A method and system for measuring an electric field change

Abstract

This invention provides a method and system for measuring electric field changes. The method includes: installing a differential electric field sensor in the area to be measured for the electric field change; collecting and preprocessing electric field change data from the area to be measured using the differential electric field sensor; establishing an electric field change distribution model based on the preprocessed electric field change data to obtain data analysis results; and generating an electric field change monitoring report based on the data analysis results. The system includes: a differential electric field sensing module for capturing electric field change signals in the area to be measured using a differential electric field sensor; a high-precision data processing unit for receiving and preprocessing the electric field change signals to obtain electric field change data; a remote communication module for real-time remote transmission of electric field change data using low-power wide-area network technology or wired communication; and an intelligent analysis platform for receiving the electric field change data, performing comprehensive analysis, and generating an electric field change monitoring report.

Description

Technical Field

[0001] This invention belongs to the field of electric field measurement technology, specifically relating to a method and system for measuring electric field changes. Background Technology

[0002] In electromagnetic environment monitoring and power systems, accurate measurement of electric field changes is of paramount importance. Changes in electric field strength can reflect the stability of the electromagnetic environment, the operating status of equipment, and potential electromagnetic interference or faults. Therefore, real-time monitoring and analysis of electric field changes are crucial for ensuring power system safety, assessing electromagnetic environment quality, and preventing electromagnetic pollution.

[0003] Traditional electric field measurement methods mainly rely on analog electric field sensors or simple digital measuring instruments. While these methods can reflect changes in the electric field to some extent, they often suffer from limitations in measurement accuracy, poor real-time data processing, and susceptibility to interference. Furthermore, traditional methods typically depend on manual on-site operation, which is not only inefficient but also makes it difficult to achieve large-scale, continuous monitoring.

[0004] The changes in electric fields are influenced by a variety of environmental factors, such as weather conditions, geographical location, and the operating status of surrounding electromagnetic equipment. These factors make the distribution and changes of electric fields highly complex and uncertain. Therefore, relying solely on traditional single-point measurement methods is insufficient to comprehensively and accurately reflect the electric field conditions in the environment. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method and system for measuring electric field changes. It aims to combine advanced differential sensing technology, high-precision data processing algorithms, and communication technology to achieve real-time monitoring, efficient transmission, and intelligent analysis of electric field changes. By constructing a distributed monitoring network and combining it with dynamic monitoring of environmental factors, this method can provide more comprehensive, accurate, and timely electric field measurement data, providing strong support for fields such as electromagnetic environment monitoring, electromagnetic compatibility analysis, and power system fault diagnosis.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for measuring electric field change, the method comprising:

[0008] S1. Install a differential electric field sensor in the region where the change in electric field is to be measured;

[0009] S2. Collect and preprocess electric field change data of the area to be measured based on differential electric field sensors;

[0010] S3. Based on the preprocessed electric field change data, establish an electric field change distribution model and obtain data analysis results;

[0011] S4. Based on the data analysis results, generate a monitoring report on the change in electric field.

[0012] Preferably, in step S1, a differential electric field sensor is installed in the region where the electric field change is to be measured, comprising:

[0013] S11. Within the area where the electric field change is to be measured, plan the layout grid of the differential electric field sensor based on the potential sources of the electric field change, topography, and electromagnetic interference.

[0014] S12. Install differential electric field sensors on the layout grid and adjust the installation angle and orientation of the differential electric field sensors.

[0015] S13. Equip each differential electric field sensor with a data acquisition module and a data processing unit, and establish a data transmission channel.

[0016] Preferably, step S2 involves collecting and preprocessing electric field change data of the region to be measured based on a differential electric field sensor, including:

[0017] S21. Collect electric field change data of the area to be measured based on differential electric field sensors;

[0018] S22. Preprocess the electric field change data, including data cleaning, outlier removal, and data smoothing;

[0019] S23. Upload the preprocessed electric field change data to the intelligent analysis platform.

[0020] Preferably, in step S3, based on the preprocessed electric field change data, an electric field change distribution model is established to obtain data analysis results, including:

[0021] S31. Analyze the preprocessed electric field change data using big data analysis and machine learning algorithms, and establish a distribution model of electric field change.

[0022] S32. Based on the electric field variation distribution model, obtain the data analysis results.

[0023] Preferably, the method further includes: automatically adjusting the operating parameters of the differential electric field sensor using an intelligent adjustment algorithm based on the environmental data of the area to be measured by real-time monitoring of electric field changes; and calibrating the differential electric field sensor using an automatic calibration algorithm within a preset period.

[0024] The present invention also provides an electric field change measurement system, which is used to implement the aforementioned electric field change measurement method. The system includes: a differential electric field sensing module, a high-precision data processing unit, a remote communication module, and an intelligent analysis platform.

[0025] The differential electric field sensing module is used to capture the electric field change signal in the area to be measured using a differential electric field sensor.

[0026] A high-precision data processing unit is used to receive electric field change signals and perform preprocessing to obtain electric field change data.

[0027] The remote communication module is used to realize the real-time remote transmission of electric field change data using low-power wide area network technology or wired communication.

[0028] The intelligent analysis platform is used to receive electric field change data, perform comprehensive analysis, and generate electric field change monitoring reports.

[0029] Preferably, the system further includes: an environmental adaptability adjustment module and an automatic calibration and verification module;

[0030] The environmental adaptability adjustment module is used to automatically adjust the operating parameters of the differential electric field sensor based on the environmental data of the area to be measured, which is monitored in real time for changes in the electric field.

[0031] The automatic calibration and verification module is used to calibrate the differential electric field sensor within a preset period.

[0032] Preferably, the high-precision data processing unit includes: an analog-to-digital converter, a signal amplification circuit, and a filtering circuit;

[0033] A filtering circuit is used to filter the received electric field change signal.

[0034] The signal amplification circuit is used to amplify the filtered electric field change signal;

[0035] An analog-to-digital converter is used to convert amplified electric field change signals into digital signals to obtain electric field change data.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] This invention provides a highly reliable differential electric field change measurement method, achieving high-precision real-time monitoring and accurate analysis of electric field changes. This method employs advanced differential electric field sensing technology and intelligent data analysis methods, enabling the capture of minute electric field changes in complex environments, providing a scientific basis for electromagnetic environment monitoring, electromagnetic compatibility analysis, and other fields. Furthermore, regular system maintenance, data backup and security strategies, as well as continuous upgrades and optimization measures, further ensure the system's stability and reliability, enhancing its application value and effectiveness in various application scenarios. Attached Figure Description

[0038] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the high-reliability differential electric field change measurement method according to an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of a high-reliability differential electric field change measurement system module according to an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] Example 1

[0044] This invention provides a method for measuring electric field change, comprising:

[0045] S1. Install a differential electric field sensor in the region where the change in electric field is to be measured;

[0046] S2. Based on the differential electric field sensor, the electric field change signal of the area to be measured is collected and preprocessed to obtain electric field change data;

[0047] S3. Based on the electric field change data, establish an electric field change distribution model and obtain data analysis results;

[0048] S4. Based on the data analysis results, generate a monitoring report on the change in electric field.

[0049] like Figure 1 As shown, the specific implementation process of the present invention is as follows:

[0050] I. System Deployment and Initialization

[0051] 1. Site selection and layout planning:

[0052] Within the area where the electric field change is to be measured, the layout grid of the differential electric field sensor should be rationally planned based on factors such as the potential sources of the electric field change, topography, and electromagnetic interference. This ensures that the sensor can capture electric field change information in key areas.

[0053] Select appropriate installation locations at key nodes of each grid, avoiding sources of strong electromagnetic interference, to ensure the measurement accuracy of the sensors.

[0054] 2. Install a differential electric field sensor:

[0055] Install high-precision differential electric field sensors at the planned locations, ensuring that the spacing and height between the sensors meet the measurement requirements.

[0056] Adjust the sensor's installation angle and orientation to maximize its sensitivity to changes in the electric field and reduce environmental interference.

[0057] 3. Configure the data acquisition and processing system:

[0058] Each sensor is equipped with a data acquisition module, including components such as an analog-to-digital converter, a signal amplifier, and a filter.

[0059] Configure a data processing unit to receive raw data from the sensor and perform preprocessing, filtering, calibration, and other operations to improve the accuracy and reliability of the data.

[0060] Establish data transmission channels to ensure that the collected data can be uploaded to a remote data center or analysis platform in real time or on a scheduled basis.

[0061] 4. Deploy a data analysis and early warning platform:

[0062] Deploy a dedicated data analysis and early warning platform on a remote server or in a data center. This platform should have powerful data processing, analysis, and early warning capabilities.

[0063] Configure platform parameters, including threshold settings for electric field changes and early warning information release methods, to adapt to different measurement needs and early warning requirements.

[0064] II. System Operation and Data Acquisition

[0065] 1. Start the sensor and data acquisition:

[0066] Start all differential electric field sensors and data acquisition modules to begin real-time acquisition of electric field change data.

[0067] The sensor will continuously monitor according to the preset sampling frequency and duration to ensure the continuity and integrity of the data.

[0068] 2. Data preprocessing and calibration:

[0069] The collected raw data is preprocessed, including data cleaning, outlier removal, and data smoothing.

[0070] The sensors should be calibrated regularly to ensure the accuracy and consistency of their measurement results.

[0071] 3. Real-time data transmission and storage:

[0072] The pre-processed data is uploaded in real time to a remote data center or analysis platform via wired or wireless means.

[0073] Establish a data backup mechanism to ensure data integrity and security. Simultaneously, establish a data indexing and query mechanism to enable rapid retrieval and analysis of historical data.

[0074] Specifically, the data processing flow is as follows:

[0075] 1) Signal Processing Stage

[0076] 1. Differential signal amplification and processing:

[0077] Signal amplification is achieved using a high-precision instrumentation amplifier (such as AD8429):

[0078] V_out=G×(V_in+-V_in-)+V_ref

[0079] Wherein: G = 100-1000 times (programmable adjustment), CMRR > 120dB@1kHz.

[0080] 2. Adaptive anti-aliasing filtering:

[0081] Hardware-level 5th-order Butterworth low-pass filter:

[0082] |H(f)|=1 / √[1+(f / f_c)^10]

[0083] The cutoff frequency f_c is adaptively adjusted according to the sampling rate (f_c = 0.4 × f_s).

[0084] Digital FIR filtering (window function method design):

[0085] y[n]=Σ_{k=0}^{N-1}h[k]x[nk]

[0086] Where h[k] represents the filter coefficients of the Kaiser window design.

[0087] 3. High-precision analog-to-digital conversion:

[0088] Use a 24-bit Σ-Δ ADC (such as ADS1256):

[0089] E_digital=(V_in×2^23) / V_ref

[0090] The number of significant bits (ENOB) is ≥ 21.

[0091] II) Data Analysis and Correction

[0092] 1. Environmental parameter compensation:

[0093] E_corrected=E_raw×[1+α(T-T0)+β(H-H0)+γ(P-P0)]

[0094] in:

[0095] α = 0.0035 / ℃ (temperature coefficient)

[0096] β = 0.0008 / %RH (humidity coefficient)

[0097] γ = 0.0002 / hPa (pressure coefficient)

[0098] T0, H0, and P0 are the calibration environmental parameters.

[0099] 2. Anomaly detection:

[0100] Based on the improved Z-score method:

[0101] z_i = |x_i - median(X)| / MAD

[0102] Where, MAD = 1.4826 × median(|x_i - median(X)|)

[0103] If z_i > 3.5, it is considered an outlier.

[0104] Time continuity test:

[0105] ΔE_t=|E_t-E_{t-1}|

[0106] If ΔE_t > 3σ_E (historical standard deviation), a review is triggered.

[0107] 3. Dynamic baseline correction:

[0108] E_final = E_corrected - B(t)

[0109] B(t) is a dynamic baseline based on an exponentially weighted moving average:

[0110] B(t)=αB(t-1)+(1-α)E_corrected

[0111] α = 0.95-0.99 (adjustable).

[0112] III. Data Analysis and Early Warning Issuance

[0113] 1. Data reception and parsing:

[0114] The data analysis and early warning platform receives real-time data from various sensors, parses and stores it. Further preprocessing and analysis are performed on the data to extract effective information about changes in the electric field.

[0115] 2. Analysis and modeling of electric field changes:

[0116] By utilizing big data analytics and machine learning algorithms, the received data is analyzed in depth to establish a model for the distribution of electric field changes.

[0117] By using models to predict the changing trends of electric fields in different regions, a scientific basis can be provided for issuing early warnings.

[0118] 3. Early Warning Issuance and Response:

[0119] When the electric field change in a certain area exceeds the preset safety threshold, the data analysis and early warning platform will automatically trigger the early warning mechanism.

[0120] Warning information is issued to relevant departments and personnel through SMS, email, and APP push notifications, and emergency response suggestions are provided.

[0121] 4. Report Generation and Feedback:

[0122] Based on the data analysis results, a detailed monitoring report on electric field changes is generated. The report includes the spatiotemporal distribution characteristics of electric field changes, the identification and assessment of abnormal regions, and other related information.

[0123] The report is presented to users through an interactive interface, and adjustments and optimizations are made based on user feedback.

[0124] Specifically,

[0125] I) Multidimensional Data Analysis

[0126] 1. Spatiotemporal feature extraction:

[0127] Time domain characteristics:

[0128] Mean: μ=(1 / N)ΣE_i

[0129] Wave intensity: σ_E=√[Σ(E_i-μ)] 2 / (N-1)]

[0130] Skewness: γ_1=[Σ(E_i-μ) 3 / N] / σ_E 3

[0131] Spatial correlation analysis:

[0132] Moran index I=(N / W)×[ΣΣw_ij(E_i-μ)(E_j-μ)] / Σ(E_i-μ) 2

[0133] w_ij is the spatial weight matrix.

[0134] 2. Machine learning modeling:

[0135] Feature engineering:

[0136] X = [E_t, ΔE_t, T, H, P, E_{t-1}, ..., E_{tk}] uses PCA for dimensionality reduction while preserving 95% of the variance.

[0137] Ensemble learning models:

[0138]

[0139] II) Intelligent Report Generation

[0140] 1. Visualization and Analysis Components:

[0141] Spatiotemporal heat map:

[0142] Based on Kriging interpolation:

[0143]

[0144] λ_i is determined by the variogram model.

[0145] Trend Analysis Chart:

[0146] Holt-Winters triple exponential smoothing was used.

[0147]

[0148] Where L, T, and S represent the horizontal, trend, and seasonal components, respectively. 2. Early Warning Decision Logic:

[0149]

[0150] 3. Reports are automatically generated:

[0151] Key metrics:

[0152] Exceedance rate = (Number of exceeding standards / Total number of standards) × 100%

[0153] Spatial autocorrelation index: 0-1 (random-clustered)

[0154] Trend slope: % / h

[0155] Typical report structure:

[0156] (1) Execution summary (key findings);

[0157] (2) Spatiotemporal distribution characteristics;

[0158] (3) Abnormal event analysis;

[0159] (4) Trend prediction;

[0160] (5) Recommended measures.

[0161] The electric field change measurement method of the present invention further includes: integrating environmental parameter monitoring components such as temperature and humidity sensors and barometers; and automatically adjusting the operating parameters of the differential electric field sensor using an intelligent adjustment algorithm based on the environmental data (such as temperature, humidity, and air pressure) of the area to be measured in real time, to ensure the stability and accuracy of the measurement results.

[0162] Specifically, the intelligent adjustment algorithm implementation scheme is as follows:

[0163] 1. Data Acquisition and Preprocessing

[0164] Environmental parameter monitoring: Through integrated temperature and humidity sensors, barometers and other components, it collects environmental parameters such as temperature, humidity and air pressure in real time.

[0165] Data preprocessing: The collected environmental parameter data is preprocessed by filtering and denoising to eliminate outliers and noise and ensure data accuracy.

[0166] 2. Feature Extraction and Analysis

[0167] Feature extraction: Extract key features from the preprocessed environmental parameter data, such as the rate of change of temperature, the range of humidity fluctuations, and the stability of air pressure.

[0168] Feature analysis: The impact of these features on the measurement performance of the differential electric field sensor is analyzed, and a correlation model between environmental parameters and sensor measurement performance is established.

[0169] 3. Working parameter adjustment strategy

[0170] Rule-based adjustment: Based on pre-defined rules, the sensor's operating parameters are automatically adjusted when environmental parameters reach or exceed a certain threshold. For example, when the temperature rises, it may be necessary to adjust the sensor's sensitivity or gain to compensate for the effect of temperature on measurement accuracy.

[0171] Adaptive adjustment: This method utilizes machine learning algorithms to learn the complex relationship between environmental parameters and sensor measurement performance, and dynamically adjusts the sensor's operating parameters based on real-time environmental parameters. This adaptive adjustment can more accurately compensate for the impact of environmental changes on measurement accuracy.

[0172] 4. Parameter adjustment execution

[0173] Parameter calculation: Based on feature analysis and adjustment strategies, calculate the working parameter values ​​that need to be adjusted.

[0174] Parameter setting: Send the calculated operating parameter values ​​to the differential electric field sensor to adjust its operating status.

[0175] 5. Feedback and Verification

[0176] Measurement result feedback: After adjusting the operating parameters, the measurement results of the differential electric field sensor are collected in real time and fed back to the intelligent adjustment algorithm.

[0177] Verification and Adjustment: Verify the adjusted measurement results. If the measurement accuracy is improved, retain the current working parameter settings. If the measurement accuracy is not significantly improved or even decreases, further adjust the working parameters based on the feedback results until the best measurement effect is achieved.

[0178] Algorithm implementation details:

[0179] Algorithm model training: Before deploying the algorithm, a large amount of historical environmental parameters and sensor measurement data are needed to train the machine learning model to learn the complex relationship between environmental parameters and sensor measurement performance.

[0180] Real-time requirement: The algorithm needs to be real-time, able to quickly respond to changes in environmental parameters and adjust the sensor's operating parameters in a timely manner.

[0181] Robustness considerations: The algorithm needs to have a certain degree of robustness, be able to handle outliers and noise, and ensure stable performance in complex and ever-changing environments.

[0182] Intelligent adjustment algorithm:

[0183] 1. Environmental parameter fusion algorithm

[0184] An environmental compensation model is constructed using multi-sensor data fusion technology.

[0185] E_corrected=E_raw×[1+α(T-T_ref)+β(H-H_ref)+γ(P-P_ref)]

[0186] in:

[0187] E_corrected: Corrected electric field strength

[0188] E_raw: Raw measurement value,

[0189] T,H,P: Real-time temperature (°C), humidity (%), and air pressure (hPa).

[0190] T_ref, H_ref, P_ref: Reference environmental conditions (usually 25℃, 50% ppm, 1013.25 hPa).

[0191] α, β, γ: Temperature / humidity / air pressure compensation coefficients (determined through calibration experiments).

[0192] 2. Dynamic parameter adjustment algorithm

[0193] Parameter adaptive adjustment based on fuzzy logic control:

[0194] 1.1 Input variable fuzzification:

[0195] Environmental change rate ΔE=(E_t-E_{t-1}) / Δt

[0196] Environmental parameter deviation D = √[(T - T_ref)] 2 +(H-H_ref) 2 +(P-P_ref) 2 ].

[0197] 1.2 Example of a fuzzy rule base:

[0198] If ΔE is large and D is high, then the sensitivity adjustment is +20%.

[0199] If ΔE is Small AND D is Low, then the gain adjustment is -5%.

[0200] 1.3 Deblurring (Center of Gravity Method):

[0201] U=Σ(μ_i×u_i) / Σμ_i

[0202] Where U is the final adjustment, μ_i is the membership degree, and u_i is the control variable.

[0203] 3. Real-time filtering processing

[0204] Adaptive Kalman filtering is used to eliminate interference from sudden environmental changes.

[0205] Equations of state:

[0206] x_k=A_k x_{k-1}+B_ku_k+w_k

[0207] Observation equation:

[0208] z_k=H_k x_k+v_k

[0209] in:

[0210] Q_k = f(ΔT, ΔH, ΔP) # Dynamic process noise covariance

[0211] R_k=g(E_var)# Dynamic observation noise covariance.

[0212] The aforementioned intelligent adjustment algorithm automatically adjusts the operating parameters of the differential electric field sensor based on real-time monitored environmental conditions, thereby improving the stability and accuracy of measurement results. This adaptive adjustment method significantly enhances the sensor's measurement performance in complex environments, providing strong support for the development of electric field measurement technology.

[0213] The electric field change measurement method of the present invention further includes: calibrating the differential electric field sensor within a preset period using a built-in high-precision calibration source and an automatic calibration algorithm to eliminate drift errors during long-term operation and ensure the long-term stability and accuracy of the measurement results.

[0214] An automatic calibration algorithm is an algorithm built into a system or device used to periodically or as needed calibrate differential electric field sensors to eliminate drift errors during long-term operation and ensure the long-term stability and accuracy of measurement results. Common automatic calibration algorithms include model-prediction-based calibration algorithms and data-driven calibration algorithms. This invention uses a data-driven automatic calibration algorithm as an example for illustration.

[0215] Algorithm principle:

[0216] Data-driven automatic calibration algorithms collect sensor output data and environmental parameter data at different time points to establish a mathematical model between the sensor output and environmental parameters. This model is then used to predict the sensor's output value under the current environment and compared with the actual measured value to calculate the calibration parameters.

[0217] Algorithm steps:

[0218] 1. Data Acquisition:

[0219] Collect the output data of the sensor at different time points.

[0220] At the same time, environmental parameter data such as temperature, humidity, and air pressure are collected at the corresponding time points.

[0221] 2. Model Establishment:

[0222] Based on the collected data, a mathematical model is established between the sensor output and environmental parameters. This model can be linear or nonlinear, depending on the characteristics of the sensor and the operating environment.

[0223] The parameters of the model are estimated to obtain the parameter values ​​of the model.

[0224] 3. Prediction and Calibration:

[0225] At the time point when calibration is required, the established model is used to predict the sensor's output value under the current environment.

[0226] The predicted value is compared with the actual measured value, and the error is calculated.

[0227] Adjust the sensor's calibration parameters, such as sensitivity and offset, based on the magnitude and direction of the error, to reduce the error.

[0228] 4. Verification and Update:

[0229] Verify the calibrated sensor to ensure that the calibration effect meets the requirements.

[0230] If the calibration results are not satisfactory, recollect the data, update the model, and repeat the calibration process described above.

[0231] Calibration implementation method:

[0232] 1. Built-in high-precision calibration source:

[0233] The automatic calibration and verification module includes a built-in high-precision calibration source to provide a known electric field strength value as a calibration reference. This calibration source can be a stable voltage source, current source, or other device capable of generating a known electric field strength.

[0234] Employing a quantized Hall effect voltage reference:

[0235] V_H=(h / e 2 (I / n)

[0236] in:

[0237] h: Planck's constant,

[0238] e: electron charge

[0239] I: constant current

[0240] n: Fill factor (integer).

[0241] Calibrate electric field strength:

[0242] E_cal=V_H / d

[0243] d: Calibration electrode spacing.

[0244] 2. Regular calibration:

[0245] Based on the sensor's usage and operating environment, a periodic calibration cycle is set. During this cycle, the automatic calibration algorithm will automatically activate and calibrate the sensor.

[0246] 3. Calibration process:

[0247] Place the sensor under a known electric field strength generated by the calibration source.

[0248] Collect the sensor's output data and compare it with known values ​​from the calibration source.

[0249] The calibration parameters are calculated using an automatic calibration algorithm, and the sensor's operating parameters are adjusted.

[0250] Verify the calibrated sensor to ensure that the calibration effect meets the requirements.

[0251] 3.1 Automatic Calibration Algorithm Flow

[0252] Zero point calibration:

[0253] E_offset = (1 / N)ΣE_i (under masked conditions)

[0254] Sensitivity calibration:

[0255] S = (E_meas - E_offset) / E_cal

[0256] Nonlinear correction (polynomial fitting):

[0257] E_true=a0+a1E_raw+a2E_raw 2 +a3E_raw 3

[0258] The coefficients are determined using the least squares method:

[0259] minΣ(E_cal_i-E_true_i) 2

[0260] Temperature drift compensation:

[0261] S(T)=S_0[1+c1(T-T0)+c2(T-T0) 2 ].

[0262] 3.2 Online Diagnostic Algorithm

[0263] The sensor used to detect the Mahalanobis distance is malfunctioning.

[0264]

[0265] in:

[0266] x: Current measurement vector [E,T,H,P]

[0267] μ: Historical data mean vector

[0268] Σ: covariance matrix

[0269] If D 2 >χ 2 (α,df) triggers a calibration request.

[0270] 4. Calibration records and traceability:

[0271] During each calibration process, information such as calibration time, calibration parameters, and calibration results are recorded. This information can be used for subsequent sensor performance analysis and troubleshooting, ensuring the measurement accuracy and reliability of the sensor.

[0272] By employing the aforementioned automatic calibration algorithm and calibration implementation method, the stability and accuracy of the differential electric field sensor can be ensured during long-term operation, thereby improving the precision and reliability of electric field change measurement.

[0273] The electric field change measurement method of the present invention further includes: developing a dynamic calibration method based on online monitoring, automatically adjusting calibration parameters according to environmental changes to ensure the stability and accuracy of the differential electric field sensor during long-term operation. Simultaneously, a compensation algorithm is used to correct the measurement results in real time, further eliminating errors.

[0274] Dynamic calibration based on online monitoring is a more intelligent and flexible calibration method. It utilizes online monitoring technology to acquire environmental parameters and sensor operating status in real time, and dynamically adjusts calibration parameters based on this real-time information. This method can more accurately compensate for the impact of environmental changes on sensor measurement performance, ensuring that the sensor maintains stable measurement accuracy even in complex and changing environments.

[0275] The dynamic calibration method based on online monitoring can be implemented through the following steps:

[0276] 1. Online monitoring and data collection:

[0277] Environmental parameter monitoring: Using environmental parameter monitoring components such as temperature and humidity sensors and barometers, parameters such as temperature, humidity and air pressure in the sensor's working environment are collected in real time.

[0278] Sensor status monitoring: By monitoring parameters such as the sensor's output signal, operating current, and voltage, the sensor's operating status and performance can be evaluated in real time. This can include detecting the sensor's zero-point drift, sensitivity changes, etc.

[0279] 2. Calculation of dynamic calibration parameters:

[0280] Establish a calibration model: Based on the sensor's characteristics and operating environment, establish a mathematical model relating calibration parameters to environmental parameters and sensor status. This model can be based on physical principles or a data-driven machine learning model.

[0281] Real-time calibration parameter calculation: By substituting the real-time acquired environmental parameters and sensor status into the calibration model, the required calibration parameters for the current environment are calculated. These parameters may include the sensor's sensitivity adjustment coefficient, offset adjustment value, etc.

[0282] 3. Calibration parameter adjustment and execution:

[0283] Calibration parameter adjustment: Based on the calculated calibration parameters, the sensor's operating parameters are automatically adjusted. This can be achieved through software algorithms, adjusting the sensor's internal circuit parameters or signal processing parameters.

[0284] Calibration execution: After adjusting the calibration parameters, perform short-term testing and verification on the sensor to ensure that the calibration effect meets the requirements. This can be achieved by comparing the measurement data before and after calibration.

[0285] 4. Application of real-time compensation algorithm:

[0286] Compensation Algorithm Design: Develop a compensation algorithm based on real-time measurement data to correct the sensor's output signal in real time. This algorithm can be designed based on the sensor's working principle, the changing patterns of environmental parameters, and historical measurement data.

[0287] Real-time calibration execution: During sensor measurement, real-time acquired environmental parameters and sensor status are substituted into the compensation algorithm to calculate the calibrated measurement value. This calibration process can be integrated into the signal processing and analysis unit to achieve real-time calibration of the measurement results.

[0288] 5. Calibration effect verification and feedback:

[0289] Calibration effectiveness verification: Periodically perform performance tests on the calibrated sensor to verify whether the calibration effect meets the requirements. This can be achieved by comparing measurement data before and after calibration, or by comparing it with other sensors of known accuracy.

[0290] Feedback and Adjustment: If the calibration results are unsatisfactory, adjust the calibration model or compensation algorithm based on the test results until a satisfactory calibration effect is achieved. This can be done by updating algorithm parameters, retraining the machine learning model, etc.

[0291] 6. Dynamic calibration strategy:

[0292] Adaptive calibration: The calibration cycle and parameters are adaptively adjusted based on the sensor's usage and changes in the working environment. For example, the calibration frequency can be increased when there are significant environmental changes, while the calibration cycle can be extended when the sensor performance is stable.

[0293] Remote calibration: Remote calibration is achieved using the data transmission and processing module. This can be done by transmitting calibration parameters to the sensor via a network or by remotely accessing the sensor's calibration interface. This allows for sensor calibration and maintenance without interrupting measurements.

[0294] Through the above steps, a dynamic calibration method based on online monitoring can be developed to ensure the stability and accuracy of differential electric field sensors during long-term operation. This method not only improves the measurement accuracy of the sensor but also reduces calibration costs and maintenance difficulty, providing strong support for the development of the field of electric field change measurement technology.

[0295] In addition, the present invention also includes system maintenance and management:

[0296] 1. Regular inspection and maintenance:

[0297] Regularly inspect and maintain the differential electric field sensor and data acquisition module, including cleaning the sensor surface, calibrating the sensitivity, and checking the stability of the data transmission line.

[0298] To ensure the continuous and efficient operation of the system and improve measurement accuracy and stability.

[0299] 2. Data Backup and Security:

[0300] Implement a strict data backup strategy, regularly back up the collected electric field change data, and store it in a safe and reliable storage medium.

[0301] Establish a data access control mechanism to ensure data confidentiality and security. Regarding data recovery, establish a rapid response mechanism to enable quick recovery in the event of accidental data loss or corruption.

[0302] 3. System upgrades and optimizations:

[0303] With the advancement of technology and the continuous development of electric field measurement technology, the system is regularly upgraded and optimized.

[0304] Updating sensor firmware, optimizing data acquisition algorithms, and enhancing the intelligence level of data analysis software are all ways to improve the system's measurement efficiency and accuracy.

[0305] Adjust the system configuration according to actual needs and environmental changes to ensure that the system can adapt to complex and ever-changing measurement environments and provide more accurate and reliable electric field change measurement services.

[0306] In summary, the improvements to the measurement method of this invention are as follows:

[0307] 1. Environmental Adaptability Optimization Strategy: For complex and variable electromagnetic environments, such as densely populated urban areas with high-rise buildings and industrial zones, the layout and sampling frequency of the differential electric field sensor are optimized to reduce environmental interference and improve measurement accuracy. Simultaneously, an environmental adaptability adjustment module is used to monitor and adjust sensor parameters in real time to adapt to different environmental conditions.

[0308] 2. Multi-parameter fusion analysis technology: Combining environmental parameters such as temperature, humidity, air pressure, and wind speed, and employing machine learning algorithms such as multiple linear regression and neural networks, a predictive model for electric field changes is established. Through multi-parameter fusion analysis, the accuracy and reliability of electric field change measurements are improved.

[0309] 3. Dynamic Calibration and Compensation Technology: A dynamic calibration method based on online monitoring is developed to automatically adjust calibration parameters according to environmental changes, ensuring the stability and accuracy of the differential electric field sensor during long-term operation. Simultaneously, a compensation algorithm is used to correct the measurement results in real time, further eliminating errors.

[0310] 4. Intelligent Path Planning and Optimization: For mobile electric field measurement systems, GIS and IoT technologies are used to intelligently plan detection paths, ensuring efficient coverage of key urban monitoring areas. Simultaneously, based on historical data and real-time monitoring results, the detection frequency and sampling point locations are dynamically adjusted to improve measurement efficiency.

[0311] 5. User-friendly interface and data analysis tools: The system features an intuitive and easy-to-use user interface, providing visualized data analysis reports, including electric field variation distribution maps, trend analysis, and abnormal event logs. It also offers data export and custom report generation functions to facilitate management decision-making and analysis.

[0312] This invention provides a novel, highly reliable differential electric field change measurement method, enabling high-precision real-time monitoring and accurate analysis of electric field variations. This method employs advanced differential electric field sensing technology and intelligent data analysis techniques, capable of capturing minute electric field changes in complex environments, providing a scientific basis for electromagnetic environment monitoring, electromagnetic compatibility analysis, and other fields. Furthermore, regular system maintenance, data backup and security strategies, as well as continuous upgrades and optimization measures, further ensure the system's stability and reliability, enhancing its application value and effectiveness in various scenarios.

[0313] This patent proposes a novel, highly reliable differential electric field change measurement method, focusing on the field of electric field measurement technology, particularly addressing the high-precision measurement needs of electric field changes in complex and variable environments. This method employs an innovative differential electric field sensor design, integrating environmental adaptability adjustment and automatic calibration functions, along with a set of efficient data processing and analysis algorithms. The differential electric field sensor can accurately capture weak electric field change signals in the environment and effectively suppress common-mode interference through differential technology, improving measurement accuracy. The environmental adaptability adjustment function automatically adjusts sensor parameters according to real-time environmental conditions (such as temperature and humidity) to ensure the stability of measurement results. The automatic calibration function periodically or as needed calibrates the sensor to further eliminate measurement errors. The accompanying data processing and analysis algorithms are responsible for quickly and accurately processing the sensor output data to generate a reliable electric field change report.

[0314] The core advantages of this invention lie in its high reliability and high precision, successfully overcoming the technical challenge of interference-prone electric field measurements in complex environments. By integrating environmental adaptability adjustment, automatic calibration, and efficient data processing and analysis technologies, this method significantly improves the accuracy and stability of electric field change measurements. Furthermore, this method is not only suitable for high-precision electric field measurement scenarios such as scientific research experiments and industrial monitoring, but can also be widely applied in fields such as urban environmental monitoring, electromagnetic compatibility testing, and lightning warning, providing strong support for the development of related fields.

[0315] This invention not only solves the technical challenges of measuring electric field changes in complex environments, but also reduces measurement costs and improves measurement efficiency, making a significant contribution to the development of electric field measurement technology.

[0316] Example 2

[0317] The present invention also provides an electric field change measurement system, which is used to implement the electric field change measurement method of the foregoing embodiments. The system includes: a differential electric field sensing module, a high-precision data processing unit, a remote communication module, and an intelligent analysis platform.

[0318] The differential electric field sensing module is responsible for capturing weak electric field changes in the environment and generating differential signals to suppress common-mode interference.

[0319] The high-precision data processing unit receives the signal output from the sensing module and performs filtering, amplification, analog-to-digital conversion, and other processing to ensure the accuracy and real-time performance of the data.

[0320] The remote communication module uses low-power wide area network technology or wired communication to achieve real-time remote transmission of measurement data.

[0321] The intelligent analysis platform receives data from various monitoring points, performs comprehensive analysis and processing, and generates electric field change distribution maps, trend analysis reports, etc., for relevant personnel to view and make decisions.

[0322] The electric field change measurement system also includes: an environmental adaptability adjustment module and an automatic calibration and verification module;

[0323] The environmental adaptability adjustment module is used to automatically adjust the operating parameters of the differential electric field sensor based on the environmental data of the area to be measured, which is monitored in real time for changes in the electric field.

[0324] An automatic calibration and verification module is used to calibrate the differential electric field sensor within a preset period. The high-precision data processing unit includes an analog-to-digital converter, a signal amplification circuit, and a filtering circuit.

[0325] A filtering circuit is used to filter the received electric field change signal.

[0326] The signal amplification circuit is used to amplify the filtered electric field change signal;

[0327] An analog-to-digital converter is used to convert amplified electric field change signals into digital signals to obtain electric field change data.

[0328] like Figure 2 As shown, this invention provides an electric field change measurement system, which consists of four core modules: a differential electric field sensing module, a high-precision data processing unit, a remote communication module, and an intelligent analysis platform. The differential electric field sensing module employs a precisely designed differential structure, effectively suppressing common-mode interference and improving measurement accuracy. The high-precision data processing unit is responsible for real-time analysis of the signal output from the sensing module, performing filtering, amplification, and analog-to-digital conversion to ensure data accuracy and real-time performance. The remote communication module uses low-power wide-area network technology or wired communication to achieve real-time remote transmission of measurement data. The intelligent analysis platform receives data from various monitoring points, performs comprehensive analysis, trend prediction, and anomaly detection, providing managers with intuitive monitoring reports and decision support.

[0329] In particular, Figure 2 The document details the structure and working principle of the differential electric field sensing module. This module employs a dual-electrode differential design, enabling it to capture weak electric field changes in the environment and generate differential signals to suppress common-mode interference. Simultaneously, the module integrates high-precision amplifiers and filters, further improving measurement sensitivity and accuracy. The data processing unit works closely with the sensing module, receiving and processing sensing signals in real time via a high-speed interface to ensure data real-time performance and accuracy. The entire system's operation flow is described in detail below. Figure 2The details of the patented technology are clearly presented, highlighting its enormous potential and value in highly reliable differential electric field change measurement.

[0330] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method of measuring an electric field change, characterized by, The method includes: S1. Install a differential electric field sensor in the region where the change in electric field is to be measured; S2. Collect and preprocess electric field change data of the area to be measured based on differential electric field sensors; S3. Based on the preprocessed electric field change data, establish an electric field change distribution model and obtain data analysis results; S4. Based on the data analysis results, generate a monitoring report on electric field changes; The method further includes: automatically adjusting the operating parameters of the differential electric field sensor using an intelligent adjustment algorithm based on the environmental data of the area to be measured by real-time monitoring of electric field changes; and calibrating the differential electric field sensor within a preset period using an automatic calibration algorithm. The intelligent adjustment algorithm includes: Environmental parameter fusion algorithm: Employing multi-sensor data fusion technology to construct an environmental compensation model. E_corrected = E_raw × [1 + α(T - T_ref) + β(H - H_ref) + γ(P - P_ref)]; Where: E_corrected: corrected electric field strength, E_raw: original measured value, T, H, P: real-time temperature, humidity, and air pressure, T_ref, H_ref, P_ref: reference environmental conditions, α, β, γ: temperature / humidity / air pressure compensation coefficients; Dynamic parameter adjustment algorithm: Achieving adaptive parameter adjustment based on fuzzy logic control. Input variable fuzzification: Environmental change rate ΔE = (E_t - E_{t-1}) / Δt; Environmental parameter deviation D = √[(T-T_ref)² + (H-H_ref)² + (P-P_ref)²]; Example of a fuzzy rule base: If ΔE is large and D is high, then adjust the sensitivity by +20%. If ΔE is small and D is low, then the gain adjustment is -5%. Deblurring: U = Σ(μ_i × u_i) / Σμ_i; Where U is the final adjustment, μ_i is the membership degree, and u_i is the control variable; Develop a dynamic calibration method based on online monitoring to automatically adjust calibration parameters according to environmental changes, including: Real-time acquisition of environmental parameters from sensors, including temperature, humidity, and air pressure. Real-time evaluation of sensor operating status and performance, including detection of sensor zero drift and sensitivity changes; Based on the characteristics of the sensor and its working environment, a calibration model is established between the calibration parameters, environmental parameters, and sensor status. The calibration model is based on physical principles or a data-driven machine learning model. The environmental parameters and sensor status collected in real time are substituted into the calibration model to calculate the calibration parameters required under the current environment. The calibration parameters include the sensor sensitivity adjustment coefficient and offset adjustment value. Based on the calculated calibration parameters, the internal circuit parameters or signal processing parameters of the sensor are automatically adjusted. After adjusting the calibration parameters, the sensor is tested and verified for a short period of time by comparing the measurement data before and after calibration.

2. The electric field change measurement method according to claim 1, characterized by, The differential electric field sensor installed in S1 in the region where the electric field change is to be measured includes: S11. Within the area where the electric field change is to be measured, plan the layout grid of the differential electric field sensor based on the potential sources of the electric field change, topography, and electromagnetic interference. S12. Install differential electric field sensors on the layout grid and adjust the installation angle and orientation of the differential electric field sensors. S13. Equip each differential electric field sensor with a data acquisition module and a data processing unit, and establish a data transmission channel.

3. The method of claim 1, wherein The S2 step involves collecting and preprocessing electric field change data of the region to be measured based on a differential electric field sensor, including: S21. Collect electric field change data of the area to be measured based on differential electric field sensors; S22. Preprocess the electric field change data, including data cleaning, outlier removal, and data smoothing; S23. Upload the preprocessed electric field change data to the intelligent analysis platform.

4. The method for measuring electric field change according to claim 1, characterized in that, In step S3, based on the preprocessed electric field change data, an electric field change distribution model is established to obtain data analysis results, including: S31. Analyze the preprocessed electric field change data using big data analysis and machine learning algorithms, and establish a distribution model of electric field change. S32. Based on the electric field variation distribution model, obtain the data analysis results.

5. A system for measuring electric field change, said system being used to implement the electric field change measurement method according to any one of claims 1-4, characterized in that, The system includes: a differential electric field sensing module, a high-precision data processing unit, a remote communication module, and an intelligent analysis platform; The differential electric field sensing module is used to capture the electric field change signal in the area to be measured using a differential electric field sensor. A high-precision data processing unit is used to receive electric field change signals and perform preprocessing to obtain electric field change data. The remote communication module is used to realize the real-time remote transmission of electric field change data using low-power wide area network technology or wired communication. The intelligent analysis platform is used to receive electric field change data, perform comprehensive analysis, and generate electric field change monitoring reports.

6. The electric field change measurement system according to claim 5, characterized in that, The system also includes: an environmental adaptability adjustment module and an automatic calibration and verification module; The environmental adaptability adjustment module is used to automatically adjust the operating parameters of the differential electric field sensor based on the environmental data of the area to be measured, which is monitored in real time for changes in the electric field. The automatic calibration and verification module is used to calibrate the differential electric field sensor within a preset period.

7. The electric field change measurement system according to claim 5, characterized in that, The high-precision data processing unit includes: an analog-to-digital converter, a signal amplification circuit, and a filtering circuit; A filtering circuit is used to filter the received electric field change signal. The signal amplification circuit is used to amplify the filtered electric field change signal; An analog-to-digital converter is used to convert amplified electric field change signals into digital signals to obtain electric field change data.

Images