Full-cycle gas management method and system for environment-friendly gas insulated power equipment, computer device and storage medium

Abstract

This application relates to the field of power systems and provides a method, system, computer equipment, and storage medium for full-cycle gas management of environmentally friendly gas-insulated power equipment. The method includes: online real-time quality verification of a prepared mixed gas; filling the gas-insulated switchgear with the online real-time quality-verified mixed gas based on a multi-stage intelligent filling principle of constant current and constant pressure; acquiring multimodal edge gateway data; analyzing the edge gateway data to generate local rapid alarm information; constructing a gas state digital twin model based on the edge gateway data and model construction data associated with the gas-insulated switchgear; and generating and displaying fault diagnosis information based on the gas state digital twin model. This method can improve the effectiveness of gas detection and leakage fault diagnosis for environmentally friendly mixed gases.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, system, computer equipment, and storage medium for full-cycle gas control of environmentally friendly gas-insulated power equipment. Background Technology

[0002] In the field of power system technology, with the advancement of global low-carbon goals, strong greenhouse gases, represented by sulfur hexafluoride (SF6), are being phased out at an accelerated pace. New environmentally friendly insulating mixed gases such as C4F7N / N2 and N2 / O2 have become the most promising alternatives and are being gradually applied in power equipment such as gas-insulated switchgear (GIS).

[0003] However, the application of environmentally friendly mixed gases has its shortcomings. For example, traditional leak detection technologies mostly detect single physical quantities and cannot effectively distinguish the cause of the leak; they generally lack sensitivity for small leaks in the early stages of equipment; and traditional systems mostly remain at the threshold alarm level, lacking the ability to fuse and analyze multi-source data and perform in-depth intelligent diagnostics.

[0004] In summary, traditional environmentally friendly mixed gas leak detection technologies are less effective in both gas detection and leak fault diagnosis. Summary of the Invention

[0005] This application provides a method, system, computer equipment, and storage medium for full-cycle gas control of environmentally friendly gas-insulated power equipment. More specifically, this application provides a method, system, computer equipment, computer storage medium, and computer program product for full-cycle gas control of environmentally friendly gas-insulated power equipment, which can improve the effectiveness of gas detection and leakage fault diagnosis of environmentally friendly mixed gases.

[0006] In a first aspect, embodiments of this application provide a method for full-cycle gas control of environmentally friendly gas-insulated electrical equipment, including: The prepared mixed gas is subjected to online real-time quality verification. Based on the multi-stage intelligent gas filling principle of constant current and constant pressure, the mixed gas that has passed the online real-time quality verification is filled into the gas-insulated switchgear. Acquire multimodal edge gateway data, analyze the edge gateway data, and generate local rapid alarm information; Based on the edge gateway data and the model construction data associated with the gas-insulated switchgear, a gas state digital twin model is constructed, and fault diagnosis information is generated and displayed based on the gas state digital twin model.

[0007] Secondly, embodiments of this application provide a full-cycle gas control system for environmentally friendly gas-insulated power equipment, which has the function of implementing the full-cycle gas control method for environmentally friendly gas-insulated power equipment provided in the first aspect above. The function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, and the modules can be software and / or hardware.

[0008] In one possible design, the system includes: The intelligent gas preparation and filling module is used to perform online real-time quality verification of the prepared mixed gas. Based on the multi-stage intelligent filling principle of constant current and constant pressure, the mixed gas that has passed the online real-time quality verification is filled into the gas-insulated switchgear. The cloud-edge collaborative multimodal leakage and fault diagnosis module is used to acquire multimodal edge gateway data, analyze the edge gateway data, and generate local rapid alarm information. The intelligent diagnostic and visualization platform based on digital twins is used to construct a gas state digital twin model based on the edge gateway data and the model construction data associated with the gas-insulated switchgear, and to generate and display fault diagnosis information based on the gas state digital twin model.

[0009] In another aspect, this application provides a computer device including at least one connected processor and a memory, wherein the memory is used to store program code, and the processor is used to call the program code in the memory to execute the methods described in the above aspects.

[0010] In another aspect, embodiments of this application provide a computer storage medium including instructions that, when executed on a computer, cause the computer to perform the methods described in the above aspects.

[0011] In another aspect, this application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods described in the above aspects.

[0012] Compared to traditional technologies, the technical solution in this application embodiment achieves real-time detection before charging through online mixed gas quality verification; it enhances the diversity and accuracy of data acquisition through multimodal sensor fusion technology; it adopts a cloud-edge collaborative architecture, utilizing edge computing to achieve real-time processing and rapid response of local sensor data, and uses artificial intelligence algorithms for in-depth analysis and trend prediction, balancing the real-time nature and accuracy of diagnosis; thus, it improves the overall effectiveness of gas detection and leak fault diagnosis for environmentally friendly mixed gases. Attached Figure Description

[0013] Figure 1This is a flowchart illustrating a full-cycle gas control method for environmentally friendly gas-insulated electrical equipment in one embodiment. Figure 2 This is a flowchart illustrating a full-cycle gas control method for environmentally friendly gas-insulated electrical equipment, as shown in another embodiment. Figure 3 This is a structural block diagram of a full-cycle gas control system for environmentally friendly gas-insulated electrical equipment in one embodiment; Figure 4 This is a schematic diagram of the overall system architecture in one embodiment; Figure 5 This is an internal structural diagram of a computer device in one embodiment; Figure 6 This is a diagram of the internal structure of a computer device in another embodiment. Detailed Implementation

[0014] The terms "first," "second," etc., used in the embodiments of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. The division of modules appearing in the embodiments of this application is only a logical division. In actual applications, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not performed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interface, and the indirect coupling or communication connection between modules may be electrical or other similar forms. None of these are limited in the embodiments of this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed among multiple circuit modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of this application.

[0015] Figure 1 This is a flowchart illustrating a full-cycle gas control method for environmentally friendly gas-insulated electrical equipment in one embodiment, as shown below. Figure 1 As shown in the embodiments of this application, the full-cycle gas control method for environmentally friendly gas-insulated power equipment includes: S110 performs online real-time quality verification of the prepared mixed gas. Based on the multi-stage intelligent gas filling principle of constant current and constant pressure, the mixed gas that has passed the online real-time quality verification is filled into the gas-insulated switchgear.

[0016] Among them, online real-time quality verification refers to the instant detection process of mixed gas indicators before filling; constant flow and constant pressure multi-stage intelligent filling refers to the method of filling gas in stages according to constant parameters.

[0017] Among them, gas-insulated switchgear refers to high-voltage complete set of power distribution equipment that uses gas as the insulating medium.

[0018] S120 acquires multimodal edge gateway data, analyzes the edge gateway data, and generates local rapid alarm information.

[0019] Among them, multimodal edge gateway data refers to various types of monitoring data collected by the edge gateway.

[0020] Among them, local rapid alarm information refers to fault warning prompts generated locally by the edge gateway, such as a prompt that "the event is determined to be a leakage event".

[0021] S130: Based on the edge gateway data and the model construction data associated with the gas-insulated switchgear, construct a gas state digital twin model, and generate and display fault diagnosis information based on the gas state digital twin model.

[0022] Among them, model building data refers to the relevant data used to build digital twin models; gas state digital twin models refer to virtual models that are synchronized with the gas state of physical devices in real time.

[0023] Among them, fault diagnosis information refers to fault-related data derived from analysis based on digital twin models, such as leakage rate assessment, fault development prediction, ultra-high precision location of leakage sources, accurate fault type diagnosis, severity level, and other data or information.

[0024] Compared to traditional technologies, this embodiment first performs online real-time quality verification on the prepared mixed gas, then charges the verified mixed gas into the gas-insulated switchgear. Next, multimodal edge gateway data is acquired and analyzed to generate local rapid alarm information. Finally, based on the edge gateway data and the model construction data associated with the gas-insulated switchgear, a digital twin model of the gas state is constructed, generating and displaying fault diagnosis information. The technical solution of this embodiment achieves real-time detection before charging through online mixed gas quality verification; improves the diversity and accuracy of data acquisition through multimodal sensor fusion technology; adopts a cloud-edge collaborative architecture, utilizing edge computing to achieve real-time processing and rapid response of local sensor data; and uses artificial intelligence algorithms for deep analysis and trend prediction, balancing real-time diagnosis and accuracy. Therefore, it comprehensively improves the gas detection and leakage fault diagnosis of environmentally friendly mixed gases.

[0025] Optionally, in some embodiments of this application, the prepared mixed gas is subjected to online real-time quality verification. Based on the multi-stage intelligent gas filling principle of constant current and constant pressure, the mixed gas that has passed the online real-time quality verification is filled into the gas-insulated switchgear. This includes: performing online real-time quality verification on the mixed gas to obtain the quality verification result; determining the preset pressure and temperature curve based on the multi-stage intelligent gas filling principle of constant current and constant pressure; and controlling the filling of the mixed gas into the gas-insulated switchgear according to the preset pressure and temperature curve when the quality verification result is that the online real-time quality verification is passed.

[0026] Among them, the quality verification result refers to the conclusion of whether the gas is qualified after online testing.

[0027] Among them, the multi-stage intelligent inflation principle of constant flow and constant pressure, also known as the constant flow-constant pressure multi-stage intelligent inflation principle, refers to the control criterion of phased steady flow and steady pressure inflation.

[0028] Among them, the pressure and temperature curves refer to the preset trajectory of pressure and temperature changes over time during the filling process.

[0029] In this embodiment, the accuracy of mixed gas filling is improved by online real-time quality verification before filling and staged constant current and constant pressure filling control, the operational stability of gas-insulated switchgear is improved, the technical capability of gas quality control is enhanced, and the risk of failure caused by unqualified gas filling is reduced.

[0030] Optionally, in some embodiments of this application, acquiring multimodal edge gateway data and analyzing the edge gateway data to generate local rapid alarm information includes: acquiring multimodal local sensor data and environmental background intelligent compensation data; using the local sensor data and environmental background intelligent compensation data as edge gateway data; analyzing the edge gateway data to obtain local analysis results; and generating local rapid alarm information based on the local analysis results.

[0031] The edge gateway data includes local sensor data and intelligent environmental background compensation data. More specifically, the local sensor data also includes data collected by sensors such as acoustic sensors, high-sensitivity parent gas sensors, and high-selectivity decomposition product sensors.

[0032] Among them, multimodal local sensor data refers to on-site monitoring data collected by multiple types of sensors; environmental background intelligent compensation data refers to corrected sensor data after eliminating environmental interference.

[0033] Among them, the local analysis results refer to the judgment conclusions obtained by the edge gateway after processing the data, such as the comparison of various indicators with thresholds.

[0034] Local rapid alarm information refers to fault warning prompts generated locally based on analysis results, such as prompts indicating whether a fault has occurred, or more specifically, prompts indicating that "the event is determined to be a leakage event".

[0035] In this embodiment, by fusing multimodal sensing data and environmental compensation data and analyzing them at the edge, the accuracy of leak fault detection is improved and the timeliness of alarm response is enhanced.

[0036] Optionally, in some embodiments of this application, a gas state digital twin model is constructed based on edge gateway data and model construction data associated with the gas-insulated switchgear. Fault diagnosis information is generated and displayed based on the gas state digital twin model, including: acquiring gas preparation data of the mixed gas and equipment operation data of the gas-insulated switchgear; using the gas preparation data and equipment operation data as model construction data; constructing a gas state digital twin model based on the edge gateway data and model construction data; analyzing the gas state digital twin model based on artificial intelligence technology to obtain fault diagnosis information; and displaying the fault diagnosis information in three-dimensional visualization on the gas state digital twin model.

[0037] The term "model building data" refers to the collective data used to prepare and operate digital twin models, including gas preparation data and equipment operation data. Specifically, gas preparation data refers to various process parameters generated during the preparation of mixed gases; equipment operation data refers to the status monitoring data of gas-insulated switchgear during operation.

[0038] Artificial intelligence technology refers to intelligent algorithm technology used for in-depth analysis of twin model data.

[0039] Among them, fault diagnosis information refers to the equipment fault-related judgment data obtained through intelligent analysis; for example, fault diagnosis information may include leakage rate assessment, fault development prediction, ultra-high precision location of leakage source, accurately diagnosed fault type, severity level, etc., or it may be data that has been further processed for easy display, such as leakage point location, gas diffusion cloud map, internal fault related parts, health index, etc.

[0040] Among them, three-dimensional visualization refers to the way of displaying fault information intuitively on a twin model. For example, the three-dimensional visualization process can intuitively display the location of the leak point, the gas diffusion cloud map, the internal fault-related parts, and the health index in three dimensions.

[0041] In addition, the full-cycle gas control method for environmentally friendly gas-insulated electrical equipment also includes: automatically generating graded early warning information and intelligent maintenance decision suggestions based on fault diagnosis information. The graded early warning information is also known as graded alarm information or hazard classification information.

[0042] In this embodiment, by fusing multi-source data to construct a digital twin model of gas state and combining it with intelligent analysis and 3D display, the accuracy of fault diagnosis is improved and the intuitiveness of operation and maintenance decisions is enhanced.

[0043] Figure 2 This is a flowchart illustrating a full-cycle gas control method for environmentally friendly gas-insulated electrical equipment, as shown in another embodiment. The method includes: S210 performs online real-time quality verification of the mixed gas and obtains the quality verification results; based on the multi-stage intelligent inflation principle of constant flow and constant pressure, it determines the preset pressure and temperature curves.

[0044] S220, when the quality verification result is that the online real-time quality verification is passed, controls the injection of mixed gas into the gas-insulated switchgear according to the preset pressure and temperature curves.

[0045] S230 acquires multimodal local sensor data and intelligent environmental background compensation data; and uses the local sensor data and intelligent environmental background compensation data as edge gateway data.

[0046] S240 analyzes the edge gateway data to obtain local analysis results; and generates local rapid alarm information based on the local analysis results.

[0047] S250: Acquire gas preparation data for mixed gases and equipment operation data for gas-insulated switchgear; use the gas preparation data and equipment operation data as model building data; S260 constructs a digital twin model of the gas state based on edge gateway data and model building data.

[0048] S270 uses artificial intelligence technology to analyze the digital twin model of the gas state to obtain fault diagnosis information.

[0049] S280 displays fault diagnosis information in a three-dimensional visualization on a digital twin model of the gas state.

[0050] It should be noted that the specific limitations of the above steps can be found in the above description of the specific limitations of a full-cycle gas control method for environmentally friendly gas-insulated power equipment, and will not be repeated here.

[0051] Any technical feature in any of the above embodiments provided in this application is also applicable to any of the following embodiments provided in this application, and similar details will not be repeated hereafter.

[0052] Figure 3 This is a structural block diagram of a full-cycle gas control system for environmentally friendly gas-insulated electrical equipment, as described in one embodiment. The full-cycle gas control system for environmentally friendly gas-insulated electrical equipment provided in this application is used to implement the full-cycle gas control method for environmentally friendly gas-insulated electrical equipment as described in any of the above embodiments. (Refer to...) Figure 3 The system includes: The intelligent gas preparation and filling module 100 is used to perform online real-time quality verification of the prepared mixed gas. Based on the multi-stage intelligent filling principle of constant current and constant pressure, the mixed gas that has passed the online real-time quality verification is filled into the gas-insulated switchgear. The cloud-edge collaborative multimodal leakage and fault diagnosis module 200 is used to acquire multimodal edge gateway data, analyze the edge gateway data, and generate local rapid alarm information. The 300 intelligent diagnostic and visualization platform based on digital twins is used to construct a gas state digital twin model based on edge gateway data and model construction data associated with gas-insulated switchgear, and to generate and display fault diagnosis information based on the gas state digital twin model.

[0053] In this embodiment of the application, based on, as follows Figure 3 The connection relationships between the various modules or units shown in the diagram can improve the effectiveness of gas detection and leak fault diagnosis for environmentally friendly mixed gases through the cooperation between these modules or units.

[0054] Figure 4 This is a schematic diagram of the overall system architecture in one embodiment, with reference to... Figure 4The full-cycle gas management system for environmentally friendly gas-insulated power equipment includes an intelligent gas preparation and filling module 100, a cloud-edge collaborative multimodal leakage and fault diagnosis module 200, and an intelligent diagnosis and visualization platform based on digital twins 300.

[0055] Specifically, the intelligent gas preparation and filling module 100 includes a multi-channel high-purity gas source and mass flow control unit 110, an adaptive dynamic gas mixing unit 120, an online mixed gas quality verification unit 130, and an intelligent filling control unit 140. The cloud-edge collaborative multimodal leakage and fault diagnosis module 200 includes a distributed multimodal sensor array 210, an edge computing gateway 220, and an intelligent environmental background compensation unit 230. The digital twin-based intelligent diagnosis and visualization platform 300 includes a data aggregation and twin mapping unit 310, an artificial intelligence diagnostic engine 320, and a 3D visualization and decision support module 330.

[0056] in addition, Figure 4 In this context, MFC refers to High-Precision Digital Mass Flow Controller, and GIS refers to Gas Insulated Switchgear, also known as GIS device.

[0057] It should be noted that the connection and configuration of the above modules or units can be determined according to... Figure 4 The illustrations and descriptions in the various embodiments of this application are clearly presented, and therefore will not be repeated here.

[0058] Optionally, in some embodiments of this application, the intelligent gas preparation and filling module includes: a multi-channel high-purity gas source and mass flow control unit for controlling the flow rate of each component gas; an adaptive dynamic mixing unit for uniformly mixing each component gas according to adaptive fine-tuning mixing ratio data; an online mixed gas quality verification unit for online real-time quality verification of preset indicators of the mixed gas before the gas is filled into the equipment; and an intelligent filling control unit for filling gas according to preset pressure and temperature curves.

[0059] Among them, the preset indicators of the mixed gas refer to key indicators such as the component ratio, purity and moisture content of the mixed gas.

[0060] Among them, the preset pressure and temperature curve, also known as the preset pressure-temperature (PT) curve, refers to the curve data pre-designed to achieve constant flow-constant pressure multi-stage intelligent inflation.

[0061] For example, the design details and functional principles of each unit are as follows.

[0062] The multi-channel high-purity gas source and mass flow control unit 110, also known as a multi-channel mass flow controller, is used to precisely control the flow rate of each component gas. It is used to connect multiple high-purity gas sources such as C4F7N, N2, and O2, and is equipped with a high-precision digital mass flow controller (MFC) to achieve closed-loop precise control of the flow rate of each component gas.

[0063] The adaptive dynamic mixing unit 120, also known as the dynamic mixing unit, is used to uniformly mix the components of the gas and adaptively fine-tune the mixing ratio. It employs an optimized static mixer array to perform turbulent mixing of the components, ensuring uniformity. The mixing ratio can be adaptively fine-tuned based on parameters such as ambient temperature and humidity, and target pressure, to achieve optimal insulation performance.

[0064] The online mixed gas quality verification unit 130, also known as the online quality verification unit, is located between the gas mixing unit and the equipment filling port. It integrates a non-contact high-precision optical analyzer, such as a Fourier transform infrared spectrometer (FTIR) or a quantum cascade laser spectrometer (QCLS).

[0065] Before the gas is introduced into the equipment, key indicators such as the component ratio, purity, and moisture content of the mixed gas are verified online in real time. If the verification fails, the gas supply circuit is automatically cut off and an alarm is triggered to prevent unqualified gas from entering the network.

[0066] The intelligent inflation control unit 140 integrates high-precision pressure and temperature sensors and an industrial-grade PLC. Based on the preset pressure-temperature (PT) curve, it realizes constant flow-constant pressure multi-stage intelligent inflation to prevent impact on the internal components of the equipment.

[0067] Optionally, in some embodiments of this application, the cloud-edge collaborative multimodal leakage and fault diagnosis module includes: a distributed multimodal sensor array for acquiring multimodal local sensor data; an edge computing gateway for real-time acquisition and processing of local sensor data and execution of local rapid alarm logic; and an environmental background intelligent compensation unit for real-time compensation of interference from environmental changes on local sensor readings.

[0068] For example, the design details and functional principles of each unit are as follows.

[0069] The distributed multimodal sensor array 210, also known as a multimodal sensor node, is deployed at critical leakage points such as GIS basin insulators, flanges, and valves, and is a distributed hybrid sensor node deployment.

[0070] Each node integrates an integrated acoustic sensor, a high-sensitivity parent gas sensor, and a highly selective decomposition product sensor.

[0071] Specifically, acoustic sensors, such as ultrasonic sensors, are used to detect ultrasonic signals generated by micro-leaks in gas. High-sensitivity parent gas sensors employ non-dispersive infrared (NDIR) or photoacoustic spectroscopy (PAS) techniques for highly sensitive detection of parent gases such as C4F7N. High-selectivity decomposition product sensors (MOS decomposition product sensors) utilize metal-oxide-semiconductor (MOS) arrays or micro-gas chromatography to specifically detect trace characteristic decomposition products at the ppb level, such as CF4, C2F6, CO, and COF2, generated by partial discharge and overheating.

[0072] The Edge Computing Gateway 220 incorporates an edge computing unit, specifically, it adopts a distributed deployment approach, with each sensor node or area controller having its own built-in edge computing unit. This unit is responsible for real-time acquisition and processing of local sensor data (noise reduction, feature extraction, and preliminary fusion), and executing local rapid alarm logic. This reduces bandwidth requirements for cloud communication and ensures millisecond-level fault response.

[0073] The intelligent environmental background compensation unit 230 integrates environmental sensors such as temperature, humidity, CO2, and O3. It uses neural network algorithms to compensate for the interference of environmental changes on local sensor readings in real time, so as to eliminate false alarms.

[0074] Optionally, in some embodiments of this application, the intelligent diagnostic and visualization platform based on digital twins includes: a data aggregation and twin mapping unit, used to construct a gas state digital twin model that is synchronized with the physical device in real time on the cloud; an artificial intelligence diagnostic engine, used to analyze the gas state digital twin model based on artificial intelligence technology to obtain fault diagnosis information; and a three-dimensional visualization and decision support module, used to display the fault diagnosis information in three-dimensional visualization on the gas state digital twin model.

[0075] For example, the design details and functional principles of each unit are as follows.

[0076] The 300-based intelligent diagnostic and visualization platform, also known as the cloud platform, is based on digital twins.

[0077] Data aggregation and twin mapping unit 310: Used to aggregate multi-source data via wireless (e.g., 5G / LoRa) or wired methods. Also used to build a digital twin model of gas state that is synchronized in real time with physical devices in the cloud.

[0078] The multi-source data includes edge gateway data, gas preparation data, and the equipment's own operating data; the equipment's own operating data includes UHF-PD (ultra-high frequency partial discharge signal) and fiber optic temperature measurement system data.

[0079] The AI ​​diagnostic engine 320 incorporates a multi-dimensional feature fingerprint database trained on massive amounts of experimental and simulation data, formatted as "leakage-decomposition-fault". This database is used to predict trends in gas time-series data (such as gas concentration and pressure) using temporal neural networks (e.g., LSTM), enabling leak rate assessment and fault development prediction. It also utilizes graph neural networks (GNNs) to analyze the spatial topology and response correlations of sensor arrays, achieving ultra-high-precision leak source location. Furthermore, it employs deep learning models to perform pattern recognition on fused features, accurately diagnosing the fault type and severity level.

[0080] Among them, the fault types obtained by accurate diagnosis include: Class A - simple physical micro-leakage; Class B - insulator surface discharge accompanied by leakage of characteristic products; Class C - conductor overheating accompanied by leakage of characteristic products.

[0081] The 3D visualization and decision support module 330 is used to visually display the location of the leak point, gas diffusion cloud map, internal fault-related parts, and health index in a 3D on a digital twin model. Based on the diagnostic results, it automatically generates graded early warning information (also known as graded alarm information or hazard classification information) and intelligent maintenance decision suggestions.

[0082] In general, the implementation process of a full-cycle gas control system for environmentally friendly gas-insulated electrical equipment includes: First, the system uses the intelligent gas preparation and filling module 100, along with the non-contact optical analyzer in the multi-channel high-purity gas source and mass flow control unit 110, the adaptive dynamic gas mixing unit 120, and the online mixed gas quality verification unit 130, to achieve 100% online real-time verification of the proportion, purity, and moisture content of the mixed gas components before filling, thus preventing unqualified gases from entering the network.

[0083] Then, through the cloud-edge collaborative multimodal leakage and fault diagnosis module 200, a distributed multimodal sensor array 210 integrating acoustic, parent gas and decomposition product sensors is deployed at the key leakage point. The local sensor data is processed and responded quickly through the edge computing gateway 220, and the environmental background is intelligently compensated by the environmental background intelligent compensation unit 230.

[0084] Finally, by utilizing the intelligent diagnostic and visualization platform 300 based on digital twins, multi-source data is aggregated to construct a digital twin model of gas state. Then, using the artificial intelligence diagnostic engine 320, based on fingerprint database, temporal neural network, graph neural network and deep learning technology, the leak rate, fault type, fault location and severity level are accurately diagnosed and predictive maintenance is performed. Finally, the 3D visualization and decision support module 330 provides decision support for users, thus constructing a new paradigm of intelligent operation and maintenance for environmental protection power equipment.

[0085] In addition, in one embodiment, based on the system structure of the full-cycle gas control system for environmentally friendly gas-insulated power equipment, the specific steps of the full-cycle gas control method for environmentally friendly gas-insulated power equipment provided in this application include: a. Environmentally friendly insulating gas is prepared by using multiple high-purity gas sources, a mass flow control unit 110, and an adaptive dynamic gas mixing unit 120; b. Before the gas is introduced into the equipment, the component ratio, purity and moisture content of the mixed gas are verified online in real time by the online mixed gas quality verification unit 130. If the gas fails to meet the requirements, the gas filling circuit is cut off. c. The qualified environmentally friendly insulating gas is injected into the electrical equipment through the intelligent gas filling control unit 140; d. Deploy a distributed multimodal sensor array 210 at the critical leakage points of electrical equipment and collect acoustic signals, parent gas concentration and decomposition product concentration in real time, while the environmental background intelligent compensation unit 230 compensates for environmental interference. e. Real-time acquisition and processing of local sensor data and execution of rapid alarm logic via edge computing gateway 220; f. By using the data aggregation and twin mapping unit 310, edge gateway data, gas preparation data, and equipment operation data are aggregated to the cloud to construct a digital twin model of the gas state; g. The AI ​​diagnostic engine 320 performs in-depth analysis of the data from the digital twin model of the gas state to predict the leakage rate, locate the leakage source, diagnose the fault type and severity level, and generate early warning and maintenance decision suggestions through the 3D visualization and decision support module 330.

[0086] The technical research process and other technical details of this application are described below with reference to a specific embodiment.

[0087] The application of environmentally friendly mixed gases in traditional technologies has also brought new technical challenges: (1) Complex quality control of mixed gases: Unlike single gases, the insulation performance of mixed gases is highly dependent on the precise proportions of each component. Traditional manual gas mixing and offline sampling and testing methods are inaccurate and inefficient, and cannot guarantee the quality of the gas filled into the equipment, posing a risk of "operating with defects".

[0088] (2) The leak detection dimension is limited and cannot be correlated with internal faults: Traditional leak detection technologies mostly detect a single physical quantity, such as ultrasonic or infrared imaging, which are mainly for large-scale gas leaks. They cannot effectively distinguish whether the leak is from the insulating gas matrix or from trace characteristic decomposition products generated by internal faults (such as partial discharge or overheating), thus losing the opportunity to provide early warning of internal faults through the composition of the leaked gas.

[0089] (3) Insufficient sensitivity and selectivity: For small leaks in the early stages of equipment (micro-leaks), or for trace decomposition gas (ppm level or even ppb level) leaks under complex electromagnetic and environmental backgrounds, existing technologies generally suffer from insufficient sensitivity, poor selectivity, and susceptibility to interference.

[0090] (4) Data silos and lack of diagnostic capabilities: Gas state data, leak monitoring data, and equipment operation data (such as partial discharge and temperature) are usually managed independently by different systems, forming data silos. Existing systems mostly remain at the threshold alarm level, lacking multi-source data fusion analysis and deep intelligent diagnostic capabilities, and are unable to accurately judge the cause of leaks, development trends, and potential internal faults.

[0091] Therefore, there is an urgent need for a full-chain solution that can achieve quality control from the gas source to intelligent diagnosis of equipment operation status, so as to ensure the safe and reliable operation of environmentally friendly insulating gas equipment.

[0092] Based on this, this application provides a full-cycle gas management system for environmentally friendly gas-insulated power equipment, which can be simply referred to as the system provided in this application, or as an environmentally friendly insulating gas intelligent preparation, online quality verification and multimodal leakage diagnosis system, or an environmentally friendly insulating gas intelligent management system. Details are as follows.

[0093] The system provided in this application can be applied to the fields of power equipment condition monitoring, intelligent sensing and artificial intelligence diagnostic technology, covering gas preparation, online quality verification before filling, and multimodal leakage and internal fault correlation diagnosis.

[0094] Among them, gas-insulated electrical equipment includes gas-insulated switchgear (GIS) using mixed gases such as C4F7N / N2 / O2.

[0095] This application controls gas quality from the source through online quality verification before charging, utilizes a cloud-edge collaborative multimodal sensor array to achieve high-precision leak detection and on-site data processing, and combines digital twins and AI diagnostic engines to achieve deep correlation diagnosis and predictive maintenance from gas leaks to internal faults, thus constructing a new paradigm of intelligent operation and maintenance for next-generation environmentally friendly power equipment.

[0096] In a specific implementation scenario, the system provided in this application is used to fill a new 220kV environmental gas GIS with gas and perform subsequent monitoring.

[0097] During the inflation phase: Maintenance personnel set the C4F7N:N2:O2 ratio to 4%:90%:6% and the target pressure to 0.7 MPa on the system interface. The system starts, and multiple high-purity gas sources and the mass flow control unit 110 precisely control the airflow into the adaptive dynamic mixing unit 120. The mixed gas flows through the online mixed gas quality verification unit 130, where a Fourier transform infrared spectrometer analyzes the gas composition in real time, confirming that the ratio error is less than 0.1% and the moisture content is less than 10 ppmv, thus passing the verification. Subsequently, the intelligent inflation control unit 140 begins to smoothly inflate the GIS chamber until the set pressure is reached. Throughout this process, all inflation data is recorded as gas preparation data and used to initialize the construction of a digital twin model of the gas state of the GIS on the cloud platform.

[0098] During the operational monitoring phase: A multimodal sensor node integrating ultrasonic sensors, NDIR parent gas sensors, and MOS decomposition product sensors was deployed around the flange of the GIS. Data from these sensors was sent to the edge computing gateway 220 for preliminary processing.

[0099] In the diagnostic scenario of a simple physical micro-leakage problem, the implementation process of the system provided in this application is as follows.

[0100] One day, a micro-leak appeared due to aging of the gasket at the flange. First, the ultrasonic sensor captured a high-frequency acoustic signal. Subsequently, the NDIR parent gas sensor detected that the C4F7N concentration slowly increased from a background value of 0 ppm to 5 ppm. The edge computing gateway 220 determined that this was a leak event and immediately uploaded this information and location ID to the cloud platform. The cloud platform's artificial intelligence diagnostic engine 320 received the data and simultaneously found that the MOS decomposition product sensor was unresponsive, and the partial discharge and temperature sensors inside the GIS were normal. The system matched the fingerprint database and diagnosed the fault type as "Class A - Simple Physical Micro-Leak" and the severity level as "Level II - Requires Attention". The 3D visualization and decision support module 330 highlighted the corresponding flange in the gas state digital twin model of the GIS and pushed a message to the maintenance personnel: "The B-phase flange in bay 3 is suspected of having a micro-leak, with a leakage rate of approximately 0.1% / year. It is recommended to focus on checking it during the next routine inspection."

[0101] In the diagnostic scenario of leakage of decomposition products caused by internal overheating, the implementation process of the system provided in this application is as follows.

[0102] If overheating occurs at a conductor connection point inside the GIS, causing the surrounding C4F7N mixed gas to decompose, there may not be a large amount of parent gas leakage at the flange, but trace amounts of decomposition products (such as CO and CF4) will seep out. The MOS decomposition product sensor detects a CO concentration of 50 ppb. The edge computing gateway 220 uploads this specific "chemical fingerprint." The cloud platform's AI diagnostic engine 320 receives this signal and integrates data from the fiber optic temperature measurement system inside the GIS, discovering an abnormal temperature rise at that location. The system immediately diagnoses the fault type as "Class C - Conductor Overheating Fault" and the severity level as "Level IV - Danger." The cloud platform immediately triggers the highest-level audible and visual alarm and pushes an emergency message: "The B-phase circuit breaker chamber in bay 3 is suspected of severe overheating. Characteristic decomposition products have been detected, posing a risk of insulation failure. It is recommended to arrange an immediate power outage for maintenance!"

[0103] Through the above methods, this application achieves closed-loop management from source quality control to in-depth operational status diagnosis, significantly improving the operational reliability and intelligent operation and maintenance level of environmentally friendly insulating gas equipment.

[0104] The advantages of the system provided in this application are as follows: (1) The online mixed gas quality verification unit 130 is embedded in the gas filling process, realizing 100% real-time detection before filling, fundamentally eliminating the problem of reduced insulation performance caused by unqualified gas ratio or excessive impurities. (2) Through multimodal sensing fusion technology, it can distinguish between physical leakage and chemical decomposition product leakage, thereby deeply linking external gas leakage monitoring with early warning of internal electrical faults (discharge, overheating) and realizing intelligent diagnosis in the form of "listening to the sound and smelling the odor". (3) Adopting a cloud-edge collaborative architecture, it uses edge computing to realize real-time processing and rapid response of local sensor data, and uses the powerful computing power and artificial intelligence algorithms of the cloud for in-depth analysis and trend prediction, taking into account both the real-time nature and accuracy of diagnosis. (4) Using advanced sensing technology and combining it with intelligent environmental background compensation algorithm, it ensures the accurate identification capability of ppb-level trace gases, greatly improving the probability of early fault detection.

[0105] In summary, this application provides a complete set of technical solutions and implementation methods, which provides key technical support for the intelligent and lean management of the entire life cycle of environmentally friendly gas-insulated power equipment, and effectively promotes the green and low-carbon transformation of the power industry.

[0106] In another embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5As shown, it includes a processor, memory, input / output interfaces, and a communication interface. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface is connected to the system bus via the input / output interfaces. The processor provides computing 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 an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores relevant data. The input / output 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. The computer program can be executed by the processor to implement the various methods described in the above embodiments.

[0107] In yet another embodiment, a computer device is provided, such as a terminal, whose internal structure diagram may be as follows: Figure 6 As shown, it includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. The computer program can be executed by the processor to implement the various methods described in the above embodiments.

[0108] Those skilled in the art will understand that Figure 5 and Figure 6 The structure shown is only a block diagram of a part of the structure related to the solution of this application, and does not constitute a limitation on the computer device on which the solution of this application is applied. It may also include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements, in order to realize the function of the terminal or server.

[0109] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0110] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the systems, devices, equipment, modules or units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, devices, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0112] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0113] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0114] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0115] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium, or a semiconductor medium (e.g., a solid-state drive), etc.

[0116] The technical solutions provided by the embodiments of this application have been described in detail above. Specific examples have been used in the embodiments of this application to illustrate the principles and implementation methods of the embodiments of this application. The description of the above embodiments is only for the purpose of helping to understand the methods and core ideas of the embodiments of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments of this application. Therefore, the content of this specification should not be construed as a limitation on the embodiments of this application.

Claims

1. A full-cycle gas management method for an environmentally friendly gas-insulated electrical device, characterized by, The full-cycle gas control method for environmentally friendly gas-insulated power equipment includes: The prepared mixed gas is subjected to online real-time quality verification. Based on the multi-stage intelligent gas filling principle of constant current and constant pressure, the mixed gas that has passed the online real-time quality verification is filled into the gas-insulated switchgear. Acquire multimodal edge gateway data, analyze the edge gateway data, and generate local rapid alarm information; Based on the edge gateway data and the model construction data associated with the gas-insulated switchgear, a gas state digital twin model is constructed, and fault diagnosis information is generated and displayed based on the gas state digital twin model.

2. The method for full-cycle gas control of environmentally friendly gas-insulated power equipment according to claim 1, characterized in that, The process of performing online real-time quality verification on the prepared mixed gas, based on the multi-stage intelligent gas filling principle of constant current and constant pressure, and filling the mixed gas that has passed the online real-time quality verification into the gas-insulated switchgear includes: The mixed gas was subjected to online real-time quality verification to obtain the quality verification results; Based on the multi-stage intelligent inflation principle of constant flow and constant pressure, a preset pressure and temperature curve is determined. If the quality verification result is that the online real-time quality verification is passed, the mixed gas is controlled to be injected into the gas-insulated switchgear according to the preset pressure and temperature curve.

3. The method for full-cycle gas control of environmentally friendly gas-insulated power equipment according to claim 1, characterized in that, The process of acquiring multimodal edge gateway data and analyzing the edge gateway data to generate local rapid alarm information includes: Acquire multimodal local sensor data and intelligent environmental background compensation data; The local sensor data and the intelligent environmental background compensation data are used as the edge gateway data; The edge gateway data is analyzed to obtain local analysis results; Local rapid alarm information is generated based on the local analysis results.

4. The method for full-cycle gas control of environmentally friendly gas-insulated power equipment according to claim 1, characterized in that, The step of constructing a gas state digital twin model based on the edge gateway data and the model construction data associated with the gas-insulated switchgear, and generating and displaying fault diagnosis information based on the gas state digital twin model includes: Obtain the gas preparation data of the mixed gas and the equipment operation data of the gas-insulated switchgear; The gas preparation data and the equipment operation data are used as the model construction data; The gas state digital twin model is constructed based on the edge gateway data and the model construction data. The fault diagnosis information is obtained by analyzing the digital twin model of the gas state based on artificial intelligence technology; The fault diagnosis information is displayed in three dimensions on the digital twin model of the gas state.

5. A full-cycle gas control system for environmentally friendly gas-insulated electrical equipment, characterized in that, For implementing the full-cycle gas control method for environmentally friendly gas-insulated electrical equipment as described in any one of claims 1 to 4, the system comprises: The intelligent gas preparation and filling module is used to perform online real-time quality verification of the prepared mixed gas. Based on the multi-stage intelligent filling principle of constant current and constant pressure, the mixed gas that has passed the online real-time quality verification is filled into the gas-insulated switchgear. The cloud-edge collaborative multimodal leakage and fault diagnosis module is used to acquire multimodal edge gateway data, analyze the edge gateway data, and generate local rapid alarm information. The intelligent diagnostic and visualization platform based on digital twins is used to construct a gas state digital twin model based on the edge gateway data and the model construction data associated with the gas-insulated switchgear, and to generate and display fault diagnosis information based on the gas state digital twin model.

6. The full-cycle gas control system for environmentally friendly gas-insulated power equipment according to claim 5, characterized in that, The intelligent gas preparation and filling module includes: Multiple high-purity gas sources and mass flow control units are used to control the flow rate of each component gas; An adaptive dynamic mixing unit is used to uniformly mix the gas components based on adaptive fine-tuning mixing ratio data. An online mixed gas quality verification unit is used to perform online real-time quality verification of the preset indicators of the mixed gas before the gas is introduced into the equipment. The intelligent inflation control unit is used to inflate the air according to a preset pressure and temperature curve.

7. The full-cycle gas control system for environmentally friendly gas-insulated power equipment according to claim 5, characterized in that, The cloud-edge collaborative multimodal leakage and fault diagnosis module includes: A distributed multimodal sensor array is used to acquire multimodal local sensor data. An edge computing gateway is used to collect and process the local sensor data in real time and execute local rapid alarm logic. An intelligent environmental background compensation unit is used to compensate for the interference of environmental changes on local sensor readings in real time.

8. The full-cycle gas control system for environmentally friendly gas-insulated power equipment according to claim 5, characterized in that, The intelligent diagnosis and visualization platform based on digital twins includes: The data aggregation and twin mapping unit is used to build a digital twin model of the gas state that is synchronized in real time with the physical device in the cloud; An artificial intelligence diagnostic engine is used to analyze the digital twin model of the gas state based on artificial intelligence technology to obtain the fault diagnosis information; The 3D visualization and decision support module is used to display the fault diagnosis information in 3D on the gas state digital twin model.

9. A computer device, characterized in that, The computer device includes: At least one processor and memory; The memory is used to store program code, and the processor is used to call the program code stored in the memory to execute the method as described in any one of claims 1 to 4.

10. A computer storage medium, characterized in that, It includes instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 4.

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