Loop resistance measuring method and device

Abstract

The invention discloses a loop resistance measuring method and device. The method comprises the following steps: generating a current calibration parameter by adopting a current calibration model, calibrating a current source based on the current calibration parameter, and controlling the calibrated current source to output a measurement current to a loop to be measured; acquiring loop current, loop voltage and loop environment data of the loop to be detected; wherein the loop environment data comprises at least one of temperature, humidity and electromagnetic parameters of the environment where the to-be-detected loop is located; determining an environment compensation coefficient corresponding to the loop environment data based on a corresponding relation between the environment data and the environment compensation coefficient; and determining the loop resistance of the loop to be measured according to the loop current, the loop voltage and the environment compensation coefficient. The method can ensure that the loop resistance measurement process has dynamic adaptability, and improves the accuracy of long-term measurement of the loop resistance.

Description

Technical Field

[0001] The present invention relates to the technical field of loop resistance measurement, and particularly to a loop resistance measurement method and device. Background Art

[0002] Loop resistance measurement is one of the basic detection technologies widely used in power systems and industrial equipment, and its measurement accuracy is crucial for the safety and operating efficiency of the system. However, due to various uncertain factors involved in the loop resistance measurement process, such as internal errors of equipment and the influence of environmental changes, etc., it often leads to deviations or distortions of measurement data. For example, traditional loop resistance measurement methods usually based on a single current source driving mode fail to effectively compensate for the errors caused by current source drift. In addition, the dynamic influence of measurement environment changes may cause resistance fluctuations of wire materials, further increasing the complexity of measurement. These problems pose severe challenges to high-precision resistance measurement, especially in application scenarios requiring high reliability.

[0003] In some related technologies, a constant current source design is adopted to reduce the measurement error caused by current source fluctuations, but its calibration technology often relies on high-cost equipment, making it difficult to meet the requirements of economy and universality. As a result, the economy and universality of the loop resistance measurement method based on a constant current source are relatively low. In addition, in related technologies, for environmental changes, a temperature compensation strategy based on fixed correction parameters is provided, but it cannot dynamically adapt to a rapidly changing temperature environment, thus limiting its applicability in complex scenarios. In summary, the current loop resistance measurement scheme does not have dynamic adaptability, resulting in relatively low measurement accuracy of loop resistance during long-term measurement of loop resistance.

[0004] Therefore, how to design a loop resistance measurement method with accurate measurement and dynamic adaptability has become an important technical problem that urgently needs to be solved in the field of loop resistance measurement. Summary of the Invention

[0005] The present invention provides a loop resistance measurement method and device to ensure that the loop resistance measurement process has dynamic adaptability and improve the accuracy of long-term loop resistance measurement.

[0006] According to one aspect of the present invention, there is provided a loop resistance measurement method, including:

[0007] Generating current calibration parameters by using a current calibration model, and calibrating a current source based on the current calibration parameters, controlling the calibrated current source to output a measurement current to a to-be-measured loop;

[0008] Obtaining the loop current, loop voltage and loop environment data of the to-be-measured loop; wherein, the loop environment data includes at least one of temperature, humidity and electromagnetic parameters of the environment where the to-be-measured loop is located;

[0009] Based on the corresponding relationship between the environmental data and the environmental compensation coefficient, determine the environmental compensation coefficient corresponding to the loop environmental data;

[0010] According to the loop current, the loop voltage, and the environmental compensation coefficient, determine the loop resistance of the loop to be measured.

[0011] Optionally, a plurality of measurement points are distributed in the loop to be measured, and a plurality of sensor modules are respectively arranged corresponding to each measurement point. The sensor modules are used to detect the node voltage, node current, and node environmental data of the corresponding measurement point; wherein, the node environmental data includes at least one of the temperature, humidity, and electromagnetic parameters of the environment where the measurement point is located;

[0012] The obtaining of the loop current, loop voltage, and loop environmental data of the loop to be measured includes:

[0013] Obtain the node voltage, node current, and node environmental data at each measurement point;

[0014] Perform fusion processing on each of the node voltages to obtain the loop voltage; perform fusion processing on each of the node currents to obtain the loop current; perform fusion processing on each of the node environmental data to obtain the loop environmental data.

[0015] Optionally, the performing of fusion processing on each of the node voltages to obtain the loop voltage includes:

[0016] By using the Kalman filtering algorithm based on Bayesian estimation, perform weighted average processing on each of the node voltages according to the time series to obtain the loop voltage;

[0017] The performing of fusion processing on each of the node currents to obtain the loop current includes:

[0018] By using the Kalman filtering algorithm based on Bayesian estimation, perform weighted average processing on each of the node currents according to the time series to obtain the loop current;

[0019] The performing of fusion processing on each of the node environmental data to obtain the loop environmental data includes:

[0020] By using the Kalman filtering algorithm based on Bayesian estimation, perform weighted average processing on each of the node environmental data according to the time series to obtain the loop environmental data.

[0021] Optionally, before performing fusion processing on each of the node voltages, it further includes: correcting each of the node voltages;

[0022] Correspondingly, fusing the node voltages includes: fusing the corrected node voltages;

[0023] Among them, for any one of the node voltages, correcting the node voltage includes:

[0024] Based on empirical mode decomposition, decomposing the node voltage into multiple decomposed voltage signals under multiple frequency components;

[0025] Based on fast Fourier transform, filtering each of the decomposed voltage signals to obtain effective voltage signals;

[0026] Constructing a compensation correction matrix, and correcting the effective voltage signal according to the compensation correction matrix to obtain the corrected node voltage.

[0027] Optionally, the process of obtaining the correspondence between the environmental data and the environmental compensation coefficient includes:

[0028] Taking the environmental data as the independent variable and the loop resistance as the dependent variable, and establishing the correspondence between the environmental data and the environmental compensation coefficient based on the multivariate regression analysis algorithm; where the environmental compensation coefficient is used to correct the error caused by the change of the environmental data to the measurement result of the loop resistance.

[0029] Optionally, the loop current is obtained at a preset frequency;

[0030] After obtaining the loop current of the to-be-tested loop each time, it further includes:

[0031] Calculating current error data according to the loop current and the measured current;

[0032] Adopting a time series analysis algorithm and a long short-term memory network model to model the temporal characteristics of the change of the current error data, and constructing a current error data temporal characteristic model;

[0033] Predicting the current error data at the next time point based on the current error data temporal characteristic model;

[0034] When the prediction result of the current error data at the next time point is greater than a preset error value, updating the current calibration model;

[0035] And / or,

[0036] The loop resistance measurement method further includes: updating the current calibration model every preset time interval.

[0037] Optionally, the process of constructing the current calibration model includes:

[0038] Obtain multiple historical error data; wherein, the obtaining process of any one of the historical error data includes: controlling the current source to provide a target current value to a reference resistor; detecting the actual current value flowing through the reference resistor; taking the difference between the actual current value and the target current value as the historical error data;

[0039] Based on a neural network algorithm, train each of the historical error data to obtain the current calibration model.

[0040] Optionally, the loop environment data includes the temperature of the environment where the loop to be measured is located;

[0041] After obtaining the loop environment data, it further includes:

[0042] Adopt a time series analysis algorithm and a long short-term memory network model to model the temporal characteristics of the temperature change of the environment where the loop to be measured is located at different time points, and construct a temperature temporal characteristic model;

[0043] Based on the temperature temporal characteristic model, predict the change rate of the temperature of the environment where the loop to be measured is located at the next time point;

[0044] When the change rate is greater than a preset rate, increase the acquisition frequency of the temperature of the environment where the loop to be measured is located.

[0045] Optionally, before generating current calibration parameters using the current calibration model, it further includes:

[0046] Determine the estimated loop resistance value of the loop to be measured;

[0047] If the estimated loop resistance value is greater than a preset resistance value, select a voltage sampling device with a first sensitivity to measure the loop voltage, and obtain the loop current, the loop voltage, and the loop environment data at a first sampling frequency;

[0048] If the estimated loop resistance value is less than the preset resistance value, select a voltage sampling device with a second sensitivity to measure the loop voltage, and obtain the loop current, the loop voltage, and the loop environment data at a second sampling frequency;

[0049] Wherein, the first sensitivity is higher than the second sensitivity, and the first sampling frequency is less than the second sampling frequency;

[0050] And / or,

[0051] After determining the loop resistance of the loop to be measured, it further includes:

[0052] Encrypt and store the current calibration parameter, the measured current, the loop current, the loop voltage, the loop environment data, the environmental compensation coefficient, and the loop resistance.

[0053] According to another aspect of the present invention, there is provided a loop resistance measuring device, including:

[0054] A current control module, configured to generate a current calibration parameter by using a current calibration model, calibrate a current source based on the current calibration parameter, and control the calibrated current source to output a measured current to a loop to be measured;

[0055] An acquisition module, configured to acquire the loop current, loop voltage, and loop environment data of the loop to be measured; wherein, the loop environment data includes at least one of the temperature, humidity, and electromagnetic parameters of the environment where the loop to be measured is located;

[0056] A first determination module, configured to determine an environmental compensation coefficient corresponding to the loop environment data based on the correspondence between the environmental data and the environmental compensation coefficient;

[0057] A second determination module, configured to determine the loop resistance of the loop to be measured according to the loop current, the loop voltage, and the environmental compensation coefficient.

[0058] The technical solution of the embodiment of the present invention can dynamically calibrate the current source through the current calibration model and adjust the calibration parameter in real time, which can ensure the long-term output stability of the current source, and can adapt to the situation of load change or large interference caused by environmental change, and ensure the accuracy of loop resistance measurement. And calibration is performed based on the current calibration model, which can improve the economy and universality of the loop resistance measurement method compared with using professional calibration equipment, and has a dynamic adaptation ability. Similarly, the loop resistance measurement process has a dynamic adaptation ability, which can ensure the long-term measurement accuracy of the loop resistance. While acquiring the loop current and loop voltage, the loop environment data is also detected, and further the environmental compensation coefficient is determined according to the correspondence between the environmental data and the environmental compensation coefficient, and the environmental compensation coefficient is used for the compensation of loop resistance measurement, which is equivalent to considering the influence of the change of the environment where the loop to be measured is located on the measurement result and making corrections, and can further improve the accuracy of loop resistance measurement.

[0059] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0061] Figure 1 It is a flowchart of a loop resistance measurement method provided by an embodiment of the present invention;

[0062] Figure 2 It is a schematic structural diagram of a loop resistance measurement device provided by an embodiment of the present invention. Detailed implementation manners

[0063] In order to enable those skilled in the art of the present technology to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and their variants are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0065] An embodiment of the present invention provides a loop resistance measurement method. This embodiment is applicable to the situation of detecting loop resistance in power systems and industrial equipment. This method can be executed by a loop resistance measurement device, and the loop resistance measurement device can be implemented in the form of hardware and / or software.

[0066] Figure 1 It is a flowchart of a loop resistance measurement method provided by an embodiment of the present invention. Refer to Figure 1 , the loop resistance measurement method includes:

[0067] S110. Generate current calibration parameters using a current calibration model, calibrate a current source based on the current calibration parameters, and control the calibrated current source to output a measurement current to a circuit under test.

[0068] Among them, the current source can be a high-precision current source with dynamic calibration and adaptive compensation capabilities. The current calibration model can be understood as an adaptive optimization algorithm based on machine learning. Exemplarily, the calibration process of the current source uses an adaptive optimization algorithm based on machine learning to dynamically adjust the current calibration parameters according to multiple calibration data of the current source, so as to calibrate the current source, so that the current source adapts to its long-term drift characteristics.

[0069] Specifically, the current source can be dynamically and automatically calibrated through the current calibration model and the current calibration parameters can be adjusted in real time, so that the measurement current output by the current source can be kept stable for a long time. Even in the case of large interference caused by load changes or environmental changes, the accuracy of loop resistance measurement can be ensured. The current calibration parameters are input control parameters of the current source and are used to control the output of the current source.

[0070] The current source provided by the embodiment of the present invention can dynamically adjust the output measurement current in combination with historical calibration data, realizes effective compensation for the drift and long-term aging of the current source, and makes the measurement current output by the current source always equal to the target current required for measurement during the measurement process of the loop resistance.

[0071] S120. Obtain the loop current, loop voltage and loop environment data of the circuit under test.

[0072] Among them, the loop environment data includes at least one of the temperature, humidity and electromagnetic parameters of the environment where the circuit under test is located.

[0073] Specifically, the loop current of the circuit under test can be detected by a current detection device. For example, the current detection device can include a current transformer; the loop voltage can be detected by a voltage detection device. For example, the voltage detection device can include a voltage sensor; the temperature in the environment data can be detected by a temperature detection device. For example, the temperature detection device can include a temperature sensor or a temperature and humidity sensor; the humidity in the environment data can be detected by a humidity detection device. For example, the humidity detection device can include a humidity sensor or a temperature and humidity sensor; the electromagnetic parameters in the environment data can be detected by an electromagnetic parameter detection device. For example, the electromagnetic parameter detection device can include an electric field sensor, a magnetic field sensor or an electromagnetic radiation sensor.

[0074] Furthermore, due to the dynamic influence of environmental data (such as temperature) changes, the resistance of the conductive material in the circuit to be measured may fluctuate, which in turn affects the measurement accuracy of the circuit resistance. By detecting the circuit environmental data during the process of obtaining the circuit current and circuit voltage, it is equivalent to considering the influence of environmental data on the circuit resistance measurement when measuring the circuit resistance, thereby improving the measurement accuracy of the circuit resistance.

[0075] S130. Determine the environmental compensation coefficient corresponding to the circuit environmental data based on the corresponding relationship between the environmental data and the environmental compensation coefficient.

[0076] Among them, the environmental data is collected in real time during the circuit resistance measurement process, and the environmental compensation coefficient is adjusted accordingly. Compared with the traditional circuit resistance measurement, where temperature compensation is performed on the circuit resistance based on fixed correction parameters, this embodiment can dynamically adapt to the rapidly changing environment and improve the applicability of this measurement method in complex scenarios.

[0077] In the embodiment of the present invention, the corresponding relationship between the environmental data and the environmental compensation coefficient can be a pre-established corresponding relationship and can be further updated continuously during the measurement process. Exemplarily, the corresponding relationship between the environmental data and the environmental compensation coefficient can be established based on a preset algorithm. During the measurement process, when the circuit environmental data is determined, the circuit environmental data is brought into the corresponding relationship between the environmental data and the environmental compensation coefficient, and the environmental compensation coefficient corresponding to the circuit environmental data can be obtained. The measurement error of the circuit resistance caused by environmental changes is corrected by the environmental compensation coefficient. For example, the measurement errors of the circuit current and circuit voltage caused by environmental changes can be compensated.

[0078] S140. Determine the circuit resistance of the circuit to be measured according to the circuit current, the circuit voltage, and the environmental compensation coefficient.

[0079] Specifically, first calculate the ratio of the circuit voltage to the circuit current, and then take the product of the obtained ratio and the environmental compensation coefficient as the circuit resistance of the circuit to be measured, and the measurement of the circuit resistance is performed in real time.

[0080] The technical solution of the embodiment of the present invention can dynamically calibrate the current source through the current calibration model and adjust the calibration parameters in real time, which can ensure the long-term output stability of the current source, adapt to the change of load or the situation of large interference caused by environmental changes, and ensure the accuracy of loop resistance measurement. And calibration based on the current calibration model can improve the economy and universality of the loop resistance measurement method compared with using professional calibration equipment, and has dynamic adaptability. Similarly, the loop resistance measurement process has dynamic adaptability, which can ensure the long-term measurement accuracy of the loop resistance. While obtaining the loop current and loop voltage, the loop environmental data is also detected, and further, the environmental compensation coefficient is determined according to the corresponding relationship between the environmental data and the environmental compensation coefficient, and the environmental compensation coefficient is used for the compensation of loop resistance measurement, which is equivalent to considering the influence of the change of the environment where the loop to be measured is located on the measurement result and making corrections, and can further improve the accuracy of loop resistance measurement.

[0081] It can be understood that the loop resistance measurement method provided in this embodiment is equivalent to performing error compensation during the measurement process.

[0082] Optionally, on the basis of the above embodiment, the construction process of the current calibration model includes:

[0083] 1) Obtain a plurality of historical error data.

[0084] Among them, the acquisition process of any historical error data includes: controlling the current source to provide a target current value to the reference resistor; detecting the actual current value flowing through the reference resistor; taking the difference between the actual current value and the target current value as the historical error data.

[0085] Among them, the reference resistor can be understood as a resistor with a known and stable resistance value and high precision. The actual current value can be used as the reference current value.

[0086] 2) Based on the neural network algorithm, train each piece of historical error data to obtain the current calibration model.

[0087] Specifically, the neural network model is used to learn and predict a plurality of historical error data collected during the calibration process of the current source. The neural network model generates a set of weights and biases through the neural network algorithm, and when the loss function converges to a smaller value or reaches the preset number of training rounds, the current calibration model is obtained. Among them, the neural network model generates a set of weights and biases through training to identify the regularity of the current source drift, extract the change pattern of its error data, and then generate a set of optimized current calibration parameters for current source compensation calibration. The current calibration parameters can be directly applied to the compensation and correction of drift errors in subsequent measurements or calibrations.

[0088] In summary, by constructing a set of dynamically calibrated high-precision current source systems (denoted as the first system) for calibrating the current source, the stability and accuracy of the measurement current output in loop resistance measurement are ensured from the source. Specifically, the first system may include a high-precision current source, a reference resistor, a controller, and a calibration algorithm module based on neural networks. Among them, the calibration algorithm module based on neural networks can be used as an independent functional unit or integrated into the controller, and stores an adaptive optimization algorithm based on machine learning. Among them, the calibration algorithm module based on neural networks has the ability of adaptive optimization, specifically by continuously learning the historical error data of the current source under different environments and working conditions.

[0089] Among them, the controller, as the core unit of the first system, mainly functions to coordinate multiple functional modules such as current source output, reference resistor detection, neural network calibration algorithm, and compensation parameter adjustment to form a closed-loop automatic calibration system; the controller can not only achieve stable control of the current source, but also collect environmental data (such as temperature) in real time and participate in error correction. Therefore, the controller in this embodiment can uniformly manage the measurement, calibration, and compensation processes.

[0090] Furthermore, the calibration algorithm module based on neural networks can establish an internal correlation model between the current source drift and environmental data (such as temperature, humidity, and electromagnetic interference, etc.). On this basis, it can dynamically predict the possible drift trend of the current source in the future working environment, so as to automatically optimize the compensation parameters during the calibration process, reduce manual intervention, and improve the stability and accuracy of the current source output.

[0091] The current source provides a stable target current value for loop resistance measurement, the reference resistor is used to calibrate the actual current value output by the current source, and the controller is responsible for monitoring the target current value provided by the current source to the reference resistor in real time and comparing it with the actual current value fed back by the reference resistor. The adaptive optimization algorithm based on machine learning learns and dynamically adjusts the historical error data generated during the calibration process.

[0092] Exemplarily, in the initial stage, the first system will collect the actual current value through the reference resistor and detect the target current value output by the current source using a high-precision digital meter. After comparing the actual current value with the target current value, the first system will generate historical error data and input it into the neural network. The neural network has been pre-trained to be able to identify error patterns and generate a set of optimized calibration parameters to adjust the output circuit of the current source to make its output approach the ideal value. At the same time, the historical error data during the calibration process will be stored by the first system for subsequent dynamic learning and adjustment.

[0093] The technical solution of the embodiment of the present invention can effectively reduce the need for human intervention by constructing a set of high-precision current source systems with dynamic calibration. Traditional current source calibration often requires operators to manually adjust the parameters of the output circuit, while this first system realizes the full-automatic management of the current source calibration process through the automatic optimization function of the neural network algorithm. At the same time, the first system is also equipped with a remote monitoring function, allowing users to view the calibration status of the current source and the stability of the output measured current in real time through the network.

[0094] In addition, the first system is designed to support modular expansion. For example, it can be used in conjunction with other measurement devices or data processing modules. When measuring the loop resistance, the calibrated high-precision current data can be directly transmitted to the measurement circuit through a dedicated interface, reducing the error accumulation during the data transmission process. This embodiment is suitable for industrial and power systems with high reliability requirements, such as substation equipment detection and rail transit signal system maintenance.

[0095] In summary, this embodiment significantly improves the stability and long-term reliability of the output measured current of the current source by combining dynamic calibration technology with neural network algorithms. It can not only solve the measurement error problem caused by current source drift but also provide strong technical support for high-precision loop resistance measurement in complex environments.

[0096] Optionally, based on the above embodiments, the loop current is obtained at a preset frequency. Then, after each acquisition of the loop current of the loop to be measured, it further includes:

[0097] 1) Calculate the current error data according to the loop current and the measured current.

[0098] Specifically, the difference between the loop current and the measured current is used as the current error data.

[0099] 2) Use the time series analysis algorithm and the long short-term memory network model to model the temporal characteristics of the change in the current error data and construct a temporal characteristic model of the current error data.

[0100] 3) Predict the current error data at the next time point based on the temporal characteristic model of the current error data.

[0101] 4) When the prediction result of the current error data at the next time point is greater than the preset error value, update the current calibration model.

[0102] Specifically, a real-time error feedback mechanism is established. Based on the current error data of the loop current and the measured current, combined with the time series analysis algorithm and the Long Short-Term Memory (LSTM) network model, a time series feature model of the current error data is constructed to analyze and predict the current measurement deviation at the next time point, and the measurement strategy is dynamically adjusted to achieve high-precision measurement of the loop resistance. Exemplarily, when the prediction result of the current error data at the next time point is greater than the preset error value, that is, when the time series feature model of the current error data predicts that the future current source drift may increase, the calibration program of the first system is triggered in advance, and the current calibration parameters are optimized according to the actual error level. That is, this embodiment can dynamically adjust the measurement strategy according to the predicted error trend during the measurement optimization stage. Among them, the role of the Long Short-Term Memory network model in this process is to identify the long-term change trend of the current source drift and provide reliable basic data for time series prediction, and its accuracy is directly related to the accuracy of the subsequent prediction results and the stability of calibration. The adjustment of the drift is achieved by dynamically correcting the output current value of the current source through the bias correction amount output by the Long Short-Term Memory network model and continuously optimizing the compensation model in combination with the feedback error data. To reduce the measurement deviation, the system also cooperates with means such as dynamic environmental coefficient compensation, noise filtering, and multi-point data fusion to effectively reduce the influence of various error sources such as environmental fluctuations, environmental noise, and current source drift on the measurement result, thereby improving the measurement accuracy and the adaptability of the system. Among them, the measurement deviation refers to the gap between the measured value and the true value caused by factors such as current source drift, environmental temperature change, environmental humidity change, or electromagnetic interference, and its accurate prediction helps to identify potential error risks in advance.

[0103] In summary, this embodiment uses the output of the time series feature model of the error current data as the calibration parameter of the current source, and continuously collects new drift data during subsequent measurements to continuously update the time series feature model of the current error data dynamically, so that it can always reflect the latest drift change trend, and solve the problem of current error fluctuation caused by environmental changes or equipment aging during long-term operation. Specifically, during the long-term operation of the first system, data such as the measured error, the output drift of the current source, and the environmental data change collected in real time will be continuously input into the short-term memory network model as training data to continuously improve the time series feature model of the current error data, so that it can dynamically adapt to various error characteristics and environmental changes during the long-term use of the current source.

[0104] In summary, the current source provided by the embodiments of the present invention can dynamically adjust the measured current it outputs according to the prediction results of the timing feature model of the current error data, ensuring the long-term stability of the calibration results. At the same time, it reduces the error fluctuations caused by changes in the external environment during the calibration process, improving the accuracy and adaptability of the calibration. The current source provided by the present invention can achieve effective compensation for the drift and long-term aging of the current source by comparing with a reference resistor, modeling and predicting the current error based on a neural network (such as a long short-term memory network model), and dynamically adjusting the output measured current in combination with historical calibration data. Therefore, the current source not only undertakes the current driving function but also integrates functional modules for real-time error compensation and adaptive calibration, and can maintain the stability of the current output in a complex environment, and can solve the problem of measurement errors caused by drift in traditional current sources.

[0105] In summary, in this embodiment, a dynamic loop resistance measurement system (denoted as the second system) can be designed by introducing an error feedback mechanism and an intelligent optimization algorithm. The second system includes an error monitoring module, an error feedback adjustment module, a measurement optimization algorithm, and a prediction analysis model, and realizes high-precision measurement of the loop resistance through multi-level feedback and optimization.

[0106] Specifically, during the operation of the second system, first, the error monitoring module monitors and analyzes the real-time measurement data. This module uses a current transformer to collect the loop current, compares it with the measured current obtained after calibration, calculates the current error value, and obtains the current error data. The error feedback adjustment module receives the output of the monitoring module and models the current error data by combining time series analysis methods. Through the long short-term memory network model, the second system can predict the error change trend in the future period of time, thereby providing decision support for subsequent measurement adjustment.

[0107] Among them, the measurement optimization algorithm will also dynamically allocate resources according to the characteristics of the error change. For example, it increases the density of measurement points at the critical stage of measurement to improve the stability and reliability of the measurement results.

[0108] The technical solution of the embodiments of the present invention sets an error feedback mechanism, and the error feedback mechanism has the ability of sustainable optimization. The second system will continuously record the error data and measurement results during operation, and use these data to iteratively update the feedback model to make it adapt to a wider range of application scenarios. In addition, the error feedback mechanism also supports remote monitoring and configuration, allowing users to adjust the feedback parameters and measurement optimization strategies according to their needs. At the same time, it can significantly reduce the dependence on manual intervention. Especially in scenarios with high reliability requirements, such as substation equipment detection and industrial equipment status monitoring, the intelligent measurement optimization technology driven by error feedback can provide users with more accurate and stable measurement results.

[0109] In summary, through the combination of the error feedback mechanism and intelligent optimization, this embodiment provides strong technical support for loop resistance measurement in complex environments. It can not only adapt to the dynamically changing environment in real time but also continuously improve the measurement ability and accuracy of the system during long-term operation.

[0110] Optionally, based on the above embodiments, the loop resistance measurement method further includes: updating the current calibration model at preset time intervals.

[0111] Wherein, the preset time can be understood as the calibration period.

[0112] Specifically, during the operation of the current source, to cope with the possible drift phenomenon of the current source during long-term use, the first system designs a periodic automatic calibration mechanism so that the current calibration model is updated at preset time intervals. The calibration period can be flexibly set according to the stability of the actual working environment and the characteristics of the current source. During each calibration period, the first system will use an adaptive optimization algorithm based on machine learning to adjust the possible drift in advance to reduce the current deviation caused by the drift.

[0113] Optionally, based on the above embodiments, the loop current, loop voltage, and loop environment data of the loop to be measured can be measured through a single measurement point set on the loop to be measured, or can be measured through multiple measurement points set on the loop to be measured. The following is a separate description.

[0114] Optionally, in one embodiment, the loop to be measured includes a measurement point corresponding to the total connection end between the loop to be measured and the current source. Detecting the voltage at this measurement point is the loop voltage, detecting the current at this measurement point is the loop current, and detecting the environmental data near this measurement point is the loop environment data.

[0115] Optionally, in another embodiment, the loop to be measured includes a plurality of measurement points arranged distributively, and each measurement point is provided with a plurality of sensor modules one by one. The sensor modules are used to detect the node voltage, node current, and node environment data of the corresponding measurement point; wherein, the node environment data includes at least one of the temperature, humidity, and electromagnetic parameters of the environment where the measurement point is located.

[0116] Wherein, each measurement point can separately collect the node voltage, node current, and node environment data.

[0117] Among them, multiple sensor modules form a distributed sensor network, which is distributed at different positions of the circuit to be measured and is used to generate node voltages, node currents, and node environmental data covering each measurement point in the circuit to be measured. Exemplarily, when the sensor module includes a temperature sensor, the multi-sensor network is used to collect temperature data of the environment where the circuit to be measured is located, and multiple temperature sensors are distributed at different positions of the circuit to be measured to generate a temperature data field covering the entire environment where the circuit to be measured is located.

[0118] Correspondingly, obtaining the loop current, loop voltage, and loop environmental data of the circuit to be measured includes:

[0119] 1) Obtain the node voltage, node current, and node environmental data at each measurement point.

[0120] 2) Perform fusion processing on each node voltage to obtain the loop voltage; perform fusion processing on each node current to obtain the loop current; perform fusion processing on each node environmental data to obtain the loop environmental data.

[0121] By performing fusion processing on each node voltage, each node current, and each node environmental data respectively, the random errors caused by environmental noise, sensor accuracy, etc. in the node data at a single measurement point can be reduced, and the overall stability and accuracy of the loop resistance measurement result can be improved. The fusion processing can specifically be direct summation or weighted average, etc., which can be set according to the actual situation.

[0122] Optionally, based on the above embodiments, performing fusion processing on each node voltage to obtain the loop voltage includes:

[0123] Through the Kalman filtering algorithm based on Bayesian estimation, each node voltage is weighted and averaged according to the time series to obtain the loop voltage. By performing fusion processing on multi-point node voltage data, the random errors, noise, and systematic errors in the node voltage measurement can be minimized, and the measurement accuracy and data consistency of the node voltage can be achieved, thereby ensuring the accuracy of the obtained loop voltage.

[0124] Optionally, based on the above embodiments, performing fusion processing on each node current to obtain the loop current includes:

[0125] Through the Kalman filtering algorithm based on Bayesian estimation, each node current is weighted and averaged according to the time series to obtain the loop current. By performing fusion processing on multi-point node current data, the random errors and systematic errors in the node current measurement can be minimized, and the measurement accuracy and data consistency of the node current can be achieved, thereby ensuring the accuracy of the obtained loop current.

[0126] Optionally, based on the above embodiments, the environmental data of each node is fused to obtain loop environmental data, including:

[0127] The environmental data of each node is weighted and averaged according to the time series through the Kalman filtering algorithm based on Bayesian estimation to obtain the loop environmental data. By fusing the environmental data of multiple nodes, the random error and systematic error in the measurement of node environmental data can be minimized, and the measurement accuracy and data consistency of node environmental data can be achieved, thus ensuring the accuracy of the obtained loop environmental data.

[0128] It can be understood that when fusing the current, voltage and environmental data of each node respectively, the fusion result of each node data can be optimized by weight adjustment.

[0129] In summary, based on the node current, node voltage and node environmental data of multiple measurement points, the Kalman filtering algorithm based on Bayesian estimation is used to fuse the node data of each measurement point respectively, further reducing the random error of node data in single-point measurement and improving the stability and accuracy of the measurement result.

[0130] Optionally, based on the above embodiments, the process of obtaining the correspondence between environmental data and environmental compensation coefficients includes:

[0131] Taking environmental data as the independent variable and loop resistance as the dependent variable, a correspondence between environmental data and environmental compensation coefficients is established based on the multivariate regression analysis algorithm; where the environmental compensation coefficient is used to correct the error caused by the change of environmental data to the measurement result of loop resistance.

[0132] Specifically, multiple sensor modules collect the environmental data of the environment where the loop to be measured is located in real time, and input the collected environmental data and the loop resistance calculated according to the loop voltage and loop current into the multivariate regression analysis algorithm to establish the correspondence between environmental data and environmental compensation coefficients, and this correspondence is adjustable in real time. Therefore, the environmental compensation coefficient can be understood as a dynamic environmental compensation coefficient, that is, the parameter table of the environmental compensation coefficient can be dynamically adjusted, and the environmental error correction of the loop resistance can be quickly completed, realizing the real-time correction of the influence of environmental data, improving the accuracy and stability of environmental data compensation, and ensuring a stable and reliable measurement result of loop resistance even under drastic environmental changes.

[0133] Exemplarily, a multivariate regression model is adopted. By introducing partial least squares regression (PLSR), neural network fitting or other non-linear regression means, a mathematical mapping relationship between environmental data and environmental compensation coefficients is established. It is not just a simple regression fitting, but provides a real-time adjustable and optimal environmental compensation coefficient, significantly improving the accuracy and stability of the environmental compensation effect.

[0134] In summary, the embodiment of the present invention designs a high-precision loop resistance measurement method applicable to complex environments based on multi-point data fusion and dynamic compensation technology. Among them, multi-point data fusion can be understood as the fusion of node currents, node voltages, and node environmental data respectively.

[0135] Specifically, the method includes a distributed sensor module, a data fusion processing unit, a dynamic compensation module, and a measurement result correction algorithm. Each node is distributed at different positions of the loop, used to collect node current, node voltage, and node environmental data, and transmits the data to the data fusion processing unit for fusion analysis through wireless communication. Among them, the data fusion processing unit can be set separately or integrated into the central processing module.

[0136] Among them, the data fusion processing unit refers to a dedicated module used to respectively perform fusion, filtering, and error compensation processing on node currents, node voltages, and node environmental data collected by multiple distributed sensing networks, and is an indispensable core functional unit in the measurement system. The data fusion processing unit can be composed of hardware (such as a dedicated signal processor, microcontroller) or software (such as an embedded data processing program, filtering algorithm, fusion algorithm). Its main function is to receive multi-source node data transmitted back from each measurement point through wireless communication, and use algorithms such as Kalman filtering, weighted average, and time series analysis to fuse data with random errors and environmental noise, so as to generate measurement data with higher confidence for subsequent use by the dynamic compensation module and the measurement result correction unit. Therefore, the data fusion processing unit not only completes data collection, but also undertakes the important functions of signal quality improvement and data preprocessing, and is the key unit to ensure measurement accuracy and reliability during the measurement process.

[0137] During the measurement of loop resistance, the node data collected by each sensor module usually contains random errors and environmental noise, which will affect the overall accuracy of the measurement. Therefore, this embodiment adopts the Kalman filtering algorithm based on Bayesian estimation to integrate and correct the data of different nodes by means of weighted average. Specifically, the Kalman filtering algorithm based on Bayesian estimation can dynamically optimize the node data in the time series, eliminate the abnormal points and generate measurement results with high confidence. For example, when the measurement data of a certain node is abnormal due to environmental interference, the Kalman filtering based on Bayesian estimation will automatically reduce its weight, thereby reducing the impact of abnormal data on the measurement result.

[0138] After the fusion of each node data is completed, it enters the dynamic compensation stage. As an important part of the dynamic compensation module, the core of environmental compensation is to establish the corresponding relationship between the loop resistance and environmental data through the multivariate regression analysis algorithm, such as a non-linear relationship. The node environmental data of each measurement point is collected through a distributed sensor network, and the loop environmental data is obtained by fusing the node environmental data of each node using the Kalman filtering algorithm based on Bayesian estimation. The loop environmental data is brought into the corresponding relationship between the loop resistance and environmental data to obtain the dynamic environmental compensation coefficient, so as to correct the measurement error of the loop resistance caused by environmental changes.

[0139] Therefore, through multi-point data fusion combined with the Kalman filtering algorithm based on Bayesian estimation, noise and random errors can be effectively eliminated, and the dynamic compensation module can quickly respond to environmental fluctuations, improving the anti-interference ability and adaptability of loop resistance measurement in complex environments.

[0140] Furthermore, to improve the reliability of the measurement, this embodiment also designs a measurement result correction algorithm. This algorithm analyzes the differences between the fused data and historical measurement results, and adjusts the parameters of the compensation model to make the final measurement result closer to the actual value. All data and parameters during the correction process will be recorded to provide data support for subsequent model optimization and system upgrade.

[0141] This embodiment is particularly suitable for complex scenarios that require high-precision measurement, such as industrial environments with high electromagnetic interference or drastic temperature changes. In these scenarios, single-point measurement often fails to meet the accuracy requirements, while the multi-point fusion method can significantly improve the stability and anti-interference ability of the measurement system. In addition, the introduction of dynamic compensation and correction algorithms enables the system to achieve a good balance between real-time performance and accuracy.

[0142] In summary, through the organic combination of multi-point data fusion and dynamic compensation technology, this embodiment provides a reliable solution for high-precision loop resistance measurement. It can not only significantly reduce the measurement error caused by environmental factors, but also has strong adaptability and scalability, making it suitable for promotion in various industrial and power applications.

[0143] Optionally, based on the above embodiments, before fusing and processing the node voltages, it further includes: correcting the node voltages. Correspondingly, fusing and processing the node voltages includes: fusing and processing the corrected node voltages.

[0144] Among them, for any node voltage, correcting the node voltage includes:

[0145] 1) Based on Empirical Mode Decomposition (EMD), decompose the node voltage into multiple decomposed voltage signals at multiple frequency components.

[0146] Specifically, the multiple decomposed voltage signals can be specifically understood as separating the noise component, drift component, and effective voltage signal in the node voltage.

[0147] 2) Filter each decomposed voltage signal to obtain the effective voltage signal.

[0148] Specifically, based on the fast Fourier transform (FFT), perform spectral analysis on each decomposed voltage signal, so as to perform filtering, separate the external interference signals in each decomposed voltage signal, and obtain the effective voltage signal. Exemplarily, a filtering algorithm based on the fast Fourier transform can be used to remove the noise component in the voltage signal and correct the loop resistance measurement error caused by the node voltage offset. Or, use an adaptive filter to dynamically adjust the filtering parameters according to the noise characteristics to eliminate the non-linear noise interference and obtain the effective voltage signal.

[0149] 3) Construct a compensation and correction matrix, and correct the effective voltage signal according to the compensation and correction matrix to obtain the corrected node voltage.

[0150] Among them, the compensation and correction matrix can be constructed by using the existing method for constructing the compensation and correction matrix, which will not be elaborated in this embodiment.

[0151] Specifically, the corrected offset of the effective voltage signal is calculated through a compensation correction matrix, and the corrected node voltage is re-input into the loop resistance measurement algorithm to achieve a higher voltage offset correction effect. Exemplarily, the effective voltage signal is input into the compensation correction matrix to obtain the offset of the corrected effective voltage signal, and the corrected node voltage is obtained by subtracting the offset of the corrected effective voltage signal from the effective voltage signal.

[0152] Optionally, based on the above embodiments, the loop environment data includes the temperature of the environment where the loop to be measured is located; the loop environment data is obtained in real time during the measurement process.

[0153] After obtaining the loop environment data, it further includes:

[0154] 1) Using a time series analysis algorithm and a long short-term memory network model to model the temporal characteristics of the temperature change of the environment where the loop to be measured is located at different time points, and constructing a temperature temporal characteristic model.

[0155] 2) Based on the temperature temporal characteristic model, predicting the change rate of the temperature of the environment where the loop to be measured is located at the next time point.

[0156] 3) When the change rate is greater than the preset rate, increasing the acquisition frequency of the temperature of the environment where the loop to be measured is located.

[0157] Specifically, the temperature temporal characteristic model compares the predicted temperature change rate with the actual detected temperature change, evaluates the effectiveness of the environmental compensation coefficient in real time, and triggers a new error feedback loop to optimize the measurement strategy when necessary. Exemplarily, when the environmental temperature changes violently, the acquisition frequency of the temperature of the environment where the loop to be measured is located will be automatically increased to ensure that the environmental compensation coefficient can reflect the latest temperature state. That is, the technical solution of this embodiment can dynamically adjust the loop resistance measurement strategy according to the prediction result of the temperature temporal characteristic model.

[0158] Optionally, based on the above embodiments, before generating the current calibration parameters using the current calibration model, it further includes:

[0159] 1) Determining the estimated loop resistance value of the loop to be measured.

[0160] Among them, the estimated loop resistance value can be determined according to experience or automatically completed through impedance determination in the early stage of measurement.

[0161] 2) If the estimated loop resistance value is greater than the preset resistance value, select a voltage sampling device with the first sensitivity to measure the loop voltage, and obtain the loop current, loop voltage, and loop environment data using the first sampling frequency.

[0162] Among them, for the convenience of description, the situation where the estimated loop resistance value is greater than the preset resistance value can be recorded as a high-resistance measurement scenario.

[0163] Specifically, in the high-resistance measurement scenario, a high-sensitivity voltage sampling device can be preferentially selected to detect the node voltage to ensure the signal resolution of the node voltage. Among them, the signal resolution can be understood as that after the node voltage is decomposed and filtered, the fidelity and resolution of the effective voltage signal are improved.

[0164] 3) If the estimated loop resistance value is less than the preset resistance value, a voltage sampling device with the second sensitivity is selected to measure the loop voltage, and the loop current, loop voltage, and loop environment data are obtained at the second sampling frequency. Among them, the first sensitivity is higher than the second sensitivity, and the first sampling frequency is less than the second sampling frequency.

[0165] Among them, for the convenience of description, the situation where the estimated loop resistance value is less than the preset resistance value can be recorded as a low-resistance measurement scenario.

[0166] Specifically, in the low-resistance measurement scenario, the influence of electromagnetic interference is reduced by increasing the frequency and filtering accuracy of the voltage sampling device to obtain the node voltage.

[0167] Furthermore, the adaptive switching between the high-resistance measurement scenario and the low-resistance measurement scenario can be realized by dynamically adjusting the magnitude of the measurement current and the acquisition frequency of the node voltage. Among them, the adaptive switching process is automatically completed by a fuzzy control algorithm, so as to ensure the measurement accuracy and reliability in multiple measurement scenarios.

[0168] In summary, when different measurement scenarios (such as high resistance or low resistance) are detected, different voltage sampling devices, signal filtering schemes, and measurement parameters can be automatically selected based on the detected impedance level, dynamically optimizing the measurement signal-to-noise ratio, and avoiding the problems of too small signal in high resistance or too large noise ratio in low resistance.

[0169] Optionally, on the basis of the above embodiments, after determining the loop resistance of the loop to be measured, it further includes:

[0170] Encrypt and store the current calibration parameter, measurement current, loop current, loop voltage, loop environment data, environmental compensation coefficient, and loop resistance.

[0171] Specifically, the encrypted storage can adopt an integrated data recording and traceability mechanism based on blockchain technology for encrypted storage to ensure the reliability of the stored data. Specifically, after each loop resistance measurement is completed, the various data involved in the detection process are stored in the blockchain network in an encrypted manner; in this way, all measurement data have the ability to be traced without being tampered with, ensuring the authenticity and reliability of the measurement data, and at the same time providing reliable historical data support for subsequent measurement optimization and algorithm improvement.

[0172] It can be understood that the parameters related to the node voltage correction and the parameters related to the environmental compensation are also stored in encrypted form.

[0173] In summary, the beneficial effects of the loop resistance measurement method provided by the present invention are:

[0174] The measurement error problem caused by the drift of the traditional current source is solved through dynamic calibration technology and adaptive optimization algorithm based on machine learning. Through periodic automatic calibration and real-time adjustment of calibration parameters, the output of the current source can remain stable for a long time, and the accuracy of the loop resistance measurement can be ensured even when the load changes or the environmental interference is large. In addition, the adaptive optimization algorithm based on machine learning and the error feedback mechanism combined with the continuous learning ability of historical data enable the system to adapt to the aging problem of equipment in long-term operation, effectively extending the service life and reliability of the equipment, thereby meeting the strict requirements of industrial and power systems for high-precision measurement. Among them, the equipment can be specifically understood as a complete measurement system or device including a current source, a current calibration module, a compensation module, a measurement module, a data processing and control unit, which works together to realize the function of loop resistance measurement error compensation. Among them, the current source, as an important part of the system, is responsible for providing the current signal required for measurement, but its output stability needs to rely on the cooperation of functions such as dynamic calibration technology and neural network algorithm to effectively solve the impact of current source drift and long-term aging.

[0175] The multi-point data fusion and dynamic environmental compensation technologies are adopted to significantly improve the anti-interference ability and adaptability of the loop resistance measurement system in complex environments. Specifically, in multi-point data fusion, through the deployment of distributed sensor networks and the application of Kalman filtering algorithms based on Bayesian estimation, random errors and noise in the measurement data can be effectively eliminated, and the data fusion results can be optimized by weight adjustment. The dynamic compensation module, combined with the multivariate regression model updated in real time, can quickly respond to the error correction requirements caused by environmental fluctuations. Therefore, whether in high electromagnetic interference scenarios or industrial environments with drastic temperature differences, the invention can achieve high stability and reliability of loop resistance measurement.

[0176] Through the error feedback mechanism and the long short-term memory network model, the system can optimize the loop resistance measurement strategy in real time, dynamically adjust the circuit calibration parameters and the environmental compensation coefficient, and effectively improve the intelligence level, measurement efficiency and measurement accuracy of the loop resistance measurement system. Through the error feedback mechanism, the data deviation in the measurement process can be monitored in real time, and the calibration and compensation parameters can be dynamically adjusted in combination with predictive analysis, so that the measurement strategy is always in the optimal state. The measurement optimization algorithm also has the function of dynamic resource allocation. For example, it can increase the density of data acquisition in the measurement stage with large errors, further reducing the uncertainty of the measurement results. This mechanism not only improves the measurement accuracy, but also reduces the need for manual intervention, providing an efficient and reliable solution for complex measurement tasks, especially suitable for long-term operation scenarios that require high precision. And through the automatic calibration mechanism, neural network algorithm optimization and error feedback mechanism, the problems of current source drift and equipment aging are solved, ensuring stable output and high-precision measurement.

[0177] An embodiment of the present invention also provides a loop resistance measurement device, which can be used to measure the loop resistance according to the loop resistance measurement method provided in any of the above embodiments. Therefore, it has corresponding beneficial effects.

[0178] Figure 2 It is a schematic structural diagram of a loop resistance measurement device provided by an embodiment of the present invention. Refer to Figure 2 , the loop resistance measurement device includes a current control module 10, an acquisition module 20, a first determination module 30 and a second determination module 40.

[0179] Among them, the current control module 10 is used to generate current calibration parameters by using a current calibration model, calibrate the current source based on the current calibration parameters, and control the calibrated current source to output a measurement current to the loop to be measured. The acquisition module 20 is used to acquire the loop current, loop voltage and loop environment data of the loop to be measured; among them, the loop environment data includes at least one of the temperature, humidity and electromagnetic parameters of the environment where the loop to be measured is located. The first determination module 30 is used to determine the environmental compensation coefficient corresponding to the loop environment data based on the correspondence between the environmental data and the environmental compensation coefficient. The second determination module 40 is used to determine the loop resistance of the loop to be measured according to the loop current, loop voltage and environmental compensation coefficient.

[0180] Only some exemplary embodiments of the present invention have been described by way of illustration above. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

[0181] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0182] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not imply the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0183] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0184] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0185] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical or other form.

[0186] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0187] In addition, each functional unit in various embodiments of the present application may be integrated in a processing unit, may exist physically alone for each unit, or two or more units may be integrated in one unit.

[0188] If the above function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0189] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0190] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitations are imposed herein.

[0191] The above specific implementation manner does not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for measuring loop resistance, characterized in that, Including: Generating current calibration parameters using a current calibration model, and calibrating a current source based on the current calibration parameters, controlling the calibrated current source to output a measurement current to a circuit under test; Obtaining the loop current, loop voltage, and loop environment data of the circuit under test; wherein, the loop environment data includes at least one of the temperature, humidity, and electromagnetic parameters of the environment where the circuit under test is located; Determining an environmental compensation coefficient corresponding to the loop environment data based on the corresponding relationship between the environmental data and the environmental compensation coefficient; Determining the loop resistance of the circuit under test according to the loop current, the loop voltage, and the environmental compensation coefficient.

2. The loop resistance measurement method according to claim 1, characterized in that The circuit under test includes a plurality of measurement points arranged distributively, and each of the measurement points is correspondingly provided with a plurality of sensor modules, and the sensor modules are used to detect the node voltage, node current, and node environment data of the corresponding measurement point; wherein, the node environment data includes at least one of the temperature, humidity, and electromagnetic parameters of the environment where the measurement point is located; The obtaining of the loop current, loop voltage, and loop environment data of the circuit under test includes: Obtaining the node voltage, node current, and node environment data at each of the measurement points; Performing fusion processing on each of the node voltages to obtain the loop voltage; performing fusion processing on each of the node currents to obtain the loop current; performing fusion processing on each of the node environment data to obtain the loop environment data.

3. The loop resistance measurement method according to claim 2, characterized in that, Performing fusion processing on each of the node voltages to obtain the loop voltage, including: Performing weighted average processing on each of the node voltages in time series through a Kalman filtering algorithm based on Bayesian estimation to obtain the loop voltage; Performing fusion processing on each of the node currents to obtain the loop current, including: Performing weighted average processing on each of the node currents in time series through a Kalman filtering algorithm based on Bayesian estimation to obtain the loop current; Performing fusion processing on each of the node environment data to obtain the loop environment data, including: Performing weighted average processing on each of the node environment data in time series through a Kalman filtering algorithm based on Bayesian estimation to obtain the loop environment data.

4. The loop resistance measurement method according to claim 2 or 3, characterized in that, Before performing fusion processing on each of the node voltages, it further includes: correcting each of the node voltages; Correspondingly, performing fusion processing on each of the node voltages includes: performing fusion processing on each of the corrected node voltages; Wherein, for any one of the node voltages, correcting the node voltage includes: Decomposing the node voltage into a plurality of decomposed voltage signals under a plurality of frequency components based on empirical mode decomposition; Filtering each of the decomposed voltage signals based on fast Fourier transform to obtain an effective voltage signal; Constructing a compensation correction matrix, and correcting the effective voltage signal according to the compensation correction matrix to obtain a corrected node voltage.

5. The loop resistance measurement method according to claim 1, wherein The obtaining process of the corresponding relationship between the environmental data and the environmental compensation coefficient includes: Taking the environmental data as the independent variable and the loop resistance as the dependent variable, based on the multivariate regression analysis algorithm, establish the corresponding relationship between the environmental data and the environmental compensation coefficient; wherein the environmental compensation coefficient is used to correct the error caused by the change of the environmental data to the measurement result of the loop resistance.

6. The loop resistance measurement method according to claim 1, characterized in that, The loop current is acquired at a preset frequency. After each acquisition of the loop current of the loop under test, it further includes: Calculating current error data according to the loop current and the measured current. Using the time series analysis algorithm and the long short-term memory network model to model the temporal characteristics of the change of the current error data, and constructing a current error data temporal characteristic model. Predicting the current error data at the next time point based on the current error data temporal characteristic model. When the prediction result of the current error data at the next time point is greater than the preset error value, updating the current calibration model. And / or The loop resistance measurement method further includes: updating the current calibration model every preset time interval.

7. The loop resistance measurement method according to claim 1 or 6, characterized in that The construction process of the current calibration model includes: Obtaining a plurality of historical error data; wherein, the obtaining process of any one of the historical error data includes: controlling the current source to provide a target current value to the reference resistor; detecting the actual current value flowing through the reference resistor; taking the difference between the actual current value and the target current value as the historical error data. Training each of the historical error data based on the neural network algorithm to obtain the current calibration model.

8. The loop resistance measurement method according to claim 1, characterized in that The loop environmental data includes the temperature of the environment where the loop under test is located. After obtaining the loop environmental data, it further includes: Using the time series analysis algorithm and the long short-term memory network model to model the temporal characteristics of the temperature change of the environment where the loop under test is located collected at different time points, and constructing a temperature temporal characteristic model. Predicting the change rate of the temperature of the environment where the loop under test is located at the next time point based on the temperature temporal characteristic model. When the change rate is greater than the preset rate, increasing the acquisition frequency of the temperature of the environment where the loop under test is located.

9. The loop resistance measurement method according to claim 1, characterized in that Before generating the current calibration parameters using the current calibration model, it further includes: Determining the estimated loop resistance value of the loop under test. If the estimated loop resistance value is greater than the preset resistance value, select a voltage sampling device with the first sensitivity to measure the loop voltage, and acquire the loop current, the loop voltage, and the loop environmental data at the first sampling frequency. If the estimated loop resistance value is less than the preset resistance value, select a voltage sampling device with the second sensitivity to measure the loop voltage, and acquire the loop current, the loop voltage, and the loop environmental data at the second sampling frequency. Wherein, the first sensitivity is higher than the second sensitivity, and the first sampling frequency is less than the second sampling frequency. And / or After determining the loop resistance of the loop under test, it further includes: Encrypting and storing the current calibration parameters, the measured current, the loop current, the loop voltage, the loop environmental data, the environmental compensation coefficient, and the loop resistance.

10. A loop resistance measuring device, characterized in that, Includes: A current control module, which is used to generate current calibration parameters by using a current calibration model, calibrate a current source based on the current calibration parameters, and control the calibrated current source to output a measurement current to a circuit under test; An acquisition module, which is used to acquire the loop current, loop voltage and loop environment data of the circuit under test; wherein, the loop environment data includes at least one of the temperature, humidity and electromagnetic parameters of the environment where the circuit under test is located; A first determination module, which is used to determine the environment compensation coefficient corresponding to the loop environment data based on the correspondence between the environment data and the environment compensation coefficient; A second determination module, which is used to determine the loop resistance of the circuit under test according to the loop current, the loop voltage and the environment compensation coefficient.

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