Anti-electromagnetic interference dynamic compensation method for gas sensor of power equipment

Abstract

The invention relates to the field of sensors, and discloses an anti-electromagnetic interference dynamic compensation method for a gas sensor of power equipment, which comprises the following steps: monitoring and collecting sensor data, uploading the sensor data to a central processing unit for data fusion, and carrying out volatility analysis on the fused sensor data, so as to obtain an anti-electromagnetic interference dynamic compensation result. The change relation of the fluctuation of the sensor data along with time is monitored in real time, and the fluctuation abnormal state of the sensor data is analyzed and detected by combining a fluctuation analysis result with real-time environmental parameters, so that the monitoring duration and the monitoring frequency of the sensor data are dynamically adjusted; analyzing the contribution degree of the electromagnetic interference to the error generated by the gas sensor, detecting whether the error interferes with the measurement state of the gas sensor, and correcting the gas concentration measurement value by using a temperature and humidity-gas response coupling correction algorithm of Kalman filtering to realize the dynamic compensation of the gas concentration measurement value. And the accuracy of the corrected gas concentration estimated value is improved.

Description

Technical Field

[0001] The present invention relates to the field of sensor technology, and more particularly to a dynamic compensation method for a gas sensor of electric power equipment that is resistant to electromagnetic interference. Background Art

[0002] The impact of electromagnetic interference on gas sensor performance mainly includes: reduced measurement accuracy, slow or distorted response time, poor long-term stability, and false alarms or no alarms. Existing anti-interference measures use hardware means to suppress some interference, but in complex power field environments, it is very difficult to completely eliminate interference. Hardware measures are often costly and may not be able to cope with all types of interference. Therefore, a dynamic compensation method for gas sensors in power equipment based on electromagnetic interference resistance has emerged. It mainly uses software algorithms and data fusion technology, combined with real-time monitoring and dynamic adjustment strategies, to make up for the shortcomings of hardware anti-interference measures and improve the performance of sensors in complex electromagnetic environments.

[0003] However, the above process still has the following disadvantages:

[0004] First, existing dynamic compensation methods for gas sensors in power equipment rely solely on data from the gas sensors themselves, ignoring other relevant factors such as the electromagnetic field strength, ambient temperature and humidity, and mechanical vibration frequency and amplitude surrounding the power equipment. These factors can directly or indirectly affect the gas sensor's measurement. This single data source makes it impossible to fully and accurately reflect the sensor's actual environment and measurement status, thus affecting the compensation effect.

[0005] Second, existing dynamic compensation methods for gas sensors in power equipment do not monitor the volatility of sensor data in a detailed manner. They lack real-time analysis of the relationship between volatility and time, making it difficult to accurately detect abnormal fluctuations in sensor data. Furthermore, when detecting abnormal fluctuations, they do not fully consider the impact of real-time environmental parameters, resulting in low accuracy and timeliness of anomaly detection.

[0006] Third, the error correction algorithm used in the existing dynamic compensation method for gas sensors in power equipment is relatively simple and does not fully consider the impact of complex factors such as temperature and humidity-gas response coupling on gas concentration measurement, resulting in large errors in the corrected gas concentration estimate. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, the present invention provides a dynamic compensation method for a gas sensor of an electric power device that is resistant to electromagnetic interference, so as to solve the problems existing in the above-mentioned background technology.

[0008] The present invention provides the following technical solution: a dynamic compensation method for a gas sensor of an electric power device that is resistant to electromagnetic interference, comprising:

[0009] S1: By monitoring and collecting gas concentration data collected by sensors, electromagnetic field strength data around power equipment, ambient temperature and humidity data, mechanical vibration frequency and amplitude data, and uploading the collected sensor data to the central processing unit for data fusion;

[0010] S2: By performing a volatility analysis on the fused sensor data, the volatility of the sensor data is monitored in real time based on the volatility analysis results;

[0011] S3: By combining the results of the volatility analysis with real-time environmental parameters, it analyzes and detects abnormal fluctuations in sensor data. Based on the abnormal fluctuation detection results, it dynamically adjusts the monitoring duration and frequency of sensor data.

[0012] S4: After adjusting the monitoring duration and monitoring frequency, the contribution of electromagnetic interference to the error of the gas sensor is analyzed. Based on the error contribution analysis results, it is detected whether the error interferes with the measurement state of the gas sensor.

[0013] S5: Based on the error contribution analysis results, the gas concentration measurement value is corrected using the temperature, humidity and gas response coupling correction algorithm of the Kalman filter;

[0014] S6: Prompt by feeding back the error compensation correction result to the terminal interface.

[0015] Preferably, the S1 deploys gas sensors with anti-electromagnetic interference design at key parts of power equipment to monitor and collect target gas concentration in real time; deploys electromagnetic field strength sensors around the equipment to monitor and collect low-frequency and high-frequency electromagnetic interference; uses high-precision digital sensors to monitor temperature and humidity; deploys mechanical vibration sensors on the equipment housing or bracket to monitor and collect vibration frequency and amplitude to capture the interference of mechanical vibration on the sensor structure;

[0016] The collected sensor data is preprocessed, including denoising, time synchronization, and outlier removal. Then, industrial-grade wireless communication or wired Ethernet is used to upload the collected sensor data to the central processing unit in real time. The sensor data uploaded to the central processing unit is fused and the statistical features of each sensor data are extracted to construct a feature vector. The feature vector is expressed as where μ C represents the mean gas concentration, σ C Indicates the standard deviation of gas concentration, C p Indicates the peak gas concentration, R C Indicates the rate of change of gas concentration, μ E represents the mean value of electromagnetic field intensity, σ EIndicates the standard deviation of electromagnetic field strength, E p Indicates the peak value of the electromagnetic field intensity, R E Indicates the rate of change of electromagnetic field intensity, μ T Indicates the mean ambient temperature, μ H Represents the average ambient humidity, represents the mean vibration frequency, Indicates the mean vibration amplitude.

[0017] Preferably, the S2 further calculates the volatility index of the sensor data based on the fused feature vector to construct a volatility description vector V t , the volatility description vector is expressed as CV T Indicates the coefficient of variation of ambient temperature, CV H represents the coefficient of variation of ambient humidity, represents the coefficient of variation of vibration frequency, Represents the coefficient of variation of the vibration amplitude; and updates V in real time with a sliding window of step size ΔN t , and then calculate the first-order difference and second-order difference of the volatility index. The calculation formula of the first-order difference is ΔV t =V t -V t-ΔN , the calculation formula of the second-order difference is Δ 2 V t =ΔV t -ΔV t-ΔN .

[0018] Preferably, the first-order difference and the second-order difference are used to comprehensively judge the volatility change trend, and the specific judgment process is:

[0019] When ΔV t >0 and Δ 2 V t When it is greater than 0, the volatility trend is judged to be rising;

[0020] When ΔV t <0 and Δ 2 V t When <0, the volatility trend is judged to be downward.

[0021] Preferably, S3 analyzes the abnormal fluctuation state of sensor data by weighted fusion of the fluctuation index of sensor data and environmental parameters, and calculates a comprehensive fluctuation index, compares the comprehensive fluctuation index with a preset abnormal fluctuation threshold, and determines whether to trigger adjustment of the monitoring time and frequency of sensor data.

[0022] Preferably, the specific process of comparing the comprehensive fluctuation index with the preset abnormal fluctuation threshold is: when the comprehensive fluctuation index is greater than the preset abnormal fluctuation threshold, the adjustment of the monitoring time and monitoring frequency of the sensor data is immediately triggered, and the monitoring time and monitoring frequency are further corrected and analyzed, and the monitoring time correction coefficient and the monitoring frequency correction coefficient are calculated. The monitoring time of the sensor data is dynamically adjusted according to the monitoring time correction coefficient, and the monitoring frequency of the sensor data is dynamically adjusted according to the monitoring frequency correction coefficient.

[0023] Preferably, after adjusting the monitoring time and monitoring frequency and determining the adjustment range of the monitoring time and monitoring frequency, the S4 selects a suitable number of sampling points, collects the output data of the gas sensor under different monitoring time and frequency, and records the corresponding electromagnetic interference signal, including electromagnetic field strength, ambient temperature, ambient humidity, vibration frequency and vibration amplitude. Based on the collected sensor data, the output error of the gas sensor under electromagnetic interference is calculated. The specific calculation formula is E a =|Y m -Y t |, where Y m Indicates the actual measurement value of the gas sensor, Y t Indicates the true value measured on the standard reference equipment during the test.

[0024] Preferably, the output error of the gas sensor under electromagnetic interference refers to the absolute difference between the actual measured value of the gas sensor and the true value measured by the standard reference equipment during the test, by collecting M sets of paired data of electromagnetic interference signals and gas sensor errors, and the paired data are expressed as (X1, E a，1 ), (X2, E a，2 ),…,(X M , E a，M ), use statistical methods to analyze the contribution of electromagnetic interference to the gas sensor error, and calculate the correlation coefficient between the electromagnetic interference signal and the sensor error. The specific calculation formula is: X j Indicates the value of the ith electromagnetic interference signal, E a，j represents the gas sensor error value corresponding to the i-th electromagnetic interference signal, represents the mean value of all samples of the electromagnetic interference signal, Represents the mean of all gas sensor error samples, M represents the total number of samples, and the error contribution is quantified by the correlation coefficient as r 2 ;

[0025] The quantization error contribution r 2 Compare it with the preset contribution threshold τ to determine whether the electromagnetic interference has a significant interference on the measurement state of the gas sensor.2 When ≥τ and p<0.05, it indicates that electromagnetic interference has a significant contribution to the error and the gas sensor needs to be dynamically compensated.

[0026] Preferably, when it is detected that electromagnetic interference has made a significant contribution to the error, the S5 first removes the power frequency noise in the sensor measurement value by using wavelet packet transform to obtain processed measurement data, and then inputs the processed measurement data into the temperature and humidity-gas response coupling correction algorithm by Kalman filtering to establish a state space model, and fuse the sensor measurement value, temperature and humidity data and system noise information to dynamically compensate the gas concentration measurement value and calculate the corrected gas concentration estimate. The specific calculation formula is: in, represents the predicted gas concentration, K k，1 Represents the first row of elements of the Kalman gain, z k Represents the original measurement value of the sensor, f(T k , H k ) represents the temperature and humidity coupling error model.

[0027] Preferably, the S6 packages the corrected gas concentration estimate into a specific data packet format through a preset data transmission protocol, and sends it to the device where the terminal interface is located. It is encrypted using encryption technology during the transmission process, and then the device where the terminal interface is located receives the data packet through the corresponding network interface. When the terminal interface receives the data packet, it performs an integrity check on it. After the check is passed, the application of the terminal interface parses the data packet, extracts the corrected gas concentration value, and displays it in the display area of ​​the terminal interface.

[0028] Technical effects and advantages of the present invention:

[0029] (1) By monitoring and collecting gas concentration data, electromagnetic field intensity data, ambient temperature and humidity data, mechanical vibration frequency and amplitude data, and uploading these multi-source sensor data to the central processing unit for data fusion, it is possible to comprehensively consider the impact of various factors on gas sensor measurements, provide a more comprehensive and accurate data basis for subsequent volatility analysis, anomaly detection and error correction, and effectively solve the problems of data uniformity and insufficient fusion.

[0030] (2) By performing volatility analysis on the fused sensor data and monitoring the relationship between the volatility of the sensor data and time in real time, the dynamic characteristics of the data can be captured more accurately. The volatility analysis results can be combined with real-time environmental parameters to comprehensively consider the impact of various environmental factors on the fluctuation of sensor data, thereby more accurately analyzing and detecting the abnormal state of sensor data fluctuations. According to the detection results of the abnormal state of fluctuations, the monitoring time and frequency of sensor data can be dynamically adjusted, further improving the timeliness and effectiveness of anomaly detection.

[0031] (3) Based on the error contribution analysis results, the Kalman filter temperature and humidity-gas response coupling correction algorithm is used to correct the gas concentration measurement value. This can more accurately consider the influence of complex factors such as temperature and humidity-gas response coupling on gas concentration measurement, realize dynamic compensation of gas concentration measurement value, and improve the accuracy of the corrected gas concentration estimation value. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A diagram showing the steps of the method of the present invention.

[0033] Figure 2 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0034] The technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. In addition, the forms of the various structures described in the following embodiments are merely examples. The dynamic compensation method for a gas sensor of an electric power equipment with resistance to electromagnetic interference involved in the present invention is not limited to the various structures described in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0035] like Figure 1 This embodiment provides a dynamic compensation method for a gas sensor of an electric power device that is resistant to electromagnetic interference, including:

[0036] S1: By monitoring and collecting the gas concentration data collected by the sensor, the electromagnetic field strength data around the power equipment, the ambient temperature and humidity data, the mechanical vibration frequency and amplitude data, and uploading the collected sensor data to the central processing unit for data fusion.

[0037] In this embodiment, S1 deploys gas sensors with anti-electromagnetic interference design at key locations of power equipment to monitor and collect target gas concentrations in real time; deploys electromagnetic field strength sensors around the equipment to monitor and collect low-frequency and high-frequency electromagnetic interference; uses high-precision digital sensors to monitor temperature and humidity; and deploys mechanical vibration sensors on the equipment housing or bracket to monitor and collect vibration frequency and amplitude to capture interference from mechanical vibration on the sensor structure.

[0038] The collected sensor data is preprocessed, including denoising, time synchronization, and outlier removal. Then, industrial-grade wireless communication or wired Ethernet is used to upload the collected sensor data to the central processing unit in real time. The sensor data uploaded to the central processing unit is fused and the statistical features of each sensor data are extracted to construct a feature vector. The feature vector is expressed as where μ C represents the mean gas concentration, σ C Indicates the standard deviation of gas concentration, C p Indicates the peak gas concentration, R C Indicates the rate of change of gas concentration, μ E represents the mean value of electromagnetic field intensity, σ E Indicates the standard deviation of electromagnetic field strength, E p Indicates the peak value of the electromagnetic field intensity, R E Indicates the rate of change of electromagnetic field intensity, μ T Indicates the mean ambient temperature, μ H Represents the average ambient humidity, represents the mean vibration frequency, Indicates the mean vibration amplitude.

[0039] It should be noted that, with the current moment as the center, a sliding window of length N is taken, and the following features are calculated for each sensor data in the window to construct the feature vector:

[0040] S2: By performing volatility analysis on the fused sensor data, the volatility of the sensor data is monitored in real time based on the volatility analysis results.

[0041] In this embodiment, S2 further calculates the volatility index of the sensor data based on the fused feature vector to construct a volatility description vector V t , the volatility description vector is expressed as CV T Indicates the coefficient of variation of ambient temperature, CV H represents the coefficient of variation of ambient humidity, represents the coefficient of variation of vibration frequency, Represents the coefficient of variation of the vibration amplitude; and updates V in real time with a sliding window of step size ΔN t , and then calculate the first-order difference and second-order difference of the volatility index. The calculation formula of the first-order difference is ΔV t =V t -V t-ΔN , the calculation formula of the second-order difference is Δ 2 V t =ΔV t -ΔV t-ΔN ;

[0042] Based on the first-order difference and the second-order difference, the volatility trend is comprehensively judged. The specific judgment process is:

[0043] When ΔV t >0 and Δ 2 V t When it is greater than 0, the volatility trend is judged to be rising;

[0044] When ΔV t <0 and Δ 2 V t When <0, the volatility trend is judged to be downward.

[0045] S3: By combining the volatility analysis results with real-time environmental parameters, we can analyze and detect abnormal fluctuations in sensor data. Then, based on the abnormal fluctuation detection results, we can dynamically adjust the monitoring duration and frequency of sensor data.

[0046] In this embodiment, S3 analyzes the abnormal fluctuation state of the sensor data by weighted fusion of the fluctuation index of the sensor data and the environmental parameters, and calculates the comprehensive fluctuation index. The comprehensive fluctuation index is compared with the preset abnormal fluctuation threshold to determine whether to trigger the adjustment of the monitoring time and frequency of the sensor data;

[0047] When the comprehensive fluctuation index is greater than the preset abnormal fluctuation threshold, the adjustment of the monitoring time and monitoring frequency of the sensor data is immediately triggered, and the monitoring time and monitoring frequency are further corrected and analyzed to calculate the monitoring time correction coefficient and the monitoring frequency correction coefficient. The monitoring time of the sensor data is dynamically adjusted according to the monitoring time correction coefficient, and the monitoring frequency of the sensor data is dynamically adjusted according to the monitoring frequency correction coefficient.

[0048] It should be noted that the specific analysis method for the comprehensive volatility index is as follows:

[0049] Step S301: Calculate the standard deviation of the gas sensor measurement value within the sliding window as Among them, C i represents the measurement value of the sensor at time i, represents the arithmetic mean of the data within the window, and N represents the size of the sliding window, that is, the number of data points used to calculate volatility;

[0050] Step S302: Calculate the high frequency electromagnetic energy Where X(f) represents the frequency domain signal, f max Indicates the upper limit of the frequency range, f min Indicates the lower limit of the frequency range

[0051] Step S303: Calculate the effective value of vibration acceleration Among them, a i Indicates the instantaneous value of acceleration;

[0052] Step S304: By weighted fusion of volatility index and environmental parameters, the comprehensive volatility index is calculated as FB(t) = α1×σ C (t)+α2×|ΔC base (t)|+α3×E wave (t)+α4×A r (t), where ΔC base (t) represents the baseline drift;

[0053] By comparing the comprehensive fluctuation index FB(t) with the preset abnormal fluctuation threshold θ FB For comparison, when FB(t)>θ FB , the monitoring strategy adjustment is triggered; then the monitoring time and monitoring frequency are corrected and analyzed, and the monitoring time correction coefficient and monitoring frequency correction coefficient are calculated. The calculation formula of the monitoring time correction coefficient is: Among them, T d represents the default monitoring duration, b represents the duration adjustment coefficient, and the calculation formula for the monitoring frequency correction coefficient is: Among them, f d represents the default frequency, and h represents the frequency adjustment coefficient.

[0054] S4: After adjusting the monitoring duration and monitoring frequency, the contribution of electromagnetic interference to the error of the gas sensor is analyzed, and based on the error contribution analysis results, whether the error interferes with the measurement state of the gas sensor is detected.

[0055] In this embodiment, after adjusting the monitoring time and monitoring frequency and determining the adjustment range of the monitoring time and monitoring frequency, the S4 selects an appropriate number of sampling points, collects the output data of the gas sensor under different monitoring time and frequency, and records the corresponding electromagnetic interference signal, including electromagnetic field strength, ambient temperature, ambient humidity, vibration frequency and vibration amplitude. Based on the collected sensor data, the output error of the gas sensor under electromagnetic interference is calculated. The specific calculation formula is Ea =|Y m -Y t |, where Y m Indicates the actual measurement value of the gas sensor, Y t Indicates the true value measured on the standard reference equipment during the test;

[0056] By collecting M sets of paired data of electromagnetic interference signals and gas sensor errors, the paired data are expressed as (X1, E a，1 ), (X2, E a，2 ),…,(X M , E a，M ), use statistical methods to analyze the contribution of electromagnetic interference to the gas sensor error, and calculate the correlation coefficient between the electromagnetic interference signal and the sensor error. The specific calculation formula is: X j Indicates the value of the ith electromagnetic interference signal, E a，j represents the gas sensor error value corresponding to the i-th electromagnetic interference signal, represents the mean value of all samples of the electromagnetic interference signal, Represents the mean of all gas sensor error samples, M represents the total number of samples, and the error contribution is quantified by the correlation coefficient as r 2 ;

[0057] The quantization error contribution r 2 Compare it with the preset contribution threshold τ to determine whether the electromagnetic interference has a significant interference on the measurement state of the gas sensor. 2 When ≥τ and p<0.05, it indicates that electromagnetic interference has a significant contribution to the error and the gas sensor needs to be dynamically compensated.

[0058] It should be noted that in the simple linear regression E a =β0+β1X+ε, then the p-value is usually calculated by t-test, as follows:

[0059] Step S401: used to calculate the estimated value of the regression coefficient, the estimated value of the slope β1 is Then the estimated value of intercept β0 is

[0060] Step S402: Calculate the standard error of the slope as

[0061] Step S403: Use the t-value to test whether β1 is significantly different from 0:

[0062] Step S404: Based on the t-distribution and the degrees of freedom df, calculate whether the two-sided p-value is significantly different from 0: p = 2 × P (T ≥ |t|);

[0063] By specifically analyzing the contribution of electromagnetic interference to the error of the gas sensor after adjusting the monitoring duration and frequency, and detecting whether the error interferes with the measurement state of the gas sensor based on the analysis results, we can more accurately understand the impact of electromagnetic interference on sensor error and provide a stronger basis for subsequent error correction.

[0064] S5: Based on the error contribution analysis results, the gas concentration measurement value is corrected using the temperature and humidity-gas response coupling correction algorithm of the Kalman filter.

[0065] In this embodiment, when the electromagnetic interference is detected to have a significant contribution to the error, the S5 first removes the power frequency noise in the sensor measurement value by using wavelet packet transform to obtain processed measurement data, and then inputs the processed measurement data into the temperature and humidity-gas response coupling correction algorithm by Kalman filtering. By establishing a state space model and fusing the sensor measurement value, temperature and humidity data and system noise information, the gas concentration measurement value is dynamically compensated and the corrected gas concentration estimate is calculated. The specific calculation formula is: in, represents the predicted gas concentration, K k，1 Represents the first row of elements of the Kalman gain, z k Represents the original measurement value of the sensor, f(T k , H k ) represents the temperature and humidity coupling error model.

[0066] It should be specifically explained that, according to the characteristics of the sensor measurement value and the frequency range of the power frequency noise, the appropriate wavelet basis function and decomposition layer number are selected, and the original measurement value of the sensor is decomposed by wavelet packet to obtain the wavelet packet coefficients of different frequency bands. Then, through threshold processing, the frequency band containing the power frequency noise is determined. The wavelet packet coefficients of these frequency bands can be processed by a soft threshold function, and the processed wavelet packet coefficients are reconstructed by wavelet packet to obtain the measurement value after removing the power frequency noise. By combining the power frequency noise processing with the Kalman filter error correction, a complete and targeted anti-electromagnetic interference and error compensation process is formed, which can more effectively solve the impact of electromagnetic interference and power frequency noise on gas sensor measurements.

[0067] S6: Prompt by feeding back the error compensation correction result to the terminal interface.

[0068] In this embodiment, the S6 packages the corrected gas concentration estimate into a specific data packet format through a preset data transmission protocol, and sends it to the device where the terminal interface is located. It is encrypted using encryption technology during the transmission process, and then the device where the terminal interface is located receives the data packet through the corresponding network interface. When the terminal interface receives the data packet, it performs an integrity check on it. After the check passes, the application of the terminal interface parses the data packet, extracts the corrected gas concentration value, and displays it in the display area of ​​the terminal interface.

[0069] like Figure 2 The embodiment shown provides an implementation system corresponding to a dynamic compensation method for a gas sensor of an electric power equipment that is resistant to electromagnetic interference, including a sensor data monitoring and acquisition module, a volatility analysis module, a volatility anomaly detection module, an error detection module, a measurement correction module and a result display module. The sensor data monitoring and acquisition module is connected to the volatility analysis module, the volatility analysis module is connected to the volatility anomaly detection module, the volatility anomaly detection module is connected to the error detection module, the error detection module is connected to the measurement correction module, and the measurement correction module is connected to the result display module.

[0070] The sensor data monitoring and acquisition module monitors and collects gas concentration data collected by the sensor, electromagnetic field strength data around the power equipment, ambient temperature and humidity data, mechanical vibration frequency and amplitude data, and uploads the collected sensor data to the central processing unit for data fusion;

[0071] The volatility analysis module performs volatility analysis on the fused sensor data and monitors the relationship between the volatility of the sensor data and time in real time based on the volatility analysis results;

[0072] The fluctuation anomaly detection module analyzes and detects the fluctuation anomaly state of sensor data by combining the fluctuation analysis results with real-time environmental parameters, and then dynamically adjusts the monitoring duration and frequency of sensor data based on the detection results of the fluctuation anomaly state;

[0073] The error detection module analyzes the contribution of electromagnetic interference to the error of the gas sensor after adjusting the monitoring duration and monitoring frequency, and detects whether the error interferes with the measurement state of the gas sensor based on the error contribution analysis result;

[0074] The measurement correction module corrects the gas concentration measurement value based on the error contribution degree analysis result using the temperature and humidity-gas response coupling correction algorithm of the Kalman filter;

[0075] The result display module provides prompts by feeding back the error compensation correction result to the terminal interface.

[0076] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0077] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A dynamic compensation method for a gas sensor of an electric power equipment against electromagnetic interference, characterized in that: include: S1: By monitoring and collecting gas concentration data collected by sensors, electromagnetic field strength data around power equipment, ambient temperature and humidity data, mechanical vibration frequency and amplitude data, and uploading the collected sensor data to the central processing unit for data fusion; S2: By performing a volatility analysis on the fused sensor data, the volatility of the sensor data is monitored in real time based on the volatility analysis results; S3: By combining the results of the volatility analysis with real-time environmental parameters, it analyzes and detects abnormal fluctuations in sensor data. Based on the abnormal fluctuation detection results, it dynamically adjusts the monitoring duration and frequency of sensor data. S4: After adjusting the monitoring duration and monitoring frequency, the contribution of electromagnetic interference to the error of the gas sensor is analyzed. Based on the error contribution analysis results, it is detected whether the error interferes with the measurement state of the gas sensor. S5: Based on the error contribution analysis results, the gas concentration measurement value is corrected using the temperature, humidity and gas response coupling correction algorithm of the Kalman filter; S6: Prompt by feeding back the error compensation correction result to the terminal interface.

2. The method for dynamic compensation of a gas sensor of an electric power equipment against electromagnetic interference according to claim 1, characterized in that: The S1 uses gas sensors with anti-electromagnetic interference design deployed at key locations of power equipment to monitor and collect target gas concentrations in real time. Electromagnetic field strength sensors are deployed around the equipment to monitor and collect low-frequency and high-frequency electromagnetic interference. High-precision digital sensors are used to monitor temperature and humidity. Mechanical vibration sensors are deployed on the equipment housing or bracket to monitor and collect vibration frequency and amplitude to capture the interference of mechanical vibration on the sensor structure. The collected sensor data is preprocessed, including denoising, time synchronization, and outlier removal. Then, industrial-grade wireless communication or wired Ethernet is used to upload the collected sensor data to the central processing unit in real time. The sensor data uploaded to the central processing unit is fused and the statistical features of each sensor data are extracted to construct a feature vector. The feature vector is expressed as where μ C represents the mean gas concentration, σ C Indicates the standard deviation of gas concentration, C p Indicates the peak gas concentration, R C Indicates the rate of change of gas concentration, μ E represents the mean value of electromagnetic field intensity, σ E Indicates the standard deviation of electromagnetic field strength, E p Indicates the peak value of the electromagnetic field intensity, R E Indicates the rate of change of electromagnetic field intensity, μ T Indicates the mean ambient temperature, μ H Represents the average ambient humidity, represents the mean vibration frequency, Indicates the mean vibration amplitude.

3. The method for dynamic compensation of a gas sensor of an electric power equipment against electromagnetic interference according to claim 2, characterized in that: The S2 further calculates the volatility index of the sensor data based on the fused feature vector to construct the volatility description vector V t , the volatility description vector is expressed as CV T Indicates the coefficient of variation of ambient temperature, CV H represents the coefficient of variation of ambient humidity, represents the coefficient of variation of vibration frequency, Represents the coefficient of variation of the vibration amplitude; and updates V in real time with a sliding window of step size ΔN t , and then calculate the first-order difference and second-order difference of the volatility index. The calculation formula of the first-order difference is ΔV t =V t -V t-ΔN , the calculation formula of the second-order difference is Δ 2 V t =ΔV t -ΔV t-ΔN .

4. The method for dynamic compensation of a gas sensor of an electric power equipment against electromagnetic interference according to claim 3, characterized in that: The first-order difference and second-order difference are used to comprehensively judge the volatility trend. The specific judgment process is: When ΔV t >0 and Δ 2 V t When it is greater than 0, the volatility trend is judged to be rising; When ΔV t <0 and Δ 2 V t When <0, the volatility trend is judged to be downward.

5. The method for dynamic compensation of a gas sensor of an electric power equipment against electromagnetic interference according to claim 4, characterized in that: The S3 analyzes the abnormal fluctuation state of the sensor data by weighted fusion of the fluctuation index of the sensor data and the environmental parameters, and calculates the comprehensive fluctuation index. The comprehensive fluctuation index is compared with the preset abnormal fluctuation threshold to determine whether to trigger the adjustment of the monitoring time and monitoring frequency of the sensor data.

6. The method for dynamic compensation of a gas sensor of an electric power equipment against electromagnetic interference according to claim 5, characterized in that: The specific process of comparing the comprehensive fluctuation index with the preset abnormal fluctuation threshold is: when the comprehensive fluctuation index is greater than the preset abnormal fluctuation threshold, the adjustment of the monitoring time and monitoring frequency of the sensor data is immediately triggered, and the monitoring time and monitoring frequency are further corrected and analyzed, and the monitoring time correction coefficient and the monitoring frequency correction coefficient are calculated. The monitoring time of the sensor data is dynamically adjusted according to the monitoring time correction coefficient, and the monitoring frequency of the sensor data is dynamically adjusted according to the monitoring frequency correction coefficient.

7. The method for dynamic compensation of a gas sensor of an electric power equipment against electromagnetic interference according to claim 6, characterized in that: After adjusting the monitoring time and frequency and determining the adjustment range of the monitoring time and frequency, the S4 selects an appropriate number of sampling points, collects the output data of the gas sensor under different monitoring time and frequency, and records the corresponding electromagnetic interference signal, including electromagnetic field strength, ambient temperature, ambient humidity, vibration frequency and vibration amplitude. Based on the collected sensor data, the output error of the gas sensor under electromagnetic interference is calculated. The specific calculation formula is E a =|Y m -Y t |, where Y m Indicates the actual measurement value of the gas sensor, Y t Indicates the true value measured on the standard reference equipment during the test.

8. The method for dynamic compensation of a gas sensor of an electric power equipment against electromagnetic interference according to claim 7, characterized in that: The output error of the gas sensor under electromagnetic interference refers to the absolute difference between the actual measured value of the gas sensor and the true value measured by the standard reference equipment during the test. By collecting M sets of paired data of electromagnetic interference signals and gas sensor errors, the paired data are expressed as (X1, E a，1 ), (X2, E a，2 ),…,(X M , E a，M ), use statistical methods to analyze the contribution of electromagnetic interference to the gas sensor error, and calculate the correlation coefficient between the electromagnetic interference signal and the sensor error. The specific calculation formula is: X j Indicates the value of the ith electromagnetic interference signal, E a，j represents the gas sensor error value corresponding to the i-th electromagnetic interference signal, represents the mean value of all samples of the electromagnetic interference signal, Represents the mean of all gas sensor error samples, M represents the total number of samples, and the error contribution is quantified by the correlation coefficient as r 2 ; The quantization error contribution r 2 Compare it with the preset contribution threshold τ to determine whether the electromagnetic interference has a significant interference on the measurement state of the gas sensor. 2 When ≥τ and p<0.05, it indicates that electromagnetic interference has a significant contribution to the error and the gas sensor needs to be dynamically compensated.

9. The method for dynamic compensation of a gas sensor of an electric power equipment against electromagnetic interference according to claim 8, characterized in that: When S5 detects that electromagnetic interference has a significant contribution to the error, it first removes the power frequency noise in the sensor measurement value by using wavelet packet transform to obtain processed measurement data, and then inputs the processed measurement data into the temperature and humidity-gas response coupling correction algorithm of Kalman filtering. By establishing a state space model and fusing the sensor measurement value, temperature and humidity data and system noise information, the gas concentration measurement value is dynamically compensated and the corrected gas concentration estimate is calculated. The specific calculation formula is: in, represents the predicted gas concentration, K k，1 Represents the first row of elements of the Kalman gain, z k Represents the original measurement value of the sensor, f(T k , H k ) represents the temperature and humidity coupling error model.

10. The method for dynamic compensation of a gas sensor of an electric power equipment against electromagnetic interference according to claim 9, characterized in that: The S6 packages the corrected gas concentration estimate into a specific data packet format through a preset data transmission protocol, and sends it to the device where the terminal interface is located. It uses encryption technology to encrypt it during the transmission process. The device where the terminal interface is located then receives the data packet through the corresponding network interface. When the terminal interface receives the data packet, it performs an integrity check on it. After the check is passed, the application program of the terminal interface parses the data packet, extracts the corrected gas concentration value, and displays it in the display area of ​​the terminal interface.

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