Equipotential connection measurement method and system based on precise low resistance

Abstract

The present application relates to the technical field of electrical variable measurement, and discloses an equipotential connection measurement method and system based on precise low resistance. The method includes: completing the initial configuration of the measurement channel according to the sampling parameters of the high-precision ADC chip and the arrangement of temperature sensors to obtain a dynamic compensation parameter database, and at the same time connecting 20 channels with an elastic crimping structure to obtain stress compensation data; using a standard resistance network to obtain dynamic calibration compensation coefficients through time-division multiplexing calibration combined with temperature-resistance and stress characteristic analysis; performing four-wire measurement based on the coefficients and sampling at 1 kHz to obtain raw data; obtaining the resistance value through compensation processing and interference identification; performing correlation analysis to obtain state parameters; and finally evaluating the stability to obtain optimized compensation parameters. The present application is used to solve the dynamic interference problem caused by temperature changes and mechanical stress during the measurement of equipotential connection resistance, and to achieve high-precision multi-channel measurement.

Description

Technical Field

[0001] This application relates to the field of electrical variable measurement, and particularly to an equipotential connection measurement method and system based on precise low resistance. Background Art

[0002] In modern industrial production and power systems, equipotential connection is a key technical measure to ensure the safe operation of equipment and personal safety. Equipotential connection connects conductive parts such as equipment enclosures, metal components, and grounding devices to the same potential to prevent dangerous voltages caused by potential differences. Currently, the resistance measurement of equipotential connection mainly adopts the four-wire measurement method, which calculates the resistance value by injecting a constant current into the measured resistance and measuring the voltage at both ends. With the large-scale and complexity of industrial equipment, the number of equipotential connection points is increasing continuously, posing higher requirements for the accuracy, stability, and reliability of the measurement system.

[0003] However, the existing equipotential connection resistance measurement technologies have obvious deficiencies. First, the measurement results are easily affected by factors such as test line resistance and contact resistance, resulting in large measurement errors. Second, traditional measurement methods are difficult to cope with dynamic interferences caused by temperature changes and mechanical stresses, lacking effective compensation mechanisms. Third, during multi-channel measurement, there is a lack of correlation analysis between channels, and it is impossible to comprehensively evaluate the overall state of the equipotential connection network. In addition, the existing technologies also have problems such as a single sampling strategy and limited data processing capabilities, making it difficult to meet the on-line monitoring requirements of large-scale equipotential connection systems. Summary of the Invention

[0004] This application provides an equipotential connection measurement method and system based on precise low resistance, which is used to solve the dynamic interference problems caused by temperature changes and mechanical stresses during the equipotential connection resistance measurement process and achieve high-precision multi-channel measurement.

[0005] In a first aspect, the present application provides an equipotential connection measurement method based on precise low resistance. The equipotential connection measurement method based on precise low resistance includes: configuring initial parameters for the measurement channels according to the sampling parameters of the high-precision ADC chip and the arrangement positions of the temperature sensors to obtain a dynamic compensation parameter database; meanwhile, physically connecting 20 measurement channels using an elastic crimping structure to obtain mechanical stress compensation data; calibrating the parameters in the dynamic compensation parameter database through time-division multiplexing using a standard resistance network, and combining the mechanical stress compensation data. After analyzing the temperature-resistance characteristics and stress-contact resistance characteristics, obtaining a multi-channel dynamic calibration compensation coefficient; according to the multi-channel dynamic calibration compensation coefficient, applying a compensation current to each measurement channel using an independent excitation source for four-wire measurement, and collecting temperature data and stress data at a sampling rate of 1 kHz to obtain multi-channel original measurement data; performing temperature compensation and stress compensation on the multi-channel original measurement data through a dynamic compensation processing unit, and analyzing the data of adjacent channels by a transient interference identification unit to obtain the equipotential connection resistance value; according to the equipotential connection resistance value, performing correlation analysis on the data characteristics of adjacent measurement channels, and increasing the sampling frequency when an abnormal trend is detected to obtain the equipotential connection network state parameters; according to the equipotential connection network state parameters, evaluating the stability of the measurement channels, and obtaining optimized compensation model parameters based on the historical data of temperature change and stress change.

[0006] In a second aspect, the present application provides an equipotential connection measurement system based on precise low resistance. The equipotential connection measurement system based on precise low resistance includes:

[0007] An acquisition module, configured to configure initial parameters for the measurement channels according to the sampling parameters of the high-precision ADC chip and the arrangement positions of the temperature sensors to obtain a dynamic compensation parameter database; meanwhile, physically connecting 20 measurement channels using an elastic crimping structure to obtain mechanical stress compensation data;

[0008] A calibration module, configured to calibrate the parameters in the dynamic compensation parameter database through time-division multiplexing using a standard resistance network, and combine the mechanical stress compensation data. After analyzing the temperature-resistance characteristics and stress-contact resistance characteristics, obtaining a multi-channel dynamic calibration compensation coefficient;

[0009] A measurement module, configured to apply a compensation current to each measurement channel using an independent excitation source for four-wire measurement according to the multi-channel dynamic calibration compensation coefficient, and collect temperature data and stress data at a sampling rate of 1 kHz to obtain multi-channel original measurement data;

[0010] A compensation module, configured to perform temperature compensation and stress compensation on the multiplexed original measurement data through a dynamic compensation processing unit, and analyze the data of adjacent channels by a transient interference identification unit to obtain an equipotential connection resistance value;

[0011] An association module, configured to perform association analysis on the data characteristics of adjacent measurement channels according to the equipotential connection resistance value, and increase the sampling frequency when an abnormal trend is detected to obtain equipotential connection network state parameters;

[0012] An evaluation module, configured to evaluate the stability of the measurement channels according to the equipotential connection network state parameters, and obtain optimized compensation model parameters based on historical data of temperature change and stress change.

[0013] In the technical solution provided by the present application, through the combination of sampling by a high-precision ADC chip and the arrangement of temperature sensors, a dynamic compensation parameter database is established. At the same time, an elastic press-fit structure is used to realize the physical connection of 20 measurement channels. This design not only ensures the accuracy of data acquisition but also solves the reliability problem of multi-channel connection. Secondly, a high-precision standard resistance network is used for time-division multiplexing calibration. Combining the analysis of temperature-resistance characteristics and stress-contact resistance characteristics, a multiplexed dynamic calibration compensation coefficient is obtained, significantly improving the measurement accuracy. Thirdly, through the design of an independent excitation source and a high-frequency sampling strategy of 1 kHz, synchronous acquisition of temperature data and stress data is realized, providing complete data support for subsequent compensation processing. In terms of data processing, the combined use of a dynamic compensation processing unit and a transient interference identification unit effectively eliminates the influence of various interference factors. In particular, the sampling frequency can be automatically increased when an abnormal trend is detected, reflecting the adaptive characteristics of the solution. Finally, through the evaluation of the stability of the measurement channels and the optimization of the compensation model parameters, continuous optimization of the measurement system is achieved. At the algorithm level, this solution innovatively combines a variety of data processing algorithms, including dynamic compensation algorithms, time-division multiplexing algorithms, transient interference identification algorithms, etc. The synergistic effect of these algorithms not only improves the measurement accuracy but also enhances the anti-interference ability and adaptability of the system. Through the comprehensive application of these technical features, the present invention successfully solves the dynamic interference problem in equipotential connection resistance measurement and achieves the technical goals of high precision, multi-channel, and real-time monitoring. Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1Schematic diagram of an embodiment of the method for measuring equipotential connection based on precise low resistance in the embodiments of the present application;

[0016] Figure 2 Schematic timing diagram for initial parameter configuration of the measurement channel in the embodiments of the present application;

[0017] Figure 3 Schematic diagram of an embodiment of the equipotential connection measurement system based on precise low resistance in the embodiments of the present application. Detailed implementation manners

[0018] The embodiments of the present application provide a method and system for measuring equipotential connection based on precise low resistance. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order different from that illustrated or described here. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the specific processes of the embodiments of the present application are described below. Please refer to Figure 1 An embodiment of the method for measuring equipotential connection based on precise low resistance in the embodiments of the present application includes:

[0020] Step S101: According to the sampling parameters of the high-precision ADC chip and the arrangement positions of the temperature sensors, perform initial parameter configuration on the measurement channels to obtain a dynamic compensation parameter database; meanwhile, physically connect 20 measurement channels using an elastic crimping structure to obtain mechanical stress compensation data;

[0021] Step S102: Perform time-division multiplexing calibration on the parameters in the dynamic compensation parameter database through a standard resistance network, and combine the mechanical stress compensation data. After analyzing the temperature-resistance characteristics and stress-contact resistance characteristics, obtain a multi-channel dynamic calibration compensation coefficient;

[0022] Step S103: According to the multi-channel dynamic calibration compensation coefficient, use an independent excitation source to apply a compensation current to each measurement channel for four-wire measurement, and collect temperature data and stress data at a sampling rate of 1 kHz to obtain multi-channel original measurement data;

[0023] Step S104: Perform temperature compensation and stress compensation on the multiplexed original measurement data through the dynamic compensation processing unit, and analyze the data of adjacent channels by the transient interference identification unit to obtain the equipotential connection resistance value;

[0024] Step S105: Perform correlation analysis on the data characteristics of adjacent measurement channels according to the equipotential connection resistance value, and increase the sampling frequency when an abnormal trend is detected to obtain the equipotential connection network state parameters;

[0025] Step S106: Evaluate the stability of the measurement channels according to the equipotential connection network state parameters, and obtain the optimized compensation model parameters based on the historical data of temperature change and stress change.

[0026] It can be understood that the execution subject of this application can be an equipotential connection measurement system based on precise low resistance, or a terminal or a server. Specifically, it is not limited here. This application embodiment is described by taking the server as the execution subject as an example.

[0027] Specifically, in the initial parameter configuration stage, the high-precision ADC chip uses a 24-bit resolution, the range is set to ±10V, and the sampling rate is initially configured to 1kHz. The temperature sensor uses a PT100 platinum resistance, which is arranged at the incoming end, middle end, and outgoing end of each measurement channel to form a temperature gradient monitoring network. The dynamic compensation parameter database stores sampling parameter configuration information, temperature sensor position coordinate data, and initial compensation coefficients. The elastic crimping structure uses a special spring contact array to physically connect 20 measurement channels. Each contact integrates a strain-type stress sensor to collect contact pressure data in real time and generate mechanical stress compensation data.

[0028] In the time-division multiplexing calibration stage, a 0.01-level standard resistance network is used as the reference. The 20 measurement channels are polled and switched through the multiplexer array, and the calibration time for each channel is 1ms. During the calibration process, temperature data and stress data are synchronously collected to establish a temperature-resistance characteristic curve and a stress-contact resistance characteristic curve. The temperature characteristic analysis uses a piecewise linear fitting method to divide the temperature coefficient change in the range of -20°C to 60°C into multiple intervals. The stress characteristic analysis is based on the inverse relationship between the contact pressure and the contact resistance, and the dynamic calibration compensation coefficient is obtained through data fitting. In the four-wire measurement stage, an independent excitation source provides a 100mA constant current excitation for each measurement channel, and the thermoelectric potential influence is eliminated through reverse excitation. The amplitude of the compensation current is modulated by the dynamic calibration compensation coefficient to achieve dynamic compensation of the measurement error. The sampling system synchronously collects voltage signals, temperature data, and stress data at a frequency of 1kHz to form a multiplexed original measurement data stream.

[0029] During the compensation processing stage, the dynamic compensation processing unit performs temperature compensation and stress compensation on the original measurement data. The temperature compensation calculates the influence of the thermoelectric potential based on the temperature gradient data, and the stress compensation takes into account the dynamic changes of the contact resistance. The transient interference is identified by the correlation analysis of the adjacent channel data. The common-mode interference components are identified by setting the correlation coefficient threshold, and the equipotential connection resistance value is obtained after the compensation processing.

[0030] During the correlation analysis stage, a trend analysis is performed on the equipotential connection resistance value. By calculating the resistance difference and its change rate between adjacent channels, the abnormal change trend is identified. When the change rate of the resistance value is detected to exceed the set threshold, the system automatically increases the sampling frequency to 2 kHz, encrypts the sampling to capture the transient characteristics, and generates the state parameters of the equipotential connection network.

[0031] During the optimization stage, the stability of the measurement channels is evaluated. By analyzing the historical data of temperature changes and stress changes, a correlation model between the measurement error and environmental factors is established. The optimization of the compensation model parameters uses the recursive least squares method, and the compensation coefficients are continuously updated according to the historical data to improve the measurement accuracy.

[0032] Taking the equipotential connection measurement of a certain lightning protection system as an example, the measurement system monitors the grounding grid nodes through 20 measurement channels. During the initial configuration stage, temperature sensors are arranged at three characteristic positions of each node to monitor the temperature gradient changes in real time. During the calibration stage, a 10 mΩ standard resistor is used for time-division multiplexing calibration, and a compensation model is established in combination with the stress data of the pressure contact points. During the measurement process, the system detects a 20% rapid increase in the resistance value of a certain node, automatically triggers the increase of the sampling frequency, reveals the problem of loose contact at this node through the high-density sampling data, and generates an alarm signal. After the continuous optimization of the compensation model parameters, the measurement system can still maintain stable measurement accuracy under the condition of large temperature fluctuations.

[0033] In the embodiments of the present application, through the combination of sampling by a high-precision ADC chip and the arrangement of temperature sensors, a dynamic compensation parameter database is established. At the same time, an elastic crimping structure is adopted to realize the physical connection of 20 measurement channels. This design not only ensures the accuracy of data acquisition but also solves the reliability problem of multi-channel connection. Secondly, a high-precision standard resistor network is used for time-division multiplexing calibration. By combining the temperature-resistance characteristics and stress-contact resistance characteristics analysis, multi-channel dynamic calibration compensation coefficients are obtained, significantly improving the measurement accuracy. Thirdly, through the design of an independent excitation source and a high-frequency sampling strategy of 1 kHz, synchronous acquisition of temperature data and stress data is achieved, providing complete data support for subsequent compensation processing. In terms of data processing, the combined use of a dynamic compensation processing unit and a transient interference identification unit effectively eliminates the influence of various interference factors. In particular, when an abnormal trend is detected, the sampling frequency can be automatically increased, reflecting the adaptive characteristics of the scheme. Finally, through the evaluation of the stability of the measurement channels and the optimization of the compensation model parameters, continuous optimization of the measurement system is achieved. At the algorithm level, this scheme innovatively combines a variety of data processing algorithms, including dynamic compensation algorithms, time-division multiplexing algorithms, transient interference identification algorithms, etc. The synergistic effect of these algorithms not only improves the measurement accuracy but also enhances the anti-interference ability and adaptability of the system. Through the comprehensive application of these technical features, the present invention successfully solves the dynamic interference problem in the measurement of equipotential connection resistance and achieves the technical goals of high-precision, multi-channel, and real-time monitoring.

[0034] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0035] (1) Set a sampling frequency of 1 kHz through the sampling control unit of the high-precision ADC chip, alternately sample a standard resistor group with different resistance values, and suppress noise for the sampled data by combining a fourth-order Butterworth filter to establish reference compensation data;

[0036] (2) Arrange temperature sensors at three key nodes, namely the introduction end, the middle end, and the lead-out end of each measurement channel respectively. Adopt the three-point temperature difference method to collect temperature data sequences, and establish temperature gradient distribution data after thermodynamic equilibrium calculation;

[0037] (3) Collect three-dimensional stress data of the X-axis, Y-axis, and Z-axis by the stress sensing unit integrated in the elastic crimping structure, and generate stress-temperature correlation data through thermal stress coupling calculation based on the temperature gradient distribution data;

[0038] (4) Write the reference compensation data, temperature gradient distribution data, and stress-temperature correlation data into the dynamic compensation parameter database according to the hierarchical storage structure through the data fusion processor;

[0039] (5) A flexible crimping structure with an automatic compensation function is used to crimp and fix 20 measurement channels at a fixed pressure value. The crimping stress value is collected in real time through a built-in strain-type stress sensor, and mechanical stress compensation data is generated by combining and judging the stress threshold.

[0040] Specifically, as Figure 2 shown, it is a timing schematic diagram for initial parameter configuration of the measurement channel in the embodiment of this application. The high-precision ADC chip first sets a sampling frequency of 1 kHz and alternately samples a standard resistor group. The sampled data is filtered by a Butterworth filter to establish reference compensation data; the temperature sensor collects temperature data at three key nodes, and the temperature gradient distribution data is obtained through three-point temperature difference method and thermodynamic equilibrium calculation; the stress sensing unit collects three-dimensional stress data and performs thermal stress coupling calculation in combination with temperature data; the data processor writes various types of data into the dynamic compensation parameter database according to a hierarchical structure; finally, the flexible crimping structure crimps and fixes 20 measurement channels, and generates mechanical stress compensation data by judging the stress threshold.

[0041] Set the sampling frequency, fix the frequency at 1 kHz, and alternately sample a group of standard resistors including 10 mΩ, 100 mΩ, and 1 Ω. During the sampling process, a 4th-order Butterworth filter is used for data processing. The cut-off frequency of the filter is set to 1 / 10 of the sampling frequency, that is, 100 Hz, and the high-frequency noise components in the sampled data are suppressed through a recursive calculation method to form stable reference compensation data.

[0042] The temperature monitoring layout adopts the three-point temperature difference method, and PT100 platinum resistance temperature sensors are installed at the introduction end, middle end, and lead-out end of each measurement channel. The core of the three-point temperature difference method lies in establishing a temperature gradient field through multi-point temperature measurement. The temperature gradient distribution calculation uses the following formula:

[0043]

[0044] Among them, is the temperature gradient distribution value; is the weight coefficient of the i-th measurement point; is the temperature value of the i-th measurement point; is the reference temperature value; is the thermal conductivity matrix; is the heat flux density; is the spatial coordinate.

[0045] This formula describes the calculation process of the temperature gradient distribution. The weight coefficient reflects the importance of different measurement point positions, and its value range is between 0-1. The weight at the introduction end is 0.4, the middle end is 0.3, and the lead-out end is 0.3. The temperature difference ( ) Reflects the deviation degree between the measured point temperature and the reference temperature. The thermal conductivity matrix Characterizes the thermal conduction characteristics in all directions of the space. The diagonal elements represent the main direction conductivity coefficients, and the non-diagonal elements represent the coupled conduction effects. The second-order partial derivative of the heat flux density reflects the curvature change of the temperature field and is used to capture the non-linear characteristics of the temperature distribution.

[0046] The triaxial stress sensing units are integrated in the elastic crimping structure to collect stress data in the X-axis, Y-axis, and Z-axis directions respectively. The thermal stress coupling calculation uses the following formula:

[0047]

[0048] is the thermal stress coupling value; is the thermal stress compensation coefficient; is the elastic modulus; is the stress weight in the k-th direction; are the triaxial stress components; is the temperature sensitivity coefficient; is the temperature gradient distribution value.

[0049] This formula establishes the coupling relationship between stress and temperature. The thermal stress compensation coefficient β reflects the thermo-mechanical characteristics of the material, and the typical value is / ℃. The elastic modulus E characterizes the stiffness characteristics of the material, and for copper it is about 110 GPa. The direction weight reflects the contribution degrees of different axial stresses. Usually, the weights of the X-axis and Y-axis are 0.3, and the weight of the Z-axis is 0.4. The square root term of the sum of squares of the triaxial stress components represents the comprehensive stress intensity. The temperature sensitivity coefficient η in the exponential term describes the sensitivity of the stress response to temperature changes, and its value is usually between 0.01 - 0.1.

[0050] The data fusion processor uses a hierarchical storage structure to manage various types of data. The underlying layer stores the reference compensation data, the middle layer stores the temperature gradient distribution data, and the top layer stores the stress-temperature correlation data. During the data writing process, a timestamp synchronization mechanism is adopted to ensure the temporal consistency of the data. For 20 measurement channels, the elastic crimping structure adopts a constant pressure value design, and the stress state of the crimping point is monitored in real time through the built-in strain type stress sensor. When the measured stress value exceeds the preset threshold range, the compensation mechanism is automatically triggered to generate mechanical stress compensation data.

[0051] Taking the equipotential connection system of a certain 220 kV substation as an example, the grounding points of the main equipment are monitored through 20 measurement channels. During the acquisition of reference compensation data, a 10 mΩ standard resistor is sampled. After the sampled data is processed by a fourth-order Butterworth filter, the noise is effectively suppressed. The temperature monitoring shows that the temperature values of three measurement points are 28.5 °C, 29.2 °C, and 30.1 °C respectively. The temperature gradient distribution is calculated through the three-point temperature difference method. The stress monitoring data shows that the stress values on the X-axis, Y-axis, and Z-axis are 15 N, 12 N, and 20 N respectively. Combining with the temperature gradient data, thermal stress coupling calculation is carried out to generate stress-temperature correlation data. The data fusion processor writes this data into the database according to the hierarchical structure. During the monitoring process, the elastic crimping structure detects that the crimping stress value of a certain grounding point drops to 8 N, which is lower than the preset threshold of 10 N, and immediately generates compensation data for correction.

[0052] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0053] (1) Divide the standard resistor network into 20 independent compensation units, and use the phase interleaved sampling method to poll and switch the parameters in the dynamic compensation parameter database to generate multi-channel interleaved compensation reference data;

[0054] (2) The piezoelectric stress sensor decomposes the mechanical stress compensation data into three-dimensional components, and combines with the non-linear deformation characteristics of the elastic crimping structure to generate stress-resistance coupling data;

[0055] (3) Perform gradient temperature compensation on the multi-channel interleaved compensation reference data through the thermocouple array, and at the same time perform dynamic correction for the temperature change rate to obtain temperature coefficient compensation data;

[0056] (4) Identify the contact resistance characteristics based on the stress-resistance coupling data, and introduce the microscopic deformation compensation of the crimping surface to obtain contact impedance compensation data;

[0057] (5) Perform non-linear weighted processing on the temperature coefficient compensation data and the contact impedance compensation data to generate a dynamic combined compensation coefficient;

[0058] (6) Perform piecewise linear fitting and interpolation calculation on the dynamic combined compensation coefficient to obtain multi-channel dynamic calibration compensation coefficients.

[0059] Specifically, a 0.01-level standard resistance network is divided into 20 independent compensation units, and each unit contains three standard resistance values of 10 mΩ, 100 mΩ, and 1 Ω. Phase-interleaved sampling is used for data acquisition, and the sampling moments between adjacent channels are staggered by 50 μs to form a time-interleaved sampling sequence. The parameters in the dynamic compensation parameter database are polled and switched through a multiplexer matrix, and the switching period for each channel is 1 ms to generate multi-channel interleaved compensation reference data with time stamps.

[0060] The piezoelectric stress sensor is made of piezoelectric crystal material and performs three-dimensional component decomposition on the mechanical stress compensation data. The stress signals in the X, Y, and Z directions are sampled simultaneously at a sampling rate of 1 kHz. The elastic crimping structure uses a special beryllium copper alloy spring sheet, which has non-linear force-deformation characteristics. Based on the stress component data and the deformation characteristic curve, the stress-resistance coupling data of the crimping point is calculated. In the thermocouple array, T-type thermocouples are used for temperature monitoring, and the temperature measurement range is from -20 °C to 60 °C. When performing temperature gradient compensation on the multi-channel interleaved compensation reference data, a temperature change rate curve is established, the temperature change speed and acceleration characteristics are calculated, and the temperature coefficient compensation data is obtained after dynamic correction.

[0061] The contact resistance characteristic identification is calculated based on the following formula:

[0062]

[0063] is the contact resistance value; Surface roughness coefficient; is the shape factor of the m-th contact point; is the resistivity of the contact point material; is the effective area of the contact point; is the pressure coefficient; is the deformation of the contact point; is the contact point pressure; is the total number of contact points.

[0064] The surface roughness coefficient μ reflects the microscopic state of the contact surface, and its value range is usually between 0.8 and 1.2. The shape factor describes the geometric characteristics of the contact point, which is about 1 for circular contact points and less than 1 for elliptical contact points. The material resistivity and the effective contact area together determine the basic resistance value. The pressure coefficient ω characterizes the influence degree of pressure on the contact resistance, and its general value range is between 0.01 and 0.1. The deformation reflects the deformation state of the contact point and has a non-linear relationship with the contact point pressure . This formula takes into account the comprehensive effects of M independent contact points.

[0065] The non - linear weighted processing of the temperature coefficient compensation data and the contact impedance compensation data adopts the following formula:

[0066]

[0067] Where: is the dynamic combined compensation coefficient; is the weight factor of the nth compensation term; is the temperature compensation weight; is the temperature coefficient compensation value; is the contact impedance compensation weight; is the time decay coefficient; is the compensation time interval; is the number of compensation terms.

[0068] Weight factor reflects the importance degree of different compensation terms, and the value is between 0 and 1. The temperature compensation weight and the contact impedance compensation weight sum up to 1, reflecting the relative contributions of the two compensation mechanisms. The temperature coefficient compensation value and the contact resistance reflect the non - linear characteristics after the n - th power operation. The time decay coefficient θ describes the decay law of the compensation effect with time, and the compensation time interval reflects the time - series characteristics of the data.

[0069] The piece - wise linear fitting and interpolation calculation adopt the following formula:

[0070]

[0071] is the multi - channel dynamic calibration compensation coefficient; is the global calibration coefficient; is the piece - wise fitting coefficient matrix; is the temperature piece - wise coefficient; is the temperature deviation; is the pressure piece - wise coefficient; is the pressure deviation.

[0072] Global calibration coefficient is used for overall calibration and is usually close to 1. The piece - wise fitting coefficient matrix stores the fitting coefficients of each piece - wise interval. The temperature piece - wise coefficient and the pressure piece - wise coefficient respectively reflect the influence of temperature and pressure changes on the compensation. The temperature deviation and the pressure deviation It represents the difference between the measured value and the reference value. I and J respectively represent the number of segments of temperature and pressure, and typical values are 3 - 5.

[0073] Taking the equipotential bonding measurement of a certain lightning protection system as an example, 20 measurement channels respectively monitor the grounding connection points of different devices. The independent compensation units of the standard resistance network are switched alternately, and the sampled data shows good time interleaving characteristics. The triaxial stress data measured by the piezoelectric stress sensor reflects the stress distribution state of the pressure contact point after processing. The thermocouple array monitors the temperature change process, and the temperature compensation coefficient is calculated accordingly. On the microscale, the contact resistance shows non-linear characteristics with the change of pressure, and the influence of surface roughness and actual contact area needs to be considered. Through non-linear weighted processing and piecewise fitting, an accurate multi-channel dynamic calibration compensation coefficient is established.

[0074] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0075] (1) Inject excitation currents with adjustable amplitudes into 20 measurement channels respectively through a high-stability current source array, use the multi-channel dynamic calibration compensation coefficient as a modulation signal, and generate a compensated excitation current sequence;

[0076] (2) Under the action of the compensated excitation current sequence, differentially collect the voltage sampling points of each measurement channel, and eliminate the thermoelectric potential interference according to the current reverse method to obtain the original voltage data;

[0077] (3) Perform synchronous demodulation and digital integration operations on the original voltage data, and combine the current amplitude information to generate an initial resistance measurement value;

[0078] (4) Perform partitioned temperature sampling on the measurement channels through a temperature sensor array, and synchronously collect stress sensing data at a frequency of 1 kHz to form temperature-stress joint sampling data;

[0079] (5) Align the time stamps and synchronize the data of the initial resistance measurement value and the temperature-stress joint sampling data to generate a multi-channel measurement data stream;

[0080] (6) Perform real-time error compensation and data reconstruction on the multi-channel measurement data stream to obtain multi-channel original measurement data.

[0081] Specifically, a high-stability current source array is used to excite 20 measurement channels. The high-stability current source uses a reference source circuit with a temperature coefficient less than 1 ppm / °C, the output current range is 10 mA - 1 A, and the resolution is 1 μA. The multi-channel dynamic calibration compensation coefficient is used as a modulation signal to adjust the amplitude of the excitation current in real time, compensate for the influence of various error factors, and thus generate a compensated excitation current sequence.

[0082] When performing differential acquisition on the voltage sampling points of each measurement channel, an instrumentation amplifier circuit is used, and the common-mode rejection ratio is greater than 120 dB. The current reversal method eliminates the influence of thermoelectric potential by periodically changing the direction of the excitation current, collecting the forward and reverse voltage values, and calculating half of their difference. The sampling frequency is set to 1 kHz, ensuring sufficient data points for subsequent processing.

[0083] The synchronous demodulation and digital integration operations of the original voltage data use the following formula:

[0084]

[0085] In this formula: is the initial resistance measurement value; is the gain of the instrumentation amplifier; is the sampling voltage time series; is the synchronous demodulation function; is the weight coefficient of the k-th current component; is the amplitude of the k-th current component; is the frequency of the k-th component; is the integration time constant; T is the integration period; K is the number of current components.

[0086] G reflects the amplification characteristics of the signal conditioning circuit. U(t) is the voltage time series after differential acquisition. The synchronous demodulation function is used to extract specific frequency components. The current information part considers the superposition effect of multiple components, and each component has its weight . The exponential term introduces the influence of the integration time constant.

[0087] The temperature sensor array performs zonal temperature sampling on the measurement channels. Three temperature measurement points are arranged in each channel to form a temperature gradient monitoring network. The stress sensing data acquisition uses a strain gauge sensor with a sensitivity of 2 mV / V. The temperature data and stress data are synchronously acquired at a sampling rate of 1 kHz to form time-aligned temperature-stress joint sampling data. The timestamp alignment uses a 64-bit timestamp with a resolution of 1 μs. The initial resistance measurement value is matched with the temperature-stress joint sampling data according to the timestamp to generate a multi-channel measurement data stream containing complete information. When performing real-time error compensation on the measurement data stream, multiple factors such as the temperature gradient effect and mechanical stress influence are considered to obtain multi-channel raw measurement data.

[0088] Taking the equipotential bonding system of a 500 kV substation as an example, 20 measurement channels respectively monitor the grounding connection points of key equipment. A high-stability current source injects an excitation current of 100 mA into each channel, and the current value is adjusted in real time according to the dynamic calibration compensation coefficient. Voltage sampling adopts a differential method, and the thermoelectric potential interference of about 5 μV is eliminated by the current reversal method. The initial resistance value is obtained after synchronous demodulation and integral operation processing. Temperature monitoring shows the temperature gradient changes at each measurement point, and is synchronously collected together with the stress data. After timestamp alignment and data synchronization processing, a multi-channel measurement data stream is formed, and an accurate measurement result of the equipotential bonding resistance is obtained through compensation processing.

[0089] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0090] (1) Divide the multiplexed original measurement data into multiple compensation unit groups according to the spatial position relationship of the measurement channels, and perform time series segmentation on the data within each group through a sliding window to obtain segmented compensation data streams;

[0091] (2) Calculate the temperature gradient of the segmented compensation data stream, combine the temperature difference coefficient between adjacent channels, generate a temperature distribution feature vector, and perform hierarchical compensation processing according to the temperature change rate to obtain a temperature compensation sequence;

[0092] (3) Cross-verify the temperature compensation sequence with the stress data, and eliminate the transient fluctuations caused by mechanical vibration to obtain steady-state compensation data;

[0093] (4) Separate the common-mode interference components in the steady-state compensation data by the dual-frequency excitation method, and combine the data correlation between adjacent channels to screen out the interference characteristic signals;

[0094] (5) Perform amplitude tracking and phase comparison on the interference characteristic signals, identify the abnormal fluctuation components, and generate an interference suppression compensation factor;

[0095] (6) Perform adaptive compensation operation on the interference suppression compensation factor and the steady-state compensation data to obtain the equipotential bonding resistance value.

[0096] Specifically, according to the physical layout relationship of 20 measurement channels, the multi-channel original measurement data is divided into 4 compensation unit groups, and each group contains 5 spatially adjacent measurement channels. A sliding window with a length of 1 s and an overlap rate of 50% is used to segment the data within each group in the time series, generating a segmented compensation data stream with time correlation. The temperature gradient calculation is based on the temperature data in the segmented compensation data stream. The temperature difference between adjacent measurement points is calculated and combined with the physical distance between the measurement points to obtain the temperature gradient value. The temperature difference coefficient between adjacent channels is obtained through normalization, which reflects the spatial distribution characteristics of the temperature field. The temperature distribution feature vector contains information in two dimensions: the temperature gradient value and the temperature difference coefficient. According to the magnitude of the temperature change rate, the temperature compensation is divided into two levels: rapid change compensation and slow change compensation, and different compensation strategies are adopted respectively to generate the temperature compensation sequence.

[0097] During the cross-validation process, the temperature compensation sequence and the stress data are compared in the time domain. By setting the stress change threshold, the transient stress fluctuations caused by mechanical vibration are identified. When the stress value shows a rapid change exceeding the threshold, the data in the corresponding time period is marked as transient fluctuations. After removing these transient fluctuations, the steady-state compensation data reflecting the stable state is obtained. The dual-frequency excitation method uses two excitation signals with different frequencies, namely 1 kHz and 2 kHz. The steady-state compensation data is subjected to spectral analysis to extract the amplitude and phase information of these two frequency components. Since the common-mode interference is consistent in each channel, while the effective signal varies among channels, by comparing the response characteristics of different channels at the two frequencies, the common-mode interference component can be separated. Combining the correlation analysis of adjacent channel data, the interference characteristic signals are further screened and confirmed.

[0098] The identified interference characteristic signals are processed. The signal intensity change is tracked through amplitude envelope detection, and the time sequence characteristics of the signal are concerned through phase comparison analysis. The amplitude and phase characteristics of the interference signal are compared with the preset normal range, and the part exceeding the range is identified as the abnormal fluctuation component. Based on these abnormal characteristics, the corresponding interference suppression compensation factor is calculated. The adaptive compensation operation adopts a weighted iteration method, and the interference suppression compensation factor is applied to the steady-state compensation data. During the compensation process, the weight coefficient will be automatically adjusted according to the dynamic characteristics of the data to ensure the accuracy of the compensation. After the compensation process, the equipotential connection resistance value is obtained.

[0099] Taking the equipotential bonding measurement of a large industrial equipment as an example, the monitoring of 20 measurement points is involved. These measurement points are divided into 4 groups according to their spatial positions, and each group contains 5 adjacent measurement points. A sliding window with a length of 1 s is used for data segmentation to generate a time series data stream. Temperature monitoring shows that there is a temperature gradient between adjacent channels, and based on this, a temperature compensation sequence is calculated. Stress monitoring discovers transient fluctuations caused by equipment vibration, and these fluctuations are removed through cross-validation. Dual-frequency excitations of 1 kHz and 2 kHz are used to separate common-mode components such as power frequency interference. The analysis of the interference signal discovers abnormal fluctuations, and after generating a compensation factor, adaptive compensation is performed to obtain accurate measurement results of the equipotential bonding resistance.

[0100] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0101] (1) Group the equipotential bonding resistance values through time-domain segmentation, construct a channel correlation matrix according to the spatial adjacent relationship of the measurement channels, and generate a multi-dimensional feature data group;

[0102] (2) Perform a difference operation on the resistance values of adjacent channels in the multi-dimensional feature data group, and combine the measurement timestamp information to obtain a sequence of the rate of change of the resistance value between channels;

[0103] (3) Perform slope tracking on the sequence of the rate of change of the resistance value between channels. When the slope exceeds a preset threshold, trigger a secondary sampling control signal to increase the sampling frequency to 2 kHz to obtain a high-frequency sampling data stream;

[0104] (4) Perform segmentation processing on the high-frequency sampling data stream through a sliding time window, extract statistical features from the data within each time window, and generate a sequence of feature parameters;

[0105] (5) Compare the sequence of feature parameters with historical data, screen out abnormal change intervals, and establish a change trend feature map;

[0106] (6) Perform state recognition and parameter reconstruction according to the change trend feature map to obtain the state parameters of the equipotential bonding network.

[0107] Specifically, perform time-domain segmentation on the equipotential bonding resistance values, and use a fixed time window of 500 ms for data grouping. Construct a 5×4-dimensional channel correlation matrix according to the spatial layout relationship of 20 measurement channels. Each element in the matrix represents the spatial distance weight between adjacent channels. Transform the grouped data through the correlation matrix to obtain a multi-dimensional feature data group containing resistance values, spatial positions, and time information.

[0108] The calculation of the resistance value difference between adjacent channels is based on relative positions, calculating the resistance differences between adjacent channels in the horizontal and vertical directions. Each set of difference data comes with timestamp information with microsecond-level precision to ensure the timeliness of the data. Through the difference calculation within a continuous time window, a sequence of resistance change rates reflecting the resistance change trend is obtained.

[0109] The slope tracking of the resistance change rate sequence adopts the following formula:

[0110]

[0111] Where: is the resistance change slope; is the channel position weight coefficient; is the resistance difference between adjacent channels; is the time decay factor; is the reference time; is the spatial distance influence factor; P, Q are matrix dimensions.

[0112] This formula describes the calculation process of the resistance change slope. The channel position weight coefficient y_p,q reflects the importance of channels at different positions, with values between 0 and 1. The time derivative of the resistance difference represents the change rate. The exponential term introduces a time decay effect, making the data farther from the current time have less influence. The spatial distance influence factor considers the influence of the physical distance between channels. When the calculated slope exceeds the preset threshold, the sampling frequency boosting mechanism is triggered, boosting the original 1 kHz sampling to 2 kHz to obtain more detailed change information.

[0113] The high-frequency sampling data stream is segmented using a sliding time window with a length of 200 ms, and the window overlap rate is 50%. Statistical features, including parameters such as mean, standard deviation, kurtosis, and skewness, are extracted within each time window to form a sequence of feature parameters. These feature parameters reflect the statistical characteristics of the resistance value change. The sequence of feature parameters is compared with the stored historical data in time series, and abnormal change intervals are screened out through the set judgment criteria. Cluster analysis is performed on the data in these abnormal intervals to establish a trend feature map reflecting the change law. According to the data distribution characteristics in the trend feature map, state recognition and parameter reconstruction are carried out to obtain the equipotential connection network state parameters.

[0114] Taking the equipotential bonding system of an industrial area as an example, 20 measurement channels monitor the key nodes of the grounding grid according to a 5×4 matrix layout. The data after time-domain segmentation shows spatial correlation, and the resistance value change trends of adjacent channels are consistent. When the contact state of a connection point changes, the resistance change rate exceeds the preset threshold, triggering the high-frequency sampling mode for in-depth analysis. Through feature extraction and comparison with historical data, it is found that the abnormal changes are mainly concentrated in specific time periods. The trend feature map generated accordingly intuitively reflects the evolution process of the connection state, providing a basis for maintenance decisions.

[0115] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0116] (1) Perform multi-window segmentation processing on the state parameters of the equipotential bonding network, establish a stability evaluation index through the calculation of data deviation between windows, and generate channel stability characteristic data;

[0117] (2) Perform time-series correlation analysis on the channel stability characteristic data and the historical temperature change data. Through temperature mutation point detection and temperature drift tracking, obtain the temperature influence characteristic sequence;

[0118] (3) Perform cross-validation on the temperature influence characteristic sequence and the historical stress change data, establish a temperature-stress coupling relationship table, and generate a multi-dimensional compensation feature quantity;

[0119] (4) Perform dynamic weighted fusion on the multi-dimensional compensation feature quantity, and combine the spatial position relationship of the measurement channels to form an optimized sequence of compensation parameters;

[0120] (5) Perform error analysis on the optimized sequence of compensation parameters by the bidirectional recursion method, and combine the steady-state and transient response characteristics to generate a corrected value of the compensation parameter;

[0121] (6) Iteratively update the corrected value of the compensation parameter and the original compensation parameter to obtain the optimized compensation model parameter.

[0122] Specifically, the state parameters of the equipotential connection network are segmented using three time windows of different lengths, namely 100 ms, 200 ms, and 500 ms, and the window overlap rate is set to 50%. Calculate the standard deviation and deviation value of the data between adjacent windows, and construct a stability evaluation index matrix. Each element of the index matrix contains information in three dimensions: mean deviation, fluctuation range, and trend characteristics, and based on this, characteristic data reflecting the stability of each measurement channel is generated. When performing time series correlation analysis on the channel stability characteristic data and the temperature change history data, the sliding window method is used to detect temperature mutation points. The determination of temperature mutation points is based on two parameters: temperature change rate and acceleration. When both of these parameters exceed the threshold, they are marked as temperature mutation points. Temperature drift tracking focuses on the slow change trend of temperature. By calculating the first and second derivatives of the temperature curve, the key characteristic points of temperature change are identified, and a characteristic sequence describing the temperature influence law is obtained.

[0123] Cross-validation of the temperature influence characteristic sequence and the stress change history data adopts data alignment and correlation analysis methods. Align the two groups of data according to the time stamp, and then calculate the correlation coefficients at different time scales to establish a temperature-stress coupling relationship table. This relationship table reflects the mapping relationship between temperature changes and stress changes. Through polynomial fitting, a temperature-stress coupling model is obtained, and then a multi-dimensional compensation characteristic quantity containing temperature coefficients, stress coefficients, and coupling coefficients is generated. The dynamic weighted fusion of the multi-dimensional compensation characteristic quantity takes into account the spatial layout characteristics of the measurement channels. According to the physical distance and connection relationship between channels, a spatial weight matrix is constructed. The compensation characteristic quantity is weighted and combined through the weight matrix, and at the same time, a time decay factor is introduced to make the recent data have a greater weight, forming an optimized sequence of compensation parameters.

[0124] The two-way recursive method is used to perform error analysis on the optimized sequence of compensation parameters, including forward and backward calculation processes. The forward recursion calculates the cumulative error, and the backward recursion searches for the error source. By comparing the steady-state response characteristics at different time points, the steady-state error is calculated; by analyzing the overshoot and adjustment time of the transient response, the transient error is obtained. Combining the error information of these two parts, a correction value of the compensation parameter is generated. The iterative update process adopts an incremental method, and the correction value of the compensation parameter is weighted and superimposed with the original compensation parameter. After each iteration, the effect of parameter update is evaluated. When the improvement amplitude of consecutive multiple updates is less than the set threshold, the iteration is stopped, and the optimized compensation model parameters are output.

[0125] Taking the isopotential connection monitoring of a certain substation as an example, 20 measurement channels monitor the grounding connection points of equipment such as main transformers and switchgear. Through multi-window segmented processing, it is found that some measurement channels show unstable states when the temperature changes violently. The temperature mutation point detection shows that when the outdoor temperature rises rapidly, the temperature of the equipment shell changes with a delay. Stress data analysis shows that the temperature change causes the deformation of the metal structure, resulting in a change in the contact resistance. Based on these data, a temperature-stress coupling model is constructed. Through dynamic weighted fusion and bidirectional recursive calculation, accurate compensation parameters are obtained, effectively suppressing the influence of temperature and stress changes on the measurement accuracy.

[0126] The above describes the isopotential connection measurement method based on precise low resistance in the embodiments of the present application. Next, the isopotential connection measurement system based on precise low resistance in the embodiments of the present application will be described. Please refer to Figure 3 , an embodiment of the isopotential connection measurement system based on precise low resistance in the embodiments of the present application includes:

[0127] An acquisition module, configured to perform initial parameter configuration on the measurement channels according to the sampling parameters of the high-precision ADC chip and the arrangement positions of the temperature sensors to obtain a dynamic compensation parameter database; at the same time, physically connect the 20 measurement channels using an elastic crimping structure to obtain mechanical stress compensation data;

[0128] A calibration module, configured to perform time-division multiplexing calibration on the parameters in the dynamic compensation parameter database through a standard resistance network, and combine the mechanical stress compensation data. After analyzing the temperature-resistance characteristics and stress-contact resistance characteristics, obtain a multi-channel dynamic calibration compensation coefficient;

[0129] A measurement module, configured to apply a compensation current to each measurement channel for four-wire measurement using an independent excitation source according to the multi-channel dynamic calibration compensation coefficient, and collect temperature data and stress data at a sampling rate of 1 kHz to obtain multi-channel original measurement data;

[0130] A compensation module, configured to perform temperature compensation and stress compensation on the multi-channel original measurement data through a dynamic compensation processing unit, and analyze the data of adjacent channels by a transient interference identification unit to obtain the isopotential connection resistance value;

[0131] An association module, configured to perform association analysis on the data characteristics of adjacent measurement channels according to the isopotential connection resistance value, and increase the sampling frequency when an abnormal trend is detected to obtain the isopotential connection network state parameters;

[0132] An evaluation module, configured to evaluate the stability of the measurement channels according to the isopotential connection network state parameters, and obtain optimized compensation model parameters based on the historical data of temperature changes and stress changes.

[0133] Through the collaborative cooperation of the above-mentioned various components, by combining the sampling of high-precision ADC chips with the arrangement of temperature sensors, a dynamic compensation parameter database is established. At the same time, an elastic crimping structure is used to achieve the physical connection of 20 measurement channels. This design not only ensures the accuracy of data acquisition but also solves the reliability problem of multi-channel connection. Secondly, a high-precision standard resistor network is used for time-division multiplexing calibration. By combining the analysis of temperature-resistance characteristics and stress-contact resistance characteristics, multi-channel dynamic calibration compensation coefficients are obtained, significantly improving the measurement accuracy. Thirdly, through the design of an independent excitation source and a high-frequency sampling strategy of 1 kHz, synchronous acquisition of temperature data and stress data is achieved, providing complete data support for subsequent compensation processing. In terms of data processing, the combined use of a dynamic compensation processing unit and a transient interference identification unit effectively eliminates the influence of various interference factors. In particular, when an abnormal trend is detected, the sampling frequency can be automatically increased, reflecting the adaptive characteristics of the solution. Finally, through the evaluation of the stability of the measurement channels and the optimization of the compensation model parameters, continuous optimization of the measurement system is achieved. At the algorithm level, this solution innovatively combines a variety of data processing algorithms, including dynamic compensation algorithms, time-division multiplexing algorithms, transient interference identification algorithms, etc. The synergistic effect of these algorithms not only improves the measurement accuracy but also enhances the anti-interference ability and adaptability of the system. Through the comprehensive application of these technical features, the present invention successfully solves the dynamic interference problem in the measurement of equipotential connection resistance and achieves the technical goals of high precision, multi-channel, and real-time monitoring.

[0134] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described system, system, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0135] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for measuring equipotential connection based on precise low resistance, characterized in that, The isopotential connection measurement method based on precise low resistance includes: According to the sampling parameters of the high-precision ADC chip and the arrangement positions of the temperature sensors, initial parameter configuration is performed on the measurement channels to obtain a dynamic compensation parameter database; meanwhile, a flexible crimping structure is used to physically connect 20 measurement channels to obtain mechanical stress compensation data; The parameters in the dynamic compensation parameter database are calibrated by time-division multiplexing through a standard resistance network, and in combination with the mechanical stress compensation data, after analysis of the temperature-resistance characteristics and stress-contact resistance characteristics, multiple-channel dynamic calibration compensation coefficients are obtained, including: dividing the standard resistance network into 20 independent compensation units, using a phase-interleaved sampling method to poll and switch the parameters in the dynamic compensation parameter database to generate multi-channel interleaved compensation reference data; decomposing the three-dimensional components of the mechanical stress compensation data by a piezoelectric stress sensor, and in combination with the non-linear deformation characteristics of the flexible crimping structure, generating stress-resistance coupling data; performing gradient temperature compensation on the multi-channel interleaved compensation reference data through a thermocouple array, and dynamically correcting for the temperature change rate to obtain temperature coefficient compensation data; identifying the contact resistance characteristics based on the stress-resistance coupling data, and introducing microscopic deformation compensation of the crimping surface to obtain contact impedance compensation data; performing non-linear weighted processing on the temperature coefficient compensation data and the contact impedance compensation data to generate a dynamic combined compensation coefficient; performing piecewise linear fitting and interpolation calculation on the dynamic combined compensation coefficient to obtain the multiple-channel dynamic calibration compensation coefficients; According to the multiple-channel dynamic calibration compensation coefficients, an independent excitation source is used to apply a compensation current to each measurement channel for four-wire measurement, and temperature data and stress data are collected at a sampling rate of 1 kHz to obtain multiple-channel raw measurement data; Temperature compensation and stress compensation are performed on the multiple-channel raw measurement data by a dynamic compensation processing unit, and adjacent channel data is analyzed by a transient interference identification unit to obtain the isopotential connection resistance value; According to the isopotential connection resistance value, correlation analysis is performed on the data characteristics of adjacent measurement channels, and when an abnormal trend is detected, the sampling frequency is increased to obtain the isopotential connection network state parameters; According to the isopotential connection network state parameters, the stability of the measurement channels is evaluated, and based on the historical data of temperature changes and stress changes, optimized compensation model parameters are obtained.

2. The method for measuring equipotential connection based on precise low resistance according to claim 1, wherein The initial parameter configuration of the measurement channels according to the sampling parameters of the high-precision ADC chip and the arrangement positions of the temperature sensors to obtain a dynamic compensation parameter database; meanwhile, the physical connection of 20 measurement channels by using a flexible crimping structure to obtain mechanical stress compensation data includes: The sampling control unit of the high-precision ADC chip sets a sampling frequency of 1 kHz, alternately samples a standard resistor group with different resistance values, and suppresses noise of the sampling data in combination with a fourth-order Butterworth filter to establish reference compensation data; The temperature sensors are respectively arranged at three key nodes, namely the inlet end, the middle end, and the outlet end of each measurement channel. The temperature data sequence is collected by the three-point temperature difference method, and the temperature gradient distribution data is established after thermodynamic equilibrium calculation. The stress sensing units integrated in the elastic crimping structure collect three-dimensional stress data in the X-axis, Y-axis, and Z-axis directions, and based on the temperature gradient distribution data, stress-temperature correlation data is generated through thermal stress coupling calculation. The reference compensation data, the temperature gradient distribution data, and the stress-temperature correlation data are written into the dynamic compensation parameter database by the data fusion processor according to the hierarchical storage structure. An elastic crimping structure with an automatic compensation function is used to crimp and fix 20 measurement channels with a fixed pressure value. The crimping stress value is collected in real time by the built-in strain type stress sensor, and the mechanical stress compensation data is generated by combining the stress threshold judgment.

3. The method for measuring equipotential connection based on precise low resistance according to claim 1, characterized in that, According to the multi-channel dynamic calibration compensation coefficient, a compensation current is applied to each measurement channel by an independent excitation source for four-wire measurement, and temperature data and stress data are collected at a sampling rate of 1 kHz to obtain multi-channel raw measurement data, including: An excitation current with an adjustable amplitude is injected into 20 measurement channels respectively by a high-stability current source array. The multi-channel dynamic calibration compensation coefficient is used as a modulation signal to generate a compensated excitation current sequence. Under the action of the compensated excitation current sequence, the voltage sampling points of each measurement channel are differentially collected, and the thermoelectric potential interference is eliminated by the current reverse method to obtain the raw voltage data. Synchronous demodulation and digital integration operations are performed on the raw voltage data, and combined with the current amplitude information, an initial resistance measurement value is generated. The measurement channels are sampled for the partition temperature by a temperature sensor array, and the stress sensing data is synchronously collected at a frequency of 1 kHz to form temperature-stress joint sampling data. The initial resistance measurement value and the temperature-stress joint sampling data are subjected to timestamp alignment and data synchronization processing to generate a multi-channel measurement data stream. Real-time error compensation and data reconstruction are performed on the multi-channel measurement data stream to obtain the multi-channel raw measurement data.

4. The method for measuring equipotential connection based on precise low resistance according to claim 1, characterized in that The multi-channel raw measurement data is subjected to temperature compensation and stress compensation by the dynamic compensation processing unit, and the adjacent channel data is analyzed by the transient interference identification unit to obtain the equipotential connection resistance value, including: The multi-channel raw measurement data is divided into multiple compensation unit groups according to the spatial position relationship of the measurement channels. The data within each group is segmented in time series by a sliding window to obtain a segmented compensation data stream. Temperature gradient calculation is performed on the segmented compensation data stream. Combining the temperature difference coefficient between adjacent channels, a temperature distribution feature vector is generated, and hierarchical compensation processing is performed according to the temperature change rate to obtain a temperature compensation sequence. The temperature compensation sequence and the stress data are cross-validated to eliminate the transient fluctuations caused by mechanical vibration to obtain the steady-state compensation data. The common-mode interference components in the steady-state compensation data are separated by the dual-frequency excitation method, and combined with the data correlation between adjacent channels, the interference characteristic signals are screened out. Perform amplitude tracking and phase comparison on the interference feature signal, identify abnormal fluctuation components, and generate an interference suppression compensation factor; Perform adaptive compensation operation on the interference suppression compensation factor and the steady-state compensation data to obtain the equipotential connection resistance value.

5. The method for measuring equipotential connection based on precise low resistance according to claim 1, characterized in that Based on the equipotential connection resistance value, perform correlation analysis on the data characteristics of adjacent measurement channels. When an abnormal trend is detected, increase the sampling frequency to obtain the equipotential connection network state parameters, including: Perform data grouping on the equipotential connection resistance value through time-domain segmentation, construct a channel correlation matrix according to the spatial adjacent relationship of the measurement channels, and generate a multi-dimensional feature data group; Perform difference operation on the resistance values of adjacent channels in the multi-dimensional feature data group, and combine the measurement timestamp information to obtain a sequence of resistance value change rates between channels; Perform slope tracking on the sequence of resistance value change rates between channels. When the slope exceeds the preset threshold, trigger a secondary sampling control signal, increase the sampling frequency to 2 kHz, and obtain a high-frequency sampling data stream; Perform segmented processing on the high-frequency sampling data stream through a sliding time window, extract statistical features from the data within each time window, and generate a sequence of feature parameters; Compare the sequence of feature parameters with historical data, screen out abnormal change intervals, and establish a change trend feature map; Perform state recognition and parameter reconstruction according to the change trend feature map to obtain the equipotential connection network state parameters.

6. The method for measuring equipotential connection based on precise low resistance according to claim 1, wherein, Based on the equipotential connection network state parameters, evaluate the stability of the measurement channels, and based on the historical data of temperature change and stress change, obtain optimized compensation model parameters, including: Perform multi-window segmented processing on the equipotential connection network state parameters, establish a stability evaluation index through calculating the data deviation between windows, and generate channel stability feature data; Perform time-series correlation analysis on the channel stability feature data and the historical data of temperature change, and obtain a temperature influence feature sequence through temperature mutation point detection and temperature drift tracking; Perform cross-validation on the temperature influence feature sequence and the historical data of stress change, establish a temperature-stress coupling relationship table, and generate multi-dimensional compensation feature quantities; Perform dynamic weighted fusion on the multi-dimensional compensation feature quantities, and combine the spatial position relationship of the measurement channels to form an optimized sequence of compensation parameters; Perform error analysis on the optimized sequence of compensation parameters through the bidirectional recursion method, and combine the steady-state and transient response characteristics to generate a corrected value of the compensation parameters; Perform iterative update on the corrected value of the compensation parameters and the original compensation parameters to obtain the optimized compensation model parameters.

7. An equipotential connection measurement system based on precise low resistance, which is used to implement the equipotential connection measurement method based on precise low resistance as described in any one of claims 1-6, characterized in that, The equipotential connection measurement system based on precise low resistance includes: An acquisition module, which is used to perform initial parameter configuration on the measurement channels according to the sampling parameters of the high-precision ADC chip and the layout position of the temperature sensor to obtain a dynamic compensation parameter database; at the same time, physically connect 20 measurement channels using an elastic crimping structure to obtain mechanical stress compensation data; A calibration module, which is used to perform time-division multiplexing calibration on the parameters in the dynamic compensation parameter database through a standard resistance network, and combine the mechanical stress compensation data. After analyzing the temperature-resistance characteristics and stress-contact resistance characteristics, a multi-channel dynamic calibration compensation coefficient is obtained, including: dividing the standard resistance network into 20 independent compensation units, using a phase-interleaved sampling method to poll and switch the parameters in the dynamic compensation parameter database to generate multi-channel interleaved compensation reference data; decomposing the three-dimensional components of the mechanical stress compensation data by a piezoelectric stress sensor, and combining the non-linear deformation characteristics of the elastic crimping structure to generate stress-resistance coupling data; performing gradient temperature compensation on the multi-channel interleaved compensation reference data through a thermocouple array, and dynamically correcting for the temperature change rate to obtain temperature coefficient compensation data; identifying the contact resistance characteristics based on the stress-resistance coupling data, and introducing micro-deformation compensation of the crimping surface to obtain contact impedance compensation data; performing non-linear weighted processing on the temperature coefficient compensation data and the contact impedance compensation data to generate a dynamic combined compensation coefficient; performing piecewise linear fitting and interpolation calculation on the dynamic combined compensation coefficient to obtain the multi-channel dynamic calibration compensation coefficient; A measurement module, which is used to apply a compensation current to each measurement channel by an independent excitation source according to the multi-channel dynamic calibration compensation coefficient for four-wire measurement, and collect temperature data and stress data at a sampling rate of 1 kHz to obtain multi-channel original measurement data; A compensation module, which is used to perform temperature compensation and stress compensation on the multi-channel original measurement data through a dynamic compensation processing unit, and analyze the data of adjacent channels by a transient interference identification unit to obtain the value of the equipotential connection resistance; An association module, which is used to perform association analysis on the data characteristics of adjacent measurement channels according to the value of the equipotential connection resistance, and increase the sampling frequency when an abnormal trend is detected to obtain the state parameters of the equipotential connection network; An evaluation module, which is used to evaluate the stability of the measurement channel according to the state parameters of the equipotential connection network, and obtain optimized compensation model parameters based on the historical data of temperature change and stress change.

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