A vehicle mileage accumulation method and system

Abstract

The application relates to a vehicle mileage accumulation method and system, which comprises the following steps: collecting multi-source running data in the running process of a vehicle; performing weighted calculation on the multi-source running data based on preset weights to obtain initial mileage accumulation parameters; performing compliance verification on the initial mileage accumulation parameters, correcting parameters exceeding a preset compliance range to boundary values of the preset compliance range to obtain target mileage accumulation parameters; and performing instrument mileage accumulation according to the target mileage accumulation parameters. The application collects multi-source running data in the running process of a vehicle, performs weighted calculation to obtain initial mileage accumulation parameters, changes the generation basis of the parameters from a single fixed value to multi-dimensional dynamic data fusion, and quantifies the influence degree of each data on mileage accumulation through the weighted calculation mode, so that the initial mileage accumulation parameters can reflect the actual running conditions of the vehicle in real time.

Description

Technical Field

[0001] This application relates to the field of automotive electronic information technology, specifically to a method and system for accumulating vehicle mileage. Background Technology

[0002] The odometer readings of commercial vehicles are an important basis for vehicle operation settlement, maintenance, and regulatory supervision. Their accuracy is directly related to the interests of all parties and compliance.

[0003] In existing technologies, the mileage accumulation parameters on commercial vehicle instrument panels are typically determined by setting fixed values ​​or manually adjusting them within a certain range. These parameter setting methods are primarily based on standard operating conditions or empirical values. However, during actual vehicle operation, the driving state is affected by a combination of dynamic factors, making it difficult for preset fixed parameters to match actual driving scenarios throughout the entire journey, easily leading to deviations in mileage measurement. Furthermore, as the vehicle's usage time increases, changes in the condition of key vehicle components may further introduce systematic errors, and existing methods lack an effective continuous correction mechanism after parameter setting, making it difficult to guarantee the stability of measurement accuracy throughout the entire lifecycle. Simultaneously, with industry metrological standards increasingly specifying error range requirements, existing technical solutions still have room for improvement in ensuring that parameters remain within compliant ranges. Summary of the Invention

[0004] This application provides a vehicle mileage accumulation method and system, which can solve the problem that the existing mileage accumulation parameter determination method is difficult to adapt to the dynamic changes in vehicle operating status, resulting in insufficient stability of mileage measurement accuracy.

[0005] In a first aspect, embodiments of this application provide a method for accumulating vehicle mileage, comprising: Collect multi-source operational data during vehicle operation; The initial mileage accumulation parameters are obtained by weighting the multi-source operation data based on preset weights. The initial mileage accumulation parameters are verified for compliance. Parameters that exceed the preset compliance range are corrected to the boundary values ​​of the preset compliance range to obtain the target mileage accumulation parameters. The instrument mileage is accumulated based on the target mileage accumulation parameters.

[0006] In conjunction with the first aspect, in one embodiment, the method further includes: Acquire historical error data generated during the vehicle's historical driving process; The correction coefficient is calculated based on historical error data, and the target mileage accumulation parameter is multiplied by the correction coefficient to obtain the updated mileage accumulation parameter. The updated mileage accumulation parameters are subjected to a second compliance check and correction, and parameters that exceed the preset compliance range are corrected to the boundary values ​​of the preset compliance range. Use the revised and updated mileage accumulation parameters to accumulate the instrument mileage.

[0007] In conjunction with the first aspect, in one implementation, acquiring historical error data generated during the vehicle's historical driving process specifically includes: During vehicle operation, a dual-source fusion algorithm is used to generate reference mileage data; The difference between the reference mileage data and the instrument's cumulative mileage is used as historical error data; The dual-source fusion algorithm includes: when the positioning signal quality meets the preset conditions, using the fusion of positioning mileage data and wheel speed mileage data as reference mileage data; when the positioning signal quality does not meet the preset conditions, using wheel speed mileage data as reference mileage data. The dual-source fusion algorithm also includes a conflict handling step: when the difference between the positioning mileage data and the wheel speed mileage data is greater than a preset ratio and continues for a preset duration, the wheel speed mileage data is forcibly used as the reference mileage data.

[0008] In conjunction with the first aspect, in one implementation, before performing weighted calculations on the multi-source operational data based on preset weights, the method further includes a step of confirming the preset weights: Collect test data and corresponding mileage true values ​​of the vehicle when it is driving under standard test conditions; Based on the test data and true mileage values, the weight combination that minimizes the mileage error is solved using the least squares method. The weight combination is used as the preset weight.

[0009] In conjunction with the first aspect, in one implementation, the multi-source operating data includes: vehicle speed data, wheel speed pulse data, GPS speed data, and acceleration data.

[0010] In conjunction with the first aspect, in one implementation, multi-source operational data is weighted and calculated based on preset weights to obtain initial mileage accumulation parameters, specifically including: Obtain the preset minimum and maximum values ​​corresponding to each multi-source runtime data; The multi-source operational data is preprocessed using the preset minimum and maximum values ​​to obtain standardized variables; The standardized variables are weighted and summed using the preset weights to obtain the initial mileage accumulation parameters.

[0011] In conjunction with the first aspect, in one embodiment, the method further includes: When the currently collected multi-source running data is greater than the corresponding preset maximum value, the preset maximum value is updated to the value of the currently collected multi-source running data; When the currently collected multi-source running data is less than the corresponding preset minimum value, the preset minimum value is updated to the value of the currently collected multi-source running data; Store the updated preset minimum or maximum value and apply it when the vehicle is initialized for the next driving cycle.

[0012] In conjunction with the first aspect, in one implementation, correcting parameters that exceed the preset compliance range to boundary values ​​of the preset compliance range specifically includes: The initial mileage accumulation parameter is compared with a preset compliance range threshold. If the initial mileage accumulation parameter is less than the lower limit of the preset compliance range threshold, then the initial mileage accumulation parameter is corrected to the lower limit. If the initial mileage accumulation parameter is greater than the upper limit of the preset compliance range threshold, then the initial mileage accumulation parameter is corrected to the upper limit value; If the initial mileage accumulation parameter is within the preset compliance range threshold, then the initial mileage accumulation parameter is used as the target mileage accumulation parameter.

[0013] In conjunction with the first aspect, in one implementation, the instrument mileage is accumulated based on the target mileage accumulation parameter, specifically including: Obtain raw mileage increment data during vehicle operation; Based on the original mileage increment data and the target mileage accumulation parameter, the corrected mileage increment data is obtained; The instrument's cumulative mileage is obtained by accumulating the corrected mileage increment data.

[0014] Secondly, this application provides a vehicle mileage accumulation system, comprising: a data acquisition module, a processing module, and an accumulation module. The data acquisition module is used to: collect multi-source operating data during vehicle operation; the processing module is used to: perform weighted calculation on the multi-source operating data based on preset weights to obtain initial mileage accumulation parameters, and perform compliance verification on the initial mileage accumulation parameters, correcting parameters exceeding the preset compliance range to the boundary values ​​of the preset compliance range to obtain target mileage accumulation parameters; the accumulation module is used to: accumulate the instrument mileage according to the target mileage accumulation parameters.

[0015] The beneficial effects of the technical solutions provided in this application include: This application provides a vehicle mileage accumulation method and system. It collects multi-source operational data during vehicle operation and performs weighted calculations based on preset weights to obtain initial mileage accumulation parameters. This transforms the parameter generation basis from a single fixed value to multi-dimensional dynamic data fusion. Since multi-source operational data can cover different state characteristics during vehicle operation, the weighted calculation method can quantify the influence of each data point on mileage accumulation, thus enabling the initial mileage accumulation parameters to reflect the vehicle's current actual operating conditions in real time. Furthermore, by performing compliance checks on the initial mileage accumulation parameters and removing parameters exceeding a preset limit... The parameters within the specified range are corrected to the boundary values ​​of the preset compliance range, thus constructing a safety constraint mechanism for parameter output. By forcibly limiting the range of parameter values, abnormal deviations from the reasonable range due to sensor fluctuations or extreme operating conditions are avoided, thereby ensuring that the obtained target mileage accumulation parameters are always within the compliance range that meets metrological requirements. Finally, the instrument mileage is accumulated based on the target mileage accumulation parameters. Since the parameters used for accumulation calculation have both operating condition adaptability and compliance, the instrument accumulated mileage data can dynamically change with the vehicle's operating status while meeting the industry metrological standards for error range limitations, thereby improving the accuracy and reliability of the mileage data. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the vehicle mileage accumulation method in this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0018] This application provides a vehicle mileage accumulation method and system, which can solve the problem that the existing mileage accumulation parameter determination method is difficult to adapt to the dynamic changes in vehicle operating status, resulting in insufficient stability of mileage measurement accuracy.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0020] In a first aspect, embodiments of this application provide a method for accumulating vehicle mileage, comprising: 101: Collect multi-source operating data during vehicle operation; the system acquires multi-dimensional physical quantities reflecting the vehicle's driving status in real time through the vehicle bus and sensor network. These data cover key variables that affect the accuracy of mileage calculation, ensuring that subsequent parameter calculations can fully perceive the actual operating conditions of the vehicle and provide a reliable and real-time data foundation for mileage accumulation.

[0021] 102: Based on preset weights, multi-source operating data are weighted and calculated to obtain initial mileage accumulation parameters. The preset weights characterize the degree of influence of different operating data on mileage error. By multiplying each variable with its corresponding weight and summing the results, the system can quantify the mileage deviation trend under the current operating conditions. These weights are usually determined based on standard operating condition calibration, thereby generating an initial parameter that reflects real-time driving characteristics and avoiding calculation bias caused by a single data source.

[0022] 103: Perform compliance verification on the initial mileage accumulation parameters, and correct parameters that exceed the preset compliance range to the boundary value of the preset compliance range to obtain the target mileage accumulation parameters; this step aims to ensure that the parameters used in the end always meet the error range required by industry metrology standards or regulations, prevent mileage parameters from going out of control due to extreme operating conditions or sensor malfunctions, and ensure the legality and consistency of mileage measurement.

[0023] 104: Accumulate the instrument mileage based on the target mileage accumulation parameters. The raw mileage increment data generated during vehicle operation is combined with the target mileage accumulation parameters to obtain a corrected mileage increment, and then accumulated to obtain the instrument's cumulative mileage. In this way, the mileage displayed on the instrument can dynamically adapt to the vehicle's operating status while maintaining high accuracy within the compliant range, achieving adaptive optimization of mileage measurement.

[0024] In this application, multi-source operational data during vehicle operation is collected, and initial mileage accumulation parameters are obtained by weighting the multi-source operational data based on preset weights. This transforms the parameter generation basis from a single fixed value to multi-dimensional dynamic data fusion. Since multi-source operational data can cover different state characteristics during vehicle operation, the weighted calculation method can quantify the influence of each data point on mileage accumulation, thus enabling the initial mileage accumulation parameters to reflect the vehicle's current actual operating conditions in real time. Based on this, a safety constraint mechanism for parameter output is constructed by verifying the compliance of the initial mileage accumulation parameters and correcting parameters exceeding the preset compliance range to the boundary values ​​of the preset compliance range. By forcibly limiting the parameter value range, abnormal deviations of parameters from the reasonable range due to sensor fluctuations or extreme operating conditions are avoided, thereby ensuring that the obtained target mileage accumulation parameters are always within the compliance range that meets metrological requirements. Finally, the instrument mileage is accumulated based on the target mileage accumulation parameters. Since the parameters used for accumulation calculation have both operating condition adaptability and compliance, the instrument accumulated mileage data can dynamically change with the vehicle's operating status while meeting the industry metrological standards for error range limitations, improving the accuracy and reliability of the mileage data.

[0025] In this embodiment, before performing weighted calculations on the multi-source operational data based on preset weights, the method further includes a step of confirming the preset weights: First, test data and corresponding mileage values ​​are collected when the vehicle is driving under standard test conditions. The system collects four types of variable data in real time through the onboard sensor network: vehicle speed data from the CAN bus or vehicle speed sensor, wheel speed pulse data from the wheel speed sensor, GPS speed data from the positioning module, and acceleration data from the accelerometer. These four types of data cover the key characteristics of vehicle kinematics and dynamics from different dimensions, including overall vehicle speed, wheel rotation, satellite positioning, and vehicle dynamic acceleration. The test process is conducted on a standard test road, which includes, for example, a 5km straight section and covers various driving conditions such as acceleration / deceleration, constant speed, and slight bumps, ensuring that the collected data samples can comprehensively reflect the vehicle's operating characteristics under different loads and road conditions. In this process, the true mileage refers to the actual mileage recorded by a high-precision differential GPS device. It serves as a reference benchmark for measuring the accuracy of the instrument mileage. Differential GPS can provide centimeter-level positioning accuracy by correcting satellite signal errors. The mileage data it records is regarded as the error-free true value. At the same time, the original mileage of the vehicle's instrument is recorded for comparison calculation. The ratio of the true mileage to the original mileage of the instrument is the true mileage coefficient, which is used to characterize the ideal mileage correction ratio.

[0026] Then, based on the test data and true mileage values, the least squares method is used to find the weight combination that minimizes the mileage error, and a correlation model is established based on a weighted summation algorithm to calculate the initial parameters. The model adopts a weighted summation linear model, and the specific formula is expressed as follows: P_init = Σ(Standardized variable_i × Weight_i), where variables need to be standardized to eliminate differences between different physical units and prevent the weight allocation from being affected by different numerical ranges. During model building, the system collects N sets of data under various operating conditions. Each set of data includes the standardized variable values ​​and the corresponding true mileage. The number of data sets N is no less than 1000 sets to ensure statistical significance and the model's generalization ability. To quantify the error and solve for the optimal weights, a loss function is defined: Loss = Σ(P_init - True Odometer Coefficient) 2 The loss function represents the sum of squared errors between the model's predicted values ​​and the actual proportions. In this calibration phase, the weights _i are treated as unknowns to be solved. The least squares method is used to find the weight combination that minimizes the loss function, thus scientifically determining the influence of each variable on the mileage error. For example, the calculated weights of vehicle speed (0.3), wheel speed (0.4), GPS speed (0.2), and acceleration (0.1) are merely examples; the specific values ​​should be based on the actual calibration results. This process transforms the weights from unknowns to known, definite values.

[0027] The weight combination is written into the control unit firmware storage area as the preset weight. This weight remains fixed during the vehicle's subsequent daily driving and does not change dynamically with operating conditions. Once the weight is calibrated, it is written into the firmware and remains unchanged during driving. This fixed method avoids the risk of abnormal mileage display due to real-time fluctuations in weight during driving, ensuring the stability of system operation and the consistency of calculation results. During daily driving, the weight _i in the formula has been converted into a known constant stored in the firmware and no longer participates in the calculation. The system collects the standardized variable _i and the fixed weight _i in real time, substitutes them into the formula, and performs a weighted sum to calculate the initial mileage accumulation parameter P_init. The preset weight provides a benchmark for the generation of the mileage accumulation parameter and a reliable basic parameter for subsequent compliance verification, enabling the system to maintain a consistent measurement standard across different vehicles and ensuring the accuracy and legality of mileage measurement throughout its entire lifecycle.

[0028] Based on the above embodiments, in this embodiment, the multi-source operating data obtained in step 101 includes: vehicle speed data, wheel speed pulse data, GPS speed data, and acceleration data. This stage continues to run after the vehicle is started and driven, executing cyclically at a frequency of seconds or milliseconds. The core task is to calculate compliant mileage parameters in real time. The system collects four types of variables in real time through the vehicle network and sensor hardware, specifically including vehicle speed data read through the CAN bus, in km / h, reflecting the overall vehicle speed; wheel speed pulse data read through the wheel speed sensor, in pulses / second, reflecting the wheel rotation frequency; GPS speed data read through the positioning module, in km / h, providing satellite positioning speed reference; and acceleration data read through the accelerometer, in m / s², reflecting the vehicle's dynamic acceleration and deceleration state. These four types of data cover the key characteristics of vehicle kinematics and dynamics, providing a multi-dimensional input basis for subsequent parameter calculations and ensuring that the system can comprehensively perceive the actual operating conditions of the vehicle.

[0029] In step 102, the multi-source operating data is weighted based on preset weights to obtain the initial mileage accumulation parameters, specifically including steps 1021 to 1023: Step 1021: Obtain the preset minimum and maximum values ​​corresponding to each multi-source operating data; the system reads the factory preset boundary ranges of each variable, such as the vehicle speed preset range of 0 to 120 and the acceleration preset range of -5 to 5.

[0030] Step 1022: Perform normalization preprocessing on the multi-source operating data using the preset minimum and maximum values ​​to obtain standardized variables. The specific calculation follows the formula below: ; in, For standardized values, This is the raw value currently being collected. and These are the preset minimum and maximum values, respectively. This process eliminates differences between different physical units, prevents variations in numerical ranges from affecting weight allocation, and maps all variables to the same numerical range. An anomaly protection mechanism is included in this process; if the denominator... If the value is 0, then the standardized value will be forced to be set to 0.5, that is: To avoid calculation errors that could lead to system anomalies, ensure the robustness of the data preprocessing stage, prevent division by zero anomalies caused by sensor malfunctions or calibration errors, and ensure that the standardized results are always within the valid numerical range.

[0031] Furthermore, when the currently collected multi-source operating data is greater than the corresponding preset maximum value, the preset maximum value is updated to the value of the currently collected multi-source operating data; when the currently collected multi-source operating data is less than the corresponding preset minimum value, the preset minimum value is updated to the value of the currently collected multi-source operating data. When the system performs boundary detection, if the currently collected original value exceeds the preset range, the boundary is marked as needing to be updated. However, the new boundary value is only written to storage and takes effect after the next vehicle startup; the old boundary is still used in the current cycle. The updated preset minimum or maximum value is stored and applied during the initialization of the next vehicle driving cycle. This delayed-effect mechanism prevents sudden boundary changes caused by instantaneous abnormal values ​​during driving, thus avoiding drastic fluctuations in the standardized results. It ensures the stability of system operation and the consistency of calculation results, and avoids jumps in mileage display during driving. The boundary value update is stored in non-volatile memory to ensure it is not lost after power failure and is loaded into the running memory during each ignition initialization phase.

[0032] Step 1023: Calculate the weighted sum of the standardized variables using the preset weights to obtain the initial mileage accumulation parameters. The system calls the factory-set weight values ​​and performs the weighted summation operation. The specific formula is as follows: ; in, This is the initial mileage accumulation parameter. Let i be the i-th standardized variable. The corresponding preset weights are used. Output dimensionless initial mileage accumulation parameters. This parameter reflects the mileage deviation trend under the current operating conditions. The preset weights remain fixed throughout the vehicle's subsequent daily driving, without dynamically changing with operating conditions, and are directly used for weighted calculations during subsequent driving. This fixed approach avoids the risk of abnormal mileage display due to real-time fluctuations in weights during driving, providing reliable basic parameters for subsequent compliance verification. It enables the system to maintain consistent measurement standards across different vehicles, ensuring the scientific validity and verifiability of the mileage accumulation parameter calculation logic, and ultimately completing the real-time generation of initial parameters, providing input data for subsequent compliance verification steps.

[0033] Based on the above embodiments, in this embodiment, in step 103, the initial mileage accumulation parameters are subjected to compliance verification, and parameters exceeding the preset compliance range are corrected to the boundary values ​​of the preset compliance range to obtain the target mileage accumulation parameters. Specifically, correcting parameters exceeding the preset compliance range to the boundary values ​​of the preset compliance range includes: The initial mileage accumulation parameter is compared with a preset compliance range threshold. This preset compliance range threshold is set based on the industry standard that the mileage measurement error of commercial vehicles must not exceed ±4%, corresponding to a parameter range of [0.96, 1.04]. After obtaining the initial mileage accumulation parameter, the system immediately calls the lower limit value of 0.96 and the upper limit value of 1.04 stored in the control unit for boundary comparison to determine whether the current parameter meets the legal measurement requirements. This comparison process is executed synchronously in each data acquisition cycle to ensure that the initial parameter generated at any time has undergone legality screening, ensuring the legality of the benchmark for subsequent accumulation calculations, preventing parameters from momentarily exceeding the limit due to transient sensor interference, and providing the first line of defense for the compliance of mileage measurement.

[0034] If the initial mileage accumulation parameter is less than the lower limit of the preset compliance range threshold, the initial mileage accumulation parameter is corrected to the lower limit; if the initial mileage accumulation parameter is greater than the upper limit of the preset compliance range threshold, the initial mileage accumulation parameter is corrected to the upper limit; if the initial mileage accumulation parameter is within the preset compliance range threshold, the initial mileage accumulation parameter is used as the target mileage accumulation parameter.

[0035] The specific correction logic is executed according to the following rules: If Injunction ;like Injunction ;like ,but .in, This is the initial mileage accumulation parameter. The target mileage accumulation parameters are added. Through the piecewise function processing described above, regardless of fluctuations in the initial calculation results, the output parameters are always limited to the compliant range, eliminating the risk of parameters exceeding limits due to extreme operating conditions. This correction process does not change the weighted restructuring contract; it only applies boundary constraints to the final output results, ensuring the traceability and consistency of parameter corrections, so that the target mileage accumulation parameters can accurately reflect the optimal correction ratio within the compliant range.

[0036] The execution principle is to perform only direct truncation, without secondary back-calculation or recalculation, to ensure real-time performance. After completing threshold judgment and correction, the system directly outputs the corrected target mileage accumulation parameters to the accumulation module, without triggering weight recalculation or model iteration. This direct truncation mechanism avoids time delays caused by complex calculations, meeting the millisecond-level real-time control requirements during vehicle operation. Simultaneously, it prevents chain calculation reactions caused by parameter correction, ensuring the stability and determinism of the mileage accumulation process. This allows the target mileage accumulation parameters to be applied instantly to the current instrument mileage accumulation step, ensuring the continuity and compliance of mileage display and avoiding mileage jumps or discontinuous display due to parameter adjustments. This completes the first truncation process for mandatory compliance verification.

[0037] Based on the above embodiments, in this embodiment, the instrument mileage is accumulated according to the target mileage accumulation parameter, specifically including: The raw mileage increment data during vehicle operation is obtained, which is usually calculated based on the product of wheel speed pulse number and tire rolling circumference.

[0038] Based on the original mileage increment data and the target mileage accumulation parameter, the corrected mileage increment data is obtained, specifically by multiplying the original mileage increment by the target mileage accumulation parameter. The corrected mileage increment data is then accumulated to obtain the instrument cluster's cumulative mileage, which is displayed in real-time on the vehicle's dashboard. Simultaneously, the system writes one record per second to the historical database, including a timestamp, four types of original variables, fused mileage, current parameter value, and real-time error, forming a data black box to provide a data foundation for subsequent optimization. This process continues after the vehicle is started and driven, executing cyclically at a frequency of seconds or milliseconds to ensure the real-time and continuous display of mileage. During the parameter application phase, the currently effective target mileage accumulation parameter is used directly to avoid calculation delays affecting the display effect.

[0039] Furthermore, the method also includes acquiring historical error data generated during the vehicle's historical driving process, a step completed in real time during vehicle operation. Specifically, a dual-source fusion algorithm is used to generate reference mileage data, and the difference between the reference mileage data and the instrument's accumulated mileage is used as historical error data. The dual-source fusion algorithm includes: when the positioning signal quality meets preset conditions, the fusion of positioning mileage data and wheel speed mileage data is used as reference mileage data; when the positioning signal quality does not meet preset conditions, wheel speed mileage data is used as reference mileage data. The specific fusion strategy involves real-time reading of the number of GPS satellites. If the number of satellites is ≥4, the signal is considered good, and the fusion mileage calculation formula is:

[0040] If the number of satellites is less than 4, the signal is considered unreliable, and the fusion mileage is equal to 100% of the wheel speed mileage increment to achieve primary / backup switching.

[0041] The dual-source fusion algorithm also includes a conflict handling step: when the difference between the positioning mileage data and the wheel speed mileage data exceeds a preset ratio and persists for a preset duration, the wheel speed mileage data is forcibly used as the reference mileage data. Specifically, the arbitration logic involves real-time comparison of the GPS mileage increment and the wheel speed mileage increment. If the difference is greater than 5% and lasts for more than 5 seconds, the system determines that the GPS is abnormal, forcibly uses the wheel speed mileage as the reference, and triggers an abnormal event recording. This process ensures the reliability of the reference true value; the generated error data is stored in the historical database in real-time during the driving process, distinct from subsequent coefficient calculation steps.

[0042] The correction coefficient is calculated based on historical error data. The target mileage accumulation parameter is multiplied by the correction coefficient to obtain the updated mileage accumulation parameter. This step is triggered when the vehicle is stationary and the network is idle. Specifically, the trigger condition is that the system clock reaches 2:00 AM every day and the vehicle is stationary. The system performs a cold start check. If the cumulative mileage is <100km or the number of valid GPS samples (records with ≥4 satellites) in the historical database is <1000, it is considered a cold start, optimization is skipped, and the correction coefficient δ remains at 1.00. If the condition is met, the historical data of the most recent 7 days is extracted, and only the data of the time period with "good GPS signal quality" (≥4 satellites and no conflict arbitration records) is retained. The error sample of each record is calculated (error = GPS fused mileage - instrument accumulation mileage). A univariate linear regression model is used for machine learning fitting. The historical error sample is input, and the correction coefficient δ is output. The coefficient is limited to the calculated δ falling within the range of [0.98, 1.02]. If it exceeds this range, the boundary value is taken.

[0043] The updated mileage accumulation parameters undergo a second compliance check and correction, adjusting parameters exceeding the preset compliance range to their boundary values. The specific synthesis formula is as follows: ; The secondary compliance verification requires that the synthesized parameters be checked again to see if they fall within the range of [0.96, 1.04]. If they exceed this range, they are forcibly truncated to the boundary. The corrected and updated mileage accumulation parameters are used to accumulate the mileage on the instrument panel. The updated parameters are written to the running variable area for use the next day. This offline optimization stage is separated from the dual-source fusion stage during driving in terms of time. The driving stage is responsible for data generation and storage, while the stationary stage is responsible for data reading and coefficient calculation. The two work together to achieve adaptive iterative upgrades. At the same time, the secondary compliance verification ensures that the final parameters always meet industry metrology standards, guaranteeing the long-term accuracy and legality of mileage accumulation.

[0044] This application provides two embodiments for illustrative purposes: Example 1 (Standard Operating Condition): A commercial vehicle with brand new standard tires is driven at a constant speed on a smooth paved road without load, with normal tire pressure and temperature. The system collects four types of variable data in real time at a frequency of seconds via CAN bus and sensors: vehicle speed, wheel speed pulse, GPS speed, and acceleration. It reads the factory-preset minimum and maximum values ​​for boundary judgment. If the current data exceeds the boundary, it is marked as updated and will take effect on the next startup. First, the baseline parameter is set to 100%. The collected variables are processed by Min-Max standardization and the fixed weights calibrated in the firmware are called. The initial parameter is calculated to be close to 100% through a weighted summation algorithm. After compliance verification, it is checked whether the parameter falls within the range of [0.96, 1.04]. If it exceeds the range, it is forcibly truncated to the boundary. After passing the verification, 100% is finally determined as the target mileage accumulation parameter. The instrument corrects and accumulates the original mileage increment based on this parameter. At the same time, the system writes the current running data and parameter values ​​into the historical database to provide standard samples for subsequent offline optimization. This process ensures that new vehicles meet the metering accuracy requirements and operate stably as soon as they roll off the production line. The consistency of standardized boundaries guarantees the repeatability of parameter calculations and avoids mileage display jumps caused by sudden boundary changes during driving, thus completing the compliant mileage metering in the initial stage.

[0045] Example 2 (Complex Working Condition): The same vehicle, with worn tires and slightly low tire pressure, is driving on an unpaved road with slight slippage under medium load. At this time, a deviation occurs between the wheel speed pulse and GPS speed. The system uses a dual-source fusion algorithm to generate reference mileage data to monitor the error, reading the number of satellites in real time. When the number of satellites is ≥4, the fused mileage is used. When the difference between the two is greater than 5% and lasts for 5 seconds, the wheel speed is forcibly used as the reference, and the anomaly is recorded. Again, using 100% as the baseline parameter, tire-related variables have the highest weight. The calculated initial parameter is below 100% but above 90%. If the initial parameter is below 0.96, it is forcibly corrected to 0.96. After verification, it is determined as the final parameter. The system collects variables in real time and updates parameters periodically. If an anomaly occurs, it will promptly trigger an alert and upload data, recording the timestamp and four types of original variables to the black box. In addition, when the vehicle is stationary, if the system clock reaches 2:00 AM every day and the mileage is ≥100km, a correction coefficient is calculated based on historical error data to perform secondary compliance verification and correction on the parameters. Parameters that are out of range are corrected to boundary values, realizing adaptive iterative upgrade of parameters. This ensures the long-term accuracy and compliance of mileage measurement under tire wear or changes in operating conditions, and completes the full lifecycle management from real-time correction to offline optimization.

[0046] In summary, this application clearly defines the parameter setting logic and considers scenario variables, collecting four types of variables: vehicle speed, wheel speed pulse, GPS speed, and acceleration. Weights are calibrated using the least squares method, and a weighted summation model is established. The system establishes a complete closed loop encompassing variable collection, parameter setting, application, calibration, and optimization. Reference truth values ​​are generated through a dual-source fusion algorithm, and historical error data is used to offline optimize correction coefficients, enabling dynamic adjustment and scientific parameter setting. This ensures that the parameter setting process is based on evidence and possesses continuous evolution capabilities.

[0047] This method ensures that parameters are within 90%-110% of the regulatory range, with a specific compliance verification interval that can be set from 0.96 to 1.04, improving the accuracy of mileage accumulation. The system adapts to different tire sizes, road conditions, and operating conditions, meeting both patent innovation and regulatory compliance requirements. By collecting variables in real time and updating parameters periodically, it addresses existing technological shortcomings, ensuring the long-term accuracy and legality of mileage measurement. It achieves adaptive setting and closed-loop optimization of mileage accumulation parameters, meeting the actual needs of commercial vehicle mileage measurement.

[0048] Secondly, this application provides a vehicle mileage accumulation system, comprising: a data acquisition module, a processing module, and an accumulation module. The data acquisition module is used to: collect multi-source operating data during vehicle operation; the processing module is used to: perform weighted calculation on the multi-source operating data based on preset weights to obtain initial mileage accumulation parameters, and perform compliance verification on the initial mileage accumulation parameters, correcting parameters exceeding the preset compliance range to the boundary values ​​of the preset compliance range to obtain target mileage accumulation parameters; the accumulation module is used to: accumulate the instrument mileage according to the target mileage accumulation parameters.

[0049] The functions of each module in the above-mentioned vehicle mileage accumulation system correspond to the steps in the above-mentioned vehicle mileage accumulation method embodiment, and their functions and implementation processes will not be described in detail here.

[0050] Thirdly, embodiments of this application provide a vehicle mileage accumulation device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0051] In this embodiment, the vehicle mileage accumulation device may include a processor, a memory, a communication interface, and a communication bus.

[0052] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0053] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the vehicle odometer, as well as interfaces used for interconnecting the vehicle odometer with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0054] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0055] The processor can be a general-purpose processor, which can call the vehicle mileage accumulation program stored in memory and execute the vehicle mileage accumulation method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the vehicle mileage accumulation program is called can be referred to in the various embodiments of the vehicle mileage accumulation method of this application, and will not be repeated here.

[0056] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0057] The present application has a computer-readable storage medium storing a vehicle mileage accumulation program, wherein when the vehicle mileage accumulation program is executed by a processor, it implements the steps of the vehicle mileage accumulation method as described above.

[0058] The method implemented when the vehicle mileage accumulation procedure is executed can be referred to in various embodiments of the vehicle mileage accumulation method of this application, and will not be repeated here.

[0059] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0060] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0061] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0062] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0063] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0064] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0065] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for accumulating vehicle mileage, characterized in that, It includes: Collect multi-source operational data during vehicle operation; The initial mileage accumulation parameters are obtained by weighting the multi-source operation data based on preset weights. The initial mileage accumulation parameters are verified for compliance. Parameters that exceed the preset compliance range are corrected to the boundary values ​​of the preset compliance range to obtain the target mileage accumulation parameters. The instrument mileage is accumulated based on the target mileage accumulation parameters.

2. The vehicle mileage accumulation method as described in claim 1, characterized in that, The method further includes: Acquire historical error data generated during the vehicle's historical driving process; The correction coefficient is calculated based on historical error data, and the target mileage accumulation parameter is multiplied by the correction coefficient to obtain the updated mileage accumulation parameter. The updated mileage accumulation parameters are subjected to a second compliance check and correction, and parameters that exceed the preset compliance range are corrected to the boundary values ​​of the preset compliance range. Use the revised and updated mileage accumulation parameters to accumulate the instrument mileage.

3. The vehicle mileage accumulation method as described in claim 2, characterized in that, Acquire historical error data generated during the vehicle's historical driving process, specifically including: During vehicle operation, a dual-source fusion algorithm is used to generate reference mileage data; The difference between the reference mileage data and the instrument's cumulative mileage is used as historical error data; The dual-source fusion algorithm includes: when the positioning signal quality meets the preset conditions, using the fusion of positioning mileage data and wheel speed mileage data as reference mileage data; when the positioning signal quality does not meet the preset conditions, using wheel speed mileage data as reference mileage data. The dual-source fusion algorithm also includes a conflict handling step: when the difference between the positioning mileage data and the wheel speed mileage data is greater than a preset ratio and continues for a preset duration, the wheel speed mileage data is forcibly used as the reference mileage data.

4. The vehicle mileage accumulation method as described in claim 1, characterized in that, Before performing weighted calculations on multi-source operational data based on preset weights, the method further includes a step of confirming the preset weights: Collect test data and corresponding mileage true values ​​of the vehicle when it is driving under standard test conditions; Based on the test data and true mileage values, the weight combination that minimizes the mileage error is solved using the least squares method. The weight combination is used as the preset weight.

5. The vehicle mileage accumulation method as described in claim 1, characterized in that: The multi-source operational data includes: vehicle speed data, wheel speed pulse data, GPS speed data, and acceleration data.

6. The vehicle mileage accumulation method as described in claim 5, characterized in that, The initial mileage accumulation parameters are obtained by weighting the multi-source operational data based on preset weights, specifically including: Obtain the preset minimum and maximum values ​​corresponding to each multi-source runtime data; The multi-source operational data is preprocessed using the preset minimum and maximum values ​​to obtain standardized variables; The standardized variables are weighted and summed using the preset weights to obtain the initial mileage accumulation parameters.

7. The vehicle mileage accumulation method as described in claim 6, characterized in that, The method further includes: When the currently collected multi-source running data is greater than the corresponding preset maximum value, the preset maximum value is updated to the value of the currently collected multi-source running data; When the currently collected multi-source running data is less than the corresponding preset minimum value, the preset minimum value is updated to the value of the currently collected multi-source running data; Store the updated preset minimum or maximum value and apply it when the vehicle is initialized for the next driving cycle.

8. The vehicle mileage accumulation method as described in claim 1, characterized in that, Correcting parameters that exceed the preset compliance range to the boundary values ​​of the preset compliance range specifically includes: The initial mileage accumulation parameter is compared with a preset compliance range threshold. If the initial mileage accumulation parameter is less than the lower limit of the preset compliance range threshold, then the initial mileage accumulation parameter is corrected to the lower limit. If the initial mileage accumulation parameter is greater than the upper limit of the preset compliance range threshold, then the initial mileage accumulation parameter is corrected to the upper limit value; If the initial mileage accumulation parameter is within the preset compliance range threshold, then the initial mileage accumulation parameter is used as the target mileage accumulation parameter.

9. The vehicle mileage accumulation method as described in claim 1, characterized in that, Accumulate the instrument mileage based on the target mileage accumulation parameters, specifically including: Obtain raw mileage increment data during vehicle operation; Based on the original mileage increment data and the target mileage accumulation parameter, the corrected mileage increment data is obtained; The instrument's cumulative mileage is obtained by accumulating the corrected mileage increment data.

10. A vehicle mileage accumulation system, characterized in that, It includes: The data acquisition module is used to collect multi-source operational data during vehicle operation. The processing module is used to: perform weighted calculation on multi-source running data based on preset weights to obtain initial mileage accumulation parameters, and perform compliance verification on the initial mileage accumulation parameters, correcting parameters that exceed the preset compliance range to the boundary values ​​of the preset compliance range, and obtaining target mileage accumulation parameters. The accumulation module is used to: accumulate the instrument mileage according to the target mileage accumulation parameters.

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