Comprehensive monitoring method and system for power cabinet based on multiple sensors, and distribution cabinet

Abstract

The present invention discloses a comprehensive monitoring method, system and distribution cabinet based on multiple sensors for power cabinets, which relates to the field of sensor monitoring technology. During operation, the system collects various sensor data from the power cabinet and its surrounding environment, monitors the electromagnetic environment, equipment status and environmental parameters inside and outside the power cabinet in real time, pre-processes the raw sensor data through a signal filtering algorithm, extracts characteristic information related to electromagnetic interference (EMI) from the processed data, and calculates the electromagnetic environment index (EMI) through a machine learning algorithm. index , conduct status assessment and fault prediction of equipment in the power cabinet, provide early warning of fault types and give maintenance suggestions. When the electromagnetic interference intensity exceeds the standard or equipment failure occurs, the intelligent judgment mechanism will automatically trigger a real-time alarm signal, and start the system adaptive control mechanism to generate an electromagnetic environment index and equipment status report to remotely monitor the power cabinet.

Description

Technical Field

[0001] The present invention relates to the field of sensor monitoring technology, and in particular to a multi-sensor based comprehensive monitoring method and system for a power cabinet and a distribution cabinet. Background Art

[0002] With the continuous development of power systems, power cabinets, as important power distribution equipment, play a key role in controlling and protecting various types of equipment in the power system. The normal operation of power cabinets is directly related to the stability and safety of the power system. Therefore, real-time monitoring of power cabinets, especially continuous monitoring of the internal and external environment and equipment status of power cabinets, has become an urgent need in the power industry. Traditional power cabinet monitoring methods mainly focus on monitoring basic parameters such as electrical parameters (such as current, voltage, etc.) and temperature. However, these methods cannot fully reflect the various interference factors that power cabinets may be subject to in complex environments, especially electromagnetic interference (EMI). Electromagnetic interference, as a potential threat to the normal operation of power cabinet equipment, may cause equipment failure, communication interference, and even system crashes. Therefore, how to efficiently and accurately monitor and evaluate the electromagnetic environment has become an important issue in improving the safety of power cabinets.

[0003] Electromagnetic interference (EMI) is a common potential hazard in power systems, particularly in critical facilities like distribution cabinets. Interference can originate not only from the external environment but also from internal equipment. Traditional power cabinet monitoring systems are limited to monitoring electrical parameters and environmental changes, making it difficult to effectively identify the impact of EMI on system stability. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a multi-sensor based comprehensive monitoring method and system for a power cabinet and a distribution cabinet, which solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-sensor based integrated monitoring system for power cabinets, comprising a data acquisition module, a preprocessing module, an EMI feature extraction module, a fault diagnosis and prediction module, a real-time alarm control module, a data analysis module and a remote monitoring module;

[0006] The data acquisition module is used to collect various sensor data from the power cabinet and its surrounding environment, monitor the electromagnetic environment, equipment status and environmental parameters inside and outside the power cabinet in real time, and transmit the data to the data preprocessing module via wireless or wired network;

[0007] The preprocessing module is used to perform denoising on the raw sensor data through a signal filtering algorithm to remove interference noise, and to complete and correct the data using smoothing and interpolation methods;

[0008] The EMI feature extraction module is used to extract characteristic information related to electromagnetic interference (EMI) from the processed data, including electric field strength, magnetic field strength, spectrum distribution and interference fluctuation parameters, and form a characteristic vector of the electromagnetic environment. The electromagnetic environment index (EMI) is calculated through a machine learning algorithm. index ;

[0009] The fault diagnosis and prediction module is used to perform status assessment and fault prediction on the equipment in the power cabinet based on machine learning, combined with real-time collected electromagnetic interference data, electrical parameters and environmental data, to provide early warning of fault types and give maintenance recommendations;

[0010] The real-time alarm control module is used to automatically trigger a real-time alarm signal and start the system adaptive control mechanism through an intelligent judgment mechanism when the electromagnetic interference intensity exceeds the standard or equipment failure occurs;

[0011] The data analysis module is used to combine the electromagnetic environment data inside and outside the power cabinet, generate an electromagnetic environment index and equipment status report through data mining and trend analysis technology, and propose power cabinet optimization suggestions, including equipment configuration and layout adjustments;

[0012] The remote monitoring module is used to remotely monitor the electromagnetic environment, equipment status and alarm information of the power cabinet through a cloud platform or a remote management system, supporting remote fault diagnosis, equipment maintenance and optimization adjustment.

[0013] Preferably, the data acquisition module includes an electromagnetic interference data acquisition unit, an equipment status data acquisition unit and an environmental parameter data acquisition unit;

[0014] The electromagnetic interference data acquisition unit is used to monitor the electromagnetic interference situation in real time inside and outside the power cabinet by deploying electromagnetic field sensors including electric field and magnetic field sensors, collect electromagnetic wave intensity, spectrum and electric field intensity parameters, and obtain the electric field intensity value E f , magnetic field strength value H f And the interference power value P emi ,The sensor collects data regularly according to the preset range and transmits the collected results to the data preprocessing module in the form of digital signals;

[0015] The device status data acquisition unit is used to collect the operating status data of the equipment inside the power cabinet through current, voltage and temperature sensors, monitor electrical parameters, including current, voltage fluctuations and device temperature, and obtain the current fluctuation amplitude value I v , voltage fluctuation amplitude value U v and the device temperature change rate T dev and transmit the data to the pre-processing module for processing;

[0016] The environmental parameter data acquisition unit is used to monitor the impact of external environmental factors of the power cabinet on electromagnetic interference through environmental sensors, including temperature and humidity sensors, air pressure sensors and distance sensors, and obtain the ambient temperature value T env , Ambient humidity value H env and interference distance value P dist .

[0017] Preferably, the preprocessing module includes a data preprocessing unit;

[0018] The data preprocessing unit is used to filter the raw data using a low-pass filter and a Kalman filter algorithm to remove high-frequency noise and interference in the sensor signal, smooth the signal using a smoothing algorithm, a moving average method, and an exponentially weighted average method to eliminate signal fluctuations caused by equipment errors or acquisition gaps, and use a linear interpolation algorithm to complete missing or incomplete sensor data.

[0019] Preferably, the EMI feature extraction module includes an electromagnetic fluctuation amplitude extraction unit and an electromagnetic environment index calculation unit;

[0020] The electromagnetic fluctuation amplitude extraction unit is used to convert the time domain signal into the frequency domain signal using the spectrum analysis method of Fast Fourier Transform (FFT), extract the distribution of electric and magnetic field intensities in different frequency bands, analyze the spectrum characteristics of electromagnetic interference, and extract the fluctuation amplitude and change rate parameters of the electromagnetic wave from the data collected by the electromagnetic field sensor to reflect the intensity and dynamic change trend of the electromagnetic interference around the power cabinet;

[0021] The electromagnetic environment index calculation unit is used to combine the extracted electromagnetic interference features and perform modeling through machine learning algorithms, including support vector machines and random forests, to calculate the electromagnetic interference intensity coefficient K emi , electrical parameter stability coefficient K elec , Environmental interference influence coefficient K env and electromagnetic environment index EMI index .

[0022] The electromagnetic interference intensity coefficient K emi Calculate by the following formula

[0023]

[0024] Where, E f,i represents the electric field intensity at the i-th collection point, H f,i represents the magnetic field intensity at the i-th acquisition point, P emi,i represents the interference power of the i-th collection point, and n represents the total number of electromagnetic interference collection points;

[0025] The electrical parameter stability coefficient Kelec Calculate by the following formula

[0026]

[0027] Where, I v,j Indicates the current fluctuation amplitude at the jth time point, U v,j Indicates the voltage fluctuation amplitude at the jth time point, T dev,j represents the device temperature change rate at the jth time point, and m represents the total number of acquisition time points;

[0028] The environmental interference influence coefficient K env Calculate by the following formula

[0029]

[0030] Where, T env,k represents the ambient temperature at the kth position, H env,k represents the ambient humidity at the kth position, P dist,k represents the distance from the interference source at the kth position to the power cabinet, and p represents the total number of environmental collection locations;

[0031] The electromagnetic environment index EMI index Calculate by the following formula

[0032] EMI index =K emi ×K elec ×K env ;

[0033] Where K emi Indicates the electromagnetic interference intensity coefficient, K elec Indicates the electrical parameter stability coefficient, K env Represents the environmental interference influence coefficient.

[0034] Preferably, the fault diagnosis and prediction module includes a fault data analysis unit, a machine learning diagnosis unit, and a maintenance suggestion generation unit;

[0035] The fault data analysis unit is used to identify potential fault signs in the operation of the power cabinet by analyzing the relationship between electromagnetic interference inside and outside the power cabinet, equipment electrical parameters and environmental data, and perform preliminary data analysis to make a preliminary assessment of the equipment status;

[0036] The machine learning diagnosis unit is used to train and predict the failure mode of the power cabinet by using machine learning models including decision trees, neural networks and support vector machines, combining historical data and real-time monitoring data, and using the electromagnetic environment index EMI indexCompare the result with the preset first qualified threshold T and the preset second qualified threshold B to evaluate the status of the equipment and predict the occurrence of potential failures;

[0037] The maintenance suggestion generating unit is used to automatically generate maintenance suggestions based on the fault diagnosis and prediction results, combined with the maintenance history and operating status of the equipment. The suggestions include equipment maintenance, repair or replacement measures to help operators perform troubleshooting and maintenance work;

[0038] The electromagnetic environment index EMI index Compare with the preset first qualified threshold T and the preset second qualified threshold B to obtain a status assessment plan;

[0039] When EMI index When ≤T, the electromagnetic environment is within the safe and qualified range, indicating that the electromagnetic interference intensity inside and outside the power cabinet has no impact on equipment operation;

[0040] When T <EMI index When ≤B, the electromagnetic interference intensity has exceeded the standard by 30%, affecting the stability of equipment operation but not causing obvious faults;

[0041] When EMI index >B, it indicates that the electromagnetic interference intensity has exceeded the standard by more than 30%, causing equipment failure or unstable system operation, and emergency measures must be taken immediately.

[0042] Preferably, the real-time alarm control module includes a real-time alarm generating unit and an intelligent judgment control unit;

[0043] The real-time alarm generation unit is used to monitor the status of the power cabinet in real time based on the electromagnetic interference intensity and equipment failure, and trigger an alarm signal, which notifies the operator through visual, audible or other forms to promptly reflect abnormal conditions;

[0044] The intelligent judgment control unit is used to intelligently judge and trigger corresponding control operations based on monitored abnormal conditions, including excessive electromagnetic interference intensity and equipment failure, including switching power supplies and activating backup equipment. According to the system's intelligent judgment results, emergency response operations are automatically executed, including adjusting loads, switching circuits, and activating backup power supplies.

[0045] Preferably, the data analysis module includes a trend analysis unit;

[0046] The trend analysis unit is used to use data mining technology to conduct in-depth analysis of historical data, identify potential regularities and trends in the operation of the power cabinet, help predict future changes in the electromagnetic environment and equipment operating conditions, perform time series analysis based on historical monitoring data, and discover long-term change trends in the electromagnetic environment and equipment status inside and outside the power cabinet. Through a comprehensive evaluation of the data mining and trend analysis results, power cabinet optimization suggestions are generated, including equipment configuration, environmental adjustment and layout optimization.

[0047] Preferably, the remote monitoring module includes a remote data monitoring unit and a remote fault diagnosis unit;

[0048] The remote data monitoring unit is used to receive data through a cloud platform or remote management system, including electromagnetic environment data inside and outside the power cabinet and equipment operating status, receive alarm information in real time, and transmit this data to the remote management platform via the network for real-time monitoring. It supports displaying the real-time status of the power cabinet through a visual interface and generates real-time electromagnetic environment maps and equipment operation maps based on different data parameters to facilitate observation and diagnosis by remote operators;

[0049] The remote fault diagnosis unit is used to provide fault diagnosis, equipment maintenance and optimization adjustment functions based on the remote platform. When the electromagnetic interference in the power cabinet exceeds the standard or the equipment fails, the remote platform automatically analyzes the status inside the power cabinet through an algorithm, identifies potential faults, and provides a fault diagnosis report. The remote operator makes remote adjustments through the system based on the diagnosis results, including modifying the power cabinet configuration, adjusting the equipment working mode or starting the adaptive control mechanism. It supports the execution of equipment maintenance operations through remote commands. If an unresolvable fault occurs, it triggers a maintenance team dispatch signal.

[0050] The comprehensive monitoring method of the power cabinet based on multiple sensors includes the following steps:

[0051] Step 1: Collect various sensor data from the power cabinet and its surrounding environment, monitor the electromagnetic environment, equipment status, and environmental parameters inside and outside the power cabinet in real time, and transmit the data to the data preprocessing module via wireless or wired network;

[0052] Step 2: De-noise the raw sensor data using a signal filtering algorithm to remove interference noise, and use smoothing and interpolation methods to complete and correct the data;

[0053] Step 3: Extract characteristic information related to electromagnetic interference (EMI) from the processed data, including electric field strength, magnetic field strength, spectrum distribution, and interference fluctuation parameters, and form a characteristic vector of the electromagnetic environment. Calculate the electromagnetic environment index (EMI) through machine learning algorithms. index ;

[0054] Step 4: Based on machine learning and combining real-time collected electromagnetic interference data, electrical parameters, and environmental data, the system performs status assessment and fault prediction on the equipment in the power cabinet, providing early warning of fault types and maintenance recommendations.

[0055] Step 5: When the electromagnetic interference intensity exceeds the standard or equipment failure occurs, the intelligent judgment mechanism automatically triggers a real-time alarm signal and activates the system's adaptive control mechanism;

[0056] Step 6: Combine the electromagnetic environment data inside and outside the power cabinet and generate an electromagnetic environment index and equipment status report through data mining and trend analysis technology, and propose power cabinet optimization suggestions, including equipment configuration and layout adjustments;

[0057] Step 7: Remotely monitor the electromagnetic environment, equipment status, and alarm information of the power cabinet through a cloud platform or remote management system to support remote fault diagnosis, equipment maintenance, and optimization adjustments.

[0058] A power distribution cabinet, comprising the following contents:

[0059] Electromagnetic interference sensor: installed at key locations inside and outside the power distribution cabinet body, used to detect the electromagnetic interference (EMI) in the power cabinet and its surrounding environment in real time. When the electromagnetic interference intensity exceeds the preset threshold, an alarm signal is triggered. The sensor uses precise electric field strength, magnetic field strength and interference power sensors to collect electromagnetic environment data and transmit the data to the data pre-processing module via wireless or wired network;

[0060] Equipment status monitor: Installed inside the power distribution cabinet, it monitors the operating status of the equipment inside the power cabinet in real time, including key electrical parameters such as current, voltage, temperature, and humidity. When it detects abnormal equipment operation, such as current fluctuations and temperature exceeding the standard, it triggers a corresponding fault alarm signal, which is provided to the fault diagnosis module for analysis;

[0061] Environmental parameter sensor: installed on the outside of the power distribution cabinet body, used to detect changes in the environment where the power distribution cabinet is located, including temperature, humidity and air pressure environmental data;

[0062] The present invention provides a multi-sensor based comprehensive monitoring method and system for power cabinets and a power distribution cabinet, which has the following beneficial effects:

[0063] (1) When the system is running, various sensor data are collected from the power cabinet and its surrounding environment, and the electromagnetic environment, equipment status and environmental parameters inside and outside the power cabinet are monitored in real time. The original sensor data is pre-processed through the signal filtering algorithm, and the characteristic information related to electromagnetic interference (EMI) is extracted from the processed data. The electromagnetic environment index (EMI) is calculated through the machine learning algorithm. index, conduct status assessment and fault prediction of equipment in the power cabinet, provide early warning of fault types and give maintenance suggestions. When the electromagnetic interference intensity exceeds the standard or equipment failure occurs, the intelligent judgment mechanism will automatically trigger a real-time alarm signal, and start the system adaptive control mechanism to generate an electromagnetic environment index and equipment status report to remotely monitor the power cabinet.

[0064] (2) The multi-sensor-based integrated monitoring system for power cabinets achieves comprehensive monitoring of the inside and outside of the power cabinet by integrating seven modules: data acquisition, preprocessing, feature extraction, fault diagnosis, real-time alarm, data analysis, and remote monitoring. The greatest advantage of this system is that through the collaborative work of multiple sensors, it can simultaneously obtain multi-dimensional information such as electrical parameters, electromagnetic environment, and equipment status, thereby improving the comprehensive perception of the operating status of the power cabinet. By collecting data on electromagnetic interference, electrical faults, and environmental changes in real time, the system can predict and warn of potential faults before problems occur, providing more reliable protection for the safe operation of the power cabinet.

[0065] (3) Compared with the existing traditional power cabinet monitoring system, the integrated monitoring system based on multiple sensors has significantly improved the intelligence level of the system. Traditional systems mostly rely on a single sensor or only rely on the monitoring of electrical parameters, making it difficult to fully understand the actual operation of the power cabinet. However, through electromagnetic interference monitoring and real-time acquisition of equipment status, the system can not only detect electrical equipment failures in a timely manner, but also monitor external electromagnetic interference and reduce equipment failures caused by the deterioration of the electromagnetic environment. The system monitors the electromagnetic environment index EMI index The comprehensive assessment of the equipment status can send out early warning signals before a fault occurs, preventing equipment from being damaged by external factors such as electromagnetic interference, thereby improving the stability and safety of the power cabinet.

[0066] (4) This system combines data analysis with remote monitoring modules to achieve remote fault diagnosis, equipment maintenance and optimization adjustments, greatly improving the maintenance efficiency of the power cabinet. Through the cloud platform or remote management system, operators can obtain the operating data and alarm information of the power cabinet in real time, and perform fault troubleshooting and remote optimization adjustments at any time. This remote monitoring capability not only improves the response speed, but also reduces the frequency and cost of on-site inspections. Compared with current traditional methods, it provides a more efficient and convenient maintenance method. Overall, the multi-sensor based integrated monitoring system for power cabinets has brought significant technological breakthroughs in fault prediction, environmental monitoring and remote management of power cabinets, effectively improving the safety, reliability and intelligence level of power cabinets. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a block diagram of the multi-sensor based power cabinet integrated monitoring system of the present invention;

[0068] Figure 2 Schematic diagram of the steps of the comprehensive monitoring method for power cabinets based on multiple sensors of the present invention. DETAILED DESCRIPTION

[0069] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0070] Example 1

[0071] The present invention provides a comprehensive monitoring system for power cabinets based on multiple sensors. Figure 1 , including data acquisition module, preprocessing module, EMI feature extraction module, fault diagnosis and prediction module, real-time alarm control module, data analysis module and remote monitoring module;

[0072] The data acquisition module is used to collect various sensor data from the power cabinet and its surrounding environment, monitor the electromagnetic environment, equipment status and environmental parameters inside and outside the power cabinet in real time, and transmit the data to the data preprocessing module via wireless or wired network;

[0073] The preprocessing module is used to perform denoising on the raw sensor data through a signal filtering algorithm to remove interference noise, and to complete and correct the data using smoothing and interpolation methods;

[0074] The EMI feature extraction module is used to extract characteristic information related to electromagnetic interference (EMI) from the processed data, including electric field strength, magnetic field strength, spectrum distribution and interference fluctuation parameters, and form a characteristic vector of the electromagnetic environment. The electromagnetic environment index (EMI) is calculated through a machine learning algorithm. index ;

[0075] The fault diagnosis and prediction module is used to perform status assessment and fault prediction on the equipment in the power cabinet based on machine learning, combined with real-time collected electromagnetic interference data, electrical parameters and environmental data, to provide early warning of fault types and give maintenance recommendations;

[0076] The real-time alarm control module is used to automatically trigger a real-time alarm signal and start the system adaptive control mechanism through an intelligent judgment mechanism when the electromagnetic interference intensity exceeds the standard or equipment failure occurs;

[0077] The data analysis module is used to combine the electromagnetic environment data inside and outside the power cabinet, generate an electromagnetic environment index and equipment status report through data mining and trend analysis technology, and propose power cabinet optimization suggestions, including equipment configuration and layout adjustments;

[0078] The remote monitoring module is used to remotely monitor the electromagnetic environment, equipment status and alarm information of the power cabinet through a cloud platform or a remote management system, supporting remote fault diagnosis, equipment maintenance and optimization adjustment.

[0079] In this embodiment, various sensor data are collected from the power cabinet and its surrounding environment to monitor the electromagnetic environment, equipment status, and environmental parameters inside and outside the power cabinet in real time. The raw sensor data is preprocessed using a signal filtering algorithm, and characteristic information related to electromagnetic interference (EMI) is extracted from the processed data to form a characteristic vector of the electromagnetic environment. The electromagnetic environment index (EMI) is calculated using a machine learning algorithm. index , conduct status assessment and fault prediction of equipment in the power cabinet, provide early warning of fault types and give maintenance suggestions. When the electromagnetic interference intensity exceeds the standard or equipment failure occurs, the intelligent judgment mechanism automatically triggers a real-time alarm signal and starts the system adaptive control mechanism. Combined with the electromagnetic environment data inside and outside the power cabinet, through data mining and trend analysis technology, it generates electromagnetic environment index and equipment status report, and puts forward power cabinet optimization suggestions, including equipment configuration and layout adjustment. Remote monitoring of the electromagnetic environment, equipment status and alarm information of the power cabinet is carried out through the cloud platform or remote management system, supporting remote fault diagnosis, equipment maintenance and optimization adjustment.

[0080] Example 2

[0081] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data acquisition module includes an electromagnetic interference data acquisition unit, a device status data acquisition unit and an environmental parameter data acquisition unit;

[0082] The electromagnetic interference data acquisition unit is used to monitor the electromagnetic interference situation in real time inside and outside the power cabinet by deploying electromagnetic field sensors including electric field and magnetic field sensors, collect electromagnetic wave intensity, spectrum and electric field intensity parameters, and obtain the electric field intensity value E f , magnetic field strength value H f And the interference power value P emi ,The sensor collects data regularly according to the preset range and transmits the collected results to the data preprocessing module in the form of digital signals;

[0083] The device status data acquisition unit is used to collect the operating status data of the equipment inside the power cabinet through current, voltage and temperature sensors, monitor electrical parameters, including current, voltage fluctuations and device temperature, and obtain the current fluctuation amplitude value I v , voltage fluctuation amplitude value U v and the device temperature change rate T dev and transmit the data to the pre-processing module for processing;

[0084] The environmental parameter data acquisition unit is used to monitor the impact of external environmental factors of the power cabinet on electromagnetic interference through environmental sensors, including temperature and humidity sensors, air pressure sensors and distance sensors, and obtain the ambient temperature value T env , Ambient humidity value H env and interference distance value P dist .

[0085] The preprocessing module includes a data preprocessing unit;

[0086] The data preprocessing unit is used to filter the raw data using a low-pass filter and a Kalman filter algorithm to remove high-frequency noise and interference in the sensor signal, smooth the signal using a smoothing algorithm, a moving average method, and an exponentially weighted average method to eliminate signal fluctuations caused by equipment errors or acquisition gaps, and use a linear interpolation algorithm to complete missing or incomplete sensor data.

[0087] The EMI feature extraction module includes an electromagnetic fluctuation amplitude extraction unit and an electromagnetic environment index calculation unit;

[0088] The electromagnetic fluctuation amplitude extraction unit is used to convert the time domain signal into the frequency domain signal using the spectrum analysis method of Fast Fourier Transform (FFT), extract the distribution of electric and magnetic field intensities in different frequency bands, analyze the spectrum characteristics of electromagnetic interference, and extract the fluctuation amplitude and change rate parameters of the electromagnetic wave from the data collected by the electromagnetic field sensor to reflect the intensity and dynamic change trend of the electromagnetic interference around the power cabinet;

[0089] The electromagnetic environment index calculation unit is used to combine the extracted electromagnetic interference features and perform modeling through machine learning algorithms, including support vector machines and random forests, to calculate the electromagnetic interference intensity coefficient K emi , electrical parameter stability coefficient K elec , Environmental interference influence coefficient K env and electromagnetic environment index EMI index .

[0090] The electromagnetic interference intensity coefficient K emi Calculate by the following formula

[0091]

[0092] Where, E f,i represents the electric field intensity at the i-th collection point, H f,i represents the magnetic field intensity at the i-th acquisition point, P emi,i represents the interference power of the i-th collection point, and n represents the total number of electromagnetic interference collection points;

[0093] The electrical parameter stability coefficient Kelec Calculate by the following formula

[0094]

[0095] Where, I v,j Indicates the current fluctuation amplitude at the jth time point, U v,j Indicates the voltage fluctuation amplitude at the jth time point, T dev,j represents the device temperature change rate at the jth time point, and m represents the total number of acquisition time points;

[0096] The environmental interference influence coefficient K env Calculate by the following formula

[0097]

[0098] Where, T env,k represents the ambient temperature at the kth position, H env,k represents the ambient humidity at the kth position, P dist,k represents the distance from the interference source at the kth position to the power cabinet, and p represents the total number of environmental collection locations;

[0099] The electromagnetic environment index EMI index Calculate by the following formula

[0100] EMI index =K emi ×K elec ×K env ;

[0101] Where K emi Indicates the electromagnetic interference intensity coefficient, K elec Indicates the electrical parameter stability coefficient, K env Represents the environmental interference influence coefficient.

[0102] In this embodiment, comprehensive monitoring of the electromagnetic environment, electrical status, and external environment inside and outside the power cabinet can be achieved. The integration of the electromagnetic interference data acquisition unit, the equipment status data acquisition unit, and the environmental parameter data acquisition unit enables the system to accurately identify and assess potential problems in the power cabinet and its environment while acquiring multi-dimensional data. The signal filtering and completion processing of the data preprocessing module ensures the quality and integrity of the data, further improving the accuracy of subsequent analysis. The EMI feature extraction module generates an electromagnetic environment index by accurately analyzing the electromagnetic fluctuation amplitude and spectrum, providing a scientific basis for system fault diagnosis and optimized design, significantly improving the safety and reliability of the power cabinet.

[0103] Example 3

[0104] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the fault diagnosis and prediction module includes a fault data analysis unit, a machine learning diagnosis unit and a maintenance suggestion generation unit;

[0105] The fault data analysis unit is used to identify potential fault signs in the operation of the power cabinet by analyzing the relationship between electromagnetic interference inside and outside the power cabinet, equipment electrical parameters and environmental data, and perform preliminary data analysis to make a preliminary assessment of the equipment status;

[0106] The machine learning diagnosis unit is used to train and predict the failure mode of the power cabinet by using machine learning models including decision trees, neural networks and support vector machines, combining historical data and real-time monitoring data, and using the electromagnetic environment index EMI index Compare the result with the preset first qualified threshold T and the preset second qualified threshold B to evaluate the status of the equipment and predict the occurrence of potential failures;

[0107] The maintenance suggestion generating unit is used to automatically generate maintenance suggestions based on the fault diagnosis and prediction results, combined with the maintenance history and operating status of the equipment. The suggestions include equipment maintenance, repair or replacement measures to help operators perform troubleshooting and maintenance work;

[0108] The electromagnetic environment index EMI index Comparing with a preset first qualified threshold T and a preset second qualified threshold B to obtain a status assessment solution, including:

[0109] When EMI index When ≤T, the electromagnetic environment is within the safe and qualified range, indicating that the electromagnetic interference intensity inside and outside the power cabinet has no impact on the operation of the equipment. The data acquisition module collects and records the electromagnetic environment data every 10 minutes to ensure the stability of the monitoring data. The real-time alarm control module displays the "electromagnetic environment normal operation" status information, and the remote monitoring module can send monitoring reports to the operator;

[0110] When T <EMI index When the value is ≤B, the electromagnetic interference intensity has exceeded the standard by 30%, affecting the operational stability of the equipment but not causing any obvious faults. The data acquisition module shortens the sampling interval from 10 minutes to 2 minutes, increases the data acquisition frequency, tracks the dynamic changes of electromagnetic interference in real time, activates the forced heat dissipation device (such as a cooling fan or ventilation system), reduces the temperature inside the power cabinet, and adjusts the humidity around the power cabinet to a reasonable range (40% to 60%) to reduce electromagnetic interference coupling caused by humidity.

[0111] When EMI indexWhen the value is >B, the electromagnetic interference intensity has exceeded the standard by more than 30%, causing equipment failure or unstable system operation. Immediate emergency measures are required. The fault diagnosis and prediction module identifies the primary source of electromagnetic interference (such as poor cable contact, current surge sources, or external electromagnetic interference sources). A switch is automatically triggered to disconnect the specific interference source circuit or switch critical equipment to a backup power source to prevent further damage. Electromagnetic shielding devices are activated, and metal shielding or conductive coatings are added inside the power cabinet to reduce electromagnetic radiation coupling. High-interference devices outside the power cabinet are physically isolated, increasing the distance between devices to a minimum of 2 meters and adding shielding devices near the interference source. High-power cooling devices are forced to activate to quickly reduce the internal temperature of the power cabinet to below 25°C. The real-time alarm control module triggers an emergency alarm, notifying operations and maintenance personnel. The remote monitoring module then pushes real-time data and fault diagnosis reports to the cloud platform.

[0112] The real-time alarm control module includes a real-time alarm generation unit and an intelligent judgment control unit;

[0113] The real-time alarm generation unit is used to monitor the status of the power cabinet in real time based on the electromagnetic interference intensity and equipment failure, and trigger an alarm signal, which notifies the operator through visual, audible or other forms to promptly reflect abnormal conditions;

[0114] The intelligent judgment control unit is used to intelligently judge and trigger corresponding control operations based on monitored abnormal conditions, including excessive electromagnetic interference intensity and equipment failure, including switching power supplies and activating backup equipment. According to the system's intelligent judgment results, emergency response operations are automatically executed, including adjusting loads, switching circuits, and activating backup power supplies.

[0115] The data analysis module includes a trend analysis unit;

[0116] The trend analysis unit is used to use data mining technology to conduct in-depth analysis of historical data, identify potential regularities and trends in the operation of the power cabinet, help predict future changes in the electromagnetic environment and equipment operating conditions, perform time series analysis based on historical monitoring data, and discover long-term change trends in the electromagnetic environment and equipment status inside and outside the power cabinet. Through a comprehensive evaluation of the data mining and trend analysis results, power cabinet optimization suggestions are generated, including equipment configuration, environmental adjustment and layout optimization.

[0117] In this embodiment, by analyzing the relationship between electromagnetic interference inside and outside the power cabinet, equipment electrical parameters and environmental data, the unit can identify potential fault signs in the power cabinet operation. Through preliminary data analysis, it provides a basis for subsequent equipment status assessment. It uses multiple machine learning algorithms, including decision trees, neural networks and support vector machines (SVM), based on historical data and real-time data training models, combined with the electromagnetic environment index EMIindex By comparing the device's condition with pre-set acceptable thresholds (T and B), the system predicts the type and timing of potential equipment failures, provides accurate equipment status assessments, and generates personalized maintenance recommendations, including maintenance, repair, or replacement measures. By integrating the device's maintenance history with its current operating status, the system helps operators quickly identify problems and take appropriate repair measures.

[0118] Example 4

[0119] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the remote monitoring module includes a remote data monitoring unit and a remote fault diagnosis unit;

[0120] The remote data monitoring unit is used to receive data through a cloud platform or remote management system, including electromagnetic environment data inside and outside the power cabinet and equipment operating status, receive alarm information in real time, and transmit this data to the remote management platform via the network for real-time monitoring. It supports displaying the real-time status of the power cabinet through a visual interface and generates real-time electromagnetic environment maps and equipment operation maps based on different data parameters to facilitate observation and diagnosis by remote operators;

[0121] The remote fault diagnosis unit is used to provide fault diagnosis, equipment maintenance and optimization adjustment functions based on the remote platform. When the electromagnetic interference in the power cabinet exceeds the standard or the equipment fails, the remote platform automatically analyzes the status inside the power cabinet through an algorithm, identifies potential faults, and provides a fault diagnosis report. The remote operator makes remote adjustments through the system based on the diagnosis results, including modifying the power cabinet configuration, adjusting the equipment working mode or starting the adaptive control mechanism. It supports the execution of equipment maintenance operations through remote commands. If an unresolvable fault occurs, it triggers a maintenance team dispatch signal.

[0122] In this embodiment, a cloud platform or remote management system receives real-time electromagnetic environment data and equipment operating status information from inside and outside the power cabinet, and transmits alarm information to the remote platform, enabling real-time monitoring. Through a visual interface, remote operators can clearly view the real-time status of the power cabinet and generate electromagnetic environment and equipment operation maps. This allows monitoring personnel to understand the health of the equipment and changes in the surrounding environment without having to be present on-site. The data and charts displayed allow operators to intuitively identify potential problems and quickly take appropriate measures, reducing delays in on-site fault diagnosis and improving work efficiency. An automated algorithm analyzes the status within the power cabinet, identifies potential faults, and generates a fault diagnosis report. This feature not only saves time for manual inspection but also enables rapid response to excessive electromagnetic interference or other abnormalities during equipment operation, providing accurate fault diagnosis. Based on the diagnostic results, remote operators can make real-time adjustments, including modifying device configurations, adjusting operating modes, or activating adaptive control mechanisms. This remote control capability greatly enhances the flexibility of equipment management, enabling problems to be resolved in the shortest possible time, and reducing the frequency and cost of on-site maintenance. By supporting remote command execution of equipment maintenance operations, especially when problems cannot be resolved through system adaptive control, a dispatch signal can be triggered to dispatch the maintenance team and arrange on-site repairs in a timely manner. This not only shortens fault response time but also ensures efficient and orderly troubleshooting, avoiding excessive equipment downtime. Furthermore, remote maintenance supports optimizing equipment operating modes and adjusting device settings in advance to avoid potential faults, thereby improving the overall stability and reliability of the power cabinet and enhancing the operational efficiency and safety of the system.

[0123] Example 5

[0124] For a comprehensive monitoring method of power cabinets based on multiple sensors, please refer to Figure 2 , specifically: including the following steps:

[0125] Step 1: Collect various sensor data from the power cabinet and its surrounding environment, monitor the electromagnetic environment, equipment status, and environmental parameters inside and outside the power cabinet in real time, and transmit the data to the data preprocessing module via wireless or wired network;

[0126] Step 2: De-noise the raw sensor data using a signal filtering algorithm to remove interference noise, and use smoothing and interpolation methods to complete and correct the data;

[0127] Step 3: Extract characteristic information related to electromagnetic interference (EMI) from the processed data, including electric field strength, magnetic field strength, spectrum distribution, and interference fluctuation parameters, and form a characteristic vector of the electromagnetic environment. Calculate the electromagnetic environment index (EMI) through machine learning algorithms. index ;

[0128] Step 4: Based on machine learning and combining real-time collected electromagnetic interference data, electrical parameters, and environmental data, the system performs status assessment and fault prediction on the equipment in the power cabinet, providing early warning of fault types and maintenance recommendations.

[0129] Step 5: When the electromagnetic interference intensity exceeds the standard or equipment failure occurs, the intelligent judgment mechanism automatically triggers a real-time alarm signal and activates the system's adaptive control mechanism;

[0130] Step 6: Combine the electromagnetic environment data inside and outside the power cabinet and generate an electromagnetic environment index and equipment status report through data mining and trend analysis technology, and propose power cabinet optimization suggestions, including equipment configuration and layout adjustments;

[0131] Step 7: Remotely monitor the electromagnetic environment, equipment status, and alarm information of the power cabinet through a cloud platform or remote management system to support remote fault diagnosis, equipment maintenance, and optimization adjustments.

[0132] In this embodiment, through the sensor data acquisition process in step one, the system can monitor the electromagnetic environment, equipment status, and environmental parameters inside and outside the power cabinet in real time, ensuring comprehensive monitoring of the power cabinet's operating conditions from multiple angles. The data is quickly transmitted to the data preprocessing module through a wireless or wired network, effectively reducing the frequency of on-site inspections and improving monitoring efficiency. Step two uses signal filtering algorithms and data completion methods to ensure that the original sensor data can provide high-quality input data after noise removal, smoothing, and interpolation completion. Through the electromagnetic interference feature extraction in step three, the system can accurately calculate the electromagnetic environment index EMI index This provides an accurate basis for power cabinet status assessment and fault prediction, reducing false alarms caused by environmental factors, equipment failures, or interference. The machine learning diagnostic and intelligent judgment mechanisms in steps 4 and 5 enable real-time status assessment and fault warning of power cabinet equipment. When electromagnetic interference intensity exceeds the standard or equipment failure occurs, the system automatically triggers an alarm signal and activates an adaptive control mechanism to quickly implement countermeasures, reducing reliance on human intervention, improving fault response speed, and providing early warning before equipment failure occurs. Step 6, using data mining and trend analysis techniques, combines electromagnetic environment data inside and outside the power cabinet to generate an electromagnetic environment index and equipment status report, providing optimization recommendations for equipment configuration and layout adjustments. This data-based optimization capability continuously improves the operational efficiency and stability of the power cabinet. Finally, through remote monitoring and fault diagnosis in step 7, remote managers can conduct comprehensive monitoring, fault diagnosis, equipment maintenance, and optimization adjustments of the power cabinet through a cloud platform or remote management system, avoiding on-site maintenance delays and improving equipment operational efficiency and maintenance response speed.

[0133] Example 6

[0134] A power distribution cabinet, comprising the following contents:

[0135] Electromagnetic interference sensor: installed at key locations inside and outside the power distribution cabinet body, used to detect the electromagnetic interference (EMI) in the power cabinet and its surrounding environment in real time. When the electromagnetic interference intensity exceeds the preset threshold, an alarm signal is triggered. The sensor uses precise electric field strength, magnetic field strength and interference power sensors to collect electromagnetic environment data and transmit the data to the data pre-processing module via wireless or wired network;

[0136] Equipment status monitor: Installed inside the power distribution cabinet, it monitors the operating status of the equipment inside the power cabinet in real time, including key electrical parameters such as current, voltage, temperature, and humidity. When it detects abnormal equipment operation, such as current fluctuations and temperature exceeding the standard, it triggers a corresponding fault alarm signal, which is provided to the fault diagnosis module for analysis;

[0137] Environmental parameter sensor: installed on the outside of the power distribution cabinet body, used to detect changes in the environment where the power distribution cabinet is located, including temperature, humidity and air pressure environmental data;

[0138] Enclosure design: The distribution cabinet body should be made of durable steel or aluminum alloy with a protection grade of IP54 or above;

[0139] Size and modularity: The distribution cabinet has sufficient space to accommodate various sensors, electrical equipment and monitoring system modules;

[0140] Heat dissipation design: The power distribution cabinet has ventilation and heat dissipation design and is equipped with fans or air conditioning modules to maintain a suitable operating temperature and prevent equipment from overheating.

[0141] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive monitoring system for power cabinets based on multiple sensors, characterized in that: It includes data acquisition module, preprocessing module, EMI feature extraction module, fault diagnosis and prediction module, real-time alarm control module, data analysis module and remote monitoring module; The data acquisition module is used to collect various sensor data from the power cabinet and its surrounding environment, monitor the electromagnetic environment, equipment status and environmental parameters inside and outside the power cabinet in real time, and transmit the data to the data preprocessing module via wireless or wired network; The preprocessing module is used to perform denoising on the raw sensor data through a signal filtering algorithm to remove interference noise, and to complete and correct the data using smoothing and interpolation methods; The EMI feature extraction module is used to extract characteristic information related to electromagnetic interference EMI from the processed data, including electric field strength, magnetic field strength, spectrum distribution and interference fluctuation parameters, and form a characteristic vector of the electromagnetic environment, and obtain the electromagnetic environment index through machine learning algorithm calculation. The EMI feature extraction module includes an electromagnetic fluctuation amplitude extraction unit and an electromagnetic environment index calculation unit. The electromagnetic fluctuation amplitude extraction unit is used to use the spectrum analysis method of fast Fourier transform FFT to convert the time domain signal into a frequency domain signal, extract the distribution of electric field and magnetic field strength in different frequency bands, analyze the spectrum characteristics of electromagnetic interference, and extract the fluctuation amplitude and change rate parameters of the electromagnetic wave from the data collected by the electromagnetic field sensor to reflect the intensity and dynamic change trend of the electromagnetic interference around the power cabinet. The electromagnetic environment index calculation unit is used to combine the extracted electromagnetic interference features and use machine learning algorithms, including support vector machine and random forest for modeling, to calculate: electromagnetic interference intensity coefficient, electrical parameter stability coefficient, environmental interference influence coefficient and electromagnetic environment index; The fault diagnosis and prediction module is used to perform status assessment and fault prediction on the equipment in the power cabinet based on machine learning, combined with real-time collected electromagnetic interference data, electrical parameters and environmental data, to provide early warning of fault types and give maintenance recommendations; The real-time alarm control module is used to automatically trigger a real-time alarm signal and start the system adaptive control mechanism through an intelligent judgment mechanism when the electromagnetic interference intensity exceeds the standard or equipment failure occurs; The data analysis module is used to combine the electromagnetic environment data inside and outside the power cabinet, generate an electromagnetic environment index and equipment status report through data mining and trend analysis technology, and propose power cabinet optimization suggestions, including equipment configuration and layout adjustments; The remote monitoring module is used to remotely monitor the electromagnetic environment, equipment status and alarm information of the power cabinet through a cloud platform or a remote management system, supporting remote fault diagnosis, equipment maintenance and optimization adjustment.

2. The multi-sensor based integrated monitoring system for power cabinets according to claim 1 is characterized in that: The data acquisition module includes an electromagnetic interference data acquisition unit, an equipment status data acquisition unit and an environmental parameter data acquisition unit; The electromagnetic interference data acquisition unit is used to monitor the electromagnetic interference situation in real time inside and outside the power cabinet by deploying electromagnetic field sensors including electric field and magnetic field sensors, collect electromagnetic wave intensity, spectrum and electric field intensity parameters, and obtain electric field intensity value, magnetic field intensity value and interference power value. The sensor regularly collects data according to a preset range and transmits the collection results in the form of digital signals to the data preprocessing module; The device status data acquisition unit is used to collect operating status data of the equipment inside the power cabinet through current, voltage, and temperature sensors, monitor electrical parameters, including current and voltage fluctuations and device temperature, obtain current fluctuation amplitude values, voltage fluctuation amplitude values, and device temperature change rate, and transmit the data to the preprocessing module for processing; The environmental parameter data acquisition unit is used to monitor the impact of external environmental factors of the power cabinet on electromagnetic interference through environmental sensors, including temperature and humidity sensors, air pressure sensors and distance sensors, and obtain environmental temperature values, environmental humidity values ​​and interference distance values.

3. The multi-sensor based integrated monitoring system for power cabinets according to claim 1, characterized in that: The preprocessing module includes a data preprocessing unit; The data preprocessing unit is used to filter the raw data using a low-pass filter and a Kalman filter algorithm to remove high-frequency noise and interference in the sensor signal, smooth the signal using a smoothing algorithm, a moving average method, and an exponentially weighted average method to eliminate signal fluctuations caused by equipment errors or acquisition gaps, and use a linear interpolation algorithm to complete missing or incomplete sensor data.

4. The multi-sensor based integrated monitoring system for power cabinets according to claim 1, characterized in that: The fault diagnosis and prediction module includes a fault data analysis unit, a machine learning diagnosis unit, and a maintenance suggestion generation unit; The fault data analysis unit is used to identify potential fault signs in the operation of the power cabinet by analyzing the relationship between electromagnetic interference inside and outside the power cabinet, equipment electrical parameters and environmental data, and perform preliminary data analysis to make a preliminary assessment of the equipment status; The machine learning diagnostic unit is used to use machine learning models including decision trees, neural networks, and support vector machines to train and predict the failure modes of the power cabinet, combine historical data and real-time monitoring data, compare the electromagnetic environment index with a preset first qualified threshold and a preset second qualified threshold, evaluate the status of the equipment, and predict the occurrence of potential failures; The maintenance suggestion generating unit is used to automatically generate maintenance suggestions based on the fault diagnosis and prediction results, combined with the maintenance history and operating status of the equipment. The suggestions include equipment maintenance, repair or replacement measures to help operators perform troubleshooting and maintenance work; The electromagnetic environment index EMI index Comparing with a preset first qualified threshold T and a preset second qualified threshold B to obtain a status assessment solution, including: When EMI index When ≤T, the electromagnetic environment is within the safe and qualified range, indicating that the electromagnetic interference intensity inside and outside the power cabinet has no impact on equipment operation; When T <EMI index When ≤B, the electromagnetic interference intensity has exceeded the standard by 30%, affecting the stability of equipment operation but not causing obvious faults; When EMI index >B, it indicates that the electromagnetic interference intensity has exceeded the standard by more than 30%, causing equipment failure or unstable system operation, and emergency measures must be taken immediately.

5. The multi-sensor based integrated monitoring system for power cabinets according to claim 1, characterized in that: The real-time alarm control module includes a real-time alarm generation unit and an intelligent judgment control unit; The real-time alarm generation unit is used to monitor the status of the power cabinet in real time based on the electromagnetic interference intensity and equipment failure, and trigger an alarm signal, which notifies the operator through visual, audible or other forms to promptly reflect abnormal conditions; The intelligent judgment control unit is used to intelligently judge and trigger corresponding control operations based on monitored abnormal conditions, including excessive electromagnetic interference intensity and equipment failure, including switching power supplies and activating backup equipment. According to the system's intelligent judgment results, emergency response operations are automatically executed, including adjusting loads, switching circuits, and activating backup power supplies.

6. The multi-sensor based integrated monitoring system for power cabinets according to claim 1, characterized in that: The data analysis module includes a trend analysis unit; The trend analysis unit is used to use data mining technology to conduct in-depth analysis of historical data, identify potential regularities and trends in the operation of the power cabinet, help predict future changes in the electromagnetic environment and equipment operating conditions, perform time series analysis based on historical monitoring data, and discover long-term change trends in the electromagnetic environment and equipment status inside and outside the power cabinet. Through a comprehensive evaluation of the data mining and trend analysis results, power cabinet optimization suggestions are generated, including equipment configuration, environmental adjustment and layout optimization.

7. The multi-sensor based integrated monitoring system for power cabinets according to claim 1, characterized in that: The remote monitoring module includes a remote data monitoring unit and a remote fault diagnosis unit; The remote data monitoring unit is used to receive data through a cloud platform or remote management system, including electromagnetic environment data inside and outside the power cabinet and equipment operating status, receive alarm information in real time, and transmit this data to the remote management platform via the network for real-time monitoring. It supports displaying the real-time status of the power cabinet through a visual interface and generates real-time electromagnetic environment maps and equipment operation maps based on different data parameters to facilitate observation and diagnosis by remote operators; The remote fault diagnosis unit is used to provide fault diagnosis, equipment maintenance and optimization adjustment functions based on the remote platform. When the electromagnetic interference in the power cabinet exceeds the standard or the equipment fails, the remote platform automatically analyzes the status inside the power cabinet through an algorithm, identifies potential faults, and provides a fault diagnosis report. The remote operator makes remote adjustments through the system based on the diagnosis results, including modifying the power cabinet configuration, adjusting the equipment working mode or starting the adaptive control mechanism. It supports the execution of equipment maintenance operations through remote commands. If an unresolvable fault occurs, it triggers a maintenance team dispatch signal.

8. A multi-sensor based integrated monitoring method for a power cabinet, applied to a multi-sensor based integrated monitoring system for a power cabinet according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: Collect various sensor data from the power cabinet and its surrounding environment, monitor the electromagnetic environment, equipment status, and environmental parameters inside and outside the power cabinet in real time, and transmit the data to the data preprocessing module via wireless or wired network; Step 2: De-noise the raw sensor data using a signal filtering algorithm to remove interference noise, and use smoothing and interpolation methods to complete and correct the data; Step 3: Extract characteristic information related to electromagnetic interference (EMI) from the processed data, including electric field strength, magnetic field strength, spectrum distribution, and interference fluctuation parameters, and form a characteristic vector of the electromagnetic environment. Calculate the electromagnetic environment index (EMIindex) using a machine learning algorithm. Step 4: Based on machine learning and combining real-time collected electromagnetic interference data, electrical parameters, and environmental data, the system performs status assessment and fault prediction on the equipment in the power cabinet, providing early warning of fault types and maintenance recommendations. Step 5: When the electromagnetic interference intensity exceeds the standard or equipment failure occurs, the intelligent judgment mechanism automatically triggers a real-time alarm signal and activates the system's adaptive control mechanism; Step 6: Combine the electromagnetic environment data inside and outside the power cabinet and generate an electromagnetic environment index and equipment status report through data mining and trend analysis technology, and propose power cabinet optimization suggestions, including equipment configuration and layout adjustments; Step 7: Remotely monitor the electromagnetic environment, equipment status, and alarm information of the power cabinet through a cloud platform or remote management system to support remote fault diagnosis, equipment maintenance, and optimization adjustments.

9. A power distribution cabinet, characterized in that: A multi-sensor integrated monitoring system for a power cabinet comprising: Electromagnetic interference sensors are installed at key locations inside and outside the power distribution cabinet body to detect the electromagnetic interference (EMI) in the power cabinet and its surrounding environment in real time. When the electromagnetic interference intensity exceeds a preset threshold, an alarm signal is triggered. The sensor uses precise electric field strength, magnetic field strength, and interference power sensors to collect electromagnetic environment data and transmit the data to the data preprocessing module via wireless or wired networks. The equipment status monitor is installed inside the power distribution cabinet body and monitors the operating status of the equipment in the power cabinet in real time, including key electrical parameters such as current, voltage, temperature and humidity. When abnormal equipment operation is detected, such as current fluctuation and temperature exceeding the standard, the corresponding fault alarm signal is triggered and provided to the fault diagnosis module for analysis; The environmental parameter sensor is installed outside the power distribution cabinet body and is used to detect changes in the environment where the power distribution cabinet is located, including temperature, humidity and air pressure environmental data.

Images