Substation environment monitoring and routing inspection control system and method

Abstract

The invention provides a substation environment monitoring inspection control system and method, and relates to the technical field of power system monitoring. Comprising a holographic sensing subsystem and a data fusion control subsystem. The holographic sensing subsystem collects substation environment and equipment state data in real time and comprises a non-contact optical fiber sensor, a temperature and humidity sensor, a gas concentration sensor, an infrared camera, an ultraviolet camera, a visible light camera and a millimeter wave radar. The data fusion control subsystem carries out redundancy elimination, complementation and standardization processing on collected data, the intelligent analysis module carries out state analysis and fault prediction based on fused data, and the control instruction generation module generates an emergency control instruction according to an analysis result to realize closed-loop feedback control; the system improves the monitoring accuracy and response speed of the transformer substation through multi-sensor data fusion and intelligent analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of power system monitoring, and in particular to a substation environment monitoring and inspection control system and method. Background Art

[0002] As an important part of the power system, the operating status of the substation directly affects the safe and stable supply of electricity. With the complexity of modern power systems, the monitoring and control technology of the substation environment has gradually become a hot research area. Currently, intelligent and holographic monitoring methods are widely used to improve the operating efficiency of substation equipment and reduce potential environmental risks; especially in dealing with complex external environmental changes and abnormal internal equipment status, the importance of holographic perception and intelligent analysis technology has become increasingly prominent.

[0003] Existing substation environmental monitoring solutions mostly use separate monitoring technologies, including separate temperature and humidity detection, gas concentration monitoring, and equipment image monitoring. These systems usually rely on independent data acquisition devices and process information manually or through simple data aggregation platforms. For example, some technologies collect environmental data through independent temperature and humidity sensors and fiber optic sensors, and combine them with image processing-based equipment appearance monitoring to determine potential problems. The main features of these solutions are simple equipment and low cost, but the monitoring range and accuracy are limited, and it is difficult to achieve real-time status assessment and intelligent prediction.

[0004] Existing technologies have significant deficiencies in data fusion and intelligent analysis. First, they lack the ability to deeply integrate multi-source data, resulting in redundancy and contradictions between monitoring information. Second, the processing method is single and cannot effectively deal with data loss or abnormal interference. Third, there is a lack of comprehensive intelligent analysis modules, making it difficult to accurately predict potential equipment failures and environmental risks. These problems limit the application effect of existing solutions in complex environments, and a more integrated and intelligent monitoring and inspection solution is urgently needed. Summary of the invention

[0005] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a substation environmental monitoring and inspection control system and method to solve the problems of insufficient multi-source data fusion capability, single data processing method and lack of intelligent analysis module in the prior art in the above-mentioned background technology, thereby failing to achieve accurate monitoring and prediction of the operating status and environmental risks of substation equipment, limiting the application effect of the monitoring system in complex environments.

[0006] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a substation environment monitoring and inspection control system and method, including a holographic sensing subsystem and a data fusion control subsystem, wherein the holographic sensing subsystem includes a sensor module, a visual monitoring module and a radar monitoring module, which are used to collect the status data of the substation environment and equipment in real time, the sensor module includes a non-contact optical fiber sensor, a temperature and humidity sensor and a gas concentration sensor, the visual monitoring module includes an infrared camera, an ultraviolet camera and a visible light camera, and the radar monitoring module includes a millimeter wave radar, which is used to detect the dynamic operation status of the equipment in the substation and the changes in the external environment; The data fusion control subsystem includes a data acquisition module, a data fusion module, an intelligent analysis module and a control instruction generation module. The data acquisition module receives monitoring data from the sensor module, the visual monitoring module and the radar monitoring module. The data fusion module performs fusion processing on the collected data, including de-redundancy, data completion and standardization processing. The intelligent analysis module performs status analysis and predicts potential faults based on the fused data. The control instruction generation module generates emergency control instructions according to the analysis results of the intelligent analysis module, and feeds back the execution status to the data fusion control subsystem to complete closed-loop feedback control.

[0007] Preferably, in the holographic sensing subsystem, the sensor module is an integrated module including multiple sensors, including a non-contact optical fiber sensor, a temperature and humidity sensor, and a gas concentration sensor. Each sensor has an independent monitoring function but can provide comprehensive monitoring functions through cooperation and data fusion with each other. The non-contact optical fiber sensor fixes the optical fiber near the key equipment of the substation to capture the temperature change signal generated during operation. The temperature and humidity sensors are distributed in the air circulation area and the equipment heat dissipation area to collect the temperature and humidity parameters of the environment. By comparing these data with the normal operating data, abnormality detection is achieved. At the same time, the gas concentration sensor is used to detect the change of SF6 gas concentration in the substation in real time and link the temperature and humidity sensors. By comparing these monitoring data with the equipment By comparing with the benchmark data during normal operation, the system can detect whether there is an abnormality; for example, if the temperature collected by the temperature and humidity sensor is higher than the preset normal operating range; for example, the normal temperature range of the equipment is 25℃ to 30℃, and it is considered abnormal if it exceeds the normal operating range by 30%, or the humidity exceeds the specified range of 40% to 60% relative humidity, it will be judged as poor heat dissipation; at the same time, when the gas concentration sensor detects a change in SF6 gas concentration, if it exceeds the safety threshold of 50ppm, the temperature and humidity sensor will be linked. If the local temperature and gas concentration data are abnormally consistent, the system will trigger an alarm, indicating that a heat dissipation problem may occur with gas leakage; and start cooling or exhaust equipment; the whole process is collected by a low-power microprocessor and transmitted to the data acquisition module, thereby ensuring the accuracy and real-time response of the monitoring data.

[0008] Preferably, the visual monitoring module includes an infrared camera, an ultraviolet camera and a visible light camera, each of which undertakes different detection tasks based on its own spectral response characteristics, wherein the infrared camera is used to capture the temperature distribution image of the device surface, and detects and marks the temperature abnormal area through the infrared thermal imaging algorithm. For example, when the temperature of a local area on the device surface exceeds the normal range by 30%, it can be marked as an abnormal area; the ultraviolet camera analyzes the possible electrical fault hidden dangers by capturing the local discharge phenomenon on the device surface, wherein the local discharge phenomenon is usually accompanied by changes in the flicker frequency, and these changes can be captured and analyzed by ultraviolet images. If the frequency is found to be within a certain range, such as continuous abnormal fluctuations of more than 5 flickers per second, it can be determined as a potential electrical fault hidden danger; the visible light camera records the appearance status of the device and the detailed changes in the operating environment with high-definition resolution. The monitoring data of these three cameras are jointly processed by the visual analysis algorithm, wherein the autoencoder neural network model (Autoencoders) is used to mark and classify the abnormal features of different categories in the image, and further combined with the real-time monitoring parameters transmitted by the data fusion module for verification to improve the reliability of abnormal detection.

[0009] Preferably, the radar monitoring module adopts millimeter-wave radar technology, and monitors the tiny vibrations, abnormal displacement states and dynamic changes of the external environment of the equipment in the substation in real time through the reflection characteristics of high-frequency electromagnetic wave signals. For example, when a certain device has irregular vibrations due to long-term operation, the millimeter-wave radar will record the vibration frequency and amplitude and transmit the data to the data fusion module. At this time, the millimeter-wave radar can determine whether irregular vibrations occur by setting a vibration frequency threshold of more than 40 times per minute and an amplitude threshold of more than 5 mm. Since the millimeter-wave radar has extremely high sensitivity and anti-interference ability, it can achieve stable dynamic monitoring in a complex environment and combine the data verification analysis of the visual monitoring module to further improve the monitoring accuracy. At the same time, when the external environment, such as wind speed exceeding 10 meters per second or precipitation exceeding 50 mm, the millimeter-wave radar can also capture these parameters in time and transmit the data to the data fusion module.

[0010] Preferably, the data fusion module includes a data de-redundancy module, a data completion module and a data standardization module, wherein the data de-redundancy module analyzes the monitoring data transmitted by the sensor module, the visual monitoring module and the radar monitoring module through an adaptive algorithm, and removes redundant information such as repeated timestamps, invalid sensor readings and noise data, and the adaptive algorithm formula is: in, is the model parameter at the current moment, is the learning rate, is the gradient of the loss function with respect to the parameters, is the input data at the current moment, is the target output. The function of the adaptive algorithm is to continuously adjust the parameters according to the changes in data and remove redundant information. The data completion module uses a prediction algorithm based on a time series model to interpolate and complete the data loss caused by communication interruption or sensor failure to ensure data continuity. The prediction algorithm based on the time series model is an autoregressive integrated moving average model (ARIMA), which is used to predict the missing data values ​​through the existing data sequence to ensure data continuity. The formula is: in, is the observed value at the current moment, is a constant term, is the autoregression coefficient, is the white noise error term, is the previous error, and are the orders of the autoregressive and sliding average terms respectively. Through this algorithm, missing values ​​can be predicted based on historical data, and interpolation can be performed to ensure data continuity. At the same time, the data standardization module converts the data from various sources into a standard format for processing by the intelligent analysis module, wherein the standard format includes a unified timestamp format, data unit and data structure to ensure data compatibility from different data sources. The data fusion module also integrates a distributed intelligent adaptive environmental recovery unit, which dynamically schedules resources to optimize data stability by real-time monitoring of changes in the internal and external environment of the system. For example, when data collection is delayed due to environmental anomalies, the processing capacity of the edge computing unit is quickly adjusted through the resource pool scheduler to ensure that the data fusion module can always provide high-quality monitoring data for subsequent processing.

[0011] Preferably, the distributed intelligent adaptive environmental recovery unit includes an edge computing unit, a resource pool scheduler and an environmental correction feedback loop. The unit dynamically monitors environmental anomalies and generates correction strategies during system operation, wherein the edge computing unit analyzes local environmental changes in a low-latency and high-efficiency manner. For example, during high temperatures in summer, when the system detects that the temperature in a certain area suddenly rises above a set threshold, or the humidity drops sharply beyond a preset range, the edge computing unit immediately generates cooling or humidification correction suggestions, and then the resource pool scheduler allocates corresponding resources according to the correction suggestions, such as starting the cooling system to lower the ambient temperature or activating the humidification equipment to improve Air humidity, and then the environmental correction feedback loop transmits the corrected effect data to the data fusion module and the intelligent analysis module to further optimize the operation performance of the whole system. For example, if the system monitoring data has not returned to the normal range after two consecutive correction measures, the resource pool scheduler will trigger a high-level recovery mode to call more resources for comprehensive environmental control, so as to ensure the safety of substation equipment and environmental stability. The high-level recovery mode is that in this mode, the resource pool scheduler not only starts conventional cooling or humidification equipment, but also calls spare high-efficiency cooling systems or dehumidification equipment, and even enables emergency backup energy to ensure environmental stability.

[0012] Preferably, the data fusion control subsystem realizes precise regulation of the substation environment and equipment status through a closed-loop feedback control mechanism. The mechanism consists of four main links: data acquisition, intelligent analysis, control instruction generation and execution feedback. The specific implementation process includes the holographic perception subsystem real-time acquisition and transmission of data to the intelligent analysis module, the intelligent analysis module generates an assessment result of the current status and predicts potential risks, the control instruction generation module generates corresponding control instructions according to the assessment results and transmits them to the actuator, and after the actuator completes the execution of the instruction, the result is re-input into the data fusion module through the feedback loop for correction of subsequent analysis and control strategies. For example, when the intelligent analysis module predicts that the equipment may fail due to overheating, the control instruction generation module immediately triggers cooling measures, and continuously detects temperature changes through the feedback link to ensure that the cooling measures are effective, and finally optimizes the monitoring and control accuracy in multiple iterations to achieve stable operation of the system.

[0013] Preferably, the intelligent analysis module performs in-depth analysis based on the fused data provided by the data fusion module. The module consists of a historical data analysis unit and a real-time status evaluation unit. The historical data analysis unit extracts the long-term regularity of equipment operation by constructing a characteristic curve model of the substation equipment operation history. The real-time status evaluation unit combines historical data with real-time monitoring data to use a machine learning algorithm to predict the potential failure risk of the equipment. For example, when the operating temperature of a certain equipment fluctuates significantly in a short period of time and deviates from the operating trajectory of historical data, the trained regression model is used to predict the possible high-temperature failure risk of the equipment and trigger a fault warning mechanism. At the same time, the module uses a multi-task learning model based on a deep neural network to realize parallel prediction of multiple types of faults and transmit the results to the control instruction generation module to guide emergency processing.

[0014] Preferably, the control instruction generation module generates precise emergency control instructions based on the equipment status assessment results provided by the intelligent analysis module. For example, an instruction to start the cooling fan is generated when the equipment temperature is too high, an instruction to start the exhaust system is generated when the gas concentration exceeds the standard, and an instruction to trigger the fire extinguishing system is generated when a fire or high-temperature short circuit risk occurs. The core implementation of this module includes an event priority scheduler to determine the response order of each emergency instruction under various abnormal conditions. For example, when temperature abnormalities and gas leaks occur at the same time, the exhaust system is triggered first to remove toxic gases, and the temperature control system is notified to cool down. The entire emergency response process continuously detects the execution effect of the instructions through the embedded closed-loop feedback mechanism and dynamically adjusts the control strategy to ensure safe operation of the system.

[0015] Preferably, the holographic perception subsystem realizes real-time monitoring of the substation environment and equipment status through the coordinated work of multiple sensing devices, including capturing tiny vibration changes of the equipment through optical fiber sensors, monitoring the temperature and humidity fluctuations of the environment through temperature and humidity sensors, detecting SF6 and other gas leakage through gas concentration sensors, obtaining high-precision images of the equipment surface and operating environment through a visual monitoring module, and capturing dynamic changes in the equipment operating status and the influence of the external environment through a radar monitoring module. The subsystem collects data in real time at a millisecond sampling frequency and transmits it to the data fusion module. At the same time, a built-in data cache mechanism is used to temporarily store high-priority critical data to ensure that the basic monitoring capability of the system can be maintained when network communications are interrupted.

[0016] Preferably, the intelligent analysis module performs a comprehensive analysis of historical data and real-time monitoring data based on multimodal data fusion technology. The historical data is feature extracted and pattern analyzed through a large amount of equipment operation information stored in the database, including time series modeling of features such as temperature and humidity, vibration frequency, and gas leakage trends. The real-time data is acquired through the holographic perception subsystem and standardized through the data fusion module. The two realize mapping analysis in a unified feature space through the multimodal fusion layer in the deep learning network. For example, when historical data shows that the equipment has experienced electrical failures under similar environmental conditions, slight changes in real-time data can serve as early warning signals to trigger risk prompts. This fusion analysis method can effectively reduce the monitoring blind spots that may be caused by a single data dimension, and at the same time, dynamically adjust the risk prediction model in combination with the pattern matching algorithm to improve the analysis accuracy.

[0017] Preferably, the radar monitoring module is used to monitor the dynamic abnormal status within the substation in real time, the visual monitoring module is used to detect abnormal conditions on the surface of the equipment in real time, the data fusion module fuses and analyzes the data of the radar monitoring module and the visual monitoring module, and combines the data of the sensor module to perform comprehensive monitoring and accurate diagnosis of the environment and equipment status of the substation, generates equipment failure prediction results through the intelligent analysis module, and transmits them to the control instruction generation module to generate corresponding emergency control instructions, the execution module executes response operations according to the emergency control instructions, and feeds back the execution status to the data fusion control subsystem to complete closed-loop verification.

[0018] (III) Beneficial effects The present invention provides a substation environment monitoring inspection control system and method. It has the following beneficial effects: 1. The present invention collects the status data of the substation environment and equipment in real time through the holographic perception subsystem, including temperature and humidity, gas concentration, equipment surface temperature, partial discharge data, visual image data and dynamic radar data; these data are de-redundant, data-completed and standardized through the data fusion control subsystem to ensure the accuracy and integrity of the data; the intelligent analysis module uses these fused data to perform status assessment and fault prediction, so as to timely discover potential safety hazards, such as equipment overheating, gas leakage or partial discharge; the control instruction generation module generates emergency control instructions according to the analysis results, such as starting the fan, exhaust system, triggering the fire extinguishing system or adjusting the temperature control system, so as to quickly respond to and deal with these safety risks; this closed-loop monitoring and control mechanism significantly improves the safety and reliability of the substation, reduces the occurrence of accidents, and ensures the stability of power supply.

[0019] 2. The data fusion module of the data fusion control subsystem in the present invention integrates a distributed intelligent adaptive environmental recovery unit, which can dynamically monitor environmental anomalies and generate correction strategies; the edge computing unit collects local environmental anomaly data in real time and calculates environmental correction strategies, and the resource pool scheduler allocates recovery resources such as cooling, exhaust or heating equipment according to these strategies; the environmental correction feedback loop feeds back the recovery effect data to the data fusion module and the intelligent analysis module to further improve the accuracy of data processing and the system operation performance; this intelligent environmental control and resource scheduling mechanism enables the substation to adapt quickly to environmental changes, optimize energy use, and reduce energy waste, while ensuring that the equipment operates in the best environment, extending equipment life and reducing maintenance costs. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1: The embodiment of the present invention provides a substation environment monitoring and inspection control system and method. In actual operation, the substation environment monitoring and inspection control system is deployed in a medium-sized substation located in a hot and humid environment. Its core goal is to achieve all-weather monitoring of equipment operating status and environmental parameters. The system first collects data through a holographic sensing subsystem, which includes a sensor module, a visual monitoring module, and a radar monitoring module. In the sensor module, a non-contact optical fiber sensor is fixedly installed near the main transformer equipment in the substation to monitor the surface temperature changes and operating status of the transformer in real time. At the same time, temperature and humidity sensors are arranged in the air circulation area and equipment The location where the heat dissipation performance of the equipment is weak is used to capture the temperature and humidity changes of the surrounding environment. For example, when the air humidity is too high, the system can give an early alarm to prevent moisture from causing a short circuit in the equipment. In addition, the gas concentration sensor is installed near the gas circulation pipeline of the substation to detect SF6 gas leakage. When the gas concentration exceeds the 50ppm threshold, the temperature and humidity sensors will be linked to determine whether there is heat dissipation anomaly or other hidden dangers in the leakage area. The visual monitoring module includes three types of cameras: infrared, ultraviolet and visible light. The infrared camera captures the temperature distribution image of the surface of the equipment with high resolution. Once the temperature of a local area is found to exceed the normal working range by 30%, the system will mark the abnormality and trigger an alarm. The ultraviolet camera is responsible for detecting partial discharge phenomena, such as arc flickering that may appear on the surface of the equipment casing, and assessing the discharge risk through abnormal changes in the flickering frequency, such as fluctuations exceeding 5 times / second; the millimeter-wave radar, as the core component of the radar monitoring module, can capture the tiny vibrations of the equipment or the dynamic changes of the environment through the reflection characteristics of high-frequency electromagnetic wave signals. For example, when a part of the equipment vibrates irregularly, such as the frequency exceeds 40 times / minute or the amplitude exceeds 5 mm, the data will be recorded and transmitted to the data fusion control subsystem; the data fusion control subsystem processes the above collected data, the data de-redundancy module removes invalid or duplicate data points, and the data completion module The time series model ARIMA algorithm predicts and fills the data loss caused by sensor failure. The data standardization module converts data from different sources into a unified format and transmits it to the intelligent analysis module. The intelligent analysis module further performs comprehensive analysis based on historical data and real-time monitoring data. For example, when the historical data analysis unit finds that a certain device has shut down due to overheating under similar operating conditions, the system will give a high-temperature failure prediction for the temperature rising trend in the real-time state, trigger the emergency cooling system, and feed back the cooling effect to the data fusion module through the environmental correction feedback loop, and continuously adjust the cooling intensity until the temperature returns to the normal range, ultimately achieving accurate monitoring of the equipment status and stable operation.

[0022] Embodiment 2: Based on the first embodiment, this embodiment focuses on the dual monitoring of low temperature and gas leakage in response to the special needs of small substations in cold environments. The holographic perception subsystem is first deployed reasonably. The non-contact optical fiber sensor in the sensor module is installed closely on the surface of the main transformer and circuit breaker to capture the abnormal temperature of the equipment that may occur in the low temperature environment. The temperature and humidity sensors are distributed around the equipment heating device and the vents of the control room to detect abnormal humidity fluctuations to prevent condensation caused by low temperature. The gas concentration sensor monitors the changes in SF6 gas concentration near the pipeline interface of key equipment. When the concentration approaches a dangerous level, such as 45ppm, the system will automatically determine the leakage risk and simultaneously analyze its cause. The visual monitoring module combines with the infrared camera to capture the thermal distribution image of the equipment surface. In a low-temperature operating environment, when the surface temperature of a part of the equipment is lower than the safety threshold by 20%, the system will determine that there may be insufficient cooling medium or a heat dissipation device failure. The ultraviolet camera detects the discharge phenomenon of electrical equipment, such as the local electric discharge that may occur on the surface of the insulator of the circuit breaker. The millimeter-wave radar is responsible for monitoring dynamic changes in the substation. For example, windy and snowy weather may cause irregular vibrations on the outside of the equipment. The system sets the wind speed threshold to 15 m / s and the vibration frequency to 20 times / min to determine the impact of the external environment on the safety of the equipment. The data fusion control subsystem processes all collected data in real time. The data completion module fills in the missing sensor data through an interpolation algorithm. The intelligent analysis module generates a global fault risk assessment diagram based on historical operation data. For example, in the low-temperature fault mode recorded in the past five years, the system compares the temperature, humidity and vibration characteristics in the real-time data through a deep neural network. If an abnormal match is found, the system will immediately trigger the emergency heating equipment and feed back the response results to the data fusion module, while optimizing the subsequent environmental control strategy. The entire system executes emergency measures, such as exhaust, heating or dehumidification operations, through the control instruction generation module, and optimizes the subsequent operation strategy through a closed-loop feedback control mechanism based on the operation results, thereby ensuring the safe and stable operation of the equipment in a cold environment.

[0023] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A substation environmental monitoring and inspection control system, characterized in that: It includes a holographic perception subsystem and a data fusion control subsystem, wherein the holographic perception subsystem includes a sensor module, a visual monitoring module and a radar monitoring module, which are used to collect status data of the substation environment and equipment in real time, the sensor module includes a non-contact optical fiber sensor, a temperature and humidity sensor and a gas concentration sensor, the visual monitoring module includes an infrared camera, an ultraviolet camera and a visible light camera, and the radar monitoring module includes a millimeter wave radar, which is used to detect the dynamic operation status of the equipment in the substation and the changes in the external environment; The data fusion control subsystem includes a data acquisition module, a data fusion module, an intelligent analysis module and a control instruction generation module. The data acquisition module receives monitoring data from the sensor module, the visual monitoring module and the radar monitoring module. The data fusion module performs fusion processing on the collected data, including de-redundancy, data completion and standardization processing, and dynamically schedules recovery resources through a distributed intelligent adaptive environmental recovery unit. The intelligent analysis module performs status analysis based on the fused data and predicts potential faults. The control instruction generation module generates emergency control instructions according to the analysis results of the intelligent analysis module, and feeds back the execution status to the data fusion control subsystem to complete closed-loop feedback control.

2. A substation environment monitoring and inspection control system according to claim 1, characterized in that: The non-contact optical fiber sensor is installed near the key equipment in the substation to monitor the operating temperature and working status of the equipment in real time. The temperature and humidity sensor is installed in the air circulation area and the equipment heat dissipation area of ​​the substation to monitor changes in ambient temperature and humidity. The gas concentration sensor is used to detect the leakage of SF6 gas concentration and other harmful gases, and works in conjunction with the temperature and humidity sensor to achieve real-time monitoring of the equipment operating environment.

3. A substation environment monitoring and inspection control system according to claim 1, characterized in that: The visual monitoring module includes an infrared camera, an ultraviolet camera and a visible light camera. The infrared camera is used to monitor the abnormal surface temperature and hot spot distribution of the equipment, the ultraviolet camera is used to detect the partial discharge phenomenon of the equipment, and the visible light camera is used to obtain the appearance image and abnormal state of the equipment. The data collected by the visual monitoring module is analyzed by an image processing algorithm and cross-validated with the data transmitted by the data fusion module to improve the accuracy of equipment monitoring.

4. A substation environment monitoring and inspection control system according to claim 1, characterized in that: The radar monitoring module includes a millimeter-wave radar, which is used to monitor dynamic objects inside the substation in real time, including tiny vibrations of equipment, abnormal movement states and dynamic changes in the external environment. The data acquired by the radar monitoring module is compared and analyzed with the data of the visual monitoring module through the data fusion module to achieve accurate detection of abnormal activities and potential hidden dangers in the substation.

5. A substation environment monitoring and inspection control system according to claim 1, characterized in that: The data fusion module includes a data de-redundancy module, a data completion module and a data standardization module. The data de-redundancy module is used to remove invalid redundant data in the monitoring data. The data completion module completes the data missing caused by sensor signal failure or communication interruption. The data standardization module is used to convert the different source data of the sensor module, visual monitoring module and radar monitoring module into a standardized format. The data processed by the data fusion module is transmitted to the intelligent analysis module for comprehensive analysis. The data fusion module also integrates a distributed intelligent adaptive environmental recovery unit for dynamically scheduling recovery resources to optimize the stability of monitoring data.

6. A substation environment monitoring and inspection control system according to claim 5, characterized in that: The distributed intelligent adaptive environmental recovery unit includes an edge computing unit, a resource pool scheduler and an environmental correction feedback loop. The edge computing unit is used to collect local environmental abnormality data in real time and dynamically calculate the environmental correction strategy. The resource pool scheduler allocates recovery resources according to the correction strategy, including starting cooling, exhaust or heating equipment. The environmental correction feedback loop feeds back the data of the recovery effect to the data fusion module and the intelligent analysis module to improve the accuracy of data processing.

7. A substation environment monitoring and inspection control system according to claim 1, characterized in that: The intelligent analysis module is used to generate an operating status map of the substation based on the data transmitted by the data fusion module. The intelligent analysis module includes a historical data analysis unit and a real-time status evaluation unit. The historical data analysis unit is used to analyze the operating history data of the substation equipment. The real-time status evaluation unit combines historical data with current monitoring data and uses a machine learning model to predict potential equipment failures and environmental risks. The analysis results are transmitted to the control instruction generation module through a fault warning mechanism.

8. A substation environment monitoring and inspection control system according to claim 1, characterized in that: The control instruction generation module generates corresponding emergency control instructions according to the status evaluation and prediction results of the intelligent analysis module. The emergency control instructions include temperature control instructions, gas emission control instructions and fire extinguishing instructions. The control instructions include starting the fan control system for air circulation, activating the exhaust system to remove leaked gas, triggering the fire extinguishing system to extinguish the fire source, and adjusting the temperature control system to reduce the operating temperature of the equipment. The control instruction generation module sets the priority response order according to the level of the emergency event.

9. A substation environment monitoring and inspection control system according to claim 1, characterized in that: The holographic perception subsystem is used to collect status data and environmental parameters of substation equipment in real time. The collected data include temperature and humidity data, gas concentration data, equipment surface temperature data, partial discharge data, visual image data and dynamic radar data. The collected data is input into the data fusion control subsystem. The data fusion module is used to remove redundancy, complete data and standardize data. The processed data is transmitted to the intelligent analysis module. The intelligent analysis module uses historical data and real-time data to perform status assessment and fault prediction. The control instruction generation module generates emergency control instructions according to the analysis results of the intelligent analysis module. The emergency control instructions include fan start-up, exhaust control, fire extinguishing trigger and temperature control adjustment. The execution result refers to the execution status of the emergency control instruction. The execution result is fed back to the data fusion control subsystem to complete the closed-loop operation of monitoring, analysis, control and feedback.

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