Calibration data processing method, device and equipment of vehicle and medium

Abstract

The application relates to a vehicle calibration data processing method, device, equipment and medium. The vehicle calibration data processing method comprises the following steps: acquiring vehicle state data, wherein the vehicle state data comprises vehicle software and hardware information and running state data; determining a to-be-calibrated object of the vehicle based on the software and hardware information and the running state data; acquiring initial calibration data matched with the to-be-calibrated object; performing parameter dynamic adjustment based on the initial calibration data and combining a prediction selection operation to obtain calibration prediction data; and performing vehicle data calibration based on the calibration prediction data to obtain a data calibration result corresponding to the to-be-calibrated object, so that the workload of vehicle calibration data can be effectively reduced, the actual needs of the vehicle can be met, the need for multiple data adjustments and optimizations in the use process of the vehicle can be reduced, and the workload and cost of vehicle maintenance can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile electronics, and in particular to a calibration data processing method and device for a vehicle, an apparatus and a medium. BACKGROUND

[0002] In the field of automobile electronics, calibration of data is usually completed during vehicle production, which mainly involves setting and adjusting parameters of various systems and components of the vehicle through dedicated equipment and tools to ensure that the performance reaches the best state. However, since the hardware configuration and use environment of each vehicle are not the same, the parameters set in advance during vehicle production may not meet the actual needs of each vehicle, which requires multiple data adjustments and optimizations during the use of the vehicle, increasing the maintenance workload and cost. SUMMARY

[0003] Therefore, the present application provides a calibration data processing method and device for a vehicle, an apparatus and a medium to reduce the maintenance cost and workload during the use of the vehicle and solve the problem of high maintenance cost caused by multiple data adjustments and optimizations during the use of the vehicle in the prior art.

[0004] In a first aspect, the present application provides a calibration data processing method for a vehicle, comprising:

[0005] obtaining vehicle state data, wherein the vehicle state data includes hardware and software information and running state data of the vehicle;

[0006] determining a to-be-calibrated object of the vehicle based on the hardware and software information and the running state data;

[0007] obtaining initial calibration data matched with the to-be-calibrated object;

[0008] performing dynamic adjustment of parameters based on the initial calibration data in combination with a prediction selection operation to obtain calibration prediction data;

[0009] performing vehicle data calibration based on the calibration prediction data to obtain a data calibration result corresponding to the to-be-calibrated object.

[0010] Optionally, the obtaining of the initial calibration data matched with the to-be-calibrated object comprises:

[0011] obtaining user login information;

[0012] determining a user security level according to the user login information;

[0013] if the permission corresponding to the user security level meets a preset permission condition, extracting module calibration data with the same version as the to-be-calibrated object from a preset calibration database.

[0014] The module calibration data is determined as the initial calibration data.

[0015] Optionally, the step of dynamically adjusting parameters based on the initial calibration data and combining it with a prediction selection operation to obtain calibration prediction data includes:

[0016] The target prediction data is determined by using the prediction selection operation described above;

[0017] Check if the vehicle's calibration database contains preset calibration function information corresponding to the target prediction data;

[0018] If the calibration database contains the preset calibration function information, then the calibration prediction data is generated based on the calibration function information and the initial calibration data.

[0019] If the calibration database does not contain the preset calibration function information, then a function fitting operation is performed based on the fitting data selection operation to obtain the target fitting function, and the calibration prediction data is generated using the target fitting function.

[0020] Optionally, the step of performing function fitting based on the selection operation of the fitted data to obtain the target fitted function includes:

[0021] Based on the aforementioned data selection operation, the data to be fitted is determined;

[0022] Data is collected for the data to be fitted, and the collected data is obtained;

[0023] Based on the collected data, data preprocessing is performed to obtain data characteristic distribution information;

[0024] Based on the data characteristic distribution information, and combined with the initial fitting parameters set in the calibration database, a function is fitted to obtain the target fitting function.

[0025] Optionally, the target fitting function is a fitting function with a preset degree of fit. The step of performing function fitting based on the data characteristic distribution information and the initial fitting parameters set in the calibration database to obtain the target fitting function includes:

[0026] Based on the data characteristic distribution information, determine the type of fitting function;

[0027] The initial fitting parameters are extracted from the calibration database according to the fitting function type;

[0028] According to the type of fitting function, the initial fitting parameters are used to start fitting until the fitting degree of the fitted function reaches the specified fitting degree.

[0029] Optionally, the vehicle status data also includes the calibration data currently used by the vehicle, and the acquisition of vehicle status data includes:

[0030] The unified diagnostic service obtains the vehicle's hardware and software information and operating status data, including the vehicle's software version information and hardware version information.

[0031] Based on the software version information and hardware version information, obtain the calibration data currently used by the vehicle.

[0032] Optionally, based on the hardware and software information and the operational status data, the vehicle to be calibrated is determined, including:

[0033] Based on the hardware and software information and the operating status data, calibration tests are performed using the calibration data to obtain calibration test results.

[0034] The calibration test results are used to determine whether each functional module in the vehicle needs to be recalibrated.

[0035] The functional modules that need to be recalibrated are identified as the objects to be calibrated.

[0036] Secondly, this application provides a vehicle calibration data processing device, comprising:

[0037] The first acquisition module is used to acquire vehicle status data, which includes the vehicle's hardware and software information and operating status data.

[0038] The calibration determination module is used to determine the calibration target of the vehicle based on the software and hardware information and the operating status data.

[0039] The second acquisition module is used to acquire initial calibration data that matches the object to be calibrated;

[0040] The dynamic adjustment module is used to dynamically adjust the parameters based on the initial calibration data and in conjunction with the prediction selection operation to obtain calibration prediction data.

[0041] The data calibration object module is used to perform vehicle data calibration based on the calibration prediction data to obtain the data calibration result corresponding to the object to be calibrated.

[0042] Thirdly, this application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0043] Memory, used to store computer programs;

[0044] A processor, when executing a program stored in memory, implements the steps of the vehicle calibration data processing method as described in any of the preceding claims of this application.

[0045] Fourthly, this application provides a computer storage medium storing computer-executable instructions for performing the steps of the vehicle calibration data processing method as described in any of the preceding claims of this application.

[0046] This application embodiment acquires vehicle status data to determine the vehicle to be calibrated based on the vehicle's hardware and software information and operating status data. Then, it acquires initial calibration data that matches the target object. Based on the initial calibration data, it dynamically adjusts parameters using a predictive selection operation to obtain calibration prediction data. Thus, vehicle data calibration can be performed based on the calibration prediction data to obtain the data calibration result corresponding to the target object. This effectively reduces the workload of vehicle calibration data, and by dynamically adjusting parameters, it reduces the need for multiple data adjustments and optimizations during vehicle use, thereby reducing the workload and cost of vehicle maintenance. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0050] Figure 1 A flowchart illustrating the steps of a vehicle calibration data processing method provided in this application embodiment;

[0051] Figure 2 A schematic diagram illustrating a calibration process for a car before it rolls off the production line, as provided in this application.

[0052] Figure 3 A structural block diagram of a vehicle calibration data processing device provided as an example of this application;

[0053] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] The following disclosure provides numerous different embodiments or examples for implementing various structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of the invention. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0056] In the field of automotive electronics, data calibration and diagnostic services are two crucial technical aspects. Data calibration addresses the inconsistency in vehicle status caused by hardware limitations among vehicles of the same model by performing specialized processing and calibration of key data for different vehicles. Diagnostic services, on the other hand, utilize specific interfaces and protocols to test and diagnose faults in various systems and components of the vehicle, ensuring its proper functioning. However, existing data calibration processes typically require disassembling the casings of vehicle components, which not only increases the complexity and difficulty of the operation but may also affect the performance and stability of the components. Furthermore, since the hardware configurations and operating environments of each vehicle are different, pre-set parameters may not meet the actual needs of each vehicle. This necessitates multiple data adjustments and optimizations during vehicle use, increasing maintenance workload and costs.

[0057] Based on the above, this application provides a vehicle calibration data processing method, apparatus, device, and medium. By acquiring vehicle status data, the method determines the vehicle to be calibrated based on the vehicle's hardware and software information and operational status data. Subsequently, it acquires initial calibration data matching the target vehicle, and dynamically adjusts parameters based on this initial calibration data using a predictive selection operation to obtain calibration prediction data. This allows for vehicle data calibration based on the calibration prediction data, yielding the calibration result corresponding to the target vehicle. This effectively reduces the workload of vehicle calibration data processing, and by dynamically adjusting parameters, it reduces the need for multiple data adjustments and optimizations during vehicle use, thus lowering the workload and cost of vehicle maintenance. Vehicle status data refers to information related to the vehicle's status, such as vehicle hardware and software information and current operational status data; this application does not limit this.

[0058] It should be noted that the vehicle's software and hardware information refers to information related to the vehicle's software and hardware. Considering that the hardware configuration and usage environment of each vehicle are different, the obtained software and hardware information may include the vehicle's software version information, hardware version information, software and hardware status information, etc. This application embodiment does not limit this. The vehicle's current operating status data may refer to information related to the current operating status of the vehicle, which may include, but is not limited to, vehicle speed, engine speed, coolant temperature, battery voltage, etc.

[0059] The embodiments of this application are described below by way of example. However, it should be noted that the embodiments of this application may have the features described below, but the following description does not constitute a limitation on the protection scope of the embodiments of this application.

[0060] Figure 1 This is a flowchart illustrating the steps of a vehicle calibration data processing method provided in an embodiment of this application. Figure 1 As shown, the vehicle calibration data processing method provided in this application embodiment may include the following steps:

[0061] Step 110: Obtain vehicle status data, which includes the vehicle's hardware and software information and operating status data.

[0062] Specifically, during the data calibration process, the Unified Diagnostic Services (UDS) protocol can be used to obtain vehicle hardware and software information. This allows for vehicle data calibration without disassembling the component housings, facilitating the management of vehicle calibration data. For example, during vehicle calibration, the UDS protocol can be used to obtain the software and hardware version information of the vehicle's internal Electronic Control Unit (ECU). Based on the ECU's software and hardware version information, the current hardware and software status of the vehicle can be determined. Furthermore, UDS can obtain other vehicle status information, such as the vehicle's current operating status data, to determine the vehicle's current operating condition. As a universal and unified diagnostic service protocol, UDS requires no modification, reducing calibration workload and simplifying the calibration process.

[0063] Step 120: Based on the software and hardware information and the operating status data, determine the vehicle to be calibrated;

[0064] Specifically, after obtaining the vehicle's hardware and software information and operating status data, this application embodiment can determine whether each functional module in the vehicle needs to be calibrated based on the hardware and software information and operating status data. Then, the functional modules in the vehicle that need to be calibrated can be identified as the calibration objects of the vehicle, so that the corresponding matching module data can be obtained for the calibration objects as the initial calibration data.

[0065] Step 130: Obtain initial calibration data that matches the object to be calibrated;

[0066] In this step, software and hardware information can be used to match calibration data in the calibration database for the object to be calibrated. Module calibration data with the same software and hardware version as the object to be calibrated can be extracted from the calibration database as initial calibration data to match the object. This allows parameters to be dynamically adjusted based on the initial calibration data to meet the actual needs of the vehicle.

[0067] Step 140: Based on the initial calibration data, the parameters are dynamically adjusted in conjunction with the prediction selection operation to obtain calibration prediction data;

[0068] The prediction selection operation can refer to the operation of selecting data to be predicted, such as a prediction selection operation submitted by a user for an object to be calibrated. Specifically, after receiving the prediction selection operation, this embodiment of the application can dynamically adjust the parameters based on the initial calibration data and the data to be predicted selected in the pre-selection operation, so that the adjusted parameters can meet the actual needs of each vehicle, and perform data prediction based on the adjusted parameters to automatically generate prediction data as calibration prediction data.

[0069] Step 150: Based on the calibration prediction data, perform vehicle data calibration to obtain the data calibration result corresponding to the object to be calibrated.

[0070] After this step, the calibration prediction data can be used to calibrate the vehicle to be calibrated in order to meet the actual needs of the vehicle and obtain the data calibration results. This reduces the need for multiple data adjustments and optimizations, thereby solving the problem caused by multiple data adjustments and optimizations during vehicle use in existing related technologies. It can effectively reduce the workload and cost of maintenance.

[0071] As can be seen, the embodiments of this application obtain vehicle status data to determine the vehicle to be calibrated based on the vehicle's hardware and software information and operating status data. Then, initial calibration data matching the target object is obtained. Based on the initial calibration data, parameters are dynamically adjusted by combining prediction selection operations to obtain calibration prediction data. Subsequently, vehicle data calibration is performed based on the calibration prediction data to obtain the data calibration result corresponding to the target object, thereby meeting the actual needs of the vehicle. This can effectively reduce the workload of vehicle calibration data and reduce the need for multiple data adjustments and optimizations during vehicle use. It solves the problem caused by multiple data adjustments and optimizations during vehicle use in existing related technologies, and reduces maintenance workload and costs.

[0072] For example, in the actual data calibration process, the On-Board Diagnostic (OBD) interface can communicate with the vehicle's ECU using the UDS protocol to obtain vehicle status data. For instance, using the UDS protocol, the OBD interface can communicate with the vehicle's electronic control unit to obtain the vehicle's operating status data. This eliminates the need to disassemble the device housing for calibration, thus reducing the complexity and difficulty of the operation. It also solves the problem in existing related technologies where disassembling the device housing may affect the device's performance and stability, ensuring the performance stability of vehicle devices and thereby reducing maintenance workload and costs.

[0073] In some optional embodiments of this application, the vehicle status data may include not only the vehicle's hardware and software information and operational status data, but also other types of data, such as the calibration data currently being used by the vehicle. This application does not impose specific limitations on this. The calibration data currently being used by the vehicle can be used for calibration testing to determine whether the vehicle's calibration target needs to be recalibrated.

[0074] Optionally, when the vehicle status data includes the calibration data currently used by the vehicle, software and hardware information, and operational status data, the vehicle status data acquisition embodiments of this application may specifically include: acquiring the vehicle's software and hardware information and operational status data through a unified diagnostic service, wherein the software and hardware information includes the vehicle's software version information and hardware version information; and acquiring the calibration data currently used by the vehicle based on the software version information and hardware version information. Here, the vehicle's software version information refers to information related to the vehicle's software version, such as the software version used by the vehicle's internal ECU, and can be used to determine the vehicle's current software status; the hardware version information refers to information related to the vehicle's hardware version, such as the hardware version used by the vehicle's internal ECU, and can be used to determine the vehicle's current hardware status.

[0075] For example, taking vehicle calibration as an example, the calibration process before a vehicle rolls off the production line is as follows: Figure 2 As shown, during the initial calibration, the current hardware and software information of the vehicle's modules can be obtained as the vehicle's hardware and software information. For example, the software and hardware versions used by the vehicle's internal ECU can be obtained through the UDS protocol as the vehicle's hardware and software information. Based on this information, the current hardware and software status of the vehicle can be determined, thus achieving the purpose of obtaining the current hardware and software status of the vehicle and facilitating the management of vehicle calibration data. Subsequently, the current operating status data of the vehicle and the calibration data used by the module that needs to be calibrated can be obtained. This allows the module that needs to be calibrated to be identified as the calibration target, and the calibration data used by the module that needs to be calibrated to be used as the calibration data currently used by the vehicle, so that this calibration data can be used for subsequent calibration tests.

[0076] In an optional embodiment of this application, determining the vehicle to be calibrated based on the hardware and software information and the operating status data specifically includes: performing calibration tests using the calibration data based on the hardware and software information and the operating status data to obtain calibration test results; using the calibration test results to determine whether each functional module in the vehicle needs to be recalibrated; and identifying the functional modules that need to be recalibrated as the objects to be calibrated. Specifically, after obtaining the vehicle's hardware and software information, operating status data, and the calibration data currently used by the vehicle, this embodiment of the application can perform calibration tests based on the vehicle's hardware and software information and operating status data, combined with the calibration data currently used by the vehicle, to determine whether each functional module in the vehicle needs to be calibrated. Thus, when a functional module in the vehicle is determined to need calibration, it can be identified as an object to be calibrated, and calibration data can be matched from the calibration database to use the calibration data recommended by the calibration database for calibration. This can greatly reduce the workload of calibration and allow for comparison of calibration data with hardware of the same batch, completing hardware troubleshooting. After completing the data calibration, it can be confirmed whether the data calibration is correct. If the data calibration is confirmed to be correct, the current calibration data can be uploaded to a private calibration database preset for the vehicle. This allows the current calibration data to be stored in the calibration database, that is, the calibration data is stored in the vehicle's calibration database, thus completing the data calibration and the storage of the calibration data.

[0077] For example, based on the above example, after obtaining the hardware and software information of the ECU of the vehicle calibration module and the current operating status data of the vehicle, it can be determined whether the vehicle to be calibrated needs to be recalibrated. If recalibration is required, the obtained hardware and software information and operating status data of the ECU of the vehicle calibration module can be used to match the predicted calibration values ​​generated in the calibration database. The predicted calibration values ​​can then be used for vehicle calibration. For example, the recommended predicted calibration values ​​can be matched with the software and hardware versions of the corresponding calibration module in the private calibration database, thereby using the predicted calibration values ​​recommended by the calibration database for calibration, greatly reducing the workload of calibration. If recalibration is not required, after the calibration personnel confirm that the calibration is correct, the current calibration values ​​can be saved to the vehicle, and the currently used calibration values ​​can be uploaded to the private calibration database, completing the update of the corresponding calibration module's software and hardware version data.

[0078] Since the vehicle may require calibration of other modules besides the MCU, such as the module that uses non-volatile flash memory (Nor Flash) to store data, this embodiment of the application can first obtain the current calibration value of the Flash memory inside the module and the software and hardware version information of the module through the UDS service before calibrating the vehicle data. Then, it can obtain the recommended calibration value of the same module with the same software and hardware version in the cloud. By comparing the two values, it can be determined whether the module needs to be calibrated, thereby identifying the vehicle to be calibrated. Furthermore, if the recommended value of the calibration module exists in the cloud, the recommended value can be used directly for calibration, which can greatly reduce calibration time and workload.

[0079] Optionally, in this embodiment, after calibration is completed by the calibration personnel, the data of the current calibration module can be synchronously stored in both the vehicle and a private calibration database. For example, calibration data stored inside the vehicle will be stored in a non-volatile storage device within the vehicle for use by the corresponding module. This data will not be lost even if the vehicle loses power. Furthermore, calibration data stored in the private database can be associated and stored in various ways, such as the corresponding vehicle module's Vehicle Identification Number (VIN), SN, software version, and hardware version. It will also store the vehicle status using the current calibration values. For instance, when uploading calibration data to the calibration database, encryption algorithms can be used to encrypt the data to protect its security. Vehicle calibration data can be stored according to the data storage format shown in Table 1. It should be noted that the encryption algorithms described in this embodiment may include, but are not limited to, SHA-256, ECDSA, etc., to protect the security of the calibration data.

[0080] NO. VIN SN HW_V SW_V DATA1 DATA2 … 1 2 …

[0081] In summary, after completing the online calibration of vehicle data, this embodiment of the application can encrypt and upload the calibration data to the calibration database, thereby updating the data in the calibration database and synchronizing the data to the storage of the calibration module. This completes the storage of the calibration data, allowing for data processing and reuse. For example, by matching software and hardware versions, during vehicle problem troubleshooting, big data processing can be performed based on the calibration data in the calibration database. By matching the software and hardware version numbers of corresponding modules and comparing the normal calibration data values ​​of vehicles with the same version, problem localization and analysis can be performed. Specifically, the strong correlation between software and hardware versions is determined by checking whether the software and hardware versions of ECUs with strong correlations within various functional modules of the vehicle are consistent. For example, by analyzing the versions of the strut motor module and the anti-pinch control module in the tailgate control function, a strong correlation between them can be determined. Furthermore, when replacing components, the optimal initial values ​​can be found by matching the calibration values ​​of modules with the same software and hardware version before calibrating the parameters, thus accelerating the efficiency of calibration data.

[0082] In some optional embodiments of this application, obtaining initial calibration data matching the object to be calibrated may specifically include the following sub-steps:

[0083] Sub-step S21: Obtain user login information;

[0084] Sub-step S22: Determine the user's security level based on the user login information;

[0085] Sub-step S23: If the permissions corresponding to the user security level meet the preset permission conditions, then extract module calibration data with the same version as the object to be calibrated from the preset calibration database.

[0086] Sub-step S24: The module calibration data is determined as the initial calibration data.

[0087] Specifically, in this embodiment, after obtaining user login information, the security level of the currently logged-in user can be determined based on the user identifier carried in the user login information. This security level is then used to determine the user's access permissions to the calibration database. The permissions corresponding to the user's security level can be determined by judging whether the user's security level reaches a preset level that allows access to the calibration database. If the user's security level reaches the preset level, it is determined that the user has access to the calibration database, meaning the permissions corresponding to the user's security level meet the preset permission conditions. Based on these permissions, module calibration data with the same version as the object to be calibrated is extracted from the preset calibration database as initial calibration data. Conversely, if the user's security level does not reach the preset level, it is determined that the user does not have access to the calibration database, meaning the permissions corresponding to the user's security level do not meet the preset permission conditions. Therefore, the data acquisition request from the calibration personnel is rejected based on the user's security level, ensuring that unauthorized users can freely access the data stored in the calibration database and guaranteeing data security.

[0088] For example, before calibration personnel log in to use the calibration database, it can be determined whether the current calibration personnel have permission to access the calibration database. If the current calibration personnel do not have permission to access the calibration database, their data acquisition request can be rejected, preventing them from accessing the securely encrypted calibration database. In other words, low-security calibration personnel cannot access securely encrypted calibration data, ensuring data security. If the current calibration personnel have permission to access the calibration database, the corresponding version of module calibration data can be obtained as initial calibration data and provided to the calibration personnel. This allows the calibration personnel to perform data calibration based on the initial calibration data, thereby quickly completing the vehicle data calibration and reducing the workload of data calibration.

[0089] In some optional embodiments of this application, calibration prediction data is obtained by dynamically adjusting parameters based on the initial calibration data and combining a prediction selection operation. Specifically, this may include: determining target prediction data using the prediction selection operation; detecting whether there is preset calibration function information corresponding to the target prediction data in the vehicle's calibration database; if the preset calibration function information exists in the calibration database, generating the calibration prediction data based on the initial calibration data using the calibration function information; if the preset calibration function information does not exist in the calibration database, performing function fitting based on the fitting data selection operation to obtain a target fitting function, and generating the calibration prediction data using the target fitting function. The target prediction data may be the prediction data selected by the user to be generated, such as the prediction data selected by the calibration personnel; the preset calibration function information may be a preset calibration function, specifically used to generate prediction values ​​using calibration data. The fitting data selection operation may refer to an operation for selecting the data to be fitted, such as the operation for the calibration personnel to select the data to be fitted.

[0090] Specifically, upon receiving a prediction selection operation, this application can use this operation to determine the prediction data selected by the user to be generated. This selected prediction data can then be designated as the target prediction data. Furthermore, for this target prediction data, a calibration database can be used to determine whether a preset calibration function exists. If a preset calibration function exists in the calibration database, it can be confirmed that the database contains information corresponding to the preset calibration function for the target prediction data. This allows for the subsequent generation of calibrated prediction data based on the calibration function information and the preset calibration function, using initial calibration data. Meanwhile, in the calibration data... If a pre-defined calibration function is not found in the calibration database, it is determined that the calibration database does not contain information on the pre-defined calibration function corresponding to the target prediction data. Users, such as calibration personnel, can select the data to be fitted and submit a data selection operation. Based on this data selection operation, data cleaning and preprocessing can be performed. The selected data is then used as input to perform function fitting, yielding the target fitting function. This target fitting function is then used to generate the calibration prediction data, which can be used for subsequent data calibration. This effectively improves vehicle data calibration efficiency, reduces data calibration costs, and saves human resources.

[0091] Optionally, embodiments of this application may perform function fitting based on fitting data selection operations to obtain a target fitting function. Specifically, this may include: determining the data to be fitted based on the fitting data selection operations; collecting data for the data to be fitted to obtain collected data; performing data preprocessing based on the collected data to obtain data characteristic distribution information; and performing function fitting based on the data characteristic distribution information and the initial fitting parameters set in the calibration database to obtain the target fitting function.

[0092] Specifically, in this embodiment, after receiving the user's submitted data selection operation, the user-selected data to be fitted can be determined as the data to be fitted. Subsequently, data can be collected from the data to be fitted, and the collected data can be determined as the collected data. The collected data can be cleaned, noise and missing values ​​removed, and necessary preprocessing such as standardization and normalization can be performed to determine the characteristics and distribution of the data. Data characteristic distribution information can be generated based on the characteristics and distribution of the data. Based on the data characteristic distribution information and the initial fitting parameters set in the calibration database, function fitting is performed to obtain the target fitting function.

[0093] The target fitting function is a fitting function whose fitting degree reaches a preset fitting degree. Optionally, in this embodiment of the application, the target fitting function is obtained by performing function fitting based on the data characteristic distribution information and the initial fitting parameters set in the calibration database. Specifically, this may include: determining the fitting function type based on the data characteristic distribution information; extracting the initial fitting parameters from the calibration database based on the fitting function type; and starting fitting using the initial fitting parameters according to the fitting function type until the fitting degree of the obtained fitting function reaches the preset fitting degree.

[0094] Specifically, after obtaining the data characteristic distribution information, this embodiment can select a suitable function type as the fitting function type based on this information and according to the characteristics and distribution of the data. This fitting function type can be a polynomial, exponential, Fourier, Gaussian, power, rational number, sine, etc., and this embodiment does not impose specific limitations on this. After determining the fitting function type, the database can set initial parameter values ​​for the fitting function as initial fitting parameters. Subsequently, the least squares method or other fitting methods can be used to start fitting based on the initial fitting parameters, generating the fitting function. Statistical indicators (such as RMSE, R², etc.) can then be used. 2(etc.) Evaluate the accuracy of the fitted function; if the generated fitted function has a high degree of fit, proceed to the next step, that is, when the fitted function reaches the preset degree of fit, use the fitted function curve to predict future data points and generate predicted calibration values ​​as calibration prediction data for calibration personnel to use; if the generated fitted function has a low degree of fit, jump back to the step of setting the initial parameter values ​​for the fitted function by the database, regenerate the initial values ​​and perform function fitting, until the generated fitted function reaches the preset degree of fit.

[0095] As can be seen, the data stored in the calibration database in this embodiment can be processed by big data and analyzed as a data source to generate recommended calibration data for the corresponding software and hardware versions. For example, when using UDS to calibrate data based on preset function prediction data, the calibration data is allowed to have its own calculation process from the initial design stage. For example, the relationship between motor speed and motor supply voltage, the conversion relationship between tailgate anti-pinch current and anti-pinch force, etc. These calibration data have planned the functional relationship between data from the initial design stage, and can directly perform data prediction and dynamic adjustment. For example, the calibration data obtained during the opening and closing of the car tailgate shows that, considering that the minimum step value of the tailgate motor each time is related to the motor's starting voltage and the maximum Hall value in the open state, the actual car The vehicle's actual starting voltage and maximum Hall effect value can be used to calculate the optimal motor step value, enabling the electric tailgate to open and close relatively smoothly. Therefore, by calibrating the electric tailgate's motor step value, it can be seen that the electric tailgate's movement speed changes very smoothly throughout the closing process, without obvious vibration, which facilitates vehicle maintenance. For calibration data without a preset calibration function, data fitting is usually performed on these data. For example, curve fitting can be performed on the data using data function models of fitting types such as polynomial, exponential, Fourier, and Gaussian. After fitting the data with the function, the curve function with the highest goodness of fit can be selected as the target fitting function. In this way, the target fitting function can be directly used to predict the calibration value during the data calibration process, reducing the amount of data and the calibration workload.

[0096] For example, when calibrating the configuration of automotive ambient lighting, it can be seen that the original data and formulas... With a high degree of overlap, when calibrating ambient lights later, the initial data can be directly set according to this function for dynamic parameter adjustment and data prediction, thereby reducing the amount of data.

[0097] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should know that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps may be performed in other orders or simultaneously.

[0098] like Figure 3 As shown in the illustration, this application also provides a vehicle calibration data processing device, which may specifically include the following modules:

[0099] The first acquisition module 310 is used to acquire vehicle status data, which includes the vehicle's hardware and software information and operating status data.

[0100] The calibration determination module 320 is used to determine the calibration target of the vehicle based on the software and hardware information and the operating status data.

[0101] The second acquisition module 330 is used to acquire initial calibration data that matches the object to be calibrated;

[0102] The dynamic adjustment module 340 is used to dynamically adjust the parameters based on the initial calibration data and in conjunction with the prediction selection operation to obtain calibration prediction data.

[0103] The data calibration object module 350 is used to perform vehicle data calibration based on the calibration prediction data to obtain the data calibration result corresponding to the object to be calibrated.

[0104] Optionally, the second acquisition module 330 includes:

[0105] The `get` submodule is used to retrieve user login information;

[0106] The security level determination submodule is used to determine the user's security level based on the user login information.

[0107] The extraction submodule is used to extract module calibration data with the same version as the object to be calibrated from the preset calibration database if the permissions corresponding to the user's security level meet the preset permission conditions.

[0108] The data determination submodule is used to determine the module calibration data as the initial calibration data.

[0109] Optionally, the dynamic adjustment module 340 includes:

[0110] The target prediction data determination submodule is used to determine the target prediction data by employing the prediction selection operation.

[0111] The calibration function determination submodule is used to detect whether the vehicle's calibration database contains preset calibration function information corresponding to the target prediction data;

[0112] The calibration function prediction generation submodule is used to generate the calibration prediction data based on the calibration function information and the initial calibration data if the preset calibration function information exists in the calibration database.

[0113] The function fitting generation submodule is used to perform function fitting based on the fitting data selection operation if the calibration database does not contain the preset calibration function information, to obtain the target fitting function, and to generate the calibration prediction data using the target fitting function.

[0114] Optionally, the step of performing function fitting based on the fitting data selection operation to obtain the target fitting function includes: determining the data to be fitted based on the fitting data selection operation; collecting data for the data to be fitted to obtain collected data; performing data preprocessing based on the collected data to obtain data characteristic distribution information; and performing function fitting based on the data characteristic distribution information and the initial fitting parameters set in the calibration database to obtain the target fitting function.

[0115] Optionally, the target fitting function is a fitting function whose fitting degree reaches a preset fitting degree. The step of performing function fitting based on the data characteristic distribution information and the initial fitting parameters set in the calibration database to obtain the target fitting function includes: determining the fitting function type based on the data characteristic distribution information; extracting the initial fitting parameters from the calibration database according to the fitting function type; and starting fitting according to the fitting function type using the initial fitting parameters until the fitting degree of the fitted function reaches the preset fitting degree.

[0116] Optionally, the vehicle status data also includes the calibration data currently used by the vehicle, and the first acquisition module 310 includes:

[0117] The first acquisition submodule is used to acquire the vehicle's software and hardware information and operating status data through a unified diagnostic service. The software and hardware information includes the vehicle's software version information and hardware version information.

[0118] The second acquisition submodule is used to acquire the calibration data currently used by the vehicle based on the software version information and hardware version information.

[0119] Optionally, the calibration determination module 320 includes:

[0120] The calibration test submodule is used to perform calibration tests based on the software and hardware information and the operating status data, using the calibration data, and obtain calibration test results;

[0121] The recalibration determination submodule is used to determine whether each functional module in the vehicle needs to be recalibrated based on the calibration test results.

[0122] The submodule for the object to be calibrated is used to identify the functional modules that need to be recalibrated as the object to be calibrated.

[0123] In a specific implementation, the aforementioned vehicle calibration data processing device can be applied to electronic devices, enabling the electronic devices to function as vehicle calibration data processing devices. Through vehicle status data, and based on the vehicle's hardware and software information and operational status data, the device determines the vehicle to be calibrated. Subsequently, it acquires initial calibration data matching the target vehicle. Based on this initial calibration data, and combined with predictive selection operations, parameters are dynamically adjusted to obtain calibration prediction data. Thus, vehicle data calibration can be performed based on the calibration prediction data, yielding the data calibration result corresponding to the target vehicle, meeting the actual needs of the vehicle. This effectively reduces the workload of vehicle calibration data processing and the need for multiple data adjustments and optimizations during vehicle use, thereby lowering the workload and cost of vehicle maintenance.

[0124] like Figure 4 As shown, this application embodiment provides an electronic device, including a processor 111, a communication interface 112, a memory 113, and a communication bus 114. The processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114. The memory 113 is used to store computer programs. When the processor 111 executes the program stored in the memory 113, it implements the steps of the vehicle calibration data processing method provided in any of the aforementioned method embodiments.

[0125] In one embodiment of this application, when the processor 111 executes the program stored in the memory 113, it implements the vehicle calibration data processing method proposed in any of the foregoing method embodiments, including: acquiring vehicle status data, the vehicle status data including vehicle hardware and software information and operating status data; determining the vehicle to be calibrated based on the hardware and software information and the operating status data; acquiring initial calibration data matching the vehicle to be calibrated; dynamically adjusting parameters based on the initial calibration data and combining a predictive selection operation to obtain calibration prediction data; and performing vehicle data calibration based on the calibration prediction data to obtain the data calibration result corresponding to the vehicle to be calibrated.

[0126] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the vehicle calibration data processing method provided in any of the foregoing method embodiments.

[0127] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0129] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0130] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for processing vehicle calibration data, characterized in that, include: Acquire vehicle status data, which includes the vehicle's hardware and software information and operating status data; Based on the hardware and software information and the operating status data, the vehicle to be calibrated is determined; Obtaining initial calibration data matching the object to be calibrated includes: using the software and hardware information, extracting module calibration data with the same software and hardware version as the object to be calibrated from a preset calibration database, and using the module calibration data as the initial calibration data; Based on the initial calibration data, the parameters are dynamically adjusted in conjunction with the prediction selection operation to obtain the calibration prediction data; Based on the calibration prediction data, vehicle data calibration is performed to obtain the data calibration result corresponding to the object to be calibrated. The step of dynamically adjusting parameters based on the initial calibration data and combining a prediction selection operation to obtain calibration prediction data includes: using the prediction selection operation to determine target prediction data; detecting whether there is preset calibration function information corresponding to the target prediction data in the vehicle's calibration database; if the preset calibration function information exists in the calibration database, then generating the calibration prediction data based on the initial calibration data using the calibration function information; if the preset calibration function information does not exist in the calibration database, then performing function fitting based on the fitting data selection operation to obtain a target fitting function, and using the target fitting function to generate the calibration prediction data. The step of performing function fitting based on the fitting data selection operation to obtain the target fitting function includes: determining the data to be fitted based on the fitting data selection operation; collecting data for the data to be fitted to obtain collected data; performing data preprocessing based on the collected data to obtain data characteristic distribution information; and performing function fitting based on the data characteristic distribution information and the initial fitting parameters set in the calibration database to obtain the target fitting function.

2. The method according to claim 1, characterized in that, Before extracting module calibration data with the same software and hardware version as the object to be calibrated from the preset calibration database using the aforementioned software and hardware information, the process further includes: Obtain user login information; The user's security level is determined based on the user login information. If the permissions corresponding to the user's security level meet the preset permission conditions, then the step of using the software and hardware information to extract module calibration data with the same software and hardware version as the object to be calibrated from the preset calibration database is executed.

3. The method according to claim 1, characterized in that, The target fitting function is a fitting function with a preset fitting degree. The step of performing function fitting based on the data characteristic distribution information and the initial fitting parameters set in the calibration database to obtain the target fitting function includes: Based on the data characteristic distribution information, determine the type of fitting function; The initial fitting parameters are extracted from the calibration database according to the fitting function type; According to the type of fitting function, the initial fitting parameters are used to start fitting until the fitting degree of the fitted function reaches the specified fitting degree.

4. The method according to any one of claims 1 to 3, characterized in that, The vehicle status data also includes the calibration data currently used by the vehicle. The acquisition of the vehicle status data includes: The unified diagnostic service obtains the vehicle's hardware and software information and operating status data, including the vehicle's software version information and hardware version information. Based on the software version information and hardware version information, obtain the calibration data currently used by the vehicle.

5. The method according to claim 4, characterized in that, Based on the hardware and software information and the operational status data, the vehicle to be calibrated is determined, including: Based on the hardware and software information and the operating status data, calibration tests are performed using the calibration data to obtain calibration test results. The calibration test results are used to determine whether each functional module in the vehicle needs to be recalibrated. The functional modules that need to be recalibrated are identified as the objects to be calibrated.

6. A vehicle calibration data processing device, characterized in that, include: The first acquisition module is used to acquire vehicle status data, which includes the vehicle's hardware and software information and operating status data. The calibration determination module is used to determine the calibration target of the vehicle based on the software and hardware information and the operating status data. The second acquisition module is used to acquire initial calibration data that matches the object to be calibrated, including: using the software and hardware information, extracting module calibration data with the same software and hardware version as the object to be calibrated from a preset calibration database, so as to use the module calibration data as the initial calibration data; The dynamic adjustment module is used to dynamically adjust the parameters based on the initial calibration data and in conjunction with the prediction selection operation to obtain calibration prediction data. The data calibration object module is used to perform vehicle data calibration based on the calibration prediction data to obtain the data calibration result corresponding to the object to be calibrated. The step of dynamically adjusting parameters based on the initial calibration data and combining a prediction selection operation to obtain calibration prediction data includes: using the prediction selection operation to determine target prediction data; detecting whether there is preset calibration function information corresponding to the target prediction data in the vehicle's calibration database; if the preset calibration function information exists in the calibration database, then generating the calibration prediction data based on the initial calibration data using the calibration function information; if the preset calibration function information does not exist in the calibration database, then performing function fitting based on the fitting data selection operation to obtain a target fitting function, and using the target fitting function to generate the calibration prediction data. The step of performing function fitting based on the fitting data selection operation to obtain the target fitting function includes: determining the data to be fitted based on the fitting data selection operation; collecting data for the data to be fitted to obtain collected data; performing data preprocessing based on the collected data to obtain data characteristic distribution information; and performing function fitting based on the data characteristic distribution information and the initial fitting parameters set in the calibration database to obtain the target fitting function.

7. An electronic device, characterized in that, include: The processor, communication interface, memory, and communication bus are connected, with the processor, communication interface, and memory communicating with each other via the communication bus. Memory, used to store computer programs; The processor, when executing a program stored in memory, implements the steps of the vehicle calibration data processing method as described in any one of claims 1-5.

8. A computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are used to perform the steps of the vehicle calibration data processing method as described in any one of claims 1-5.

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