Motor vehicle detection equipment metering self-checking method and system based on digital traceability

Abstract

The invention discloses a motor vehicle detection equipment metering self-checking method and system based on digital traceability, and relates to the technical field of motor vehicle detection equipment metering, and the method comprises the steps: carrying out the instantiation modeling of a physical measurement channel participating in the electric quantity measurement in motor vehicle detection equipment, and constructing a digital traceability identification structure; performing stability judgment on the output state to form a traceability reference condition; performing continuous sampling of a reference point on each metering object instance, outputting an error index fusing a measurement deviation and a sampling dispersion degree, and generating a self-checking judgment quantity; and meanwhile, synchronously associating the compensation parameter version information with the digital traceability identification structure, and generating a metering self-checking record. According to the invention, autonomous metering and self-checking are realized by constructing the metering object digital traceability model and combining the built-in electric quantity reference source, the measurement deviation can be dynamically corrected in the equipment operation process, a continuous and traceable checking record is formed, and the accuracy, consistency and reliability of the metering result of the motor vehicle detection equipment are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of metrology technology for motor vehicle testing equipment, specifically to a self-calibration method and system for motor vehicle testing equipment based on digital traceability. Background Technology

[0002] With the increasing demands for vehicle safety, emissions, and performance testing, vehicle testing equipment is gradually evolving from traditional mechanical and analog devices to digital testing equipment centered on electrical measurement. Existing vehicle testing equipment generally integrates multiple electrical data acquisition channels, such as voltage, current, frequency, and pulse, to support functions like braking performance, headlight intensity, exhaust gas analysis, and power parameters. To ensure the accuracy and consistency of test results, related metrology technologies are gradually incorporating digital calibration, online monitoring, and equipment condition assessment mechanisms. Some equipment is beginning to possess self-testing, self-diagnostic, or periodic calibration functions. Simultaneously, with the increasing demands for metrological supervision and testing compliance, the traceability and verifiability of testing equipment metrological results are becoming important development directions. Metrological traceability technology, digital calibration records, and equipment operation status management technology are being continuously applied and explored in the field of vehicle testing.

[0003] Although existing motor vehicle testing equipment has continuously improved in terms of digital measurement and automation, its metrological assurance methods still mainly rely on manual verification or periodic calibration, and the equipment itself has limited ability to sense the metrological status. In the existing technology, most testing equipment can only perform simple self-checks on the working status of the measurement channel, and it is difficult to perform substantive verification of the metrological accuracy of the electrical quantity measurement channel. It is also impossible to detect measurement drift problems caused by component aging, environmental changes, or long-term operation in a timely manner during equipment operation.

[0004] Furthermore, existing metrological calibration technologies typically store calibration results as static parameters, lacking a digital description of the calibration data formation process. The correlation between metrological results and specific measurement channels, reference sources, and historical states is insufficient, making continuous traceability of the metrological process difficult. When parameters of testing equipment are adjusted or software is upgraded, existing technologies struggle to accurately trace the source of metrological changes, leading to unclear definitions of metrological responsibility.

[0005] Regarding self-verification, existing technologies mostly employ single-point verification or simple threshold judgment methods, failing to fully consider the combined impact of measurement deviation and sampling stability. This can easily lead to occasional stability masking systematic errors, making it difficult to form a reliable metrological judgment basis. Furthermore, existing technologies handle measurement compensation parameters in a rather crude manner after verification, lacking version management and traceability mechanisms. This makes it impossible to guarantee the traceability of the compensation parameter evolution process and to support continuous review of the metrological consistency of testing equipment in regulatory scenarios. Therefore, existing metrological methods for motor vehicle testing equipment struggle to achieve the unified metrological assurance goal of self-verification, dynamic compensation, and continuous recording based on traceability benchmarks. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by this invention is that existing methods for metrological verification of motor vehicle testing equipment suffer from problems such as reliance on manual calibration for metrological status, lack of continuous traceability of metrological information in measurement channels, difficulty in reflecting changes in measurement stability during the verification and judgment process, and how to achieve autonomous metrological verification and compensation records based on traceability benchmarks during equipment operation.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: a self-calibration method for motor vehicle testing equipment based on digital traceability, comprising: instantiating and modeling the physical measurement channels involved in electrical quantity measurement in the motor vehicle testing equipment; constructing a digital traceability identifier structure; calling the corresponding built-in electrical quantity reference source based on the traceability identifier; determining the stability of the output state to form traceability reference conditions; generating a self-calibration excitation sequence according to the nominal output value set recorded in the digital traceability identifier structure; performing continuous sampling of the reference point on each measurement object instance; outputting an error index that fuses the measurement deviation and sampling dispersion; generating a self-calibration judgment quantity; outputting and updating the compensation parameters of the measurement object instance based on the relationship between the self-calibration judgment quantity and the measurement data; simultaneously associating the version information of the compensation parameters with the digital traceability identifier structure; and generating a self-calibration record.

[0009] As a preferred embodiment of the self-calibration method for motor vehicle testing equipment based on digital traceability as described in this invention, the instantiation modeling of the physical measurement channels involved in electrical quantity measurement in the motor vehicle testing equipment includes, when instantiating the physical measurement channels involved in electrical quantity measurement in the motor vehicle testing equipment, taking a single physical measurement channel as the minimum modeling object, fixing and binding the signal input port, signal conditioning circuit, analog-to-digital conversion unit, and data register address corresponding to the analog-to-digital conversion unit to the physical measurement channel, and assigning a unique metrology object identifier to the physical measurement channel, which remains unchanged during equipment operation.

[0010] As a preferred embodiment of the self-calibration method for motor vehicle testing equipment based on digital traceability described in this invention, the construction of the digital traceability identification structure includes recording the upper limit value of the measurement range, the lower limit value of the measurement range, the resolution parameter, the sampling bit width parameter, and the measurement type code corresponding to the measurement object instance in the digital traceability identification structure, and writing the digital traceability identification structure into a non-volatile storage area with a fixed field order and a fixed storage format.

[0011] As a preferred embodiment of the self-calibration method for motor vehicle testing equipment based on digital traceability described in this invention, the step of calling the corresponding built-in power reference source according to the traceability identifier and determining the stability of the output state to form traceability reference conditions includes mapping the reference source number recorded in the digital traceability identifier structure to the built-in power reference source when calling the corresponding built-in power reference source according to the digital traceability identifier structure, and performing continuous sampling on the metering object instance bound to the reference source after the reference source output is established, comparing the continuous sampling results with the pre-written stability threshold to determine whether traceability reference conditions for metering self-calibration calculation are formed.

[0012] As a preferred embodiment of the self-calibration method for motor vehicle testing equipment based on digital traceability as described in this invention, the step of generating a self-calibration excitation sequence according to the nominal output value set recorded in the digital traceability identification structure includes a self-calibration excitation sequence containing reference output points, and sequentially loading them into the built-in power reference source according to the output order fixed during the equipment initialization stage. Under the condition that the output value of each reference output point remains constant, continuous sampling is performed on the measurement object instance to form an original sampling data set corresponding to each reference output point.

[0013] As a preferred embodiment of the self-calibration method for motor vehicle testing equipment based on digital traceability described in this invention, the step of outputting and updating the compensation parameters of the measurement object instance according to the relationship between the self-calibration judgment quantity and the measurement data includes: after outputting the error index of the fusion measurement deviation and sampling dispersion and generating the self-calibration judgment quantity, calculating the compensation parameters of the measurement object instance based on the relationship between the self-calibration judgment quantity and the measurement data corresponding to each benchmark output point; and before writing the compensation parameters, comparing the differences between the new compensation parameters and the historical compensation parameters, and updating the compensation parameters in a versioned manner when the difference comparison result meets the preset restriction conditions.

[0014] As a preferred embodiment of the self-calibration method for motor vehicle testing equipment based on digital traceability described in this invention, the step of simultaneously associating the compensation parameter version information with the digital traceability identifier structure and generating a self-calibration record includes, after completing the calculation and update of the compensation parameters, generating a self-calibration record by taking the self-calibration judgment quantity formed by the measurement object instance in the current self-calibration cycle, the compensation parameter version information, and the traceability association information corresponding to the digital traceability identifier structure, and writing the self-calibration record into a non-volatile storage area according to the self-calibration cycle number.

[0015] Another objective of this invention is to provide a self-calibration system for motor vehicle testing equipment based on digital traceability. This system can generate a self-calibration excitation sequence by following the set of nominal output values ​​recorded in the digital traceability identification structure, perform continuous sampling of reference points on each instance of the metrology object, output an error index that integrates measurement deviation and sampling dispersion, and generate a self-calibration judgment quantity. This solves the problem that current methods for metrology verification of motor vehicle testing equipment often fail to reflect changes in measurement stability during the verification judgment process.

[0016] As a preferred embodiment of the self-calibration system for motor vehicle testing equipment based on digital traceability described in this invention, the system includes: a traceability modeling module, a benchmark self-calibration module, and a compensation traceability module; the traceability modeling module is used to instantiate and model the physical measurement channels involved in electrical quantity measurement in the motor vehicle testing equipment, and establish a digital traceability identifier structure corresponding to the measurement channel parameters and benchmark sources; the benchmark self-calibration module is used to call the built-in electrical quantity benchmark source based on the digital traceability identifier and generate a self-calibration excitation sequence, perform multi-benchmark point sampling on the measurement object, and form a self-calibration judgment quantity; the compensation traceability module is used to update the measurement compensation parameters according to the self-calibration judgment quantity, and associate the compensation parameter version with the traceability identifier to generate a measurement self-calibration record.

[0017] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement a method for self-calibration of motor vehicle testing equipment based on digital traceability.

[0018] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a digital traceability-based self-calibration method for motor vehicle testing equipment.

[0019] The beneficial effects of this invention are as follows: The self-calibration method for motor vehicle testing equipment based on digital traceability provided by this invention instantiates and models the physical measurement channels involved in electrical quantity measurement in motor vehicle testing equipment, and constructs a digital traceability identifier structure that corresponds one-to-one with the measurement channel parameters, reference source, and historical status. This ensures that each measurement channel has a clear, unique, and traceable metrological identity during the self-calibration process, fundamentally solving the problem of scattered and difficult-to-associate metrological information of measurement channels in existing technologies. Furthermore, by calling the built-in electrical quantity reference source based on the traceability identifier and determining the stability of its output status, the self-calibration process is established on controlled and reliable traceability reference conditions, effectively avoiding interference from instability of the reference source on the metrological judgment results. Further, by generating a self-calibration excitation sequence covering multiple nominal output values ​​and performing continuous sampling on each metrological object instance, the measurement deviation and sampling dispersion are fused and evaluated to form a self-calibration judgment quantity that can simultaneously reflect accuracy and stability, overcoming the problem of insufficient reliability of judgment results caused by relying solely on single-point errors or mean values ​​in existing technologies. Finally, by updating the metrological compensation parameters based on the self-verification judgment quantity and synchronously associating the compensation parameter version information with the digital traceability identifier structure and self-verification records, a complete binding between the metrological correction results and their generation conditions is achieved, ensuring continuous traceability of the evolution process of the compensation parameters. In summary, this invention, without relying on frequent manual calibration, achieves autonomous verification, dynamic correction, and unified management of traceability records for the metrological status of motor vehicle testing equipment, significantly improving the accuracy, consistency, and auditability of metrological results. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The first embodiment of the present invention provides an overall flowchart of a self-calibration method for motor vehicle testing equipment based on digital traceability. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1 As an embodiment of the present invention, a self-calibration method for the metrology of motor vehicle testing equipment based on digital traceability is provided, comprising: S1: Instantiate and model the physical measurement channels involved in electrical quantity measurement in the motor vehicle testing equipment, construct a digital traceability identifier structure, call the corresponding built-in electrical quantity reference source based on the traceability identifier, and determine the stability of the output state to form traceability reference conditions.

[0024] Furthermore, before the motor vehicle testing equipment is put into operation, a data modeling system for the self-calibration objects of the measurement is constructed based on the hardware structure of the equipment involved in electrical quantity measurement and signal processing. This modeling system uses physical measurement channels as basic units, independently modeling each electrical signal path input from an external testing object into the equipment and participating in measurement calculations. The modeled objects at least cover the signal input port, signal conditioning circuit, analog-to-digital conversion unit, and the data register address corresponding to each analog-to-digital conversion unit, ensuring that each measurement channel logically forms a unique and fixed instance of the measurement object.

[0025] During the modeling process, an immutable channel identifier is assigned to each instance of a measurement object. This channel identifier is generated by combining the hardware sequence information and channel number information fixed at the time of device manufacture, and is written to a non-volatile storage area in binary or hexadecimal form. This channel identifier remains unchanged throughout the entire lifecycle of the device, uniquely corresponding to a specific physical measurement channel, thus avoiding confusion of measurement objects due to software upgrades or parameter updates.

[0026] After instantiating the metering object, a digital traceability identifier structure is established for each metering object. This digital traceability identifier structure is stored as a dataset, containing the metering object identifier, upper and lower range limits, resolution parameters, nominal sampling bit width, and the corresponding metering type code. The metering type code is used to clearly identify the electrical quantity attribute corresponding to the metering object; its value is written according to hardware design parameters during device initialization.

[0027] The digital traceability identification structure further records traceability information fields related to the metrological benchmark. These fields include the benchmark source number, the benchmark source nominal output value set identifier, and the benchmark source uncertainty level identifier. A one-to-one mapping relationship is formed between the benchmark source number and the controllable electrical energy benchmark source configured within the device. This mapping relationship is completed during the device initialization phase and written to the storage area via a verification code.

[0028] It should be noted that, in addition, a historical verification association field is set in the digital traceability identifier structure to store the summary value of the certificate number corresponding to the most recent manual verification or calibration. This summary value is calculated from the complete certificate number using a fixed algorithm and is used only for association and consistency verification; it does not store the complete certificate text content. This field is written after manual verification is completed and participates in record generation only as traceability association data during self-verification; it does not participate in error calculation.

[0029] After completing the above modeling and identification, the instances of measurement objects and their corresponding digital traceability identifiers are organized in the form of an index table. Each record in the index table contains a mapping relationship between the measurement object identifier and the storage address of the traceability identifier. This index table is loaded into the runtime environment when the device starts up and serves as the sole reference for self-verification calculations, parameter updates, and traceability record generation.

[0030] Furthermore, after completing the data modeling of the metering object instance and establishing the corresponding digital traceability identifier structure, the built-in power reference source bound in its digital traceability identifier structure is called for each metering object instance according to the mapping relationship recorded in the index table. The built-in power reference source has completed the numbering and solidification during the device initialization stage, and its numbering information is consistent with the reference source number field in the digital traceability identifier structure, which is used to uniquely correspond to the traceability reference source of the metering object instance during the self-verification process.

[0031] Based on the mapping relationship, activation operations are performed on the corresponding built-in energy reference sources according to the order of the metering object instances in the index table. The activation operation includes controlling the conduction of the reference source output path and loading the nominal output value recorded in the digital traceability identification structure into the reference source control parameter area, ensuring that the reference source output value is consistent with the range and resolution parameters recorded by the metering object instance. The output value remains constant during activation and does not dynamically change with the sampling process.

[0032] After the reference source output is established, a stability determination process is performed on the measurement object instance bound to that reference source. The stability determination is based on continuous sampling, that is, under the condition that the reference source output value remains unchanged, multiple consecutive samples are taken from the sampling channel corresponding to the measurement object instance, and the sampling results are stored in the running buffer in the sampling order. The number of samplings and the sampling time interval adopt fixed parameter values ​​written during device initialization.

[0033] It should be noted that the numerical analysis performed on the continuous sampling results includes calculating the mean, dispersion, and numerical offset of the sampled data as the sampling sequence changes. The analysis results are then compared with the stability threshold set by the reference source during the factory calibration phase. This threshold parameter is stored in a non-volatile memory area as a fixed value and accessed via an index using the reference source number.

[0034] When the continuous sampling analysis results meet the corresponding threshold conditions, the reference source is recorded as being in a stable output state within the current self-calibration cycle, and a stable flag is written into the running status field associated with the measurement object instance; when the analysis results do not meet the threshold conditions, the reference source number, measurement object identifier, and corresponding sampling data set are recorded, and the error calculation process of the measurement object instance within this round of self-calibration cycle is terminated.

[0035] After activating and determining the stability of the built-in power reference source corresponding to all metering object instances, a reference source stability state set is generated. This state set uses the metering object identifier as an index key to record the stability determination result of the reference source corresponding to each metering object instance within the current self-verification cycle.

[0036] S2: Generate a self-verification excitation sequence according to the set of nominal output values ​​recorded in the digital traceability identifier structure, perform continuous sampling of the benchmark point for each measurement object instance, output the error index of fusion measurement deviation and sampling dispersion, and generate a self-verification judgment quantity.

[0037] Furthermore, based on the established measurement object instance and its digital traceability identifier structure, a self-verification excitation sequence is generated for the measurement object instance that has passed the stability determination. The self-verification excitation sequence consists of multiple reference output points, the values ​​of which are derived from the set of nominal output values ​​recorded in the digital traceability identifier structure, and maintain a corresponding relationship with the upper limit value, lower limit value, and resolution parameter recorded in the measurement object instance.

[0038] The self-calibration excitation sequence is arranged according to pre-written sequence rules during generation. These rules are fixed during device initialization and include the number of reference output points, the order of output points, and the switching interval between each output point. The excitation sequence remains unchanged throughout the self-calibration cycle to ensure that different measurement object instances perform sampling operations under the same conditions.

[0039] During the execution of the self-calibration excitation sequence, each reference output point is sequentially loaded into the built-in electrical reference source bound to the metering object instance according to the output order defined in the excitation sequence, so that the reference source outputs the corresponding reference value at each output point. The reference value remains constant at each output point until the sampling operation corresponding to that output point is completed, and then the process switches to the next output point.

[0040] During the stabilization period of each reference output point, multiple sampling operations are performed on the sampling channel corresponding to the measurement object instance. The sampling operations are based on the recorded sampling bit width and resolution parameters. The number of samples uses a fixed value set during the device initialization phase and remains consistent throughout the self-calibration process. The sampling results are stored in chronological order and associated with the corresponding measurement object identifier and the current reference output point number.

[0041] It should be noted that after sampling a single reference output point is completed, the sampling result data is written into the running data area corresponding to the measurement object instance, and the index number of the reference output point is recorded. Then, the process switches to the next reference output point in the excitation sequence and repeats the reference output loading and sampling process until all excitation sequence output points corresponding to the measurement object instance are sampled.

[0042] After sampling all excitation sequences for a measurement object instance, a raw sampled data set for that measurement object instance within the current self-calibration cycle is generated. This raw sampled data set is indexed by the measurement object identifier and contains multiple benchmark output point numbers and their corresponding sampled data sequences. This raw sampled data set remains unchanged as input data for error calculation and determination during the current self-calibration process.

[0043] Once all instances of the measurement object have completed multi-point sampling corresponding to their respective excitation sequences, the data acquisition process ends, and the original sampled data set and the formed reference source stability state set are retained together.

[0044] Furthermore, after completing multi-point sampling of the self-verification excitation sequence corresponding to each metering object instance, based on the generated original sampled data set, a fusion calculation and judgment process for the metering error is performed on each metering object instance. The metering error calculation is based on the established metering object instance and its digital traceability identifier structure as the index, and uses the built-in power reference source confirmed to be in a stable output state as the metering reference source.

[0045] For a given measurement object instance, at each corresponding benchmark output point, a sampling data sequence corresponding to that benchmark output point is extracted from the original sampling data set, and the measurement mean and dispersion parameter are calculated based on the sampling data sequence. The measurement mean is used to characterize the stable measurement result of the measurement object instance at that benchmark output point, and the dispersion parameter is used to characterize the numerical fluctuation of the measurement result during the sampling process.

[0046] Based on this, the fusion error index is calculated for each reference output point. , represented as:

[0047] in, Indicates the index number of the reference output point; Indicates the instance of the measurement object in the first... The average value of samples taken at each benchmark output point; Indicates the first The traceability benchmark value corresponding to each benchmark output point; Indicates the instance of the measurement object in the first... The sampling standard deviation at each reference output point; This represents the repeatability weighting coefficient, a fixed parameter written during the device initialization phase, used to adjust the degree of participation of sampling dispersion in the fusion error index.

[0048] It should be noted that the fusion error index, based on traditional relative error calculation, incorporates the sampling dispersion into a unified calculation expression, so that the error calculation result of each benchmark output point simultaneously reflects both measurement deviation and sampling stability. During the calculation process, all parameters involved are directly derived from the defined measurement object instance attributes, the digital traceability identifier structure, and the formed original sampling data set, without introducing external correction data.

[0049] After calculating the fusion error index for all benchmark output points of the measurement object instance, a channel-level self-verification judgment quantity is generated based on the fusion error index. , represented as:

[0050] in, This represents the channel-level self-verification judgment quantity, and its value is equal to the maximum value of the fusion error index calculated at all benchmark output points for this measurement object instance.

[0051] Subsequently, the channel-level self-verification judgment quantity is... Error threshold parameters recorded in the digital traceability identifier structure A comparison is performed. The error threshold parameter is written during the equipment's factory verification or manual calibration stage and stored in a one-to-one correspondence with the metrology object instance. After the comparison is completed, the metrology judgment result for the metrology object instance within the current self-verification cycle is generated, and the judgment result and the corresponding fusion error index set are written into the operation data area.

[0052] S3: Based on the relationship between the self-calibration judgment quantity and the measurement data, output and update the compensation parameters of the measurement object instance, and simultaneously associate the compensation parameter version information with the digital traceability identifier structure, and generate a measurement self-calibration record.

[0053] Furthermore, after completing the fusion error calculation and self-verification of the measurement object instances, for measurement object instances whose judgment results meet preset conditions, the measurement compensation parameters are calculated and updated based on their corresponding fusion error index set. These measurement compensation parameters maintain a corresponding relationship with the range parameters, resolution parameters, and sampling bit width parameters recorded by the measurement object instances, and are used to ensure the consistency of the measurement data.

[0054] For a given measurement object instance, based on the numerical relationship between the measurement mean values ​​corresponding to each benchmark output point and the traceability benchmark values, a compensation parameter calculation dataset for that measurement object instance within the current self-calibration period is constructed. This dataset uses the measurement object identifier as an index and includes the number of each benchmark output point, the corresponding traceability benchmark value, and the measurement mean data, maintaining the original numerical format without normalization or scaling.

[0055] During the compensation parameter calculation process, zero-point compensation parameters and proportional compensation parameters are calculated based on the dataset. The zero-point compensation parameters are obtained by statistically analyzing the measurement deviations at low-range reference output points, while the proportional compensation parameters are calculated by fitting the numerical relationships between measurement data from multiple reference output points and traceability reference values. The compensation parameter calculation process employs fixed calculation rules written during the equipment initialization phase, and no external empirical parameters or dynamic correction factors are introduced during the calculation.

[0056] After obtaining new compensation parameters, a difference comparison is performed between these new parameters and the historical compensation parameters already stored in the current metering object instance. This difference comparison includes calculating the magnitude of the change in the compensation parameter value and verifying the consistency of the change direction. The comparison rules are written during the device initialization phase and remain unchanged during operation. The compensation parameter update phase is only allowed when the magnitude of the compensation parameter change meets preset limitations and the change direction is consistent with historical trends.

[0057] During the compensation parameter update phase, new compensation parameters are written to the non-volatile storage area corresponding to the metering object instance in a versioned manner. Each compensation parameter write generates a new parameter version identifier, which is then associated with the corresponding metering object identifier and the current self-verification cycle number. Existing compensation parameters are not overwritten after the new parameters are written, but are retained as historical versions.

[0058] It should be noted that, simultaneously with writing the compensation parameters, the digital traceability identifier structure of the measurement object instance is updated synchronously. The traceability identifier structure records the current compensation parameter version identifier and its generation time stamp. This time stamp is generated by the device's internal clock and is used to establish a correspondence with the measurement data during the self-verification record generation process.

[0059] When the difference in compensation parameters does not meet the preset constraints, the set of fusion error indicators and the judgment result corresponding to the measurement object instance are recorded, and the compensation parameter writing process is skipped, keeping the original compensation parameters of the measurement object instance unchanged. Regardless of whether the compensation parameter update is completed, a compensation processing status record for the measurement object instance in the current self-verification cycle is generated and associated with the formed self-verification judgment result.

[0060] At this point, the calculation of compensation parameters and the updating of traceability parameters for the measurement object instance within the current self-calibration cycle are completed. The generated compensation parameter version information, difference comparison results, and associated traceability identification information remain unchanged as input data for the generation of self-calibration records and traceability storage.

[0061] Furthermore, after completing the calculation of compensation parameters and the update of traceability parameters for the measurement object instances, the measurement self-verification records are generated and associated with digital traceability based on the status data of the measurement object instances formed during this self-verification cycle. The measurement self-verification records use the measurement object instance as the smallest record unit and a defined measurement object identifier as the index key, which is used to distinguish and associate the self-verification processes of each measurement object instance at the record level.

[0062] When generating metering self-calibration records, for each metering object instance, its corresponding set of fusion error indicators, channel-level self-calibration judgment quantities, compensation parameter version identifiers, and compensation processing status information for the current self-calibration cycle are written into the record data area according to a predetermined data structure order. The field order, field length, and encoding method in the record data area are determined during the device initialization phase and remain unchanged during operation, thereby ensuring the structural consistency of records generated in different self-calibration cycles.

[0063] Traceability association information is synchronously written into the measurement self-calibration record. This traceability association information includes the measurement object identifier, the reference source number, the set of reference output point numbers, and the corresponding digital traceability identifier structure summary value. The summary value is calculated by a preset algorithm from fixed fields in the digital traceability identifier structure, and is used to achieve a deterministic association between the self-calibration record and the traceability information without copying the complete traceability data.

[0064] After generating a self-verification record for a single measurement object instance, the record content undergoes integrity identification processing. Integrity identification involves sequentially concatenating all fields in the record data area and calculating a summary value based on the concatenation result. This summary value establishes a sequential relationship with the summary value generated in the previous self-verification cycle and is written into the current self-verification record, thus forming a chronologically ordered association structure at the record level.

[0065] It should be noted that after completing the generation and traceability association of self-verification records for all metering object instances, the current metering status of the equipment is updated based on the judgment results and compensation processing status of each metering object instance. The metering status update is recorded at the metering object instance level, and the status information is written into the operating status area in the form of flag bits, maintaining a one-to-one correspondence with the corresponding metering object identifier.

[0066] When a metering object instance fails to update its compensation parameters within the current self-calibration cycle, or when its channel-level self-calibration judgment exceeds the error threshold recorded in the digital traceability identifier structure, the metering object instance is marked as restricted in the operating status area. This restricted status is recorded only as part of the metering status data and does not affect the writing of status data for other metering object instances.

[0067] After completing the metering status update, the set of metering self-verification records, the set of traceability association summary values, and the metering status data generated within the current self-verification cycle are written into a complete dataset in the non-volatile storage area. This dataset is indexed and stored using the self-verification cycle number, and is used for comparing traceability associations with historical statuses within the cycle.

[0068] Example 2, one embodiment of the present invention, provides a self-calibration method for motor vehicle testing equipment based on digital traceability. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment.

[0069] First, this embodiment selects a set of motor vehicle testing equipment actually used in a motor vehicle safety technical inspection agency as the test object. This testing equipment integrates multiple electrical quantity measurement channels to collect and process voltage, current, and pulse signals generated during vehicle testing. To verify the feasibility and effectiveness of the metrological self-calibration method based on digital traceability described in this invention, a metrological self-calibration experiment was conducted on the key physical measurement channels involved in electrical quantity measurement within the equipment without changing the equipment hardware structure.

[0070] Before the experiment began, the physical measurement channels involved in the electrical quantity measurement in the testing equipment were first instantiated and modeled. Each actual physical measurement channel was treated as an independent measurement object instance, and its signal input port number, analog-to-digital conversion bit width, range, and resolution parameters were recorded accordingly. A unique measurement object identifier was also established for each measurement object instance. Based on this, a digital traceability identification structure corresponding one-to-one with each measurement object instance was constructed. The measurement object identifier, range parameters, sampling parameters, and the corresponding built-in electrical quantity reference source number were uniformly bound and written into the non-volatile storage area of ​​the equipment to ensure that the relevant parameters remained consistent throughout the entire experiment.

[0071] After completing the above preparations, the built-in power reference source integrated within the device is invoked based on the reference source number recorded in the digital traceability identifier structure. The reference source remains in a preheated state before outputting, and then, while maintaining a constant target output value, continuous sampling is performed on the metering object instance bound to it. By analyzing the numerical fluctuations of the continuous sampling results, it is confirmed that the reference source output state meets the preset stability conditions, thus forming the traceability reference conditions for this round of metering self-calibration.

[0072] After the reference source stabilizes, a self-verification excitation sequence is generated according to the set of nominal output values ​​recorded in the digital traceability identification structure. This excitation sequence covers multiple reference output points within the measurement channel's range. Each output point maintains a constant output value, and under this condition, multiple consecutive samples are performed on the measurement object instance, recording the original sampled data corresponding to each output point. Based on the sampled data, the measurement deviation and sampling dispersion are calculated for each reference output point and fused to form a self-verification error index, further generating a self-verification judgment quantity for the measurement object instance.

[0073] After completing the self-verification judgment, the compensation parameters of the measurement object instance are updated based on the correspondence between the self-verification judgment quantity and the measurement data of multiple benchmark output points. Before updating the compensation parameters, a limiting verification is performed on the change range between the old and new compensation parameters. Parameter writing is only performed when preset conditions are met, and the data is saved in a versioned manner. Finally, the judgment results, compensation parameter version information, and corresponding digital traceability identifier structure association information generated during this round of self-verification are used to generate a measurement self-verification record for historical traceability and status comparison.

[0074]

[0075] As shown in Table 1, by introducing a metrological self-calibration method based on digital traceability, the accuracy and stability of multiple electrical measurement channels in the motor vehicle testing equipment have been significantly improved. Taking the comparison of the maximum measurement deviation before and after self-calibration as an example, each measurement channel had varying degrees of measurement deviation before self-calibration, with the maximum deviation reaching 0.60%. This level of deviation can easily affect the reliability of the test results under long-term operating conditions.

[0076] After implementing the method of this invention, the maximum measurement deviation of each measurement channel was significantly reduced, and the maximum deviation after self-calibration was controlled within the range of 0.10% to 0.18%, with a more concentrated distribution of deviations among different channels. This result demonstrates that, through the synergistic effect of the digital traceability identification structure and the built-in electrical reference source, the metrological status of the measurement channels can be effectively corrected without relying on external manual calibration, overcoming the shortcomings of long calibration cycles and slow response in existing technologies.

[0077] Further analysis of the sampling dispersion data reveals that, under the premise that the stability of the reference output is met, the sampling dispersion of each measurement channel remains within a reasonable range, and the variation between different reference output points is small. This indicates that the present invention does not only focus on a single measurement deviation during the self-verification process, but incorporates measurement stability into a unified evaluation system, effectively avoiding the problem in existing technologies that rely solely on single-point error judgment while ignoring sampling fluctuations.

[0078] Furthermore, the changes in the compensation parameter version number indicate that the compensation parameters are not simply rewritten, but updated and recorded in a versioned manner. This approach ensures that every parameter change can be traced back to the specific self-calibration cycle and traceability identification information, overcoming the technical shortcomings of existing technologies where the source of compensation parameters is unclear and historical status is difficult to trace. All measurement channels successfully generated metrological self-calibration traceability records, demonstrating that this method possesses good scalability and consistency in multi-channel environments.

[0079] Based on the above data analysis results, it can be confirmed that the present invention achieves autonomous perception and continuous correction of the measurement status of motor vehicle testing equipment through digital traceability modeling, benchmark-driven self-verification calculation, and collaborative processing of compensation and recording. It is significantly superior to the existing technology in terms of measurement accuracy, stability, and traceability, and has outstanding practical value and technological progress.

[0080] Example 3, one embodiment of the present invention, provides a self-calibration system for the metrology of motor vehicle testing equipment based on digital traceability, including a traceability modeling module, a benchmark self-calibration module, and a compensation traceability module.

[0081] The traceability modeling module is used to instantiate and model the physical measurement channels involved in the electrical measurement in motor vehicle testing equipment, and establish a digital traceability identifier structure corresponding to the measurement channel parameters and the reference source; the reference self-calibration module is used to call the built-in electrical reference source based on the digital traceability identifier and generate a self-calibration excitation sequence, perform multi-reference point sampling on the metering object and form a self-calibration judgment quantity; the compensation traceability module is used to update the metering compensation parameters according to the self-calibration judgment quantity, and associate the compensation parameter version with the traceability identifier to generate a metering self-calibration record.

[0082] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0083] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0084] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0085] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A self-calibration method for motor vehicle testing equipment based on digital traceability, characterized in that, include: The physical measurement channels involved in electrical quantity measurement in motor vehicle testing equipment are instantiated and modeled to construct a digital traceability identifier structure. Based on the traceability identifier, the corresponding built-in electrical quantity reference source is called, and the stability of the output state is determined to form traceability reference conditions. A self-verification excitation sequence is generated based on the set of nominal output values ​​recorded in the digital traceability identification structure. Continuous sampling of the benchmark point is performed on each instance of the measurement object. An error index that integrates measurement deviation and sampling dispersion is output, and a self-verification judgment quantity is generated. Based on the relationship between the self-calibration judgment quantity and the measurement data, the compensation parameters of the measurement object instance are output and updated. At the same time, the version information of the compensation parameters is synchronously associated with the digital traceability identification structure, and a measurement self-calibration record is generated.

2. The self-calibration method for motor vehicle testing equipment based on digital traceability as described in claim 1, characterized in that: The instantiation modeling of the physical measurement channels involved in electrical quantity measurement in the motor vehicle testing equipment includes instantiating and modeling the physical measurement channels involved in electrical quantity measurement in the motor vehicle testing equipment by taking a single physical measurement channel as the minimum modeling object, fixing and binding the signal input port, signal conditioning circuit, analog-to-digital conversion unit and the data register address corresponding to the analog-to-digital conversion unit to the physical measurement channel, and assigning a unique measurement object identifier to the physical measurement channel. The measurement object identifier remains unchanged during the operation of the equipment.

3. The self-calibration method for motor vehicle testing equipment based on digital traceability as described in claim 2, characterized in that: The construction of the digital traceability identifier structure includes recording the upper limit value of the measurement range, the lower limit value of the measurement range, the resolution parameter, the sampling bit width parameter, and the measurement type code corresponding to the measurement object instance in the digital traceability identifier structure, and writing the digital traceability identifier structure into the non-volatile storage area with a fixed field order and a fixed storage format.

4. The self-calibration method for motor vehicle testing equipment based on digital traceability as described in claim 3, characterized in that: The process of calling the corresponding built-in power reference source based on the traceability identifier and determining the stability of the output state to form traceability reference conditions includes mapping the reference source number recorded in the digital traceability identifier structure to the built-in power reference source when calling the corresponding built-in power reference source based on the digital traceability identifier structure. After the reference source output is established, continuous sampling is performed on the metering object instance bound to the reference source. The continuous sampling results are compared with the pre-written stability threshold to determine whether traceability reference conditions for meter self-verification calculation are formed.

5. The self-calibration method for motor vehicle testing equipment based on digital traceability as described in claim 4, characterized in that: The process of generating a self-verification excitation sequence based on the nominal output value set recorded in the digital traceability identifier structure includes the self-verification excitation sequence containing reference output points, and sequentially loading them into the built-in power reference source according to the output order fixed during the device initialization phase. Under the condition that the output value of each reference output point remains constant, continuous sampling is performed on the metering object instance to form the original sampling data set corresponding to each reference output point.

6. The self-calibration method for motor vehicle testing equipment based on digital traceability as described in claim 5, characterized in that: The step of outputting and updating the compensation parameters of the measurement object instance based on the relationship between the self-verification judgment quantity and the measurement data includes: after outputting the error index of the fused measurement deviation and sampling dispersion and generating the self-verification judgment quantity, calculating the compensation parameters of the measurement object instance based on the relationship between the self-verification judgment quantity and the measurement data corresponding to each benchmark output point, and comparing the differences between the new compensation parameters and the historical compensation parameters before writing the compensation parameters. When the difference comparison result meets the preset restriction conditions, the compensation parameters are updated in a versioned manner.

7. The self-calibration method for motor vehicle testing equipment based on digital traceability as described in claim 6, characterized in that: The simultaneous association of compensation parameter version information and digital traceability identifier structure, and the generation of measurement self-verification records, includes generating measurement self-verification records by generating the self-verification judgment quantity, compensation parameter version information, and traceability association information corresponding to the digital traceability identifier structure formed by the measurement object instance in the current measurement self-verification cycle after the completion of compensation parameter calculation and update, and writing the measurement self-verification records into the non-volatile storage area according to the measurement self-verification cycle number.

8. A system employing the self-calibration method for motor vehicle testing equipment based on digital traceability as described in any one of claims 1 to 7, characterized in that: It includes a source tracing modeling module, a benchmark self-calibration module, and a compensation source tracing module; The traceability modeling module is used to instantiate and model the physical measurement channels involved in electrical measurement in motor vehicle testing equipment, and to establish a digital traceability identification structure corresponding to the measurement channel parameters and the reference source. The reference self-calibration module is used to call the built-in power reference source based on the digital traceability identifier and generate a self-calibration excitation sequence, perform multi-reference point sampling on the metering object and form a self-calibration judgment quantity; The compensation traceability module is used to update the metering compensation parameters according to the self-verification judgment quantity, and associate the compensation parameter version with the traceability identifier to generate a metering self-verification record.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the self-calibration method for motor vehicle testing equipment based on digital traceability as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the self-calibration method for the measurement of motor vehicle testing equipment based on digital traceability as described in any one of claims 1 to 7.

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