results. Then, the model iteration module can reversely determine the processing score of the data processing results based on the evaluation results, update the model parameters of the corresponding diagnostic model based on the processing score of the data processing results, optimize the diagnostic model, and obtain the optimized diagnostic model.

[0065] It should be noted that the cloud-edge collaborative data analysis module synchronously stores various diagnostic models deployed in the collaborative acquisition and edge computing modules.

[0066] In the above embodiments, the cloud-edge collaborative data analysis module uses the data processing results to assist in re-evaluating the device status of all detection data to obtain the evaluation results. On the other hand, it uses the evaluation results to optimize the diagnostic model corresponding to the data processing results, and then pushes the optimized diagnostic model down to the collaborative acquisition and edge computing module to realize the iterative evolution of the system.

[0067] In some embodiments, the collaborative acquisition and edge computing module includes a synchronous acquisition unit; the synchronous acquisition unit is used to connect multiple sensing probes included in the distributed intelligent sensing unit, and to provide synchronous trigger clock signals to the multiple sensing probes through a clock source.

[0068] A clock source is a device or circuit used to provide a stable and accurate time reference or clock signal.

[0069] In the above embodiments, by providing a synchronous trigger clock signal to multiple sensing probes through a clock source, the consistency of the data sensed by the multiple sensing probes in time can be ensured, thereby facilitating data correlation analysis.

[0070] In some embodiments, the collaborative acquisition and edge computing module further includes an encapsulation unit and a communication unit; the encapsulation unit is used to encapsulate the probe data and data processing results according to the target format to obtain encapsulated data; the communication unit is used to upload the encapsulated data to the cloud-edge collaborative data analysis unit.

[0071] In some embodiments, data communication can be achieved through industrial communication protocols such as Ethernet, 4G, 5G, and fiber optics.

[0072] In other embodiments, the communication unit is also used to send a synchronous trigger clock signal to the sensing probe and to receive a diagnostic model from the cloud-edge collaborative data analysis module.

[0073] In the above embodiments, standardized data transmission is achieved through the encapsulation unit and the communication unit, thereby improving data transmission efficiency.

[0074] In some embodiments, the cloud-edge collaborative data analysis module includes a data storage unit and a status assessment unit; the storage unit is used to store historical data; the historical data includes at least one of historical detection data, historical data processing results, and historical assessment results; the status assessment unit is used to call the status assessment model to perform equipment assessment based on historical data, detection data, and data processing results to obtain the assessment results of the power equipment.

[0075] The state evaluation model can be a pre-trained deep network model.

[0076] In some embodiments, the storage unit may also be configured with a data expiration time to monitor historical data; specifically, if the storage time of historical data exceeds the data expiration time, the historical data can be deleted.

[0077] In the above embodiments, by combining historical data, it is possible to perform temporal correlation analysis of the data, which further improves the comprehensiveness and accuracy of the data analysis.

[0078] Please see Figure 3 , Figure 3 The diagram illustrates an application scenario of a multi-source collaborative power equipment evaluation system according to an embodiment of this application. The multi-source collaborative power equipment evaluation system includes a distributed intelligent sensing layer, a collaborative acquisition and edge computing layer, and a cloud-edge collaborative data analysis layer.

[0079] The distributed intelligent sensing layer and the collaborative acquisition and edge computing layer are connected via wireless sensor networks or cables; the collaborative acquisition and edge computing layer and the cloud-edge collaborative data analysis layer are connected via optical fiber, 4G or 5G.

[0080] The distributed intelligent sensing layer includes various sensors, such as SF6 decomposition gas monitoring sensors, gas density sensors, trace moisture content sensors, coil current sensors, ultra-high frequency (UHF) partial discharge sensors, ultrasonic sensors, vibration sensors, travel sensors, and energy storage motor sensors.

[0081] The collaborative acquisition and edge computing layer includes a multi-channel synchronous acquisition module, an edge computing module, a data compression and encapsulation module, and a communication module.

[0082] For example, please refer to Figure 4 , Figure 4 The diagram illustrates the framework of the collaborative acquisition and edge computing layer. The multi-channel synchronous acquisition module includes sensor signal interfaces and a high-precision clock source. The sensor signal interfaces offer various input options, including analog, digital, and frequency signals, to accommodate different types of sensors. The high-precision clock source (such as a BeiDou module) provides a unified hardware-level synchronous trigger clock signal for all input channels, ensuring strict synchronization of the acquisition timestamps for all data, such as micro-water, density, partial discharge, and temperature, with an accuracy down to the microsecond level. Furthermore, the multi-channel synchronous acquisition module can also perform signal conditioning and sampling frequency control functions.

[0083] The edge computing module is used to implement functions such as data preprocessing, local collaborative diagnostic algorithms, and local display of diagnostic results. It can be implemented through a built-in high-performance processor. Data preprocessing refers to filtering, noise reduction, and feature extraction of raw data. Local collaborative diagnostic algorithms can be implemented through various lightweight diagnostic models deployed locally. Local display of diagnostic results can be achieved through voice broadcasting, prompts, and other methods.

[0084] Data compression and encapsulation are used to achieve functions such as signal digital-to-analog conversion, data encryption and decryption, signal conditioning, and data packaging.

[0085] The communication module is used to provide communication via Ethernet, 4G, and 5G communication protocols.

[0086] The cloud-edge collaborative data analysis platform can realize functions such as massive data storage and management, deep data fusion and intelligent diagnosis, partial discharge signal localization, equipment health status assessment, panoramic status visualization, model distribution and system iteration.

[0087] Among them, massive data storage and management are used to store all historical synchronized data to form a database covering the entire lifecycle of GIS equipment; deep data fusion and intelligent diagnosis are used for in-depth analysis based on machine learning algorithms; partial discharge signal localization is used to jointly locate partial discharge signals using data from multiple sites; equipment health status assessment is used to establish a more accurate equipment health status assessment model for status assessment; panoramic status visualization is used to comprehensively display all status quantities such as density, micro-water, partial discharge spectrum, and temperature distribution of each air chamber in the GIS in the form of 3D digital twin models, so as to achieve "panoramic capture" and clear understanding of the operating status; model distribution and system iteration are used to distribute the trained optimized diagnostic model to the edge side, so that the entire system has the ability to continuously learn and evolve.

[0088] Based on the aforementioned multi-source collaborative power equipment evaluation system, this application also proposes a multi-source collaborative power equipment diagnostic method, applied to the multi-source collaborative power equipment evaluation system provided in the embodiments of this application. The multi-source collaborative power equipment diagnostic system includes a distributed intelligent sensing unit, a collaborative data acquisition and edge computing unit, and a cloud-edge collaborative data analysis unit. Please refer to [link to relevant documentation]. Figure 5 , Figure 5 A flowchart illustrating a multi-source collaborative power equipment diagnostic method according to an embodiment of this application is shown, including steps 201 to 203. Wherein:

[0089] Step 201: The collaborative acquisition and edge computing module controls multiple sensing probes included in the distributed intelligent sensing unit to synchronously detect the power equipment and obtain detection data.

[0090] Specifically, the collaborative acquisition and edge computing module can provide a synchronous trigger clock signal to multiple sensing probes through a clock source, thereby controlling the multiple sensing probes included in the distributed intelligent sensing unit to synchronously detect power equipment and obtain detection data.

[0091] In some embodiments, the detection data may include GIS body parameters, such as SF6 gas density, trace water content, UHF partial discharge signal, ultrasonic signal, temperature and vibration signal; circuit breaker mechanical parameters, such as opening / closing coil current waveform, moving contact stroke-time curve, operating mechanism vibration signal; and surge arrester status parameters, such as total leakage current, bus voltage reference signal, and body temperature.

[0092] Step 202: The collaborative acquisition and edge computing module performs local data processing based on the detection data to obtain the data processing results.

[0093] Local data processing can include data preprocessing, data feature extraction, and data correlation analysis.

[0094] For example, data preprocessing can include noise reduction and filtering; for instance, filtering and noise reduction of partial discharge signals.

[0095] Data feature extraction refers to processing the detected data in one step to obtain the required feature data. For example, after filtering and denoising the partial discharge signal, a phase-resolved partial discharge spectrum is generated; for coil current waveforms and stroke curves, feature time points such as core start-up, tripping of the tripping mechanism, and contact separation, as well as feature parameters such as current peak value and time interval, are extracted; time-frequency analysis of vibration signals is performed to extract the amplitude, frequency components, and occurrence time of vibration events; and harmonic analysis is used to extract the fundamental peak value of resistive current based on leakage current and voltage reference signals.

[0096] Data correlation analysis refers to the correlation, fusion, and diagnosis of different detection data based on spatiotemporal references, thereby analyzing the internal relationships between the detection data; for example, when a partial discharge signal is detected, the vibration signal, SF6 gas decomposition product content, and temperature data at the time of discharge are queried simultaneously to comprehensively determine the nature, location, and severity of the discharge.

[0097] Step 203: The cloud-edge collaborative data analysis module evaluates the equipment based on the detection data and data processing results to obtain the evaluation results of the power equipment.

[0098] In some embodiments, a condition assessment model can be used to assess the equipment based on probe data and data processing results to obtain the assessment results of the power equipment.

[0099] In the above embodiments, by utilizing synchronously collected multi-source detection data and through collaboration between local edge devices and cloud devices, a comprehensive assessment of power equipment is achieved, improving the real-time performance, reliability, and accuracy of power equipment status assessment.

[0100] In some embodiments, step 202 includes steps 301 to 302. Wherein:

[0101] Step 301: The collaborative acquisition and edge computing module preprocesses the detection data to obtain preprocessed data.

[0102] Preprocessing includes one or more of the following: filtering, noise reduction, feature extraction, and format conversion.

[0103] Step 302: The collaborative acquisition and edge computing module performs local data diagnosis based on the preprocessed data to obtain the data processing results.

[0104] In some embodiments, local data diagnosis can be performed on the preprocessed data using at least one diagnostic model to obtain the data processing results output by each diagnostic model.

[0105] Furthermore, different diagnostic models are used to analyze the correlations between data from different sources.

[0106] In the above embodiments, data preprocessing was used to standardize the detection data from different sources, thereby ensuring the effectiveness of the input for local data diagnosis and improving the accuracy of local data diagnosis.

[0107] In some embodiments, the multi-source collaborative power equipment diagnostic method further includes steps 401 to 420. Wherein:

[0108] Step 401: Based on the assessment results of the power equipment, generate a prompt message; the prompt message includes at least one of early warning information and maintenance suggestions.

[0109] In some embodiments, the prompt information may be displayed through voice broadcast, pop-up window, SMS push, or other means.

[0110] In some embodiments, the prompt information may also include abnormal data and the source of the abnormal data, so as to facilitate the investigation of abnormalities based on the prompt information.

[0111] Step 402: Map the evaluation results of the power equipment to the digital twin model of the power equipment to obtain a display model for displaying the evaluation results.

[0112] Among them, the digital twin model of the power equipment is established in advance; further, mapping the evaluation results to the digital twin model of the power equipment means that, based on the attributes and meanings of different indicators in the evaluation results, they are reasonably mapped to the corresponding parameters or features of the digital twin model, thereby realizing the visualization of the evaluation results.

[0113] In the above embodiments, abnormal warnings and strategy recommendations are realized by generating prompt information, which is beneficial for subsequent maintenance. The status visualization of power equipment is realized by generating display models.

[0114] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0115] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores evaluation data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-source collaborative power equipment evaluation method.

[0116] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0117] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0118] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0119] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0120] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0121] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0122] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0123] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A multi-source collaborative power equipment evaluation system, characterized in that, The system includes: The distributed intelligent sensing module includes multiple sensing probes distributed on the power equipment for detecting the power equipment and obtaining detection data; The collaborative acquisition and edge computing module is used to control multiple sensing probes included in the distributed intelligent sensing unit to conduct synchronous detection, and to perform local data processing based on the detection data to obtain data processing results; The cloud-edge collaborative data analysis module is used to assess the equipment status based on the detection data and the data processing results, and obtain the assessment results of the power equipment.

2. The system according to claim 1, characterized in that, The collaborative acquisition and edge computing module includes a data processing unit; The data processing unit is used to call the diagnostic model to perform local data diagnosis based on the probe data and obtain the data processing results.

3. The system according to claim 2, characterized in that, The cloud-edge collaborative data analysis module also includes a model iteration module and a model distribution module; The model iteration module is used to optimize and train the diagnostic model based on the evaluation results, the data processing results, and the detection data to obtain an optimized diagnostic model. The model distribution module is used to distribute the optimized diagnostic model to the collaborative acquisition and edge computing module.

4. The system according to claim 1, characterized in that, The collaborative acquisition and edge computing module includes a synchronous acquisition unit; The synchronous acquisition unit is used to connect multiple sensing probes included in the distributed intelligent sensing unit, and to provide synchronous trigger clock signals to the multiple sensing probes through a clock source.

5. The system according to claim 1, characterized in that, The collaborative acquisition and edge computing module also includes an encapsulation unit and a communication unit; The encapsulation unit is used to encapsulate the detection data and the data processing results according to a target format to obtain encapsulated data; The communication unit is used to upload the encapsulated data to the cloud-edge collaborative data analysis unit.

6. The system according to claim 1, characterized in that, The cloud-edge collaborative data analysis module includes a data storage unit and a status assessment unit; The storage unit is used to store historical data; the historical data includes at least one of historical detection data, historical data processing results, and historical evaluation results. The status assessment unit is used to call the status assessment model to perform equipment assessment based on the historical data, the detection data, and the data processing results, and obtain the assessment result of the power equipment.

7. A multi-source collaborative power equipment diagnostic method, characterized in that, The method is applied to a multi-source collaborative power equipment diagnostic system, which includes a distributed intelligent sensing unit, a collaborative data acquisition and edge computing unit, and a cloud-edge collaborative data analysis unit. The method includes: The collaborative acquisition and edge computing module controls multiple sensing probes included in the distributed intelligent sensing unit to synchronously detect the power equipment and obtain detection data. The collaborative acquisition and edge computing module performs local data processing based on the detection data to obtain the data processing results. The cloud-edge collaborative data analysis module performs equipment evaluation based on the detection data and the data processing results to obtain the evaluation result of the power equipment.

8. The method according to claim 7, characterized in that, The collaborative acquisition and edge computing module performs local data processing on the detected data to obtain data processing results, including: The collaborative acquisition and edge computing module preprocesses the detection data to obtain preprocessed data; The collaborative acquisition and edge computing module performs local data diagnosis based on the preprocessed data to obtain the data processing results.

9. The method according to claim 7, characterized in that, The method further includes: Based on the evaluation results of the power equipment, a prompt message is generated; the prompt message includes at least one of early warning information and maintenance suggestions; and... The evaluation results of the power equipment are mapped onto the digital twin model of the power equipment to obtain a display model for showing the evaluation results.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 7 to 9.

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