An automatic verification and calibration method and system for motor vehicle metrological parameters
By constructing a topological network model for the automatic verification and calibration of motor vehicle measurement parameters, the problems of low efficiency and insufficient accuracy in existing technologies have been solved. This has enabled the systematic connection and calibration of multiple parameters, and has adapted to the requirements of large-scale production and supervision.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INST OF ACOUSTICS CHINA ACAD OF TESTING TECH
- Filing Date
- 2026-04-13
- Publication Date
- 2026-06-02
AI Technical Summary
Existing technologies suffer from low efficiency, insufficient accuracy, and low level of intelligence in the verification and calibration of metrological parameters before motor vehicles leave the factory. This makes it difficult to meet the requirements of large-scale production and strict supervision, and it is also difficult to achieve systematic connection and calibration of multiple parameters.
The measurement parameters are abstracted into nodes using a topological network model to construct an equivalent topological network. Through multi-level preprocessing, pseudo-abnormal parameters are identified, and automatic verification and calibration are performed in combination with parameter correlation to generate a traceable verification and calibration report.
It enables collaborative verification of multiple parameters, improves the accuracy and efficiency of verification and calibration, adapts to the needs of large-scale production, and meets the traceability requirements of metrological supervision.
Smart Images
Figure CN122130140A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automatic detection technology, and more specifically to an automatic verification and calibration method and system for motor vehicle metrological parameters. Background Technology
[0002] Currently, the verification and calibration of metrological parameters before motor vehicles leave the factory still faces many technical bottlenecks that urgently need to be addressed, making it difficult to meet the requirements of large-scale production and strict supervision. The existing technologies have the following prominent problems: First, the verification and calibration efficiency is low. Motor vehicle metrological parameters are numerous and highly correlated; decentralized testing leads to disconnects in the testing processes for each parameter, making it impossible to achieve rapid verification and calibration of batch vehicles and failing to meet the high-efficiency production needs of modern production lines. Second, the accuracy of verification and calibration is insufficient. Manual operation is easily affected by subjective factors, and the test data for different parameters are independent, making it impossible to consider the inherent correlation between parameters, easily leading to missed detections, incorrect detections, or calibration deviations, making it difficult to guarantee the consistency and reliability of calibration results. Third, the level of intelligence is low. There is a lack of systematic integration and analysis of metrological parameters, making it impossible to achieve automatic identification and accurate calibration of deviations through the correlation between parameters, and it is difficult to achieve full traceability of calibration data, failing to meet the new regulations' requirements for software and data traceability.
[0003] While some existing technologies attempt to use digital means for metrological testing, they only address single types of metrological parameters or specific scenarios. They cannot meet the comprehensive verification and calibration needs of multiple parameters and scenarios before motor vehicles leave the factory, and they do not achieve systematic serialization and collaborative calibration of metrological parameters. This makes it difficult to solve the core pain point of automatic verification and calibration of metrological parameters before motor vehicles leave the factory, and cannot meet the requirements of large-scale production and strict metrological supervision.
[0004] Furthermore, with the continuous expansion of motor vehicle production scale and the ongoing updates to metrological technical specifications, the traditional decentralized, manually assisted verification and calibration model has become a key factor restricting the improvement of motor vehicle production efficiency and product quality. Therefore, how to overcome the limitations of existing technologies and achieve automated and systematic verification and calibration of motor vehicle metrological parameters before they leave the factory, while balancing efficiency, accuracy, and traceability, and adapting to the latest metrological technical specifications, has become a pressing technical challenge in the field of motor vehicle metrology, and is also an important support for promoting the high-quality development of the motor vehicle industry. Summary of the Invention
[0005] In view of this, the present invention provides an automatic verification and calibration method and system for motor vehicle metrological parameters to solve the problems existing in the background art.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: An automatic verification and calibration method for motor vehicle metrological parameters includes the following steps: Obtain and preprocess the various metrological parameters that need to be verified and calibrated before the motor vehicle leaves the factory to obtain a valid set of metrological parameters; An equivalent topological network is constructed based on the effective measurement parameter set. Each effective measurement parameter is abstracted as a node of the topological network and bound with parameter attribute information. The inherent relationship between different measurement parameters is abstracted as the edge between nodes, forming a topological network model that includes a set of nodes, a set of edges, and an adjacency matrix. Based on the topology network model, the system automatically verifies each valid measurement parameter, determines whether each measurement parameter meets the preset measurement standard requirements, and outputs the verification results including abnormal parameter identifiers, deviation values, and abnormal time periods. Based on the verification results, the calibration compensation coefficient is calculated by combining the correlation relationship of the measurement parameters in the topology network, and the calibration is completed.
[0007] Optionally, it also includes automatic calibration of measurement parameters that do not meet the preset measurement standard requirements, and re-verification after calibration until all measurement parameters meet the preset measurement standard requirements, thus completing the entire process of automatic verification and calibration.
[0008] Optionally, the specific process for preprocessing the various measurement parameters is as follows: Based on the pre-defined physical relationship model of measurement parameters, the theoretical expected value range of each measurement parameter is calculated. If the actual measured value of a certain measurement parameter exceeds its corresponding theoretical expected value range, it is marked as a pseudo-anomaly candidate parameter. For the pseudo-abnormal candidate parameters, it is determined whether the corresponding vibration value exceeds the dynamically set vibration-parameter coupling reference threshold. If so, it is determined to be a pseudo-abnormal parameter caused by environmental mechanical interference and is removed from the set of measurement parameters. The remaining pseudo-anomaly candidate parameters after elimination are used as parameters to be reconstructed. The measured values of each parameter to be reconstructed, the corresponding vibration values, and the ambient temperature and humidity values are input into the adaptive residual compensation network to obtain parameter compensation values. The measured values of each parameter to be reconstructed are then used to reconstruct the parameters online to obtain the reconstructed parameter values. Based on the reconstructed parameter values and all original measurement parameter values that were not marked as pseudo-anomaly candidate parameters, a valid measurement parameter set is formed.
[0009] Optionally, the vibration-parameter coupling reference threshold is dynamically set based on the correlation analysis results of historical vibration data and abnormal records of measurement parameters. The threshold size is determined by fitting the relationship curve between vibration intensity and the probability of abnormal parameter occurrence, combined with a preset confidence level, and updated at a fixed period to adapt to environmental changes.
[0010] Optionally, the specific process of constructing an equivalent topological network is as follows: mapping the attribute information of each effective measurement parameter to a three-dimensional topological coordinate system to generate a parameter association vector set; extracting strong association relationships between parameters as turning point node information, and determining the dwell threshold of the association vectors based on the turning point node information; traversing and filtering parameter association pairs that meet the dwell threshold, segmenting the parameter association vector set according to the association pairs, and outputting the topological coordinates corresponding to each parameter; using the topological coordinates as nodes, constructing an edge set according to the parameter association relationships to form an equivalent topological network.
[0011] Optionally, it also includes a verification and calibration data traceability step: recording all process data, including original metrological parameters, environmental interference data, preprocessing records, topology network construction logs, verification deviation data, calibration compensation coefficients, and parameter comparison data before and after calibration, to form a traceable verification and calibration report.
[0012] Optionally, the specific process for completing the automatic verification of measurement parameters is as follows: loading the standard parameter change curve corresponding to the measurement parameter, and extracting the standard reference value according to the preset sampling period; performing time-series alignment and matching between the effective measurement parameter and the standard reference value to generate a matching offset; calculating the difference between the matching offset and the preset allowable offset threshold, and determining that the parameter is abnormal if the difference exceeds the threshold range; performing collaborative verification by combining the associated node data of the abnormal node in the topology network to eliminate misjudgments caused by environmental interference, and generating the final verification result.
[0013] An automatic verification and calibration system for motor vehicle metrological parameters includes: Effective metrological parameter set generation module: used to obtain various metrological parameters that need to be verified and calibrated before the motor vehicle leaves the factory and preprocess them to obtain an effective metrological parameter set; Topology network model construction module: used to construct an equivalent topology network based on the effective measurement parameter set. Each effective measurement parameter is abstracted as a node of the topology network and bound with parameter attribute information. The inherent relationship between different measurement parameters is abstracted as the edge between nodes, forming a topology network model containing a set of nodes, a set of edges, and an adjacency matrix. Verification result output module: It is used to automatically verify various valid measurement parameters based on the topology network model, determine whether each measurement parameter meets the preset measurement standard requirements, and output the verification results including abnormal parameter identification, deviation value and abnormal time period. Calibration module: It is used to calculate the calibration compensation coefficient based on the verification results and the correlation of the measurement parameters in the topology network, and to automatically calibrate the measurement parameters that do not meet the preset measurement standard requirements. After calibration, it is re-verified until all measurement parameters meet the preset measurement standard requirements, thus completing the fully automatic verification and calibration process.
[0014] As can be seen from the above technical solutions, compared with the prior art, the present invention provides an automatic verification and calibration method and system for motor vehicle metrological parameters. Through a multi-level preprocessing process, it accurately identifies and processes pseudo-abnormal parameters, improving data quality; through the construction of a topology network, it realizes the systematic connection of multiple parameters, achieving collaborative verification and avoiding the limitations of isolated detection; combined with the core logic of relevant patents, it improves the accuracy and intelligence level of verification and calibration; the entire process does not require much manual intervention, greatly improving the efficiency of verification and calibration, and adapting to the needs of large-scale production. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0016] Figure 1 This is a schematic diagram of the method flow provided by the present invention; Figure 2 This is a schematic diagram of the system structure provided by the present invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] This invention discloses an automatic verification and calibration method for motor vehicle metrological parameters, such as... Figure 1 As shown, it includes the following steps: Obtain and preprocess the various metrological parameters that need to be verified and calibrated before the motor vehicle leaves the factory to obtain a valid set of metrological parameters; An equivalent topological network is constructed based on the effective measurement parameter set. Each effective measurement parameter is abstracted as a node of the topological network and bound with parameter attribute information. The inherent relationship between different measurement parameters is abstracted as the edge between nodes, forming a topological network model that includes a set of nodes, a set of edges, and an adjacency matrix. Based on the topology network model, the system automatically verifies each valid measurement parameter, determines whether each measurement parameter meets the preset measurement standard requirements, and outputs the verification results including abnormal parameter identifiers, deviation values, and abnormal time periods. Based on the verification results, the calibration compensation coefficient is calculated by combining the correlation relationship of the measurement parameters in the topology network, and the calibration is completed.
[0019] Furthermore, it also includes automatic calibration of measurement parameters that do not meet the preset measurement standard requirements, and re-verification after calibration until all measurement parameters meet the preset measurement standard requirements, thus completing the entire process of automatic verification and calibration.
[0020] Furthermore, the specific process for preprocessing each measurement parameter is as follows: Based on the pre-defined physical relationship model of measurement parameters, the theoretical expected value range of each measurement parameter is calculated. If the actual measured value of a certain measurement parameter exceeds its corresponding theoretical expected value range, it is marked as a pseudo-anomaly candidate parameter. For the pseudo-abnormal candidate parameters, it is determined whether the corresponding vibration value exceeds the dynamically set vibration-parameter coupling reference threshold. If so, it is determined to be a pseudo-abnormal parameter caused by environmental mechanical interference and is removed from the set of measurement parameters. The remaining pseudo-anomaly candidate parameters after elimination are used as parameters to be reconstructed. The measured values of each parameter to be reconstructed, the corresponding vibration values, and the ambient temperature and humidity values are input into the adaptive residual compensation network to obtain parameter compensation values. The measured values of each parameter to be reconstructed are then used to reconstruct the parameters online to obtain the reconstructed parameter values. Based on the reconstructed parameter values and all original measurement parameter values that were not marked as pseudo-anomaly candidate parameters, a valid measurement parameter set is formed.
[0021] Furthermore, the vibration-parameter coupling reference threshold is dynamically set based on the correlation analysis results of historical vibration data and abnormal records of measurement parameters. The threshold size is determined by fitting the relationship curve between vibration intensity and the probability of abnormal parameter occurrence, combined with a preset confidence level, and is updated at a fixed period to adapt to environmental changes.
[0022] Furthermore, the specific process of constructing the equivalent topological network is as follows: mapping the attribute information of each effective measurement parameter to a three-dimensional topological coordinate system to generate a parameter association vector set; extracting strong association relationships between parameters as turning point node information, and determining the dwell threshold of the association vectors based on the turning point node information; traversing and filtering parameter association pairs that meet the dwell threshold, segmenting the parameter association vector set according to the association pairs, and outputting the topological coordinates corresponding to each parameter; using the topological coordinates as nodes, constructing an edge set according to the parameter association relationships to form an equivalent topological network.
[0023] Furthermore, it also includes a verification and calibration data traceability step: recording all process data, including original metrological parameters, environmental interference data, preprocessing records, topology network construction logs, verification deviation data, calibration compensation coefficients, and parameter comparison data before and after calibration, to form a traceable verification and calibration report.
[0024] Furthermore, the specific process for completing the automatic verification of measurement parameters is as follows: loading the standard parameter change curve corresponding to the measurement parameter, and extracting the standard reference value according to the preset sampling period; performing time-series alignment and matching between the effective measurement parameter and the standard reference value to generate a matching offset; calculating the difference between the matching offset and the preset allowable offset threshold, and determining that the parameter is abnormal if the difference exceeds the threshold range; performing collaborative verification by combining the associated node data of the abnormal node in the topology network to eliminate misjudgments caused by environmental interference, and generating the final verification result.
[0025] The following details the above content with a specific process: A multi-source collaborative acquisition system was established to simultaneously acquire various metrological parameters and environmental interference data required for vehicle verification and calibration before leaving the factory. The metrological parameters cover three main categories: power performance, safety and environmental protection, and basic verification of measuring instruments. Environmental interference data mainly includes vibration values and ambient temperature and humidity values. This ensures the comprehensiveness and synchronicity of the acquired data, providing reliable data support for subsequent preprocessing and verification / calibration work. To address the issue of pseudo-anomaly parameters interfering with the accuracy of verification, this invention designs a multi-level preprocessing workflow. First, a pre-set physical relationship model of the metrological parameters is used to initially screen out pseudo-anomaly candidate parameters that exceed the theoretical expected value range. Then, combined with a dynamically set vibration-parameter coupling reference threshold, pseudo-anomaly parameters caused by environmental mechanical interference are identified and eliminated. Finally, an adaptive residual compensation network is used to reconstruct the remaining parameters online, combining them with the unlabeled original metrological parameters to form an effective set of metrological parameters, improving data quality from the source.
[0026] Based on the preprocessed set of effective measurement parameters, an equivalent topological network is constructed to break down the "data silos" of traditional independent measurement parameter detection. This invention employs graph theory modeling, abstracting each effective measurement parameter as a node in the topological network and binding it with attributes such as parameter name, acquisition accuracy, and maximum permissible error. The inherent relationships between different measurement parameters (such as the coupling relationship between power performance and braking performance, and the relationship between exhaust pollutant concentration and engine parameters) are abstracted as edges between nodes, forming a complete topological network model containing a set of nodes, a set of edges, and an adjacency matrix. Simultaneously, the topological network is optimized by eliminating redundant nodes and invalid connections, introducing a linear Voronoi diagram for density constraints, and completing the edges connecting isolated nodes, ensuring the connectivity and integrity of the topological network and providing structural support for multi-parameter collaborative verification.
[0027] Subsequently, based on the constructed topological network model, automatic verification of measurement parameters is carried out. Specifically, the standard parameter variation curves corresponding to each measurement parameter are first loaded, and standard reference values are extracted according to a preset sampling period. Then, the effective measurement parameters and standard reference values are time-series aligned and matched, the matching offset is calculated, and compared with a preset allowable offset threshold to preliminarily determine parameter anomalies. Finally, collaborative verification is performed using the associated node data of the abnormal nodes in the topological network to effectively eliminate misjudgments caused by environmental interference. The final output includes accurate verification results containing abnormal parameter identifiers, deviation values, and abnormal time periods, achieving multi-parameter collaborative verification and improving verification accuracy and efficiency.
[0028] Finally, based on the verification results, automatic calibration is performed. Combining the correlation of metrological parameters in the topology network and integrating the patented compensation mechanism, a piecewise linear compensation algorithm is used to calculate the calibration compensation coefficient. This coefficient is not only related to the magnitude of parameter deviation exceeding the threshold but also incorporates the parameter's environmental sensitivity coefficient and the influence of verification deviations at associated nodes, ensuring calibration accuracy. After calibration, the parameters are returned to the verification stage for re-testing, forming a closed loop of "verification-calibration-re-verification" until all metrological parameters meet the preset metrological standard requirements, completing the fully automated verification and calibration process. Furthermore, this invention adds a data traceability function, recording all relevant data throughout the process to generate a traceable verification and calibration report, meeting the traceability requirements of metrological supervision.
[0029] and Figure 1 Corresponding to the method shown, the present invention also discloses an automatic verification and calibration system for motor vehicle metrological parameters. Figure 1 The implementation of the method, specifically its structure, is as follows: Figure 2 As shown, it includes: Effective metrological parameter set generation module: used to obtain various metrological parameters that need to be verified and calibrated before the motor vehicle leaves the factory and preprocess them to obtain an effective metrological parameter set; Topology network model construction module: used to construct an equivalent topology network based on the effective measurement parameter set. Each effective measurement parameter is abstracted as a node of the topology network and bound with parameter attribute information. The inherent relationship between different measurement parameters is abstracted as the edge between nodes, forming a topology network model containing a set of nodes, a set of edges, and an adjacency matrix. Verification result output module: It is used to automatically verify various valid measurement parameters based on the topology network model, determine whether each measurement parameter meets the preset measurement standard requirements, and output the verification results including abnormal parameter identification, deviation value and abnormal time period. Calibration module: It is used to calculate the calibration compensation coefficient based on the verification results and the correlation of the measurement parameters in the topology network, and to automatically calibrate the measurement parameters that do not meet the preset measurement standard requirements. After calibration, it is re-verified until all measurement parameters meet the preset measurement standard requirements, thus completing the fully automatic verification and calibration process.
[0030] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.
[0031] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. An automatic verification and calibration method for motor vehicle metrological parameters, characterized in that, Includes the following steps: Obtain and preprocess the various metrological parameters that need to be verified and calibrated before the motor vehicle leaves the factory to obtain a valid set of metrological parameters; An equivalent topological network is constructed based on the effective measurement parameter set. Each effective measurement parameter is abstracted as a node of the topological network and bound with parameter attribute information. The inherent relationship between different measurement parameters is abstracted as the edge between nodes, forming a topological network model that includes a set of nodes, a set of edges, and an adjacency matrix. Based on the topology network model, the system automatically verifies each valid measurement parameter, determines whether each measurement parameter meets the preset measurement standard requirements, and outputs the verification results including abnormal parameter identifiers, deviation values, and abnormal time periods. Based on the verification results, the calibration compensation coefficient is calculated by combining the correlation relationship of the measurement parameters in the topology network, and the calibration is completed.
2. The automatic verification and calibration method for motor vehicle metrological parameters according to claim 1, characterized in that, It also includes automatic calibration of measurement parameters that do not meet the preset measurement standards, and re-verification after calibration until all measurement parameters meet the preset measurement standards, thus completing the entire process of automatic verification and calibration.
3. The automatic verification and calibration method for motor vehicle metrological parameters according to claim 1, characterized in that, The specific process for preprocessing various measurement parameters is as follows: Based on the pre-defined physical relationship model of measurement parameters, the theoretical expected value range of each measurement parameter is calculated. If the actual measured value of a certain measurement parameter exceeds its corresponding theoretical expected value range, it is marked as a pseudo-anomaly candidate parameter. For the pseudo-abnormal candidate parameters, it is determined whether the corresponding vibration value exceeds the dynamically set vibration-parameter coupling reference threshold. If so, it is determined to be a pseudo-abnormal parameter caused by environmental mechanical interference and is removed from the set of measurement parameters. The remaining pseudo-anomaly candidate parameters after elimination are used as parameters to be reconstructed. The measured values of each parameter to be reconstructed, the corresponding vibration values, and the ambient temperature and humidity values are input into the adaptive residual compensation network to obtain parameter compensation values. The measured values of each parameter to be reconstructed are then used to reconstruct the parameters online to obtain the reconstructed parameter values. Based on the reconstructed parameter values and all original measurement parameter values that were not marked as pseudo-anomaly candidate parameters, a valid measurement parameter set is formed.
4. The automatic verification and calibration method for motor vehicle metrological parameters according to claim 3, characterized in that, The vibration-parameter coupling reference threshold is dynamically set based on the correlation analysis results of historical vibration data and abnormal records of measurement parameters. The threshold size is determined by fitting the relationship curve between vibration intensity and the probability of abnormal parameter occurrence, combined with a preset confidence level, and is updated at a fixed period to adapt to environmental changes.
5. The automatic verification and calibration method for motor vehicle metrological parameters according to claim 1, characterized in that, The specific process of constructing an equivalent topological network is as follows: the attribute information of each effective measurement parameter is mapped to a three-dimensional topological coordinate system to generate a parameter association vector set; the strong correlation between parameters is extracted as turning point information, and the dwell threshold of the association vector is determined based on the turning point information. The parameter association pairs that meet the dwell threshold are traversed and filtered. The parameter association vector set is segmented according to the association pairs, and the topological coordinates corresponding to each parameter are output. The topological coordinates are used as nodes, and an edge set is constructed according to the parameter association relationship to form an equivalent topological network.
6. The automatic verification and calibration method for motor vehicle metrological parameters according to claim 1, characterized in that, It also includes a verification and calibration data traceability step: recording all process data, including original metrological parameters, environmental interference data, preprocessing records, topology network construction logs, verification deviation data, calibration compensation coefficients, and parameter comparison data before and after calibration, forming a traceable verification and calibration report.
7. The automatic verification and calibration method for motor vehicle metrological parameters according to claim 1, characterized in that, The specific process for completing the automatic verification of measurement parameters is as follows: load the standard parameter change curve corresponding to the measurement parameter, extract the standard reference value according to the preset sampling period; perform time-series alignment and matching between the effective measurement parameter and the standard reference value to generate the matching offset; Calculate the difference between the matching offset and the preset allowed offset threshold. If the difference exceeds the threshold range, it is determined that the parameter is abnormal. By combining the data of associated nodes of abnormal nodes in the topology network for collaborative verification, misjudgments caused by environmental interference are eliminated, and the final verification result is generated.
8. An automatic verification and calibration system for motor vehicle metrological parameters, characterized in that, include: Effective metrological parameter set generation module: used to obtain various metrological parameters that need to be verified and calibrated before the motor vehicle leaves the factory and preprocess them to obtain an effective metrological parameter set; Topology network model construction module: used to construct an equivalent topology network based on the effective measurement parameter set. Each effective measurement parameter is abstracted as a node of the topology network and bound with parameter attribute information. The inherent relationship between different measurement parameters is abstracted as the edge between nodes, forming a topology network model containing a set of nodes, a set of edges, and an adjacency matrix. Verification result output module: It is used to automatically verify various valid measurement parameters based on the topology network model, determine whether each measurement parameter meets the preset measurement standard requirements, and output the verification results including abnormal parameter identification, deviation value and abnormal time period. Calibration module: It is used to calculate the calibration compensation coefficient based on the verification results and the correlation of the measurement parameters in the topology network, and to automatically calibrate the measurement parameters that do not meet the preset measurement standard requirements. After calibration, it is re-verified until all measurement parameters meet the preset measurement standard requirements, thus completing the fully automatic verification and calibration process.