Switch cabinet quality comprehensive evaluation system and method based on Internet of Things technology

Abstract

The invention discloses a switch cabinet quality comprehensive evaluation system and method based on the Internet of Things technology, and particularly relates to the technical field of switch cabinets. Electromagnetic interference detection equipment is installed, the electromagnetic field intensity around a switch cabinet and the operation state of the equipment are monitored in real time, and electromagnetic field data and state data in different time periods are obtained; carrying out weighted calculation on the electromagnetic interference index to generate an overall electromagnetic interference index; the fuzzy logic is utilized to comprehensively analyze the electromagnetic interference index and the real-time state data of the switch cabinet, a health assessment report is generated, the health condition of the equipment is accurately displayed, the operation monitoring and maintenance optimization capability of the switch cabinet is effectively improved, potential faults can be predicted, and misjudgment can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of switch cabinets, and in particular to a switch cabinet quality comprehensive evaluation system and method based on Internet of Things technology. Background Art

[0002] The comprehensive evaluation of switchgear quality based on IoT technology refers to the use of IoT technology to monitor the status and performance of switchgear in power systems in real time, collect data and analyze it, so as to comprehensively evaluate and manage its operation quality. This method uses technical means such as sensors, data transmission, cloud computing and intelligent algorithms to collect and process the operating parameters of switchgear, such as temperature, humidity, vibration, current, voltage, etc., and then analyzes these data to determine the health status of the switchgear, predict possible failures, and optimize maintenance. The IoT platform relies on wireless signal transmission and sensor devices for remote monitoring and control. However, if the environment where the switchgear is located has strong electromagnetic interference, it may have a serious impact on sensors, communication networks, and even the electronic components of the switchgear itself. Strong electromagnetic fields may cause wireless signal interference, resulting in data transmission interruption or error, causing the data received by the monitoring platform to be distorted, resulting in incorrect status judgment. At the same time, data transmission interruption caused by electromagnetic interference will prevent the IoT platform from obtaining the real-time status data of the switchgear. If real-time data is missing, the system may rely on outdated or erroneous data for judgment, and then make incorrect assessments. For example, the system may rely on previous normal data and believe that the device is still in a healthy state, but in fact the device may have failed or overloaded. Summary of the invention

[0003] The purpose of the present invention is to provide a switch cabinet quality comprehensive evaluation system and method based on Internet of Things technology to solve the shortcomings of the background technology.

[0004] In order to achieve the above object, the present invention provides the following technical solution: a comprehensive evaluation method for switch cabinet quality based on Internet of Things technology, comprising the following steps: S1: Install electromagnetic interference detection equipment around the switch cabinet and at several communication nodes, and use the Internet of Things platform to monitor electromagnetic interference in real time, obtain electromagnetic field strength data in different time periods and real-time status data of the switch cabinet; S2: Determine the weight coefficient of the electromagnetic field strength data in different time periods, and calculate the overall electromagnetic interference index of the switch cabinet after weighted averaging the weight coefficients of the electromagnetic field strength data in different time periods; S3: After comprehensively analyzing the calculated overall electromagnetic interference index of the switch cabinet and the real-time status data of the switch cabinet using fuzzy logic, a comprehensive evaluation report is generated to display the health status of the switch cabinet.

[0005] Preferably, in S1, electromagnetic field strength data and real-time status data of the switch cabinet in different time periods are obtained, wherein the different time periods include: a high load period, a medium load period and a low load period.

[0006] Preferably, in S2, the weight coefficient of the electromagnetic field strength data in different time periods is determined, wherein the electromagnetic field strength data includes an electromagnetic interference frequency anomaly index and a geomagnetic field polarization swing index; and the electromagnetic interference frequency anomaly index is obtained by: Collect the time domain signal of the electromagnetic signal in the Q time period and mark it as x[n], its length is N, and set the sampling frequency , use the FFT algorithm to convert the discrete signal x[n] in the time domain into the frequency domain signal X[k], the expression is: ; In the formula, X[k] is the kth frequency component of the frequency domain signal, k is the frequency index, N is the total number of sampling points, x[n] is the nth sampling point in the time domain, j represents the imaginary unit, X[k] obtained by FFT is a complex number, and the amplitude of the spectrum is expressed as: ; Re(X[k]) and Im(X[k]) are the real and imaginary parts of X[k] respectively; the frequency corresponding to each k is The calculation expression is: ;in, is the sampling frequency, the normal operating frequency of the switch cabinet system is fnormal, and the frequency tolerance range is set to , the allowed normal operating frequency range is ; In the spectrum, the frequency components outside [fnormal−Δf, fnormal+Δf] are marked as abnormal frequencies, and the total energy of all frequency components is calculated , the expression is: ; Calculate the sum of the energy of all abnormal frequencies , the expression is: ; Calculate the electromagnetic interference frequency anomaly index, the expression is: ; In the formula, It is the electromagnetic interference frequency anomaly index.

[0007] Preferably, the method for obtaining the geomagnetic field polarization swing index is: The time series signal of the polarization data of the geomagnetic field is marked as x(t). The geomagnetic field signal x(t) is decomposed into several intrinsic mode functions IMFs through EMD. IMFs represent the signal components on different time scales. By gradually extracting the local maximum and minimum values ​​in the signal, the original signal x(t) is decomposed into several IMFs, which are expressed as: ;in, is the ith eigenmode function, is the remaining residual, n is the total number of signals, find the local maximum and minimum in x(t), and construct the upper envelope and lower envelope respectively by interpolation, calculate the average value of the upper envelope and the lower envelope, generate the local trend curve of the signal, gradually remove the trend curve from the original signal, extract the first IMF, and decompose the signal into several IMFs; for each Perform Hilbert transform to obtain instantaneous frequency and instantaneous amplitude. Convert to complex analytic signal , defined as: ;in, is the instantaneous amplitude, is the instantaneous phase, j is the imaginary unit, and the instantaneous frequency It is calculated by the derivative of the phase, and the expression is: ; Calculate the geomagnetic field polarization swing index, the expression is: ; In the formula, and is the starting point and end point of the time interval. is the index of the Earth's magnetic field polarization swing.

[0008] Preferably, the electromagnetic interference frequency anomaly index and the geomagnetic field polarization swing index are converted into a first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model uses each group of first eigenvectors to predict the weight coefficient value label of the electromagnetic field strength data in different time periods as the prediction target, and takes minimizing the sum of the prediction errors of the weight coefficient value labels of the electromagnetic field strength data in all different time periods as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The weight coefficient values ​​of the electromagnetic field strength data in different time periods are determined according to the model output results, wherein the machine learning model is a polynomial regression model, and the weight coefficients of the electromagnetic field strength data in different time periods are weighted averaged to calculate the overall electromagnetic interference index of the switch cabinet.

[0009] Preferably, in S3, after comprehensively analyzing the calculated overall electromagnetic interference index of the switch cabinet and the real-time status data of the switch cabinet using fuzzy logic, a comprehensive evaluation report is generated; The real-time status data of the switch cabinet includes the load balance fluctuation index of the switch cabinet. The load balance fluctuation index is obtained by collecting the three-phase current of the switch cabinet and marking them as , represents the current value of the three-phase system, sets the sampling frequency f to monitor the changes of the three-phase load in real time, and calculates the average value of the three-phase current. The calculation expression is: ; In the formula, is the average value of the three-phase current; calculate the deviation of each group of currents from the average value, the expression is: ; Quantify the fluctuation of load balance and calculate its deviation from the average value, the expressions are: ; Combine the deviation values ​​of each group through RMS calculation to get the load balance fluctuation index, which is expressed as: ; Where LB is the load balancing fluctuation index.

[0010] Preferably, the calculated load balance fluctuation index LB and overall electromagnetic interference index MY are used as input items of fuzzy logic, and the health degree QE of the switch cabinet is used as the output item of fuzzy logic; a fuzzy set is defined for each input item and output item, and a membership function is used to map the input value to the fuzzy value; the fuzzy set combination of the input items is defined according to the fuzzy rule base to obtain the fuzzy set of the output item; the input items are mapped to the output items using a fuzzy reasoning mechanism; for each rule, the membership of the input items is calculated; the results of all rules are combined through a fuzzy reasoning mechanism; the fuzzy set is converted into a specific numerical result using a defuzzification method, and a health assessment report of the switch cabinet is generated according to the fuzzy logic output result.

[0011] The present invention also provides a switch cabinet quality comprehensive evaluation system based on Internet of Things technology, including a data acquisition module, an overall electromagnetic interference evaluation module and a comprehensive evaluation module; Data acquisition module: Install electromagnetic interference detection equipment around the switch cabinet and at several communication nodes, and monitor electromagnetic interference in real time through the Internet of Things platform to obtain electromagnetic field strength data in different time periods and real-time status data of the switch cabinet; Overall electromagnetic interference evaluation module: determine the weight coefficient of the electromagnetic field strength data in different time periods, and calculate the overall electromagnetic interference index of the switch cabinet after weighted averaging the weight coefficients of the electromagnetic field strength data in different time periods; Comprehensive evaluation module: After comprehensively analyzing the calculated overall electromagnetic interference index of the switch cabinet and the real-time status data of the switch cabinet using fuzzy logic, a comprehensive evaluation report is generated to display the health status of the switch cabinet.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention effectively evaluates the operating quality of the switch cabinet by real-time monitoring of the electromagnetic field strength and equipment status data around the switch cabinet, especially in the presence of electromagnetic interference. By installing electromagnetic interference detection equipment, using algorithms such as FFT and EMD to analyze the electromagnetic interference frequency anomaly index and the geomagnetic field polarization swing index, and using machine learning models to optimize the weight coefficients of different time periods, the overall electromagnetic interference index of the switch cabinet is finally calculated. Combined with the real-time status data of the switch cabinet, such as the load balance fluctuation index, fuzzy logic comprehensive analysis is used to generate a health assessment report to help predict possible equipment failures and optimize maintenance strategies.

[0013] 2. The present invention can not only accurately obtain the real-time status of the switch cabinet in a strong electromagnetic interference environment and ensure the accuracy of data transmission and evaluation, but also introduce fuzzy logic and machine learning models to enable the system to more intelligently process complex data analysis and generate a comprehensive health assessment report. At the same time, it improves the monitoring accuracy of the switch cabinet operation status, helps operation and maintenance personnel predict potential problems, optimize maintenance decisions, and thus improve the reliability and safety of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0015] Figure 1 The figure is a flow chart of the method of the present invention.

[0016] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown, the switch cabinet quality comprehensive evaluation method based on the Internet of Things technology described in this embodiment includes the following steps: S1: Install electromagnetic interference detection equipment around the switch cabinet and at several communication nodes, and use the Internet of Things platform to monitor electromagnetic interference in real time, obtain electromagnetic field strength data in different time periods and real-time status data of the switch cabinet; S2: Determine the weight coefficient of the electromagnetic field strength data in different time periods, and calculate the overall electromagnetic interference index of the switch cabinet after weighted averaging the weight coefficients of the electromagnetic field strength data in different time periods; S3: After comprehensively analyzing the calculated overall electromagnetic interference index of the switch cabinet and the real-time status data of the switch cabinet using fuzzy logic, a comprehensive evaluation report is generated to display the health status of the switch cabinet.

[0019] Among them, in S1, the electromagnetic interference detection equipment is installed around the switch cabinet and at several communication nodes, and the real-time monitoring of electromagnetic interference is carried out through the Internet of Things platform to obtain the electromagnetic field strength data in different time periods and the real-time status data of the switch cabinet, specifically: The steps to install electromagnetic interference (EMI) detection equipment around the switch cabinet and at several communication nodes need to be properly planned to ensure that the interference detection coverage is comprehensive, the data is reliable, and it can help identify the source and impact of EMI.

[0020] Select appropriate EMI detection equipment based on the frequency range and interference source characteristics of the EMI. The detection equipment should support a wide frequency range (e.g., from low frequency 50 Hz to high frequency wireless signals of several GHz) and be able to accurately capture interference intensity and patterns. Select portable devices for mobile measurements and fixed sensors for continuous monitoring. Fixed devices should have remote monitoring and data transmission capabilities for integration with the IoT platform. Ensure that the detection equipment has high sensitivity and can detect weak EMI, especially in the signal transmission area of ​​communication lines and switch cabinets.

[0021] Top and bottom detection: Install an electromagnetic interference detection device on the top and bottom of the switch cabinet to monitor possible electromagnetic radiation from above and below, especially interference near high-voltage cables and substation equipment. Install a device on the front and back of the switch cabinet to capture spatial radiation around the equipment, especially transient electromagnetic interference caused by switch operation or indirect radiation from other equipment. Install electromagnetic interference sensors at the communication interface of the switch cabinet to detect interference when the equipment communication module is connected to the external network to prevent electromagnetic interference from being introduced into the external transmission line. Select several typical nodes (such as signal amplifiers, distributors or interfaces) on the key signal transmission lines between the switch cabinet and other equipment to install electromagnetic interference detection devices to ensure that the signals of these nodes are not affected by interference. Install detection equipment near sensors and wireless communication modules (such as Wi-Fi, RFID modules, etc.) to monitor close-range electromagnetic interference and prevent wireless communication from being distorted due to interference.

[0022] Use a metal bracket or a support frame made of insulating material to fix the electromagnetic interference detection equipment in the specified position. Ensure that the equipment is kept at an appropriate distance from the tested area to avoid direct contact and reduce the interference of the equipment itself. Configure a dedicated signal line or power line for each electromagnetic interference detection device, ensure that the wiring is reasonable and avoid high-voltage lines or other signal cables to avoid introducing additional electromagnetic interference. Wireless communication node layout: If the device supports wireless communication, ensure that the wireless signal does not conflict with the signal of nearby devices. Reasonably configure the location of the communication antenna according to the coverage of the wireless network.

[0023] Ensure that the electromagnetic interference detection equipment supports compatible communication protocols with the IoT platform (such as TCP / IP, Modbus, ZigBee, etc.). Upload the data of the detection equipment to the IoT monitoring platform in real time via Wi-Fi, Ethernet or 4G / 5G network connection. After the equipment is installed, perform a data transmission test to confirm that the equipment can stably send interference data to the platform. Check the stability and signal strength of data transmission to ensure that there is no data packet loss or transmission interruption.

[0024] Real-time monitoring of electromagnetic interference through the IoT platform, obtaining electromagnetic field strength data in different time periods and real-time status data of the switch cabinet, requires the use of sensors and IoT technology to realize data collection, transmission, storage and analysis. The main components of the system include: Electromagnetic interference sensor, which is used to detect the electromagnetic field strength in the environment. The sensor can capture electromagnetic waves of different frequency ranges in real time (such as low-frequency interference, high-frequency wireless interference, etc.). Switch cabinet status sensor, which is used to monitor the key parameters of the switch cabinet, such as current, voltage, temperature, etc., to understand its operating status in real time. The gateway device is responsible for transmitting the collected sensor data to the IoT platform via wired or wireless means (such as Wi-Fi, LoRa, 4G / 5G network). The IoT platform is used to receive and store sensor data, and supports data visualization, analysis and alarm functions.

[0025] The load change of the equipment has a great impact on electromagnetic interference, so the key time periods can be divided according to the working load of the switch cabinet: High load period: This is the period when the switch cabinet is running at full load or close to full load. Usually the current and voltage loads are high and the electromagnetic interference is strong. Typical time periods include: industrial production peak (such as daytime production period), when high energy consumption equipment is running (such as when motors and power generation equipment are started); Medium load period: The switch cabinet operates at a medium load level, and the electromagnetic interference is relatively low during the high load period. It is common in the period when some equipment is turned on but the load is small, such as: the equipment is shut down for part of the time at noon, and part of the operation time at night; Low-load period: The switch cabinet is running at low load or near no-load state, when the electromagnetic interference is weak. For example: at night or when the equipment is not fully started, during shutdown maintenance or non-working hours.

[0026] The electromagnetic interference sensors installed around the switch cabinet collect the strength and spectrum information of the electromagnetic field in real time. These data include the electromagnetic field strength in different frequency bands (unit: μT or nT), interference waveform, timestamp, etc. Set the sampling frequency of the sensor (such as collecting data once per second) to ensure that the dynamically changing electromagnetic interference in the environment can be captured. High-frequency sampling can help detect instantaneous interference waves. The real-time operating status data of the switch cabinet is collected through the temperature sensors, current sensors, voltage sensors, etc. installed in the switch cabinet. These data include the temperature inside the equipment, switch status, load current, input voltage, etc. The frequency of data collection can also be set to compare and analyze the status changes of the switch cabinet when electromagnetic interference occurs.

[0027] All collected data is transmitted to the IoT platform through the gateway. Available transmission methods: Wi-Fi / LoRa: Suitable for local monitoring environments, such as factories or substations where local network coverage is good. 4G / 5G network: Used for long-distance transmission to ensure stable data upload during remote monitoring. Wired network: If the environmental interference intensity is extremely high, wired transmission may be used to avoid interference from wireless signals.

[0028] S2: Determine the weight coefficients of the electromagnetic field strength data in different time periods, and calculate the overall electromagnetic interference index of the switch cabinet after weighted averaging the weight coefficients of the electromagnetic field strength data in different time periods.

[0029] Determine the weight coefficient of the electromagnetic field strength data in different time periods, where the electromagnetic field strength data includes the electromagnetic interference frequency anomaly index and the geomagnetic field polarization swing index; the electromagnetic interference frequency anomaly index refers to the degree of deviation of the electromagnetic interference signal from the normal operating frequency, which is used to evaluate whether abnormal high-frequency or low-frequency interference occurs in the operating frequency band of the equipment. This index is usually used to detect whether external interference sources (such as wireless communication equipment or industrial equipment) affect the system within the frequency range that should not exist, thereby causing the operating frequency of the equipment to shift or the signal to distort.

[0030] The geomagnetic field polarization swing index describes the amplitude and frequency of changes in the polarization direction of the geomagnetic field in a short period of time, reflecting the stability of the geomagnetic environment. When the polarization of the geomagnetic field swings violently, it may interfere with the electronic components and sensors of the equipment, especially for equipment that relies on accurate magnetic field measurement or stable operation. This index can help evaluate the potential impact of external natural magnetic field changes on equipment operation.

[0031] Then the method for obtaining the electromagnetic interference frequency anomaly index is: The time domain signal of the electromagnetic signal in the Q time period is collected and marked as x[n], whose length is N (number of sampling points). Set the sampling frequency (Unit: Hz), which determines the number of samples collected per second. Use the FFT algorithm to convert the discrete signal x[n] in the time domain into the frequency domain signal X[k], expressed as: ; In the formula, X[k] is the kth frequency component of the frequency domain signal, k is the frequency index, N is the total number of sampling points, x[n] is the nth sampling point in the time domain, j represents the imaginary unit, X[k] obtained by FFT is a complex number, and the amplitude of the spectrum is expressed as: ; Re(X[k]) and Im(X[k]) are the real and imaginary parts of X[k] respectively; the frequency corresponding to each k is The calculation expression is: ;in, is the sampling frequency. The normal operating frequency of the switch cabinet system is fnormal, which can be the power supply frequency of the equipment (such as 50 Hz or 60 Hz) or other signal frequency. Set the frequency tolerance range to , the allowed normal operating frequency range is ; In the spectrum, the frequency components outside [fnormal−Δf, fnormal+Δf] are marked as abnormal frequencies, and the total energy of all frequency components is calculated , the expression is: ; Calculate the sum of the energy of all abnormal frequencies , the expression is: ; Calculate the electromagnetic interference frequency anomaly index, the expression is: ; Where FAI is the electromagnetic interference frequency anomaly index.

[0032] The larger the electromagnetic interference frequency anomaly index is, the more severe the electromagnetic interference intensity around the switch cabinet is, and the more interference components the frequency deviates from the normal working frequency. This means that outside the normal frequency range, there are a large number of high-frequency or low-frequency abnormal signal interferences, which may have a significant impact on the electronic components, communication modules or control systems of the switch cabinet. A larger frequency anomaly index indicates that these abnormal signals not only increase the noise of equipment operation, but may also cause misoperation, data loss or equipment failure, thereby threatening the stability and safety of the switch cabinet.

[0033] Among them, the method for obtaining the geomagnetic field polarization swing index is: The time series signal of the polarization data of the geomagnetic field is marked as x(t), which represents the component of the geomagnetic field polarization direction changing with time. The geomagnetic field signal x(t) is decomposed into several physically meaningful intrinsic mode functions (IMFs) through EMD. IMFs represent signal components on different time scales, usually arranged from high frequency to low frequency. By gradually extracting the local maximum and minimum values ​​in the signal, the original signal x(t) is decomposed into several IMFs, expressed as: ;in, is the ith eigenmode function, is the remaining residual, usually the trend component, n is the total number of signals, find the local maximum and minimum in x(t), and construct the upper envelope and lower envelope respectively by interpolation, calculate the average value of the upper envelope and the lower envelope, generate the local trend curve of the signal, gradually remove the trend curve from the original signal, extract the first IMF, and decompose the signal into several IMFs; for each Perform Hilbert transform to obtain instantaneous frequency and instantaneous amplitude. Converted into a complex analytic signal , defined as: ;in, is the instantaneous amplitude, is the instantaneous phase, j is the imaginary unit, and the instantaneous frequency It is calculated by the derivative of the phase, and the expression is: ; Calculate the geomagnetic field polarization swing index, the expression is: ; In the formula, and are the starting point and end point of the time interval, and GM is the geomagnetic field polarization oscillation index.

[0034] The larger the geomagnetic field polarization swing index is, the more violent the geomagnetic field polarization swing is, and the greater the change in instantaneous amplitude and frequency is, indicating that the fluctuation amplitude of the geomagnetic field polarization direction is large and the frequency changes rapidly, which may have a greater impact on sensitive equipment. The smaller the geomagnetic field polarization swing index is, the more stable the geomagnetic field polarization swing is, the smaller the swing amplitude is, and the stable frequency is, which means that the geomagnetic environment is relatively stable.

[0035] The electromagnetic interference frequency anomaly index and the geomagnetic field polarization swing index are converted into the first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model uses each group of first eigenvectors to predict the weight coefficient value label of the electromagnetic field strength data in different time periods as the prediction target, and takes minimizing the sum of the prediction errors of the weight coefficient value labels of the electromagnetic field strength data in all different time periods as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The weight coefficient value of the electromagnetic field strength data in different time periods is determined according to the model output results, wherein the machine learning model is a polynomial regression model, and the overall electromagnetic interference index of the switchgear is calculated by weighted averaging the weight coefficients of the electromagnetic field strength data in different time periods.

[0036] The method for obtaining the weight coefficient value of the electromagnetic field strength data in different time periods is: from the first eigenvector training data of the trained machine learning model, obtain the corresponding function expression: ; In the formula, F is the output function of the model, FAI is the electromagnetic interference frequency anomaly index, GM is the geomagnetic field polarization swing index, and CW is the weight coefficient value of the electromagnetic field strength data in different time periods.

[0037] S3: After comprehensively analyzing the calculated overall electromagnetic interference index of the switch cabinet and the real-time status data of the switch cabinet using fuzzy logic, a comprehensive evaluation report is generated to display the health status of the switch cabinet.

[0038] The real-time status data of the switch cabinet includes the load balance fluctuation index of the switch cabinet. The load balance fluctuation index is obtained by collecting the three-phase current of the switch cabinet and marking them as , represents the current value of the three-phase system, sets the sampling frequency f to monitor the changes of the three-phase load in real time, and calculates the average value of the three-phase current. The calculation expression is: ; In the formula, is the average value of the three-phase current; calculate the deviation of each group of currents from the average value, the expression is: ; To quantify the fluctuation of load balance and calculate its deviation from the average value, the expressions are: ; Combine the deviation values ​​of each group through RMS calculation to get the load balance fluctuation index, which is expressed as: ; Where LB is the load balancing fluctuation index.

[0039] The calculated load balance fluctuation index LB and overall electromagnetic interference index MY are used as input items of fuzzy logic, and the health level QE of the switch cabinet is used as the output item of fuzzy logic; Fuzzy sets are defined for each input and output term, and membership functions are used to map input values ​​to fuzzy values.

[0040] Load balance fluctuation index (LB): can be divided into three fuzzy sets: small, medium, and large, so that the health level (QE): defined as healthy, sub-healthy, and unhealthy, using triangular membership function or trapezoidal membership function to represent. For example: Low: small load balance fluctuation. Medium: moderate load balance fluctuation. High: large load balance fluctuation. Membership function definition example (assuming that the value range of LB is 0 to 1): ; Overall EMI Index (MY): Also divided into three fuzzy sets: low, medium, and high, represented by similar membership functions. Low: Low EMI. Medium: Moderate EMI. High: Strong EMI. Membership function definition example (assuming MY value range is 0 to 1): ; Health level (QE): defined as healthy, sub-healthy, and unhealthy, using membership functions to represent output items. Healthy: The equipment is in normal condition and does not require immediate maintenance. Sub-healthy: The equipment is slightly abnormal and may require monitoring. Unhealthy: The equipment is in poor condition and may face failure and require maintenance. The health level membership function can be defined based on the expected value of the equipment status.

[0041] The fuzzy rule base defines how to derive the fuzzy set of the output item based on the combination of the fuzzy sets of the input items. Fuzzy rules usually take the form of "if...then...". Example fuzzy rules: Rule 1: If LB is small and MY is low, then QE is healthy. Rule 2: If LB is large and MY is high, then QE is unhealthy. Rule 3: If LB is medium and MY is medium, then QE is sub-healthy. Rule 4: If LB is small and MY is medium, then QE is healthy. Rule 5: If LB is large and MY is low, then QE is sub-healthy. The combination of these rules is determined by expert experience or historical data, and can be comprehensively evaluated through multiple rules.

[0042] Use fuzzy inference mechanisms such as the Mamdani model to map inputs to outputs.

[0043] For each rule, calculate the membership of the input item. Combined with the "if...then..." rule, determine the membership of the output item based on the membership of the input item.

[0044] The "min" operation is used to determine the applicability of the rule, that is, the output fuzzy value of the rule is the minimum value of the input membership.

[0045] The results of all rules are combined through the fuzzy inference mechanism. A common method is to use the "max" operation to merge the fuzzy sets output by all rules into the final fuzzy output.

[0046] Use defuzzification methods to convert fuzzy sets into specific numerical results. Common defuzzification methods include the centroid method, the maximum membership method, etc. Calculate the centroid position of the fuzzy result and output the corresponding health value.

[0047] Generate a health assessment report for the switch cabinet based on the fuzzy logic output results: Summarize the calculation results of the load balance fluctuation index (LB), the overall electromagnetic interference index (MY) and the health level (QE). Generate a health status report for the switch cabinet based on the health level (QE) output by the fuzzy logic, showing the current status of the equipment and recommended maintenance measures.

[0048] For example, the report may include: the current load balance fluctuation index (LB) and the overall electromagnetic interference index (MY) values. The value of the comprehensive health index (QE) and the corresponding status description (such as "healthy" or "sub-healthy"). Maintenance recommendations: If the health level is low, it is recommended to arrange preventive maintenance or inspection.

[0049] Through fuzzy logic, the load balance fluctuation index (LB) and the overall electromagnetic interference index (MY) of the switch cabinet are used as input items, and the health level (QE) is generated as the output item. Through the fuzzy rule base, fuzzy reasoning and defuzzification, the health assessment results of the switch cabinet are finally obtained, and a comprehensive assessment report is generated to show the health status of the equipment and provide maintenance suggestions.

[0050] In this embodiment, during the electromagnetic interference monitoring and health assessment process of the switch cabinet, first, by installing electromagnetic interference detection equipment around the switch cabinet and at the communication nodes, and using the Internet of Things platform for real-time monitoring, the electromagnetic field strength data in different time periods and the real-time status data of the switch cabinet are obtained. Then, the electromagnetic field strength data in different time periods are assigned weight coefficients, and the overall electromagnetic interference index of the switch cabinet is calculated by weighted average. Finally, the overall electromagnetic interference index and the real-time status data of the switch cabinet are comprehensively analyzed using fuzzy logic to generate a comprehensive evaluation report, which displays the health status of the switch cabinet, helps predict the operating status of the equipment, and provides maintenance suggestions.

[0051] Example 2, please refer to Figure 2 As shown, the switch cabinet quality comprehensive evaluation system based on the Internet of Things technology described in this embodiment includes a data acquisition module, an overall electromagnetic interference evaluation module and a comprehensive evaluation module; Data acquisition module: Install electromagnetic interference detection equipment around the switch cabinet and at several communication nodes, and monitor electromagnetic interference in real time through the Internet of Things platform to obtain electromagnetic field strength data in different time periods and real-time status data of the switch cabinet; Overall electromagnetic interference evaluation module: determine the weight coefficient of the electromagnetic field strength data in different time periods, and calculate the overall electromagnetic interference index of the switch cabinet after weighted averaging the weight coefficients of the electromagnetic field strength data in different time periods; Comprehensive evaluation module: After comprehensively analyzing the calculated overall electromagnetic interference index of the switch cabinet and the real-time status data of the switch cabinet using fuzzy logic, a comprehensive evaluation report is generated to display the health status of the switch cabinet.

[0052] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0053] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0054] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0055] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0056] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Claims

1. A comprehensive evaluation method for switch cabinet quality based on Internet of Things technology, characterized by: The following steps are involved: S1: Install electromagnetic interference detection equipment around the switch cabinet and at several communication nodes, and use the Internet of Things platform to monitor electromagnetic interference in real time, obtain electromagnetic field strength data in different time periods and real-time status data of the switch cabinet; S2: Determine the weight coefficient of the electromagnetic field strength data in different time periods, and calculate the overall electromagnetic interference index of the switch cabinet after weighted averaging the weight coefficients of the electromagnetic field strength data in different time periods; S3: After comprehensively analyzing the calculated overall electromagnetic interference index of the switch cabinet and the real-time status data of the switch cabinet using fuzzy logic, a comprehensive evaluation report is generated to display the health status of the switch cabinet.

2. According to claim 1, a comprehensive switch cabinet quality assessment method based on Internet of Things technology is characterized by: In S1, electromagnetic field strength data and real-time status data of the switch cabinet in different time periods are obtained, wherein the different time periods include: a high load period, a medium load period and a low load period.

3. According to claim 1, a comprehensive switch cabinet quality assessment method based on Internet of Things technology is characterized by: In S2, the weight coefficient of the electromagnetic field strength data in different time periods is determined, wherein the electromagnetic field strength data includes an electromagnetic interference frequency anomaly index and a geomagnetic field polarization swing index; and the electromagnetic interference frequency anomaly index is obtained by: Collect the time domain signal of the electromagnetic signal in the Q time period and mark it as x[n], its length is N, and set the sampling frequency , use the FFT algorithm to convert the discrete signal x[n] in the time domain into the frequency domain signal X[k], the expression is: ; In the formula, X[k] is the kth frequency component of the frequency domain signal, k is the frequency index, N is the total number of sampling points, x[n] is the nth sampling point in the time domain, j represents the imaginary unit, X[k] obtained by FFT is a complex number, and the amplitude of the spectrum is expressed as: ; Re(X[k]) and Im(X[k]) are the real and imaginary parts of X[k] respectively; the frequency corresponding to each k is The calculation expression is: ;in, is the sampling frequency, the normal operating frequency of the switch cabinet system is fnormal, and the frequency tolerance range is set to , the allowed normal operating frequency range is ; In the spectrum, the frequency components outside [fnormal−Δf, fnormal+Δf] are marked as abnormal frequencies, and the total energy of all frequency components is calculated , the expression is: ; Calculate the sum of the energy of all abnormal frequencies , the expression is: ; Calculate the electromagnetic interference frequency anomaly index, the expression is: ; In the formula, It is the electromagnetic interference frequency anomaly index.

4. The switch cabinet quality comprehensive evaluation method based on Internet of Things technology according to claim 3 is characterized by: in, The method for obtaining the geomagnetic field polarization oscillation index is: The time series signal of the polarization data of the geomagnetic field is marked as x(t). The geomagnetic field signal x(t) is decomposed into several intrinsic mode functions IMFs through EMD. IMFs represent the signal components on different time scales. By gradually extracting the local maximum and minimum values ​​in the signal, the original signal x(t) is decomposed into several IMFs, which are expressed as: ;in, is the ith eigenmode function, is the remaining residual, n is the total number of signals, find the local maximum and minimum in x(t), and construct the upper envelope and lower envelope respectively by interpolation, calculate the average value of the upper envelope and the lower envelope, generate the local trend curve of the signal, gradually remove the trend curve from the original signal, extract the first IMF, and decompose the signal into several IMFs; for each Perform Hilbert transform to obtain instantaneous frequency and instantaneous amplitude. Convert to complex analytic signal , defined as: ;in, is the instantaneous amplitude, is the instantaneous phase, j is the imaginary unit, and the instantaneous frequency It is calculated by the derivative of the phase, and the expression is: ; Calculate the geomagnetic field polarization swing index, the expression is: ; In the formula, and is the starting point and end point of the time interval. is the index of the Earth's magnetic field polarization swing.

5. The switch cabinet quality comprehensive evaluation method based on Internet of Things technology according to claim 4 is characterized in that: The electromagnetic interference frequency anomaly index and the geomagnetic field polarization swing index are converted into the first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model uses each group of first eigenvectors to predict the weight coefficient value label of the electromagnetic field strength data in different time periods as the prediction target, and takes minimizing the sum of the prediction errors of the weight coefficient value labels of the electromagnetic field strength data in all different time periods as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The weight coefficient value of the electromagnetic field strength data in different time periods is determined according to the model output results, wherein the machine learning model is a polynomial regression model, and the overall electromagnetic interference index of the switchgear is calculated by weighted averaging the weight coefficients of the electromagnetic field strength data in different time periods.

6. The switch cabinet quality comprehensive evaluation method based on Internet of Things technology according to claim 5 is characterized by: In S3, after comprehensively analyzing the calculated overall electromagnetic interference index of the switch cabinet and the real-time status data of the switch cabinet using fuzzy logic, a comprehensive evaluation report is generated; The real-time status data of the switch cabinet includes the load balance fluctuation index of the switch cabinet. The load balance fluctuation index is obtained by collecting the three-phase current of the switch cabinet and marking them as , represents the current value of the three-phase system, sets the sampling frequency f to monitor the changes of the three-phase load in real time, and calculates the average value of the three-phase current. The calculation expression is: ; In the formula, is the average value of the three-phase current; calculate the deviation of each group of currents from the average value, the expression is: ; Quantify the fluctuation of load balance and calculate its deviation from the average value, the expressions are: ; Combine the deviation values ​​of each group through RMS calculation to get the load balance fluctuation index, which is expressed as: ; Where LB is the load balancing fluctuation index.

7. The switch cabinet quality comprehensive evaluation method based on Internet of Things technology according to claim 6 is characterized by: The calculated load balance fluctuation index LB and overall electromagnetic interference index MY are used as input items of fuzzy logic, and the health level QE of the switch cabinet is used as the output item of fuzzy logic; fuzzy sets are defined for each input item and output item, and membership functions are used to map input values ​​to fuzzy values; Define the fuzzy set combination of input items according to the fuzzy rule base to obtain the fuzzy set of output items; Use fuzzy reasoning mechanism to map input items to output items; for each rule, calculate the membership of the input items; combine the results of all rules through fuzzy reasoning mechanism; use defuzzification method to convert fuzzy sets into specific numerical results, and generate a health assessment report for the switch cabinet based on the fuzzy logic output results.

8. A switch cabinet quality comprehensive evaluation system based on the Internet of Things technology, used to implement the switch cabinet quality comprehensive evaluation method based on the Internet of Things technology according to any one of claims 1 to 7, characterized in that: It includes data acquisition module, overall electromagnetic interference assessment module and comprehensive assessment module; Data acquisition module: Install electromagnetic interference detection equipment around the switch cabinet and at several communication nodes, and monitor electromagnetic interference in real time through the Internet of Things platform to obtain electromagnetic field strength data in different time periods and real-time status data of the switch cabinet; Overall electromagnetic interference evaluation module: determine the weight coefficient of the electromagnetic field strength data in different time periods, and calculate the overall electromagnetic interference index of the switch cabinet after weighted averaging the weight coefficients of the electromagnetic field strength data in different time periods; Comprehensive evaluation module: After comprehensively analyzing the calculated overall electromagnetic interference index of the switch cabinet and the real-time status data of the switch cabinet using fuzzy logic, a comprehensive evaluation report is generated to display the health status of the switch cabinet.

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