Error analysis method and system for wire-drawn displacement sensor

Abstract

The present invention discloses a method and system for analyzing the error of a wire-drawn displacement sensor, and relates to the technical field of sensor error analysis. The method comprises: interactively obtaining the environmental adaptability range of the displacement sensor to be tested, setting a standard test environment and a plurality of non-standard test environments; performing multiple rounds of data acquisition tests, obtaining reference displacement data and a plurality of test displacement data; obtaining a plurality of measurement error sets; performing error type analysis, and outputting a sensor error information set; performing error compensation analysis, and obtaining an error calibration strategy; performing precision recovery verification after performing directional error calibration. The present invention solves the technical problems of insufficient measurement accuracy and poor environmental adaptability of wire-drawn displacement sensors in the prior art, and achieves the technical effect of improving the measurement accuracy and environmental adaptability of wire-drawn displacement sensors under different working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor error analysis, and in particular to a method and system for error analysis of a wire-drawn displacement sensor. Background Art

[0002] During the use of the wire-type displacement sensor, its measurement accuracy is easily affected by various external factors, such as mechanical wear, temperature, humidity changes, electromagnetic interference, etc. These factors will cause errors in the measurement results of the sensor, which will in turn affect the normal operation of the equipment and the accuracy requirements of the system. Especially in complex working environments, displacement sensors often face challenges of extreme conditions, such as high and low temperature differences, vibration, etc., which makes it difficult to guarantee the long-term stability and accuracy of the sensor. Most of the existing displacement sensor error calibration methods rely on fixed condition tests in laboratory environments, which cannot fully reflect the performance of the sensor in actual use. This method ignores the types of errors that may occur in the sensor under different working conditions and their changing laws, resulting in unreliable measurement results in actual applications. At the same time, the existing error analysis and calibration strategies are often limited to the correction of a single factor, and lack an in-depth analysis of the errors under the combined effect of multiple environmental factors.

[0003] The existing technology has technical problems that the wire-type displacement sensor has insufficient measurement accuracy and poor environmental adaptability. Summary of the invention

[0004] The present application provides a method and system for error analysis of a draw-wire displacement sensor, which are used to solve the technical problems of insufficient measurement accuracy and poor environmental adaptability of draw-wire displacement sensors in the prior art.

[0005] In view of the above problems, the present application provides a method and system for analyzing the error of a wire-type displacement sensor.

[0006] In a first aspect of the present application, a method for analyzing an error of a wire-type displacement sensor is provided, the method comprising:

[0007] The environmental adaptability range of a displacement sensor to be tested is interactively obtained, wherein the displacement sensor to be tested is a wire-type displacement sensor; a standard test environment and multiple non-standard test environments are set with the environmental adaptability range as a value constraint; multiple rounds of data acquisition tests are performed on the displacement sensor to be tested using the standard test environment and the multiple non-standard test environments to obtain reference displacement data and multiple test displacement data; the reference displacement data is synchronously compared with the multiple test displacement data to obtain multiple measurement error sets; error type analysis is performed according to the multiple non-standard test environments and the multiple measurement error sets, and a sensor error information set is output; error compensation analysis is performed according to the sensor error information set to obtain an error calibration strategy; after directional error calibration is performed on the displacement sensor to be tested based on the error calibration strategy, accuracy recovery verification of the displacement sensor to be tested is performed based on the multiple non-standard test environments and the reference displacement data.

[0008] A second aspect of the present application provides a wire-type displacement sensor error analysis system, the system comprising:

[0009] An environmental adaptability range acquisition module, the environmental adaptability range acquisition module is used to interactively obtain the environmental adaptability range of the displacement sensor to be tested, wherein the displacement sensor to be tested is a wire-type displacement sensor; a test environment setting module, the test environment setting module is used to set a standard test environment and multiple non-standard test environments with the environmental adaptability range as a value constraint; a displacement data acquisition module, the displacement data acquisition module is used to perform multiple rounds of data acquisition tests on the displacement sensor to be tested using the standard test environment and multiple non-standard test environments to obtain reference displacement data and multiple test displacement data; a measurement error set acquisition module, the measurement error set acquisition module is used to convert the reference displacement data Synchronously compare with multiple test displacement data to obtain multiple measurement error sets; a sensor error information set output module, the sensor error information set output module is used to perform error type analysis according to the multiple non-standard test environments and the multiple measurement error sets, and output a sensor error information set; an error calibration strategy acquisition module, the error calibration strategy acquisition module is used to perform error compensation analysis according to the sensor error information set to obtain an error calibration strategy; an accuracy recovery verification module, the accuracy recovery verification module is used to perform accuracy recovery verification of the displacement sensor to be tested based on the multiple non-standard test environments and reference displacement data after calibrating the orientation error of the displacement sensor to be tested based on the error calibration strategy.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Interactively obtain the environmental adaptability range of the displacement sensor to be tested; set the standard test environment and multiple non-standard test environments with the environmental adaptability range as the value constraint; use the standard test environment and multiple non-standard test environments to perform multiple rounds of data acquisition tests to obtain reference displacement data and multiple test displacement data; synchronously compare the reference displacement data with multiple test displacement data to obtain multiple measurement error sets; perform error type analysis and output sensor error information sets; perform error compensation analysis based on the sensor error information sets to obtain error calibration strategies; perform accuracy recovery verification of the displacement sensor to be tested after calibrating the orientation error of the displacement sensor to be tested based on the error calibration strategy. The technical effect of improving the measurement accuracy and environmental adaptability of the wire-drawn displacement sensor under different working conditions is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic flow chart of a method for analyzing an error of a wire-type displacement sensor provided in an embodiment of the present application;

[0014] Figure 2 This is a schematic diagram of the structure of the error analysis system of the wire-type displacement sensor provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: environmental adaptability range acquisition module 10 , test environment setting module 20 , displacement data acquisition module 30 , measurement error set acquisition module 40 , sensor error information set output module 50 , error calibration strategy acquisition module 60 , accuracy recovery verification module 70 . DETAILED DESCRIPTION

[0016] The present application provides a method and system for analyzing an error of a draw-wire displacement sensor, which is used to solve the technical problems of insufficient measurement accuracy and poor environmental adaptability of draw-wire displacement sensors in the prior art.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a method for analyzing the error of a wire-type displacement sensor, the method comprising:

[0019] Step S100: interactively obtaining the environmental adaptability range of the displacement sensor to be tested, wherein the displacement sensor to be tested is a wire-type displacement sensor.

[0020] Specifically, an interactive operation is performed to obtain the environmental adaptability range of the wire-type displacement sensor to be tested. This process is crucial because it provides key basic information for a series of subsequent tests and analyses. The interactive method is to read the environmental adaptability parameters of the sensor model that have been stored from the database. The displacement sensor to be tested is a wire-type, which means that it measures the displacement of an object by retracting the wire. This type of sensor will have different performance under different environmental conditions. The environmental adaptability range includes parameters in many aspects. For example, the temperature range is a range from extremely low temperatures to extremely high temperatures. The sensor should be able to work normally and maintain a certain measurement accuracy within this temperature range. The humidity range is also an important factor. Too high or too low humidity will affect the electronic components and mechanical structure of the sensor. In addition, the pressure range, vibration amplitude, etc. are also included in the environmental adaptability range. After obtaining the information of these environmental adaptability ranges through interaction, the subsequent test environment can be set according to this range to ensure the comprehensiveness and effectiveness of the test, so as to accurately analyze the performance and error of the sensor in various possible working environments.

[0021] Step S200: taking the environmental adaptability range as a value constraint, setting a standard test environment and multiple non-standard test environments.

[0022] Specifically, the standard test environment and various non-standard test environments are set based on the environmental adaptability range of the wire-type displacement sensor to be tested. For the standard test environment, it is an environment where no deviation has occurred. It is selected under the relatively ideal state of the environmental adaptability range, and the more moderate and stable parameter combinations in the environmental adaptability range are selected to construct the standard test environment. In such an environment, the sensor can theoretically work in the best state without obvious deviation. For the setting of various non-standard test environments, it is more targeted and diverse. According to the environmental adaptability range, different parameter combinations are selected in its boundary area and middle area to cover various situations where problems occur. For example, a combination of temperature and humidity close to the lower limit of the environmental adaptability range, or a combination close to the upper limit, is selected. At the same time, the non-standard test environment is set in combination with historical fault records and special situations that may be encountered in actual application scenarios. If the historical fault records show that the sensor is prone to deviation under specific environmental conditions, then such an environment is specially set as a non-standard test environment for key testing. In this way, based on the environmental adaptability range, a standard test environment is set as a benchmark reference, and a variety of non-standard test environments are set to fully simulate various actual working conditions, providing rich and diverse test scenarios for subsequent error analysis of the wire-type displacement sensor.

[0023] Step S300: performing multiple rounds of data acquisition tests on the displacement sensor to be tested using the standard test environment and multiple non-standard test environments to obtain reference displacement data and multiple test displacement data.

[0024] Specifically, multiple rounds of data acquisition tests are performed on the wire-type displacement sensor to be tested using the set standard test environment and multiple non-standard test environments. First, a test scenario is constructed and a displacement measurement target with a displacement change constraint is introduced. The role of this displacement measurement target is to provide a measurable displacement change under different test environments so that the sensor can collect corresponding data. For the standard test environment, the test scenario is adjusted to meet the requirements of the standard test environment. In this adjusted standard test scenario, when the displacement measurement target moves according to its displacement change constraint, the pre-called reference displacement sensor is used to synchronously collect displacement data. Since the reference displacement sensor usually has high accuracy and stability under the standard test environment, the collected data can be used as the reference displacement data. Then, for multiple non-standard test environments, the test scenario is adjusted for multiple rounds to simulate different non-standard test environments respectively. In each non-standard test scenario, when the displacement measurement target moves according to the displacement change constraint, the wire-type displacement sensor to be tested is used to synchronously collect displacement data. In this way, multiple test displacement data can be obtained in multiple rounds of testing, and each test displacement data corresponds to a sensor measurement result under a non-standard test environment. Through this multiple rounds of data collection tests, the measurement data of the sensor under different environments are obtained, providing rich information for subsequent analysis and error calibration.

[0025] Step S400: synchronously comparing the reference displacement data with a plurality of test displacement data to obtain a plurality of measurement error sets.

[0026] Specifically, the reference displacement data obtained under the standard test environment is synchronously compared with multiple test displacement data obtained under various non-standard test environments to determine the measurement error of the sensor under different environments. First, a data acquisition step is predefined, which will be used as a basic unit for subsequent data processing. Then, the displacement change constraint of the displacement measurement target is used to perform data alignment on the reference displacement data and multiple test displacement data, so that the comparison can be made under the same displacement change conditions and the accuracy of error analysis can be improved. After data alignment, the updated reference data and multiple updated test data are obtained. Then, the measurement points of the updated reference data and multiple updated test data are screened with the predefined data acquisition step as the screening constraint. Through this step, representative measurement points can be selected from a large amount of data to reduce the complexity of data processing. After screening, a reference measurement point sequence and multiple test measurement point sequences are obtained. The data indicators of each measurement point are relatively rich, including displacement value, motion speed, acceleration, power supply voltage, power supply current, and wire tension. These indicators can reflect the working state and measurement status of the sensor from different aspects. Then, the scene error calculation is performed on multiple test measurement point sequences according to the reference measurement point sequence, and multiple sets of scene error sequences are obtained by comparing the difference between each measurement point under different test environments and the reference environment. Based on these scene error sequences, multiple sets of scene error curves can be fitted. These curves intuitively show the error changes of the sensor under different non-standard test environments relative to the standard test environment. Finally, according to the mapping relationship between multiple non-standard test environments and multiple test displacement data, multiple sets of scene error curves are associated with the corresponding non-standard test environments and stored, thereby obtaining multiple measurement error sets. These measurement error sets provide specific data support for subsequent error type analysis, which helps to gain a deeper understanding of the error characteristics of sensors under different environments and lay the foundation for formulating accurate error calibration strategies.

[0027] Step S500: performing error type analysis according to the multiple non-standard test environments and multiple measurement error sets, and outputting a sensor error information set.

[0028] Specifically, error type analysis is performed based on multiple non-standard test environments and multiple measurement error sets to output a sensor error information set. Multiple scene error curves corresponding to displacement values ​​are extracted from multiple sets of scene error curves of multiple measurement error sets. These curves reflect the differences between the displacement measurement of the sensor and the standard test environment under different non-standard test environments. Then, these scene error curves are offset quantified based on the Euclidean distance. By calculating the distance between the curves, an offset uniformity coefficient can be obtained, which can preliminarily reflect the error consistency of the sensor under different environments. If the offset uniformity coefficient meets the preset offset uniformity threshold, it means that the error is relatively consistent and is caused by problems with mechanical components. At this time, the displacement value is used to traverse the pre-constructed mechanical error table to locate the first mechanical error component that may have problems. This mechanical error table is constructed based on previous experience and understanding of the sensor structure, which contains the possible mechanical error types and corresponding component information under different displacement values. If the offset uniformity coefficient does not meet the offset uniformity threshold, it means that the error is relatively complex and is caused by environmental factors. At this time, environmental correlation error analysis is performed on multiple scene error curves based on multiple non-standard test environments. First, multiple non-standard test environments are aggregated based on single-factor variables to obtain multiple groups of single-factor change environments. Then, according to the mapping relationship between multiple non-standard test environments and multiple scene error curves, multiple groups of single-factor change environments are used to group multiple scene error curves to obtain multiple groups of scene error curves. Next, the first group of single-factor change environments and the first group of scene error curves are extracted from the mapping of multiple groups of single-factor change environments and multiple groups of scene error curves. The first group of single-factor change environments is serialized based on the environmental factor change scale, and the first group of scene error curves is serialized based on the sorting result to obtain a first error curve sequence, which includes multiple time series curves. The first time series curve is extracted from the first error curve sequence, and its environmental sensitivity is set to 1. The environmental sensitivity of other time series curves is calculated based on the first time series curve. The environmental sensitivity limit is preset, and the limit is used to traverse all environmental sensitivities to locate the first single-factor error interval. By analogy, multiple groups of single-factor change environments and multiple groups of scene error curves are analyzed to obtain multiple single-factor error intervals. Finally, these single-factor error intervals are integrated and stored as the first environmental error interval. Through the above analysis process, mechanical errors and environmental errors are comprehensively considered to obtain K mechanical error components and W environmental error intervals. The W environmental error intervals are solved for union and the sensor environmental error interval is output. Then, the K mechanical error components and sensor environmental error intervals are integrated and stored to obtain the sensor error information set. This information set contains various error types and specific error source information that may occur in sensors under different non-standard test environments, providing a clear direction and basis for subsequent error calibration.

[0029] Step S600: performing error compensation analysis according to the sensor error information set to obtain an error calibration strategy.

[0030] Specifically, first, carefully study the sensor error information set. This information set contains K mechanical error components and W environmental error intervals determined by analyzing multiple non-standard test environments and multiple measurement error sets. For mechanical error components, it is necessary to have a deep understanding of the specific ways in which they affect the sensor measurement results. For example, the wear of a mechanical component may cause the measurement value to be too large or too small, or unstable fluctuations. For the environmental error interval, analyze the degree and law of the influence of different environmental factors such as temperature, humidity, and pressure on the sensor performance. Then, for mechanical error components, consider using mechanical adjustment or replacement of components to compensate for the errors. If a component is loose, it can be tightened; if it is a severely worn component, it can be replaced with a more durable or more accurate component. For errors caused by unreasonable mechanical structure, the mechanical structure of the sensor can be redesigned or optimized to improve its measurement accuracy and stability. For the environmental error interval, formulate corresponding compensation algorithms according to the influence of different environmental factors. For example, for the influence of temperature on the sensor, by establishing a temperature compensation model, the measurement results are corrected according to the temperature value during the actual measurement. If humidity has a significant impact on the sensor's measurement results, take anti-moisture measures or add a humidity compensation factor to the measurement algorithm. For other environmental factors such as pressure, the corresponding compensation model and algorithm can be established similarly. In the process of error compensation analysis, it is necessary to comprehensively consider the interaction of various error factors. Some errors are caused by the combined action of multiple factors. At this time, a comprehensive error calibration strategy needs to be formulated. For example, in a high temperature and high humidity environment, the performance of mechanical components will be more affected. At this time, it is necessary to consider both environmental compensation and mechanical adjustment methods. Through a comprehensive analysis of the sensor error information set and comprehensive consideration of various error factors, a complete set of error calibration strategies is finally determined. This strategy includes specific compensation methods and operating steps for different error types, which provides clear guidance for the subsequent directional error calibration of the sensor.

[0031] Step S700: after calibrating the orientation error of the displacement sensor to be tested based on the error calibration strategy, performing accuracy recovery verification of the displacement sensor to be tested based on the multiple non-standard test environments and reference displacement data.

[0032] Specifically, the directional error calibration is performed on the wire-type displacement sensor to be tested according to the error calibration strategy. This calibration process is a targeted adjustment for the various error types analyzed previously. After completing the directional error calibration, the sensor is then verified for accuracy recovery based on a variety of non-standard test environments and benchmark displacement data. A variety of non-standard test environments cover a variety of complex situations that may cause errors in the sensor. By verifying in these environments, the performance of the calibrated sensor under different harsh conditions is comprehensively tested. For each non-standard test environment, the displacement measurement target is re-run, and the displacement data is collected using the calibrated displacement sensor to be tested. At the same time, the collected displacement data is compared with the benchmark displacement data. The benchmark displacement data is collected by a high-precision benchmark displacement sensor under a standard test environment and has high accuracy and reliability. By comparing the calibrated test displacement data with the benchmark displacement data, the measurement error of the sensor under different non-standard test environments is calculated. If the error is within an acceptable range, it means that the calibration strategy is effective and the accuracy of the sensor has been restored; if the error is still large, the calibration strategy needs to be re-evaluated, and the calibration method needs to be further adjusted or optimized. This accuracy recovery verification based on a variety of non-standard test environments and benchmark displacement data ensures that the calibrated sensor can maintain high measurement accuracy under various actual working conditions, providing a strong guarantee for the reliable application of the sensor.

[0033] In a possible implementation, step S200 further includes:

[0034] Step S210: Perform network data call according to the model information of the displacement sensor to be tested to obtain historical fault records.

[0035] Step S220: performing fault condition distribution analysis according to the historical fault records to obtain multiple error frequencies in multiple environmental change intervals.

[0036] Step S230: presetting the test frequency, and using the multiple error frequencies to randomly select the test environment in the multiple environmental change intervals to obtain multiple groups of non-standard test environments.

[0037] Step S240: Integrate the multiple groups of non-standard test environments to obtain the multiple non-standard test environments.

[0038] Step S250: performing difference calculation on the multiple environmental change intervals and the environmental adaptability range to obtain the standard test environment.

[0039] Specifically, it is a key step to make it clear that the displacement sensor to be tested is a wire-type displacement sensor and to call the network data according to its model information. The model information of the sensor is like a unique identifier, which contains various key features and parameters about the sensor. Through the network connection to the relevant database or data storage system, the system will search and match according to the input model information. These databases are maintained by sensor manufacturers, industry organizations or special monitoring platforms. A large amount of various data about different models of sensors in actual use is stored in it, including historical fault records. When the system finds data that matches the model of the sensor to be tested, it will extract the historical fault records. These records include the time, location, and specific fault manifestations of the fault, such as excessive deviation of the measured value, no response of the sensor, unstable data, etc. It also contains information on environmental conditions when the fault occurred, such as temperature, humidity, pressure, etc. Through this network data call method, rich historical fault information can be obtained, which provides an important reference for the subsequent analysis of the performance of the sensor in different environments and possible errors.

[0040] The fault condition distribution analysis is performed on the acquired historical fault records, and the environmental conditions when the fault occurs are classified and sorted, and multiple environmental change intervals are divided. At the same time, the number of faults in each environmental change interval is counted to obtain multiple error frequencies. These error frequencies reflect the possibility of sensor failure under different environmental conditions.

[0041] A test frequency is preset to determine how many times the test environment needs to be selected. Then, the test environment is randomly selected in multiple environmental change intervals using the multiple error frequencies obtained. According to the error frequency, the probability of selecting environmental change intervals that are prone to failure is relatively high. Through this random selection method, multiple groups of non-standard test environments are obtained, which will be used to conduct more comprehensive tests on sensors.

[0042] The obtained multiple sets of non-standard test environments are integrated, the test environments with similar environmental conditions are merged, and the duplicate parts are removed, and finally a variety of non-standard test environments are obtained. These non-standard test environments cover various situations that may cause sensor failures, providing rich test scenarios for subsequent error analysis.

[0043] In order to determine the standard test environment, a difference calculation is performed on multiple environmental change intervals and environmental adaptability ranges. First, multiple environmental change intervals are obtained by analyzing historical fault records. These intervals represent various combinations of environmental conditions when sensors failed in the past, while the environmental adaptability range is determined based on the design specifications and performance indicators of the sensor. It represents the range of environmental conditions in which the sensor can work normally. When performing the difference calculation, the parts belonging to multiple environmental change intervals are excluded from the environmental adaptability range, which means that the standard test environment will be a combination of environmental conditions that are within the environmental adaptability range but have not had problems in the historical fault records. Since the standard test environment is an environment where no deviation has occurred, the standard test environment determined in this way can be used as a benchmark for comparing the performance of sensors under non-standard test environments. In this environment, the sensor should theoretically be able to work stably and accurately without obvious deviations. This calculation method helps to ensure the reliability and representativeness of the standard test environment and provide a stable reference point for subsequent error analysis and calibration of the sensor. At the same time, by comparing with non-standard test environments, we can better understand the performance changes and error sources of sensors under different environmental conditions.

[0044] In a possible implementation, step S300 further includes:

[0045] Step S310: pre-construct a test scenario, and introduce a displacement measurement target into the test scenario, wherein the displacement measurement target has a displacement change constraint.

[0046] Step S320: Use the standard test environment to adjust the test scenario parameters to obtain a standard test scenario.

[0047] Step S330: when the displacement measurement target is operated based on the displacement change constraint in the standard test scenario, displacement data is synchronously collected using a pre-called reference displacement sensor to obtain the reference displacement data.

[0048] Step S340: Use the multiple non-standard test environments to perform multiple rounds of parameter adjustment on the test scenarios to obtain multiple non-standard test scenarios.

[0049] Step S350: when the displacement measurement target is operated based on the displacement change constraint in the multiple non-standard test scenarios, displacement data are synchronously collected using the displacement sensor to be tested to obtain the multiple test displacement data.

[0050] Specifically, the test scenario is pre-built first. This test scenario is specially designed to simulate various situations that may be encountered by the wire-drawn displacement sensor in actual applications. The test scenario can be a laboratory environment or a space that simulates an actual workplace. In this test scenario, a displacement measurement target is introduced. This displacement measurement target can be a variety of objects that move in a straight line, such as moving parts of mechanical equipment. These parts often need to accurately measure their displacement in industrial production to ensure the normal operation of the equipment and the quality of production. Such displacement change constraints can ensure that the movement of the displacement measurement target is predictable and repeatable during the test, thereby facilitating accurate evaluation of the performance of the sensor. By introducing a displacement measurement target with a displacement change constraint, a stable and reliable test object can be provided for subsequent sensor testing, helping to better analyze the measurement accuracy and error of the wire-drawn displacement sensor under different conditions.

[0051] The previously determined standard test environment is used to adjust the parameters of the pre-built test scene to obtain a standard test scene. The standard test environment is determined after considering the environmental adaptability range of the wire-type displacement sensor to be tested, and represents a relatively ideal environmental condition without any deviation. The process of adjusting the parameters of the test scene is to gradually adjust the various environmental parameters in the test scene to match the standard test environment. According to the temperature requirements in the standard test environment, the temperature control system of the test scene is set to ensure that the temperature in the test scene is stable within the range specified by the standard test environment. If the standard test environment requires the temperature to be a specific value, then the temperature of the test scene is accurately adjusted to this value through heating or cooling equipment. Then, for the humidity parameter, the humidity of the test scene is also adjusted using a humidity adjustment device. Make the humidity in the test scene consistent with the humidity in the standard test environment. In addition, if the standard test environment also includes other parameters, such as pressure, light, etc., these parameters in the test scene also need to be adjusted accordingly. Through the precise control and adjustment of various environmental parameters, the test scene is fully in line with the requirements of the standard test environment. The final standard test scene will provide a stable environment that can be used as a benchmark for subsequent tests. In this environment, using a pre-called reference displacement sensor to collect displacement data can obtain accurate and reliable reference displacement data, laying the foundation for comparison with data collected in a non-standard test environment.

[0052] When the standard test scenario is ready, the displacement measurement target is run based on the displacement change constraints it has. In this standard test scenario, all environmental parameters have been adjusted to an ideal state. The displacement measurement target starts to move according to the established displacement change constraints, and at the same time, the pre-called reference displacement sensor is used to synchronously collect displacement data. This reference displacement sensor has extremely high accuracy and stability, and after strict calibration, it can accurately measure displacement under the standard test scenario. When the displacement measurement target moves, the reference displacement sensor monitors its position change in real time and records the collected data. Since the standard test scenario is a carefully designed ideal environment and the performance of the reference displacement sensor is reliable, the collected data can be used as the reference displacement data, which will serve as an important basis for subsequent comparison with the data collected by the wire-type displacement sensor to be tested in a non-standard test environment. Through comparison, the error of the sensor to be tested in different environments can be determined, and then error analysis and calibration can be performed.

[0053] In order to comprehensively test the performance of the wire-type displacement sensor in different non-ideal environments, multiple non-standard test environments are used to adjust the parameters of the pre-built test scenarios in multiple rounds to obtain multiple non-standard test scenarios. First of all, the multiple non-standard test environments are determined after considering the environmental adaptability range of the sensor to be tested and historical fault records. These environments cover various complex situations that may cause errors in the sensor, such as a combination of high temperature, low temperature, high humidity, low humidity, strong magnetic field, vibration and other environmental conditions. For each non-standard test environment, a round of parameter adjustment is performed on the test scenario. During the parameter adjustment process, various environmental parameters in the test scenario are accurately adjusted according to the specific requirements of the non-standard test environment. After multiple rounds of parameter adjustment, each round corresponds to a non-standard test environment, and finally multiple non-standard test scenarios are obtained. These non-standard test scenarios simulate various harsh environmental conditions that sensors may encounter in actual applications, providing a rich test environment for subsequent use of the displacement sensor to be tested in these scenarios for data collection, which helps to more comprehensively understand the performance and error of the sensor in different complex environments.

[0054] When multiple non-standard test scenarios are ready, start running the displacement measurement target in these scenarios and use the pull-wire displacement sensor to be tested to collect displacement data at the same time. In each non-standard test scenario, the displacement measurement target starts to move according to its established displacement change constraint. This displacement change constraint remains consistent in different test scenarios, ensuring the comparability of data collection. For example, whether in a non-standard test scenario with high temperature and high humidity, or in a non-standard test scenario with certain vibration interference, the displacement measurement target moves regularly in a straight line direction at the same speed and acceleration. At this time, the pull-wire displacement sensor to be tested is used to synchronously collect displacement data. Since the non-standard test scenario simulates various complex actual working environments, the sensor is affected to varying degrees in these environments. For example, in a high temperature environment, the performance of the sensor's electronic components will change; in an environment with magnetic field interference, the sensor's measurement accuracy will be affected. As the displacement measurement target moves, the displacement sensor to be tested monitors its position change in real time and records the corresponding displacement data. Multiple rounds of data collection are performed in each non-standard test scenario to ensure the reliability and stability of the data. Through data collection in multiple non-standard test scenarios, multiple test displacement data are finally obtained. These data reflect the measurement results of the sensor to be tested in different complex environments, and provide an important basis for subsequent comparative analysis with the benchmark displacement data, determining the sensor error type and specific error calibration method.

[0055] In a possible implementation, step S400 further includes:

[0056] Step S410: predefine data collection step length.

[0057] Step S420: using the displacement change constraint to perform data alignment between the reference displacement data and a plurality of test displacement data, to obtain updated reference data and a plurality of updated test data.

[0058] Step S430: screening the updated reference data and the multiple updated test data by measuring points with the data acquisition step length as a screening constraint to obtain a reference measurement point sequence and multiple test measurement point sequences.

[0059] Step S440: performing scene error calculation on the plurality of test measurement point sequences according to the reference measurement point sequence to obtain a plurality of groups of scene error sequences.

[0060] Step S450: fitting multiple groups of scene error curves based on the multiple groups of scene error sequences, and associating and storing the multiple non-standard test environments and the multiple groups of scene error curves according to the mapping relationship between the multiple non-standard test environments and the multiple test displacement data, to obtain the multiple measurement error sets.

[0061] Specifically, the predefined data acquisition step size provides a unified scale for subsequent data processing and analysis. The determination of this step size needs to take into account factors such as the measurement accuracy of the sensor, the efficiency of data processing, and the need for error analysis.

[0062] The displacement change constraint of the displacement measurement target is used to align the reference displacement data with multiple test displacement data. Since different data have time deviations, the displacement change constraint can be used to determine the corresponding data points under the same displacement state, thereby obtaining updated reference data and multiple updated test data. Such an alignment operation ensures that in the subsequent error analysis, the comparison is made under the same displacement conditions, which improves the accuracy of the analysis.

[0063] The updated benchmark data and multiple updated test data are screened for measurement points with the predefined data acquisition step as an important screening constraint. First, the data acquisition step determines the interval scale in the data processing process. The selection of this step needs to comprehensively consider the accuracy of the sensor, the trend of data changes, and the needs of subsequent analysis. If the step is set too large, some key measurement points will be lost, affecting the accuracy of error analysis; if the step is set too small, the complexity of data processing will increase and the efficiency will be reduced. For the updated benchmark data, the measurement points are selected in sequence according to the data acquisition step. For multiple updated test data, the measurement points are also screened with the data acquisition step. For each set of test data, the measurement points are selected according to the same step rule as the benchmark data to obtain multiple test measurement point sequences. Each test measurement point sequence corresponds to a data screening result under a non-standard test environment. The benchmark measurement point sequence and multiple test measurement point sequences screened in this way retain the key features of the data and reduce the amount of data, providing a more concise and effective data basis for the subsequent scene error calculation. These measurement point sequences will be used in subsequent analysis to compare the differences between different test environments and the benchmark environment, so as to determine the error of the sensor.

[0064] The scene error calculation is performed on multiple test measurement point sequences according to the reference measurement point sequence to determine the error of the sensor under different non-standard test environments relative to the standard test environment. First, the reference measurement point sequence is a representative set of measurement points obtained by screening the data acquisition step length under the standard test environment. These measurement points reflect the measurement results of the sensor on the displacement measurement target under an ideal state. For multiple test measurement point sequences, each sequence corresponds to the data screening results under a non-standard test environment. When performing the scene error calculation, each test measurement point is compared with the corresponding measurement point in the reference measurement point sequence. For each specific data indicator of each measurement point, such as displacement value, movement speed, acceleration, power supply voltage, power supply current and wire tension, the difference between the non-standard test environment and the standard test environment is calculated. For example, for the displacement value indicator, the difference between the displacement value of the test measurement point and the displacement value of the corresponding position in the reference measurement point sequence is calculated. By performing such calculations on each measurement point in multiple test measurement point sequences, multiple groups of scene error sequences can be obtained. Each group of scene error sequences corresponds to a non-standard test environment, reflecting the deviation of the measurement results of the sensor under this environment from the standard test environment. These scenario error sequences provide specific data support for the subsequent analysis of the sensor’s error characteristics, help gain a deeper understanding of the sensor’s performance in different non-standard test environments, and lay the foundation for developing accurate error calibration strategies.

[0065] Curve fitting operations are performed based on multiple sets of scene error sequences. These scene error sequences are obtained by comparing and calculating the measurement data under different non-standard test environments with the data under standard test environments, reflecting the error of the sensor under various non-standard test environments relative to the standard environment. Using mathematical fitting methods, the data points in each set of scene error sequences are fitted into a scene error curve. Since the horizontal axis of each scene error curve is the measurement timestamp of the measurement point, the error change trend can be observed in the time dimension. The vertical axis is a single indicator deviation of the measurement point, such as displacement value deviation or power supply current deviation, so that in-depth analysis can be performed on specific indicators. At the same time, according to the mapping relationship between multiple non-standard test environments and multiple test displacement data, multiple non-standard test environments are associated with multiple sets of scene error curves for storage, which means that each non-standard test environment corresponds to a specific set of scene error curves. Through this associated storage, the error performance of each indicator of the sensor under different environmental conditions can be clearly understood. Finally, multiple measurement error sets are obtained through this associative storage. Each measurement error set contains various error information of the sensor under a specific non-standard test environment, providing detailed and specific data support for subsequent error type analysis and formulation of error calibration strategies. These measurement error sets help to fully understand the performance characteristics and error laws of sensors in different environments, so as to carry out targeted error calibration and performance optimization.

[0066] In a possible implementation, step S430 further includes:

[0067] Step S430: In the reference measurement point sequence and the plurality of test measurement point sequences, the data indicators of each measurement point include displacement value, movement speed, acceleration, power supply voltage, power supply current and wire tension.

[0068] Specifically, in the generated reference measurement point sequence and multiple test measurement point sequences, each measurement point has a rich data indicator composition. The displacement value is the most intuitive indicator that reflects the change in the position of the measurement target. By recording and analyzing the displacement value, we can understand the sensor's measurement accuracy of the target displacement in different environments. For example, in a standard test environment, the displacement value more accurately reflects the actual displacement, but in a non-standard test environment, there will be a displacement value deviation, which indicates that the measurement accuracy of the sensor in this environment is affected.

[0069] Movement speed is an indicator that describes how fast the target is moving. The sensor will have different responses at different movement speeds. If the sensor does not accurately measure a high-speed moving target, it is because its response time is not fast enough or there are other problems with dynamic performance. For slow-moving targets, it will be affected by other factors such as friction and noise.

[0070] Acceleration reflects the change in the motion state of the measurement target. In some dynamic environments, the change in acceleration will affect the measurement results of the sensor. For example, when the measurement target suddenly accelerates or decelerates, the sensor needs a certain amount of time to adapt to this change, which will lead to measurement errors.

[0071] The supply voltage and supply current are indicators related to the working power supply of the sensor. Unstable supply voltage or current will affect the performance and measurement accuracy of the sensor. Too high or too low supply voltage will cause distortion of the sensor's output signal, while changes in supply current will affect the sensitivity and stability of the sensor.

[0072] Wire tension is a unique indicator of wire displacement sensors. Changes in wire tension will affect the sensor's measurement results. If the wire tension is too high or too low, the sensor's measurement accuracy will decrease or malfunction will occur. For example, when the wire tension is too high, the sensor's mechanical structure will be deformed, affecting the accuracy of the measurement; and when the wire tension is too low, the wire will loosen, affecting the sensor's response speed and accuracy.

[0073] Through comprehensive analysis of these data indicators, we can have a more comprehensive understanding of the sensor's performance and error sources under different test environments, providing richer information for subsequent error analysis and calibration.

[0074] In a possible implementation, step S500 further includes:

[0075] Step S510: extracting a plurality of scene error curves corresponding to displacement values ​​from the plurality of groups of scene error curves of the plurality of measurement error sets.

[0076] Step S520: performing offset quantization on the multiple scene error curves based on the Euclidean distance, and outputting an offset uniformity coefficient.

[0077] Step S530: If the offset uniformity coefficient meets a preset offset uniformity threshold, the displacement value is used to traverse a pre-constructed mechanical error table to locate the first mechanical error component.

[0078] Step S540: If the offset uniformity coefficient does not satisfy the offset uniformity threshold, performing environment-related error analysis on the multiple scene error curves based on the multiple non-standard test environments, and outputting a first environment error interval.

[0079] Step S550: Similarly, error type analysis is performed according to the multiple non-standard test environments and multiple measurement error sets to obtain K mechanical error components and W environmental error intervals.

[0080] Step S560: performing a union solution on the W environmental error intervals and outputting a sensor environmental error interval.

[0081] Step S570: Integrate and store the K mechanical error components and sensor environment error intervals to obtain the sensor error information set.

[0082] Specifically, multiple scene error curves corresponding to displacement values ​​are extracted from multiple sets of scene error curves of multiple measurement error sets. As a key measurement indicator, the error curve of displacement value can intuitively reflect the accuracy of displacement measurement of the sensor under different test environments. By extracting the error curve of displacement value alone, the error of the sensor in displacement measurement can be analyzed more focusedly.

[0083] The offset of the extracted error curves of multiple scenes is quantified based on the Euclidean distance, which can measure the degree of difference between different curves. By calculating the Euclidean distance between every two curves and performing statistical analysis on all distance values, an offset uniformity coefficient can be obtained, which reflects the overall offset degree and consistency of the error curves of multiple scenes.

[0084] When the offset uniformity coefficient meets the preset offset uniformity threshold, it means that the offsets of the error curves of multiple scenes show high consistency. This consistency is caused by mechanical problems. At this time, the displacement value is used to traverse the pre-constructed mechanical error table to locate the first mechanical error component. This mechanical error table is pre-constructed based on in-depth research and analysis of the structure, performance and possible mechanical failures of the sensor. The table records in detail the various types of mechanical errors that may occur in different displacement value ranges and the corresponding mechanical components that may have problems. First, a specific displacement value is selected as the starting point, and the search is started in the mechanical error table. This displacement value is compared with each displacement value range in the table one by one to determine the displacement value interval to which it belongs. Once the corresponding displacement value interval is found, the possible mechanical error types recorded in the interval are further checked. Then, combined with the actual error curve performance, the mechanical error type corresponding to the displacement value interval in the table is compared and analyzed. If the characteristics of the error curve match a specific mechanical error type, it can be preliminarily determined that the corresponding mechanical component may have problems. In this way, the displacement value is used to traverse the mechanical error table. When the error curve offset is consistent, the mechanical components that may cause the error can be found more accurately, providing a clear direction for subsequent error calibration and maintenance work.

[0085] If the offset uniformity coefficient does not meet the preset offset uniformity threshold, this indicates that the offset of multiple scene error curves is more complicated and is not caused by mechanical factors. At this time, it is necessary to perform environmental correlation error analysis on multiple scene error curves based on multiple non-standard test environments. First, considering that different non-standard test environments may have different effects on sensors, various non-standard test environments are classified and sorted in detail. These non-standard test environments include various complex conditions such as high temperature, low temperature, high humidity, strong magnetic field, vibration, etc. Then, for each non-standard test environment, the characteristics of the scene error curve in this environment are analyzed. Observe the change trend of the error curve under the action of different environmental factors, such as whether the deviation of the displacement value has obvious rules when the temperature changes; whether the power supply current fluctuates abnormally when the humidity increases. Through the comprehensive analysis of multiple scene error curves under different non-standard test environments, it is possible to determine which environmental factors have a greater impact on the sensor error, associate these environmental factors with the corresponding error range, and gradually narrow the possible environmental error range. For example, if it is found that the displacement value deviation of the sensor shows a specific change rule in a high temperature environment, and this deviation is not obvious when other environmental factors change, then a temperature-related environmental error range can be preliminarily determined. This interval covers a certain temperature range, within which the sensor's error performance is more prominent, thus outputting the first environmental error interval. Through this environmental correlation error analysis, when the offset uniformity coefficient does not meet the threshold, the environmental factor interval that causes the sensor error can be found, providing an important basis for subsequent error calibration and sensor performance optimization.

[0086] By analogy, a comprehensive error type analysis is performed on a variety of non-standard test environments and multiple measurement error sets. By repeating the above steps, K mechanical error components and W environmental error intervals can be obtained. These components and intervals represent the various error types and sources that may occur in the sensor under different test environments.

[0087] These W environmental error intervals are obtained by analyzing a variety of non-standard test environments and multiple measurement error sets in the previous steps. Each environmental error interval represents the influence range of specific environmental factors on sensor errors. When performing the union solution, these W intervals are merged one by one. If two or more intervals have overlapping parts, then the overlapping area will be included in the union result. If there is no overlap between the intervals, then they will be simply combined together. In this way, a sensor environmental error interval covering all possible error ranges caused by environmental factors can be obtained. This interval reflects the overall range of possible errors of the sensor under various environmental conditions. Through the union solution, a comprehensive sensor environmental error interval is obtained, which includes the influence range of multiple environmental factors such as temperature, humidity and magnetic field strength. The output sensor environmental error interval provides an important reference for the subsequent environmental adaptability improvement and error calibration of the sensor. It can help engineers more accurately understand the performance of the sensor in different environments, take targeted measures to reduce the impact of environmental factors on the sensor, and improve the measurement accuracy and reliability of the sensor.

[0088] The K mechanical error components and sensor environmental error intervals are integrated and stored to obtain the sensor error information set. This information set contains various error types and specific error source information that may occur in the sensor under different test environments, providing a clear direction and basis for subsequent error calibration.

[0089] In a possible implementation, step S540 further includes:

[0090] Step S541: Aggregate the multiple non-standard test environments based on single factor variables to obtain multiple groups of single factor change environments.

[0091] Step S542: according to the mapping relationship between the multiple non-standard test environments and the multiple scene error curves, the multiple scene error curves are divided into groups using the multiple groups of single-factor change environments to obtain multiple groups of scene error curves.

[0092] Step S543: extracting a first group of single factor change environments and a first group of scene error curves from the multiple groups of single factor change environments and multiple groups of scene error curve mappings.

[0093] Step S544: Serialize the first group of single-factor change environments based on the environmental factor change scale, and serialize the first group of scene error curves based on the sorting result to obtain a first error curve sequence, wherein the first error curve sequence includes H time series curves.

[0094] Step S545: extracting a first timing curve from the first error curve sequence, and setting the environmental sensitivity of the first timing curve to 1.

[0095] Step S546: Taking the first timing curve as a reference, calculate H-1 environmental sensitivities of H-1 timing curves.

[0096] Step S547: preset an environmental sensitivity limit, and use the environmental sensitivity limit to traverse H environmental sensitivities to locate the first single factor error interval.

[0097] Step S548: Similarly, the multiple groups of single-factor change environments and multiple groups of scene error curves are analyzed to obtain multiple single-factor error intervals.

[0098] Step S549: Integrate and store the multiple single-factor error intervals as the first environmental error interval.

[0099] Specifically, in order to more accurately analyze the impact of various non-standard test environments on sensor errors, multiple non-standard test environments are aggregated based on single-factor variables. First, determine the environmental factors that need to be considered. These factors include temperature, humidity, pressure, magnetic field strength, etc. For each environmental factor, multiple non-standard test environments are classified according to the factor. For example, for the temperature factor, all different temperature conditions in the non-standard test environment are extracted to form a set of single-factor change environments that only contain temperature changes. In this way, multiple groups of single-factor change environments can be obtained, each of which focuses on a specific environmental factor, so that the impact of the factor on the sensor error can be more clearly observed in subsequent analysis. For each environmental factor, it is very important to analyze the change trend of the error curve within its value change range, which means that in a specific single-factor change environment, observe the changes in the scene error curve of the sensor as the value of the environmental factor changes. For example, if it is a temperature factor, observe the performance of indicators such as the displacement value deviation and the power supply current deviation of the sensor in the scene error curve within different temperature ranges. By analyzing the change trend of the error curve, the specific impact of the environmental factor on the error can be evaluated. If the error curve shows a clear upward or downward trend within a certain temperature range, it means that the temperature factor has a greater impact on the sensor error. On the contrary, if the error curve changes more gently within a certain temperature range, it means that the temperature factor has less impact on the sensor error. This method of aggregating non-standard test environments based on single-factor variables helps to gain a deeper understanding of the independent impact of each environmental factor on the sensor error, providing a more specific basis for subsequent environmental correlation error analysis and error calibration.

[0100] According to the mapping relationship between multiple non-standard test environments and multiple scene error curves, multiple scene error curves are divided into groups using multiple groups of single-factor change environments. Since different non-standard test environments correspond to different scene error curves, when the non-standard test environment is decomposed into a single-factor change environment, the scene error curve can also be divided into multiple groups accordingly, so that each group of scene error curves can correspond to a specific single-factor change environment, which is convenient for the subsequent analysis of errors in the single-factor environment.

[0101] From the mapping relationship between multiple groups of single-factor change environments and the corresponding multiple groups of scene error curves, the first group of single-factor change environments and the first group of scene error curves are extracted. First, since a variety of non-standard test environments are aggregated into multiple groups of single-factor change environments based on single-factor variables, and each group of single-factor change environments corresponds to a specific scene error curve. At this time, the first group of data is selected from this mapping relationship and randomly selected. The first group of single-factor change environments represents the changes of a specific environmental factor under different values, such as changes in a specific temperature range, and the first group of scene error curves reflects the error performance of the sensor under this specific single-factor change environment, including the changes of indicators such as displacement value deviation and power supply current deviation under different conditions. By extracting this first group of data, a specific analysis object and starting point are provided for the subsequent analysis of sensor errors under a single-factor environment.

[0102] First, the first group of single-factor change environments are serialized based on the scale of environmental factor changes. If the single factor is temperature, the group of environments is arranged in order from low to high or from high to low. This can clearly present the changes of environmental factors under different values. Then, according to the sorting results of the first group of single-factor change environments, the corresponding first group of scene error curves are also serialized. Since the scene error curve reflects the error performance of the sensor under different environmental conditions, after serializing it according to the sorting of environmental factors, the first error curve sequence is obtained. This sequence includes H time series curves, each of which represents the change of sensor error over time or other factors under a specific environmental factor value. These time series curves provide a detailed data basis for further analysis of the impact of environmental factors on sensor errors. By observing the changing trends and fluctuations of the curves, we can gain an in-depth understanding of the error characteristics of the sensor under different environmental conditions.

[0103] A curve is selected from the first error curve sequence as the first timing curve. This selection is random. Once the first timing curve is determined, its environmental sensitivity is set to 1. Environmental sensitivity is an indicator used to measure the sensitivity of a curve to changes in environmental factors. Setting the environmental sensitivity of the first timing curve to 1 means that it is used as the baseline curve for subsequent analysis, and this curve is used as the standard to measure the relative sensitivity of other curves to changes in environmental factors. This setting provides a starting point and reference standard for subsequent calculations of the environmental sensitivity of other curves and analysis of the impact of environmental factors on sensor errors.

[0104] Taking the first timing curve as the benchmark, the environmental sensitivity of H-1 timing curves other than the first timing curve in the first error curve sequence is calculated. Since the environmental sensitivity of the first timing curve has been set to 1, it becomes a reference for measuring the sensitivity of other curves. By comparing the error changes of each curve with the first timing curve under the same environmental factor changes, their sensitivity to environmental factor changes can be determined. Analyze the characteristics of the curve such as slope change and fluctuation amplitude, as well as the degree of difference from the first timing curve under different environmental conditions. For example, if the change amplitude of the displacement value deviation or power supply current deviation of a timing curve is significantly greater than that of the first timing curve when the environmental factor changes, then its environmental sensitivity will be relatively high. Through such calculations, H-1 environmental sensitivity values ​​can be obtained, which reflect the different response degrees of different timing curves to environmental factor changes, and provide a quantitative basis for further analysis of the impact of environmental factors on sensor errors.

[0105] An environmental sensitivity limit is preset. This limit value is determined based on the performance requirements of the sensor, actual application scenarios, experience and other factors. It represents a threshold for judging whether the environmental factors have a significant impact on the sensor error. Then, this environmental sensitivity limit is used to traverse the H environmental sensitivities in the first error curve sequence. For each environmental sensitivity, it is compared with the environmental sensitivity limit. If a certain environmental sensitivity exceeds this limit, it means that under the corresponding environmental conditions, the error of the sensor is greatly affected by the environmental factor. Through this traversal comparison, the first single factor error interval is located. This interval is the range of environmental factors with large sensor errors under a specific single factor environment. For example, if the environmental factor is temperature, then the first single factor error interval may be a specific temperature range, within which indicators such as the displacement value deviation and the power supply current deviation of the sensor are obviously beyond the acceptable range.

[0106] By analogy, the same analysis is performed on multiple sets of single-factor change environments and multiple sets of scene error curves to obtain multiple single-factor error intervals. Each single-factor error interval represents the interval where a specific environmental factor has a greater impact on the sensor error within a certain range.

[0107] Multiple single-factor error intervals are integrated and stored as the first environmental error interval. This first environmental error interval comprehensively considers the impact of multiple environmental factors on sensor errors, providing an important basis for subsequent error analysis and calibration. By analyzing this interval, we can understand the error characteristics of the sensor under different environmental factors, and take targeted measures to reduce the impact of environmental factors on the sensor.

[0108] Embodiment 2, based on the same inventive concept as the error analysis method of the wire-type displacement sensor in the above embodiment, Figure 2 As shown, the present application provides a wire-type displacement sensor error analysis system, and the system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0109] The environmental adaptability range acquisition module 10 is used to interactively obtain the environmental adaptability range of the displacement sensor to be tested, wherein the displacement sensor to be tested is a wire-type displacement sensor.

[0110] The test environment setting module 20 is used to set a standard test environment and multiple non-standard test environments based on the environmental adaptability range as a value constraint.

[0111] The displacement data acquisition module 30 is used to perform multiple rounds of data acquisition tests on the displacement sensor to be tested using the standard test environment and multiple non-standard test environments to obtain reference displacement data and multiple test displacement data.

[0112] The measurement error set acquisition module 40 is used to synchronously compare the reference displacement data with a plurality of test displacement data to obtain a plurality of measurement error sets.

[0113] The sensor error information set output module 50 is used to perform error type analysis according to the multiple non-standard test environments and multiple measurement error sets, and output a sensor error information set.

[0114] The error calibration strategy acquisition module 60 is used to perform error compensation analysis according to the sensor error information set to obtain an error calibration strategy.

[0115] The accuracy recovery verification module 70 is used to perform accuracy recovery verification of the displacement sensor to be tested based on the multiple non-standard test environments and reference displacement data after calibrating the orientation error of the displacement sensor to be tested based on the error calibration strategy.

[0116] Furthermore, the test environment setting module 20 also includes:

[0117] A historical fault record acquisition unit is used to perform network data call according to the model information of the displacement sensor to be tested to obtain historical fault records.

[0118] An error frequency acquisition unit, wherein the error frequency acquisition unit is used to perform fault condition distribution analysis according to the historical fault records to obtain multiple error frequencies in multiple environmental change intervals.

[0119] A non-standard test environment acquisition unit is used to preset a test frequency, and use the multiple error frequencies to randomly select a test environment in the multiple environmental change intervals to obtain multiple groups of non-standard test environments.

[0120] A multiple non-standard test environment acquisition unit is used to integrate the multiple groups of non-standard test environments to obtain the multiple non-standard test environments.

[0121] A standard test environment acquisition unit is used to perform difference calculation on the multiple environmental change intervals and the environmental adaptability range to obtain the standard test environment.

[0122] Furthermore, the displacement data acquisition module 30 also includes:

[0123] A displacement measurement target introduction unit is used to pre-build a test scenario and introduce a displacement measurement target into the test scenario, wherein the displacement measurement target has a displacement change constraint.

[0124] A standard test scenario acquisition unit is used to adjust the parameters of the test scenario using the standard test environment to obtain a standard test scenario.

[0125] A reference displacement data acquisition unit is used to synchronously collect displacement data using a pre-called reference displacement sensor to obtain the reference displacement data when the displacement measurement target is operated based on the displacement change constraint in the standard test scenario.

[0126] Multiple non-standard test scenario acquisition units, the multiple non-standard test scenario acquisition units are used to use the multiple non-standard test environments to perform multiple rounds of parameter adjustment on the test scenarios to obtain multiple non-standard test scenarios.

[0127] A plurality of test displacement data acquisition units are used to synchronously collect displacement data using the displacement sensor to be tested to obtain the plurality of test displacement data when the displacement measurement target is operated based on the displacement change constraint in the plurality of non-standard test scenarios.

[0128] Furthermore, the measurement error set acquisition module 40 further includes:

[0129] A collection step unit, wherein the collection step unit is used to predefine a data collection step.

[0130] An update data acquisition unit is used to use the displacement change constraint to perform data alignment between the reference displacement data and a plurality of test displacement data to obtain updated reference data and a plurality of updated test data.

[0131] A measurement point sequence acquisition unit is used to perform measurement point screening on the updated reference data and the multiple updated test data with the data acquisition step length as a screening constraint to obtain a reference measurement point sequence and multiple test measurement point sequences.

[0132] A scene error sequence acquisition unit is used to perform scene error calculation on the multiple test measurement point sequences according to the reference measurement point sequence to obtain multiple groups of scene error sequences.

[0133] An associated storage unit, wherein the associated storage unit fits a plurality of sets of scene error curves based on the plurality of sets of scene error sequences, and associates and stores the plurality of non-standard test environments with the plurality of sets of scene error curves according to a mapping relationship between the plurality of non-standard test environments and the plurality of test displacement data, so as to obtain the plurality of measurement error sets.

[0134] Furthermore, the measurement point sequence acquisition unit further includes:

[0135] In the reference measurement point sequence and the plurality of test measurement point sequences, the data indicators of each measurement point include displacement value, movement speed, acceleration, power supply voltage, power supply current and wire tension.

[0136] Furthermore, the sensor error information set output module 50 further includes:

[0137] A scene error curve extraction unit is used to extract a plurality of scene error curves corresponding to the displacement values ​​from the plurality of groups of scene error curves of the plurality of measurement error sets.

[0138] An offset uniform coefficient output unit is configured to perform offset quantization on the plurality of scene error curves based on the Euclidean distance and output an offset uniform coefficient.

[0139] A first mechanical error component positioning unit is configured to locate the first mechanical error component by traversing a pre-constructed mechanical error table using a displacement value if the offset uniformity coefficient meets a preset offset uniformity threshold.

[0140] The first environmental error interval output unit is used to perform environmental correlation error analysis on the multiple scene error curves based on the multiple non-standard test environments and output a first environmental error interval if the offset uniformity coefficient does not meet the offset uniformity threshold.

[0141] The environmental error interval acquisition unit is used to perform error type analysis according to the multiple non-standard test environments and multiple measurement error sets to obtain K mechanical error components and W environmental error intervals.

[0142] A sensor environment error interval output unit is used to solve the union of the W environment error intervals and output the sensor environment error interval.

[0143] A sensor error information set acquisition unit is used to integrate and store the K mechanical error components and sensor environment error intervals to obtain the sensor error information set.

[0144] Furthermore, the first environmental error interval output unit further includes:

[0145] A single factor change environment acquisition unit is configured to aggregate the multiple non-standard test environments based on the single factor variable to obtain multiple groups of single factor change environments.

[0146] A multiple-group scene error curve acquisition unit is used to group the multiple scene error curves according to the mapping relationship between the multiple non-standard test environments and the multiple scene error curves using the multiple groups of single-factor change environments to obtain multiple groups of scene error curves.

[0147] A mapping extraction unit is used to map and extract a first group of single-factor change environments and a first group of scene error curves from the multiple groups of single-factor change environments and the multiple groups of scene error curves.

[0148] A first error curve sequence acquisition unit is configured to serialize the first group of single-factor change environments based on the environmental factor change scale, and to serialize the first group of scene error curves based on the sorting result to obtain a first error curve sequence, wherein the first error curve sequence includes H time series curves.

[0149] A first timing curve extraction unit is used to extract a first timing curve from the first error curve sequence and set the environmental sensitivity of the first timing curve to 1.

[0150] An environmental sensitivity calculation unit, wherein the environmental sensitivity calculation unit is used to calculate H-1 environmental sensitivities of H-1 timing curves based on the first timing curve.

[0151] The first single factor error interval positioning unit is used to preset an environmental sensitivity limit, and use the environmental sensitivity limit to traverse H environmental sensitivities to locate the first single factor error interval.

[0152] A plurality of single factor error interval acquisition units are used to analyze the plurality of groups of single factor change environments and the plurality of groups of scene error curves by analogy to obtain a plurality of single factor error intervals.

[0153] A single factor error interval integration unit is used to integrate and store the multiple single factor error intervals as the first environmental error interval.

[0154] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0155] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0156] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for analyzing the error of a wire-type displacement sensor, characterized in that: The method comprises: Interactively obtaining the environmental adaptability range of the displacement sensor to be tested, wherein the displacement sensor to be tested is a wire-type displacement sensor; Taking the environmental adaptability range as a value constraint, a standard test environment and multiple non-standard test environments are set; Using the standard test environment and multiple non-standard test environments to perform multiple rounds of data acquisition tests on the displacement sensor to be tested, to obtain reference displacement data and multiple test displacement data; Synchronously comparing the reference displacement data with a plurality of test displacement data to obtain a plurality of measurement error sets; Perform error type analysis according to the multiple non-standard test environments and multiple measurement error sets, and output a sensor error information set; Perform error compensation analysis based on the sensor error information set to obtain an error calibration strategy; After calibrating the orientation error of the displacement sensor to be tested based on the error calibration strategy, performing accuracy recovery verification of the displacement sensor to be tested based on the multiple non-standard test environments and reference displacement data; Among them, error type analysis is performed according to the multiple non-standard test environments and multiple measurement error sets, and a sensor error information set is output, including: extracting a plurality of scene error curves corresponding to the displacement values ​​from the plurality of groups of scene error curves of the plurality of measurement error sets; Performing offset quantization on the multiple scene error curves based on the Euclidean distance, and outputting an offset uniformity coefficient; If the offset uniformity coefficient satisfies a preset offset uniformity threshold, traversing a pre-constructed mechanical error table using the displacement value to locate the first mechanical error component; If the offset uniformity coefficient does not meet the offset uniformity threshold, performing environment-related error analysis on the multiple scene error curves based on the multiple non-standard test environments, and outputting a first environment error interval; Perform error type analysis according to the multiple non-standard test environments and multiple measurement error sets to obtain K mechanical error components and W environmental error intervals; Solving the union of the W environmental error intervals and outputting the sensor environmental error interval; Integrate and store the K mechanical error components and sensor environmental error intervals to obtain the sensor error information set; Performing environment-related error analysis on the multiple scene error curves based on the multiple non-standard test environments, and outputting a first environment error interval, includes: Aggregating the multiple non-standard test environments based on single-factor variables to obtain multiple groups of single-factor change environments; According to the mapping relationship between the multiple non-standard test environments and the multiple scene error curves, the multiple scene error curves are divided into groups using the multiple groups of single-factor change environments to obtain multiple groups of scene error curves; Extracting a first group of single factor change environments and a first group of scene error curves from the multiple groups of single factor change environments and multiple groups of scene error curve mappings; Serializing the first group of single-factor change environments based on the environmental factor change scale, and serializing the first group of scene error curves based on the sorting result to obtain a first error curve sequence, wherein the first error curve sequence includes H time series curves; Extracting a first timing curve from the first error curve sequence, and setting the environmental sensitivity of the first timing curve to 1; Taking the first timing curve as a reference, calculating H-1 environmental sensitivities of H-1 timing curves; Preset an environmental sensitivity limit, and use the environmental sensitivity limit to traverse H environmental sensitivities to locate a first single factor error interval; By analogy, the multiple groups of single-factor change environments and multiple groups of scene error curves are analyzed to obtain multiple single-factor error intervals; The multiple single-factor error intervals are integrated and stored as the first environmental error interval.

2. The error analysis method of the wire-type displacement sensor according to claim 1, characterized in that: Taking the environmental adaptability range as a value constraint, a standard test environment and multiple non-standard test environments are set, and the method includes: Performing online data call according to the model information of the displacement sensor to be tested to obtain historical fault records; Perform fault condition distribution analysis based on the historical fault records to obtain multiple error frequencies in multiple environmental change intervals; Preset the test frequency, and use the multiple error frequencies to randomly select the test environment in the multiple environmental change intervals to obtain multiple groups of non-standard test environments; Integrate the multiple groups of non-standard test environments to obtain the multiple non-standard test environments; A difference calculation is performed on the multiple environmental change intervals and the environmental adaptability range to obtain the standard test environment.

3. The error analysis method of the wire-type displacement sensor according to claim 1, characterized in that: The method comprises: performing multiple rounds of data acquisition tests on the displacement sensor to be tested by using the standard test environment and multiple non-standard test environments to obtain reference displacement data and multiple test displacement data. Pre-constructing a test scenario, and introducing a displacement measurement target into the test scenario, wherein the displacement measurement target has a displacement change constraint; Using the standard test environment to adjust the parameters of the test scenario to obtain a standard test scenario; When the displacement measurement target is operated based on the displacement change constraint in the standard test scenario, displacement data is synchronously collected using a pre-called reference displacement sensor to obtain the reference displacement data; Using the multiple non-standard test environments to perform multiple rounds of parameter adjustment on the test scenarios to obtain multiple non-standard test scenarios; When the displacement measurement target is operated based on the displacement change constraint in the multiple non-standard test scenarios, the displacement sensor to be tested is used to synchronously collect displacement data to obtain the multiple test displacement data.

4. The error analysis method of the wire-type displacement sensor according to claim 3, characterized in that: The reference displacement data is synchronously compared with a plurality of test displacement data to obtain a plurality of measurement error sets, the method comprising: Predefine data collection step size; Using the displacement change constraint to perform data alignment between the reference displacement data and a plurality of test displacement data, to obtain updated reference data and a plurality of updated test data; Using the data acquisition step length as a screening constraint, the updated reference data and the multiple updated test data are screened for measurement points to obtain a reference measurement point sequence and multiple test measurement point sequences; Performing scene error calculation on the plurality of test measurement point sequences according to the reference measurement point sequence to obtain a plurality of groups of scene error sequences; Based on the multiple sets of scene error sequences, multiple sets of scene error curves are fitted, and according to the mapping relationship between the multiple non-standard test environments and the multiple test displacement data, the multiple non-standard test environments and the multiple sets of scene error curves are associated and stored to obtain the multiple measurement error sets.

5. The error analysis method of the wire-type displacement sensor according to claim 4, characterized in that: In the reference measurement point sequence and the plurality of test measurement point sequences, the data indicators of each measurement point include displacement value, movement speed, acceleration, power supply voltage, power supply current and wire tension.

6. The error analysis system of the wire-type displacement sensor is characterized by: The system comprises: An environmental adaptability range acquisition module, the environmental adaptability range acquisition module is used to interactively obtain the environmental adaptability range of the displacement sensor to be tested, wherein the displacement sensor to be tested is a wire-type displacement sensor; A test environment setting module, the test environment setting module is used to set a standard test environment and multiple non-standard test environments with the environmental adaptability range as a value constraint; A displacement data acquisition module, the displacement data acquisition module is used to perform multiple rounds of data acquisition tests on the displacement sensor to be tested using the standard test environment and multiple non-standard test environments to obtain reference displacement data and multiple test displacement data; A measurement error set acquisition module, the measurement error set acquisition module is used to synchronously compare the reference displacement data with a plurality of test displacement data to obtain a plurality of measurement error sets; A sensor error information set output module, the sensor error information set output module is used to perform error type analysis according to the multiple non-standard test environments and multiple measurement error sets, and output a sensor error information set; An error calibration strategy acquisition module, the error calibration strategy acquisition module is used to perform error compensation analysis according to the sensor error information set to obtain an error calibration strategy; An accuracy recovery verification module, the accuracy recovery verification module being used to perform accuracy recovery verification of the displacement sensor to be tested based on the multiple non-standard test environments and reference displacement data after calibrating the orientation error of the displacement sensor to be tested based on the error calibration strategy; Wherein, the sensor error information set output module includes: extracting a plurality of scene error curves corresponding to the displacement values ​​from the plurality of groups of scene error curves of the plurality of measurement error sets; Performing offset quantization on the multiple scene error curves based on the Euclidean distance, and outputting an offset uniformity coefficient; If the offset uniformity coefficient satisfies a preset offset uniformity threshold, traversing a pre-constructed mechanical error table using the displacement value to locate the first mechanical error component; If the offset uniformity coefficient does not meet the offset uniformity threshold, performing environment-related error analysis on the multiple scene error curves based on the multiple non-standard test environments, and outputting a first environment error interval; Perform error type analysis according to the multiple non-standard test environments and multiple measurement error sets to obtain K mechanical error components and W environmental error intervals; Solving the union of the W environmental error intervals and outputting the sensor environmental error interval; Integrate and store the K mechanical error components and sensor environmental error intervals to obtain the sensor error information set; Wherein, the sensor error information set output module further includes: Aggregating the multiple non-standard test environments based on single-factor variables to obtain multiple groups of single-factor change environments; According to the mapping relationship between the multiple non-standard test environments and the multiple scene error curves, the multiple scene error curves are divided into groups using the multiple groups of single-factor change environments to obtain multiple groups of scene error curves; Extracting a first group of single factor change environments and a first group of scene error curves from the multiple groups of single factor change environments and multiple groups of scene error curve mappings; Serializing the first group of single-factor change environments based on the environmental factor change scale, and serializing the first group of scene error curves based on the sorting result to obtain a first error curve sequence, wherein the first error curve sequence includes H time series curves; Extracting a first timing curve from the first error curve sequence, and setting the environmental sensitivity of the first timing curve to 1; Taking the first timing curve as a reference, calculating H-1 environmental sensitivities of H-1 timing curves; Preset an environmental sensitivity limit, and use the environmental sensitivity limit to traverse H environmental sensitivities to locate a first single factor error interval; By analogy, the multiple groups of single-factor change environments and multiple groups of scene error curves are analyzed to obtain multiple single-factor error intervals; The multiple single-factor error intervals are integrated and stored as the first environmental error interval.

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