Electric control calibration data processing method and device and computer readable storage medium

Abstract

The application discloses an electric control calibration data processing method and device and a computer readable storage medium. The method comprises the following steps: obtaining historical project data corresponding to a plurality of historical projects, wherein the plurality of historical projects correspond to a plurality of vehicle models, and the historical project data comprises electric control data of attributes included in the corresponding vehicle model; obtaining to-be-calibrated data in a target project and determining attributes of the to-be-calibrated data in the target project; selecting a target historical project from the plurality of historical projects based on the attributes of the to-be-calibrated data in the target project, and obtaining target historical project data corresponding to the target historical project; and calibrating the to-be-calibrated data in the target project based on the target historical project data to obtain target calibration data of the target project. The application solves the technical problems of low data calibration efficiency and low accuracy of data calibration results when calibration is performed by referring to related project data in an artificial manner.

Description

Technical Field

[0001] This invention relates to the field of electronic intelligent technology, and more specifically, to an electronic control calibration data processing method, apparatus, and computer-readable storage medium. Background Technology

[0002] Currently, once the vehicle framework is determined—for example, after the engine, the entire vehicle, the system algorithm (control strategy), and peripheral components are finalized—software data can be optimized to achieve satisfactory vehicle performance, meet user requirements, and achieve predetermined standards. This optimization process can be simply referred to as data calibration. In related technologies, vehicle data calibration is generally performed manually by referencing data from a specific relevant project. However, this manual calibration method suffers from low efficiency and low accuracy.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides an electronic control calibration data processing method, apparatus, and computer-readable storage medium to at least solve the technical problems of low data calibration efficiency and low accuracy of data calibration results when manually referencing data from a related project for calibration in the related art.

[0005] According to one aspect of the present invention, an electronic control calibration data processing method is provided, comprising: acquiring historical project data corresponding to multiple historical projects, wherein the multiple historical projects correspond to multiple vehicle models, and the historical project data includes electronic control data of attributes included in the corresponding vehicle model; acquiring data to be calibrated in a target project, and determining the attributes of the data to be calibrated in the target project; selecting a target historical project from the multiple historical projects based on the attributes of the data to be calibrated in the target project, and acquiring target historical project data corresponding to the target historical project; and calibrating the data to be calibrated in the target project based on the target historical project data to obtain target calibration data of the target project.

[0006] Optionally, the historical project data corresponding to the various historical projects is parsed, and attributes are set for the parsed historical project data corresponding to the various historical projects to obtain the attributes of the historical project data corresponding to the various historical projects; the attributes of the data to be calibrated in the target project are compared with the attributes of the historical project data corresponding to the various historical projects to obtain the comparison result; based on the comparison result, the target historical project is determined, and the target historical project data corresponding to the target historical project is obtained.

[0007] Optionally, based on the comparison results, historical projects that correspond to the attributes of the data to be calibrated in the target project are identified as preliminary target historical projects; the attributes included in the preliminary target historical projects are compared with the attributes of the data to be calibrated in the target project to obtain the quantity values ​​corresponding to the preliminary target historical projects, wherein the quantity value is the number of attributes included in the preliminary target historical projects that correspond to the attributes of the data to be calibrated; historical projects with the quantity value greater than a predetermined quantity threshold are selected from the preliminary target historical projects as target historical projects; and target historical project data corresponding to the target historical projects are obtained.

[0008] Optionally, if there are multiple target historical projects, the target historical project data of the multiple target historical projects are statistically analyzed to obtain statistical values; the data to be calibrated is calibrated based on the statistical values ​​to obtain the target calibration data of the target project.

[0009] Optionally, the data type of the data to be calibrated is determined; the target data statistical method corresponding to the data type is determined; and the target data statistical method is used to statistically analyze the target historical data of the multiple target historical items to obtain statistical values.

[0010] Optionally, based on the historical project data corresponding to the various historical projects, the data range of the data to be calibrated is determined; based on the statistical value and the data range, the data to be calibrated is calibrated to obtain the target calibration data of the target project.

[0011] Optionally, the target calibration data is checked according to predetermined check conditions; if the target calibration data meets the predetermined check conditions, the target calibration data is sent to the vehicle controller corresponding to the target item.

[0012] According to another aspect of the present invention, an electronic control calibration data processing device is also provided, comprising: a first acquisition module, configured to acquire historical project data corresponding to multiple historical projects, wherein the multiple historical projects correspond to multiple vehicle models, and the historical project data includes electronic control data of attributes included in the corresponding vehicle model; a second acquisition module, configured to acquire data to be calibrated in a target project and determine the attributes of the data to be calibrated in the target project; a third acquisition module, configured to select a target historical project from the multiple historical projects based on the attributes of the data to be calibrated in the target project, and acquire target historical project data corresponding to the target historical project; and a calibration module, configured to calibrate the data to be calibrated in the target project based on the target historical project data to obtain target calibration data of the target project.

[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform any of the electronic control calibration data processing methods described above.

[0014] According to another aspect of the present invention, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program; the processor is configured to execute the computer program stored in the memory, wherein when the computer program is executed, the processor performs any of the electronic control calibration data processing methods described in the present invention.

[0015] In this embodiment of the invention, by acquiring historical project data corresponding to multiple historical projects, wherein the multiple historical projects correspond to multiple vehicle models, and the historical project data includes the electronic control data of the configuration items included in the corresponding vehicle model; acquiring the data to be calibrated in the target project, and determining the attributes of the data to be calibrated in the target project; selecting the target historical project from the multiple historical projects based on the attributes of the data to be calibrated in the target project, and acquiring the target historical project data corresponding to the target historical project; calibrating the data to be calibrated in the target project based on the target historical project data, and obtaining the target calibration data of the target project, that is, selecting the target historical project from the multiple historical projects, acquiring the target historical project data corresponding to the target historical project; and calibrating the data to be calibrated in the target project based on the target historical project data, the aim of quickly and accurately calibrating the data to be calibrated is achieved, thereby realizing the technical effect of intelligent calibration of the data to be calibrated based on mature historical project data, and thus solving the technical problems of low data calibration efficiency and low accuracy of data calibration results when using manual methods to refer to a certain related project data for calibration. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of an electronic control calibration data processing method provided according to an embodiment of the present invention;

[0018] Figure 2 It is a histogram of the data range provided by an optional embodiment of the present invention;

[0019] Figure 3 It is a box plot of the data range provided by an optional embodiment of the present invention;

[0020] Figure 4 This is a data list diagram of a transmission model provided according to an optional embodiment of the present invention;

[0021] Figure 5 This is a data list diagram of the exhaust temperature model provided according to an optional embodiment of the present invention;

[0022] Figure 6 This is a flowchart illustrating the electronic control calibration data processing method provided according to an embodiment of the present invention;

[0023] Figure 7 This is a schematic diagram of the attribute configuration method provided by an embodiment of the present invention;

[0024] Figure 8 This is a schematic diagram of the structure of the electronic control calibration data processing device provided in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Terminology Explanation

[0028] Data calibration, in this application, refers to the process of optimizing software data after the engine, vehicle, system algorithms (control strategies), and peripheral devices have been determined, in order to obtain satisfactory vehicle performance, meet user requirements, and achieve predetermined standards.

[0029] ECU (Electronic Control Unit), also known as "vehicle computer" or "on-board computer," is a microcomputer controller specifically designed for automobiles.

[0030] DCM (Data Conservation Format) is a standard data file format with the file extension ".dcm".

[0031] VVT (Variable Valve Timing) is a technology used in automotive piston engines. VVT technology can adjust the overlap time and timing of the engine's intake and exhaust systems (part or all of them), reducing fuel consumption and improving efficiency.

[0032] EGR (Exhaust Gas Recirculation) is short for exhaust gas recirculation. Exhaust gas recirculation refers to the process of returning some of the exhaust gas discharged from the engine to the intake manifold and re-entering the cylinders with fresh air-fuel mixture.

[0033] GPF (Gasoline Particulate Filter) is a gas-powered particulate filter for gasoline engines.

[0034] According to an embodiment of the present invention, a method embodiment of an electronic control calibration data processing method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] Figure 1 This is a flowchart of an electronic control calibration data processing method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0036] Step S102: Obtain historical project data corresponding to multiple historical projects, wherein multiple historical projects correspond to multiple vehicle models, and the historical project data includes electronic control data of the attributes included in the corresponding vehicle model;

[0037] Step S104: Obtain the data to be calibrated in the target project and determine the attributes of the data to be calibrated in the target project;

[0038] Step S106: Based on the attributes of the data to be calibrated in the target project, select the target historical project from multiple historical projects and obtain the target historical project data corresponding to the target historical project.

[0039] Step S108: Based on the target historical project data, calibrate the data to be calibrated in the target project to obtain the target calibration data of the target project.

[0040] In this embodiment of the invention, a target historical project is selected from multiple historical projects, and the target historical project data corresponding to the target historical project is obtained. Based on the target historical project data, the data to be calibrated in the target project is calibrated to obtain the target calibration data of the target project. This achieves the purpose of quickly and accurately calibrating the data to be calibrated, thereby realizing the technical effect of intelligent calibration of the data to be calibrated based on mature historical project data. This solves the technical problems of low data calibration efficiency and low accuracy of data calibration results when using manual methods to refer to relevant project data for calibration.

[0041] As an optional embodiment, the aforementioned multiple historical projects correspond to multiple vehicle models. These multiple vehicle models can correspond to different vehicle types or different configurations of the same vehicle type; that is, different configurations of the same vehicle type correspond to multiple vehicle models in this application. Furthermore, the aforementioned different configurations can correspond to different hardware configurations, different software configurations, or combinations of different hardware and different software configurations, etc.

[0042] As an optional embodiment, when selecting a target historical project from the multiple historical projects based on the attributes of the data to be calibrated in the target project, and obtaining the target historical project data corresponding to the target historical project, various processing methods can be adopted. For example, the following methods can be adopted: parse the historical project data corresponding to the multiple historical projects, set attributes for the parsed historical project data corresponding to the multiple historical projects, and obtain the attributes of the historical project data corresponding to the multiple historical projects; compare the attributes of the data to be calibrated in the target project with the attributes of the historical project data corresponding to the multiple historical projects to obtain a comparison result; based on the comparison result, determine the target historical project and obtain the target historical project data corresponding to the target historical project.

[0043] Optionally, to achieve planned storage of historical project data corresponding to the aforementioned multiple historical projects, this data can be stored using a predetermined database format and encoded using a predetermined programming language. Therefore, after acquiring the historical project data corresponding to the aforementioned multiple historical projects, the acquired data can be parsed, and attributes can be set for the parsed data to obtain its attributes. For example, when storing historical project data corresponding to multiple historical projects, i.e., after acquiring data from the vehicle's ECU, mature formats such as A2L files, hex files, or DCM files can be used to store the data in a predetermined database. The A2L file defines the communication information required between the host computer and the ECU during the calibration process using the database data, while the hex file is machine language. Parsing the historical project data converts the machine language into a language that can be used for subsequent attribute setting, facilitating unified attribute setting, attribute identification, and attribute comparison for the historical project data.

[0044] Optionally, after setting attributes for historical project data and obtaining the attributes of historical project data corresponding to the aforementioned multiple historical projects, the attributes of the data to be calibrated in the target project can be compared with the attributes of the historical project data corresponding to the aforementioned multiple historical projects to obtain a comparison result. Based on the comparison result, the target historical project is determined, and the target historical project data corresponding to the target historical project is obtained. By comparing the attributes of the data to be calibrated in the target project with the attributes of the historical project data corresponding to the aforementioned multiple historical projects, the target historical project is determined from the aforementioned multiple historical projects. Compared with related technologies that directly calibrate data based on only one project, the comparison method allows for the selection of more references corresponding to the data to be calibrated. Therefore, the target historical projects referenced by the attributes of the data to be calibrated are more specific to the data to be calibrated, thus effectively improving the reliability of the reference.

[0045] As an optional embodiment, based on the comparison results, determining the aforementioned target historical items and obtaining the target historical item data corresponding to the aforementioned target historical items can be achieved through various processing methods. For example, the following methods can be used: Based on the comparison results, historical items that correspond to the attributes of the data to be calibrated in the aforementioned target items are identified as preliminary target historical items; the attributes included in the aforementioned preliminary target historical items are compared with the attributes of the data to be calibrated in the aforementioned target items to obtain the quantity values ​​corresponding to the aforementioned preliminary target historical items, wherein the quantity values ​​are the number of attributes included in the aforementioned preliminary target historical items corresponding to the attributes of the data to be calibrated; historical items with quantity values ​​greater than a predetermined quantity threshold are selected from the aforementioned preliminary target historical items as the aforementioned target historical items; and the target historical item data corresponding to the aforementioned target historical items is obtained. Through the above methods, the attributes included in the aforementioned preliminary target historical items are compared with the attributes of the data to be calibrated in the aforementioned target items to obtain the quantity values ​​corresponding to the aforementioned preliminary target historical items, and the target historical items are determined based on the quantity values. Since the quantitative value represents the number of attributes included in the initial target historical project that correspond to the attributes of the data to be calibrated, this quantitative value reflects the similarity between the target project and multiple historical projects. In other words, it reflects, to a certain extent, the reliability of each historical project as a reference for the target project. Therefore, determining the target historical project based on quantitative values ​​not only allows for a qualitative assessment of the likelihood of each historical project serving as a reference, but also a quantitative assessment of its quantifiability as a reference. This improves the accuracy in determining whether each historical project can serve as a target historical project, thereby enhancing the accuracy of subsequent data calibration.

[0046] It should be noted that the aforementioned predetermined quantity threshold can be determined based on the standard requirements of data calibration, and there are various ways to determine it. For example, it can be determined manually or obtained by statistical analysis based on historical data corresponding to multiple historical projects.

[0047] The following optional example illustrates the above method of determining target historical items based on quantity values ​​and obtaining target historical data for target historical items.

[0048] For example, selecting a target historical project from multiple historical projects can be done in several ways. For instance, the target project might contain data to be calibrated, named A1, A2, and A3, with the attribute "Sports Driving Mode." The target project might also contain uncalibrated data, named B1, B2, and B3, with the attribute "Cruise Control Type."

[0049] Historical project 1 contains historical project data named A1, A2, and A3, with the attribute of Sport Driving Mode. It also contains historical project data named B1, B2, and B3, with the attribute of Adaptive Cruise Control.

[0050] Historical project 2 contains historical project data, named A1 and A2, with the attribute of sport driving mode. There is also historical project data named B1 and B2, with the attribute of cruise control type.

[0051] Historical item 3 contains historical item data named A1 and A2, with the attribute of sport driving mode. It also contains historical item data named B1, B2, and B3, with the attribute of cruise control type.

[0052] Historical item 4 contains historical item data, named A1, A2, and A3, with the attribute of economy driving mode, and also contains data named B1, B2, and B3, with the attribute of cruise control type.

[0053] Historical item 5 contains historical item data named C1, C2, and C3, with the attribute of Sport Driving Mode. It also contains historical item data named B1, B2, and B3, with the attribute of Cruise Control Type.

[0054] According to the name and corresponding attributes of the data to be calibrated for the target project, compare them with historical projects 1, 2, 3, 4, and 5 to obtain historical projects 1, 2, and 3 as the preliminary target historical projects 1, 2, and 3;

[0055] Further comparisons were made among the preliminary target historical projects 1, 2, and 3 according to the non-to-be-calibrated names of the target projects and their corresponding attributes. The number of matching items was as follows: 3 for preliminary target historical project 1, 4 for preliminary target historical project 2, and 5 for preliminary target historical project 3.

[0056] When the threshold for the number of matching items is set to 3, the initial target historical items 1, 2, and 3 all meet the threshold requirements, and the initial target historical items 1, 2, and 3 are selected as target historical items 1, 2, and 3.

[0057] When the threshold for the number of matching items is set to 4, the initial target historical items 2 and 3 meet the threshold requirements, while the initial target historical item 1 does not meet the threshold requirements. Therefore, the initial target historical items 2 and 3 are selected as the target historical items 2 and 3.

[0058] By selecting preliminary target historical projects according to the name and corresponding attributes of the data to be calibrated in the above manner, setting a matching quantity threshold can reflect the requirements for the similarity between the target historical projects and the target projects to a certain extent. Taking into account the selection of target historical projects can effectively improve the similarity between the selected target historical projects and the target projects, which is conducive to obtaining calibration results with high maturity.

[0059] As an optional embodiment, based on the aforementioned target historical project data, the data to be calibrated in the aforementioned target projects is calibrated to obtain the target calibration data of the aforementioned target projects. Various processing methods can be adopted. For example, the following methods can be adopted: when there are multiple target historical projects, the target historical project data of the multiple target historical projects are statistically analyzed to obtain statistical values; based on the aforementioned statistical values, the data to be calibrated is calibrated to obtain the target calibration data of the aforementioned target projects.

[0060] In cases where there are multiple target historical items, statistical analysis is performed on the data of these multiple target historical items to obtain statistical values. The number of target historical items can vary; if the number of obtained target historical items is lower than a predetermined threshold, a message indicating a shortage of target historical items is output. Since the data to be calibrated is based on the processed target historical item data, selecting too few target historical items will result in an insufficient sample size for statistical processing, leading to unrepresentative statistical values ​​and reduced accuracy of the calibration results. To ensure the representativeness of the historical data by the statistical values, a condition can be set where the number of target historical items exceeds a predetermined threshold. This predetermined threshold is a settable parameter representing the required number of target historical items. By calibrating the data to be calibrated based on the aforementioned statistical values ​​to obtain the target calibration data for the target items, the statistical values ​​can provide a reference for multiple target historical item data to a certain extent. Compared to data based on a single historical item or directly specifying a single historical item, this method effectively avoids the randomness of data anomalies, improves the reference value of the target historical item data, and thus enhances the accuracy of data calibration.

[0061] As an optional implementation, when statistically analyzing the target historical data of the aforementioned multiple target historical projects to obtain statistical values, different data statistical methods can be adopted based on the different data types of the target historical project data. This can, to a certain extent, make the obtained statistical values ​​more accurate. Therefore, the data type of the data to be calibrated can be determined first; then, the target data statistical method corresponding to the data type can be determined; and finally, the target data statistical method can be used to statistically analyze the target historical data of the aforementioned multiple target historical projects to obtain statistical values.

[0062] Optionally, statistical analysis can be performed on the target historical data of multiple target historical projects. First, the data type of the data to be calibrated is determined, and then the target data statistical method corresponding to the data type is determined and processed. The above statistical method can take multiple forms.

[0063] When determining the data type to be calibrated, it is necessary to obtain the correlation distribution of the target historical data. Based on the number of correlation parameters, the target historical data can be categorized into various data types. Correlation parameters are those that influence the target historical data; the data type is determined according to the specific circumstances. For example:

[0064] If the target historical data is not affected by other parameters, the data type is a single value, such as: technical configuration items such as start-stop, cruise, VVT, EGR, activation conditions or boundary values ​​such as water temperature, altitude, speed, load, vehicle speed, system constants, control strategy switching modes, single-value filtering, coefficient correction, etc.

[0065] If the target historical data changes with a certain parameter value, then the data type is two-dimensional. For example, the curve in the target historical data is of type XZ, where the X-axis represents the associated parameter and the Z-axis represents the target historical data. Some sensor characteristics, such as those of water temperature sensors, canister solenoid valves, and voltage corrections for injector opening times, are correlated with some component control inputs, such as booster pumps, air conditioners, fans, and thermal management modules. When the target historical data is from a water temperature sensor, the water temperature sensor characteristics are related to the control input of the booster pump and change with the control input of the booster pump; therefore, the target historical data can be considered a two-dimensional type.

[0066] In addition, there are some target historical data related to powertrain calibration principles, such as the starting module, idle speed module, and emission module. These functional modules follow certain physical laws. For example, the amount of fuel injection enrichment during startup will gradually decrease as the coolant temperature increases; the target idle speed of the entire vehicle will gradually decrease as the coolant temperature or startup time increases. These target historical data can also be regarded as two-dimensional data.

[0067] If the target historical data changes with the values ​​of two parameters, then the data type is three-dimensional data. For example, the corresponding curve of the target historical data is of type XYZ, where the X-axis can be parameters such as speed, water temperature, gear, throttle opening, etc., the Y-axis can be parameters such as load, torque, etc., all of which are data generated in actual calibration, and the Z-axis is the target historical data.

[0068] Optionally, the method for determining the target data statistics corresponding to the data type is based on the distribution of the target's historical data, for example:

[0069] If the target historical data is determined to be single-valued, and 10 target historical items A1, A2...A10 are selected, with the corresponding target historical data having only two states: 0 and 1 (8 target historical data points are 1, and 2 target historical data points are 0), then when selecting the target data statistical method, the mode statistical method will be output according to the preset statistical rules. If the target historical data B is determined to be single-valued, and 10 target historical items B1, B2...B10 are selected, and the values ​​of the corresponding target historical data differ significantly, then when selecting the target data statistical method, the median statistical method will be output according to the preset rules. If the target historical data C is the start-up concentration factor, and the corresponding curve of the start-up concentration factor is XZ type, with the X-axis representing water temperature and the Z-axis representing the start-up concentration factor, and the start-up concentration factor changes with water temperature, then the target historical data C is determined to be two-dimensional data. If 10 target historical items C1, C2...C10 are selected, and the values ​​of the corresponding target historical data differ relatively little, then when selecting the target data statistical method, the average statistical method will be output according to the preset rules. Table 5 shows the statistical values ​​output by the above statistical methods. It can be seen that using the above method, the obtained statistical values ​​do not deviate significantly from the target historical data, reducing data risk. If the values ​​of the corresponding target historical data differ greatly, the median statistical method will be output according to preset rules when selecting the target data statistical method.

[0070] If the differences between the corresponding historical data values ​​exceed the established data range, the historical data that exceeds the data range needs to be removed to ensure the rationality of the data source.

[0071] If the parameters affecting the target historical project data show differences such as expanded axes, missing axes, or misaligned axes, various fitting methods can be adopted, such as linear fitting, quadratic square fitting, and cubic polynomial fitting, for statistical processing.

[0072] If the target's historical data consists of sensor characteristic curves or certain feature attributes, then by identifying the data attributes and sensor type, the target calibration data can be accurately located and output directly from multiple historical projects.

[0073] If the target historical data is determined to be a three-dimensional data type, the corresponding curve is of type XYZ. When the specific physical meaning of the target historical data is normally closed or normally open, driving style type, or optimized performance data, such as target VVT angle or target rail pressure, the target data statistical method is to output a fixed value. Since three-dimensional data types involve three dimensions, the statistical method of outputting the average or median is more often chosen. To reduce data risk, the statistical method uses the output average, median, average, or a combination of median, based on the specific physical characteristics of the target historical data. For example, for basic ignition angle and enrichment protection, the result is compared between the median and the average, and the smaller value is output; for knock threshold, misfire diagnosis, and exhaust temperature model, the result is compared between the median and the average, and the larger value is output.

[0074] The preset rules are various statistical methods corresponding to data types. For example, single values ​​correspond to the statistical methods of outputting fixed values, mode, median, and average; two-dimensional data corresponds to the statistical methods of outputting fixed values, average, median, and fitted curve values; and three-dimensional data corresponds to the statistical methods of outputting fixed values, average, and median.

[0075] It should be noted that other specific implementation methods that can perform statistical processing based on data type are all part of the embodiments of this invention, and will not be described in detail here.

[0076] Furthermore, some target calibration data need to meet specific rules. As regulations are continuously upgraded, electronic control systems are also upgraded, causing changes in calibration data. Pre-setting according to the required regulations ensures that the data to be calibrated for the target item meets the mapping conditions of the required regulations. This compliant data does not change with the target item and can be automatically preset into the target item. Simultaneously, for on-board diagnostic systems, such as lighting strategies, fault reporting, and repair strategies, regulatory requirements must be met.

[0077] As an optional embodiment, the above-mentioned data to be calibrated is calibrated based on the above-mentioned statistical values ​​to obtain the target calibration data of the above-mentioned target project. Various processing methods can be adopted. For example, the following methods can be adopted: determine the data range of the above-mentioned data to be calibrated based on the historical project data corresponding to the above-mentioned multiple historical projects; calibrate the above-mentioned data to be calibrated based on the above-mentioned statistical values ​​and the above-mentioned data range to obtain the target calibration data of the above-mentioned target project.

[0078] Determining the data range of the data to be calibrated based on historical project data from various historical projects can be achieved in several ways. For example, the data range can be obtained using maximum, minimum, and normal distributions, with different methods for determining these values ​​depending on the data type. For single-valued data, a normal distribution plot or histogram can be used to obtain the data range, while for two-dimensional and three-dimensional data, a box plot can be used. Figure 2 This is a schematic diagram illustrating the use of histograms to solve for the range of single-valued data as proposed in this embodiment of the invention. Figure 3 This is a schematic diagram illustrating the use of box plots to solve for the data range of three-dimensional data types, as proposed in this embodiment of the invention. By employing this method—that is, calibrating the data to be calibrated based on statistical values ​​and the data range of the data to be calibrated—the probability of distorted data in the data to be calibrated can be reduced, thereby improving the accuracy of the pre-defined calibration data.

[0079] It should be noted that the data range is established based on historical project data corresponding to multiple historical projects. This is because the number of historical project data corresponding to multiple historical projects is no less than the number of target historical project data corresponding to the target historical project, which expands the sample size, reduces the one-sidedness of the data range establishment, and lowers the possibility of false alarms.

[0080] As an optional embodiment, after calibrating the data to be calibrated based on the target historical project data to obtain the target calibration data of the target project, the method may further include: verifying the target calibration data according to predetermined verification conditions; and if the target calibration data meets the predetermined verification conditions, sending the target calibration data to the vehicle controller corresponding to the target project.

[0081] Among these methods, the target calibration data is checked according to predetermined verification conditions. Many of the data to be calibrated exhibit certain physical patterns, specifically fixed data trends and reciprocities. To avoid basic errors or anomalies, various verification methods can be employed. For data trend checks, they can be based on corresponding data curves, and can be performed according to various data trends such as left-high-right-low, left-low-right-high, top-high-bottom-low, top-low-bottom-high, U-shaped, parabolic, vertical equality, horizontal equality, reciprocity checks, coordinate axis trends, and fixed values. For example:

[0082] If the target calibration data is a three-dimensional data type, i.e., XYZ type, where the X-axis represents the gear ratio, gradually increasing from high to low gears, the Y-axis represents the engine speed, gradually increasing from top to bottom, and the Z-axis represents the target calibration data, then theoretically, to mitigate driving shock, the lower the gear, the higher the engine speed. Therefore, the target calibration data should increase with increasing engine speed and decrease with increasing gear, which can be checked according to the rule of lower at the top and higher at the bottom, and lower on the left and higher on the right (e.g., ...). Figure 4 (As shown).

[0083] If the target calibration data is exhaust temperature model data, both the rotational speed and load are related to the target calibration data. They should increase with increasing rotational speed and simultaneously increase with increasing load. This can be checked according to the rule of lower at the top and higher at the bottom, or lower on the left and higher on the right (e.g., ...). Figure 5 (As shown)

[0084] If the target calibration data is the start-up enrichment factor, and the fuel injection coefficient gradually decreases as the water temperature rises, then it conforms to the inspection rule of left high and right low.

[0085] If the target calibration data are optimal ignition angle, basic ignition angle, and minimum ignition angle, the logical relationship between the three target calibration data is defined as follows: the optimal ignition angle must be greater than or equal to the basic ignition angle, and the optimal ignition angle must also be greater than or equal to the minimum ignition angle. The magnitude relationship between the three target calibration data must meet the requirement that it increases with increasing engine speed and simultaneously increases with increasing load.

[0086] If the trend of the target calibration data does not meet the requirements, a prompt will be given for the target calibration data, and manual confirmation will be required.

[0087] The above-mentioned rationality analysis of the target calibration data is noteworthy. It's important to understand that establishing the data range is the first step in verifying the target calibration data. If only compliance with the data range is considered, unreasonable data within that range may still exist. Pre-setting verification conditions for each target calibration data point based on expert experience, and then conducting a second verification, can improve the efficiency of data checking and further enhance the quality of the target calibration data.

[0088] In this embodiment of the invention, when the target calibration data meets the predetermined verification conditions, the target calibration data is sent to the vehicle controller corresponding to the target project. The output operation can be performed in the following ways: for example, the target calibration data can be selected and output and saved as a part of the calibration data in the target project; or all the calibration data of the target project can be generated in batches.

[0089] Based on the above embodiments and optional embodiments, an optional implementation method is provided. Figure 6 This is a data calibration flowchart provided by an optional embodiment of the present invention, such as... Figure 6 As shown, the process includes the following steps:

[0090] (1) Obtain data for each batch production project, upload A2L & hex files or relatively mature DCM files for projects A, B, C, etc. The uploaded new data will be named in a specific format, and then the corresponding file name association information will be saved in the thesaurus to unify and classify the data.

[0091] (2) Define data attributes related to the data to be calibrated, including engine attributes, transmission attributes, vehicle attributes, component attributes and other attributes. These attributes can be obtained from the system constants or calibration values ​​inside the ECU controller. For example, engine displacement, cylinder bore, stroke, intake manifold volume, etc., can all be mapped to the ECU calibration values.

[0092] 1. Set engine attributes. Table 1 is the engine attribute table for an optional embodiment of the present invention.

[0093] Table 1

[0094] Serial Number Attribute Name Serial Number Attribute Name Serial Number Attribute Name 1 Engine displacement 10 VVT type 20 Intake manifold volume 2 Ignition sequence 11 Intake and exhaust VVT activated 21 Intensifier relief valve 3 cylinder bore, stroke 12 EGR type 22 Coolant protection limit 4 External characteristic torque, power 13 turbocharger type 23 Oil temperature protection 5 Reverse torque 14 Intake flow meter type 24 engine maximum speed 6 Compression ratio 15 GPF type 25 flywheel teeth 7 Timing signal category 17 High-pressure injection pressure 26 Oil pump type 8 Exhaust back pressure 18 VVL type 27 Thermostat type 9 Maximum boost pressure 19 Long and short pipe types

[0095] 2. Set the transmission attributes. Table 2 shows the transmission attribute table for an optional embodiment of the present invention.

[0096] For example, the transmission type and the ECU's on / off selection in the drivability calibration module, the transmission ratio can be extracted from the coordinate axes of the drivability calibration diagram, and the shift pattern can be obtained from the bus signal output, etc.

[0097] Table 2

[0098] Serial Number Attribute Name Remark 1 Transmission type AT, DCT, MT, CVT 2 Gear ratios Transmission ratio value 3 Final Drive Ratio Final reduction ratio value 4 Transmission shifting patterns Economy, Sport, Comfort, Rain / Snow, Off-road, Slope 5 Torque demand type Torque limit, torque increase, torque decrease, speed request, etc. 6 Maximum torque capacity of the transmission Torque value

[0099] 3. Set component attributes. Table 3 is a component attribute table for optional embodiments of the present invention.

[0100] By establishing a component characteristic curve index rule, once the sensor or actuator type is determined, the corresponding sensor characteristic value can be located, such as voltage, resistance, current, duty cycle, etc.

[0101] Table 3

[0102] Serial Number Attribute Name Serial Number Attribute Name Serial Number Attribute Name 1 Intake pressure sensor characteristics 8 Injector characteristics 15 EGR temperature sensor characteristics 2 Intake pressure sensor characteristics 9 Rail pressure sensor characteristics 16 GPF pressure sensor characteristics 3 Throttle opening characteristics 10 High-pressure oil pump characteristics 17 GPF Temperature Exhaust Sensor Features 4 Knock sensor characteristics 11 Low-pressure oil pump characteristics 18 EGR pressure sensor characteristics 5 Crankshaft position sensor characteristics 12 Pre-oxygen sensor characteristics 19 Boost sensor characteristics 6 Camshaft position sensor characteristics 13 Post-oxygen sensor characteristics 20 Characteristics of charcoal canister solenoid valve 7 Coolant temperature sensor characteristics 14 Wheel speed sensor characteristics 21 Secondary air pump characteristics

[0103] 4. Set vehicle attributes. Table 4 shows the vehicle attribute table for optional embodiments of the present invention.

[0104] Table 4

[0105] Serial Number Attribute Name Remark 1 Driving Mode Sporty, comfortable, economical, off-road, etc. 2 Start-stop type 12V, 48V, HEV, etc. 3 Cruise type Cruise control, adaptive cruise control, etc. 4 Ambient temperature signal type ECU reading, CAN acquisition, etc. 5 Air conditioning torque signal type ECU reading, CAN acquisition, etc. 6 Fan type Single-level, two-level, and stepless 7 Types of electronic water pumps Single-stage, stepless 8 Hybrid type HEV, 48V, PHEV, pure electric 9 Thermal Management System Electronic thermostat, intelligent thermal management module 10 Generator type Ordinary generators and intelligent generators 11 Other controller types ESP, APA, etc. 12 Catalyst diagnostic strategies Amplitude diagnosis, concentration / dilutement diagnosis 13 Idle torque reserve Torque value 14 Evaporation system diagnostic types 15 Bad road diagnosis types Sensor method, software method 16 Oxygen sensor aging diagnosis types Software method, sensor method 17 Emissions regulations National V, National VI, RDE 18 GPF regeneration instrument display Carbon loading classification and grading

[0106] (3) Based on the above-set attributes, the target configuration attributes are associated with the predetermined rule values ​​(e.g., professional experience values) by setting fixed values. Different attributes correspond to different values. The predetermined rule values ​​can be set according to expert experience. There are multiple ways to set the preset rule values, such as using a template or generation tool to facilitate manual updating and saving of the rule setting method for the associated values. Figure 7 This is a schematic diagram illustrating the attribute configuration method of an optional embodiment of the present invention. For example... Figure 7As shown, the horizontal axis represents the set attributes, and the vertical axis represents the functional modules to be calibrated. Taking the drivability module as an example, the configuration attributes corresponding to the drivability module include engine attributes, transmission attributes, and vehicle attributes. Select all or some of the above configuration attributes according to specific needs. For example, select engine attributes as the set attribute, and filter the target historical items according to the engine attributes.

[0107] Furthermore, for more detailed functional sub-modules or parameters within the drivability module, such as the cruise control module and the start-stop module, the cruise control module can be set to vehicle attributes, and the start-stop module can be set to transmission attributes. By using this method for attribute setting, the target historical items selected by the cruise control module and the start-stop module can be different historical items. Based on the characteristics and requirements of the specific data to be calibrated, different attributes are selected with emphasis, and specific presets are made for the data to be calibrated, which helps to improve the data maturity of the overall project.

[0108] (4) Based on the set attributes and target configuration attributes, identify and parse the attributes of each calibration data in the database, read the A2L & hex file, and query based on system constants, key calibration data, CAN bus configuration, function switch instructions, etc.

[0109] (5) Develop pre-defined data rules to extract and statistically analyze the data in the database, obtain the correlation distribution of the target data in historical data, and design multiple pre-defined rules such as average, maximum, minimum, median, mode, and fitted value. Examples of these rules are as follows:

[0110] a. Among them, the preset rules corresponding to the single value are fixed value, mode, median, and average.

[0111] The single values ​​in the calibration parameters are generally related to certain technical configurations (start-stop, cruise control, VVT, EGR, etc.), the boundaries or conditions for activating a certain function (water temperature, altitude, engine speed, load, vehicle speed, etc.), system constants, etc. These values ​​generally do not change under the same or similar assemblies, so they can be automatically identified and output according to fixed values ​​during data preset. Other parameters involve the switching modes of the control strategy used, single-value filtering, coefficient correction, etc., which can be automatically output according to the median or mode.

[0112] For example, Table 6 is the result table of data single value A in historical projects in the optional embodiment of the present invention. As shown in Table 6, the data preset of the target data single value A (which has only two states, 0 and 1) needs to be obtained. After filtering according to the above attributes, there are 10 projects, of which 8 projects have data of 1 and the remaining 2 have data of 0. According to the preset rules, it does not belong to the fixed value (data is the same) rule. The system automatically identifies according to the statistical results and outputs it according to the mode rule. Then the preset data of the target data single value A is 1.

[0113] Table 6

[0114] Historical projects A-1 A-2 A-3 A-4 A-5 A-6 A-7 A-8 A-9 A-10 Corresponding data 1 1 0 0 1 1 1 1 1 1

[0115] Table 7 shows the results of data unit value A in historical projects in an optional embodiment of the present invention. As shown in Table 7, if the target data unit value A, after being statistically analyzed by the batch production database, yields the following results, then according to the rules set by expert experience, the preset value of A should be the average of 10 values. If the 10 values ​​differ significantly, the median value is used for statistical output.

[0116] Table 7

[0117] Historical projects A-1 A-2 A-3 A-4 A-5 A-6 A-7 A-8 A-9 A-10 Corresponding data 1.2 1.3 1 1.1 1.4 1.2 1.5 1.2 1.1 1

[0118] In addition, by identifying the characteristics of some single-value data types, such as project attributes and assembly information, like accelerator pedal characteristics and throttle opening characteristics, the values ​​can be directly determined.

[0119] b. Among them, the preset rules corresponding to the two-dimensional data type are fixed value, average value, median value, and fitted curve.

[0120] The two-dimensional data type in the target historical project is of type XZ. The output value changes with a certain value. Some of these values ​​are related to sensor characteristics (water temperature sensor, charcoal canister solenoid valve, voltage correction for injector opening time, etc.) and some component control inputs (boost water pump, air conditioning, fan, thermal management module, etc.). These values ​​can be automatically identified and output directly according to fixed values ​​based on the sensor types configured in the vehicle or engine during preset. Other values ​​are related to powertrain calibration principles, such as the starting module, idle speed module, and emissions module. These calibration values ​​also follow certain physical laws. For example, the fuel injection enrichment during starting gradually decreases with water temperature; the target idle speed of the vehicle also gradually decreases with water temperature or starting time. Therefore, when presetting these functional modules, the average value, median value, and fitted curve from the historical project can be used for mathematical calculations and statistical output to obtain the required data.

[0121] Taking the preset parameter B (start-up concentration factor) in historical projects as an example, Table 8 shows the results of parameter B of the two-dimensional data type in historical projects in the optional embodiment of the present invention. As shown in Table 8, the Z value of the start-up concentration factor is the data value of each project, and the X value is the water temperature. Projects that meet the target data are filtered according to attributes. If all Z values ​​are the same in historical projects, a fixed value is output. As shown in the distribution in the table below, the average output can be used, that is, the average value of each Z value under the same X value. This ensures that the data of the target project does not deviate much from that of the historical project, and the data risk is also reduced.

[0122] Table 8

[0123]

[0124]

[0125] Table 9 shows the preset values ​​of the activation concentration factor B in the optional embodiments of the present invention.

[0126] Table 9

[0127] Water temperature / ℃ -30 -20 0 20 40 60 80 100 Statistical value 1.5 1.4 1.2 1.1 1.1 1.08 1.05 1

[0128] If the Z-values ​​in historical projects have large deviations, but are within a certain range, the output can be preset according to the median rule. If the deviation range is even larger, the data of that project can be directly removed to ensure the rationality of the data source.

[0129] If there are discrepancies in the X-axis (expanded axis, missing axis, misaligned axis), the system can adopt various fitting methods, such as linear, quadratic, and cubic polynomial, to preset the data.

[0130] If it is a sensor characteristic curve or some characteristic attributes, then by defining the sensor type and project attributes, the preset values ​​will be accurately located and output directly from the historical database, saving engineers valuable time.

[0131] c. Among them, the preset rules corresponding to the three-dimensional data type are fixed value, average value, and median value.

[0132] The three-dimensional data type in the calibration parameters corresponds to the XYZ type curve. The X-axis is generally the speed, water temperature, gear, throttle opening, etc., and the Y-axis is the load, torque, etc. All of them are data generated in actual calibration according to the development goals. Therefore, the fixed values, average values, and medians (in order from front to back) from historical projects are used for statistics.

[0133] Because the XYZ type involves three dimensions, very few data points in historical projects meet the fixed values. These are generally for functions that are normally closed or open, driving styles of certain vehicle models, or optimized performance data (target VVT angle, target rail pressure, etc.). Other data generally use averages or medians, but data safety must be ensured. For example, the setting rules for basic ignition angle and enrichment protection use the smaller of the median and average values. Conversely, settings for knock threshold, misfire diagnosis, and exhaust temperature models use the larger of the median and average values. This ensures that the preset data does not deviate too much while protecting the engine and related components, avoiding data risks due to unreasonable preset data.

[0134] d. Some of the data is pre-defined according to specific rules.

[0135] As regulations continue to evolve, electronic control systems are also being updated, leading to changes in calibration variables. A data dictionary function has been developed to allow for preset mappings that meet certain conditions. Simultaneously, for on-board diagnostic systems, such as lighting strategies, fault reporting, and repair strategies, regulatory requirements must be met. These data do not change with project variations, making them compliant data that can be automatically preset into the target data.

[0136] e. Among these, establishing statistical rules for calibration data facilitates the setting of target data boundaries.

[0137] The data pre-settings generally follow the "majority rules" principle, but further understanding of the data optimization boundaries is still needed. This can be achieved through historical data statistics. Single-value types include: normal distribution plots and histograms; two-dimensional data types (XY) and three-dimensional data types (XYZ) include: box plots. See above. Figure 2 , Figure 3 This allows us to determine the maximum, minimum, and normal distribution of each value in historical projects, clarifying the data range for each calibrated value and further ensuring data security and rationality.

[0138] (6) Set data inspection rules

[0139] To avoid basic errors or anomalies in the preset data, further rationality checks are necessary. This is because many calibrations follow physical laws: engine output torque increases with throttle pedal opening; the basic ignition angle increases with engine speed and decreases with load, etc. Therefore, the preliminary preset data needs to be checked for rationality. Checking rules are set for each calibration value, such as left-high-right-low, left-low-right-high, top-high-bottom-low, top-low-bottom-high, U-shaped, parabolic, vertical equality, horizontal equality, reversible checks, coordinate axis trends, and fixed values—15 checking methods in total. Figure 4 The horizontal axis represents the gear ratio, which gradually increases from high to low gears, and the vertical axis represents the engine speed, which gradually increases from top to bottom. This parameter is designed to address drivability issues. Due to inherent limitations in the vehicle's transmission system, theoretically, lower gears increase both clearance and driving force, while higher engine speeds reduce the impact of transmission system clearance on drivability. In control strategies, this parameter reduces engine output torque. It should increase with engine speed and decrease with higher gears, and can be checked using the rule of lower at the top and higher at the bottom, and lower on the left and higher on the right. Similarly, exhaust temperature data can be analyzed using this method, showing that exhaust temperature increases with engine speed and load, as described above. Figure 5Furthermore, as mentioned in the examples above, the starting enrichment factor should gradually decrease as the water temperature rises, conforming to the check rule of "higher on the left, lower on the right." There are also checks involving inverse data, such as ignition angle efficiency curves, secondary charge models, and optimal model torque. In addition, multiple calibration parameters are verified, such as the optimal ignition angle parameter being greater than or equal to the basic ignition angle parameter, and the minimum ignition angle parameter being greater than or equal to the minimum ignition angle parameter. These rules are all based on the experience of calibration experts, realizing the assetization of knowledge. The results of the checks can be displayed visually in the form of bar charts, making it easy to identify which preset data have problems. Calibration engineers can further confirm the data using visualizations such as red or yellow status lights, improving data checking efficiency and data quality.

[0140] (7) Based on the above preset rules, preset a specific functional module of the target data. Taking engine bench calibration as an example, you can filter configuration attributes related to calibration in the set rules, such as displacement, stroke, bore, maximum power, or torque, and find data that is the same as or similar to the target engine for download. The preset data includes the charging model, torque model, basic ignition angle, VVT, ignition angle efficiency, etc. This version of data can be directly applied to bench calibration. The selected data module outputs and saves a DCM file, or a batch of DCM files can be generated by module.

[0141] (8) The flowchart of the optional embodiments is shown in [link to flowchart]. Figure 6 The steps to obtain the target calibration data corresponding to the target project are as follows: 1) Obtain historical project data; 2) Parse and process historical project data; 3) Define the attributes of the data to be calibrated; 4) Determine whether the data attribute rules are met; 5) Read historical data from historical projects that meet the data attributes; 6) Define the preset rules for the data to be calibrated; 7) Select the statistical method corresponding to the data type; 8) Determine whether the verification requirements are met; 9) Automatically output the calibration data.

[0142] The specific process is as follows:

[0143] S602, retrieve historical data from the ECU;

[0144] S604, After parsing, the data is uniformly classified and attribute identified to obtain multiple historical items and their corresponding historical item data, and then proceeds to S608;

[0145] S608, define the data attributes of the data to be calibrated in the target project, then proceed to S610;

[0146] S610, based on the attributes of the data to be calibrated in the target project, determine whether there are corresponding attributes in multiple historical projects. If the determination result is that there are multiple historical projects that correspond to the attributes of the data to be calibrated in the target project, proceed to S612; if the determination result is that there are no multiple historical projects that correspond to the attributes of the data to be calibrated in the target project, proceed to S608, and redefine the attributes of the data to be calibrated.

[0147] S612, Read historical data that meets the data attributes in the historical projects, remove historical projects that do not meet the preset similarity requirements and quantity requirements, and obtain the target historical project and the corresponding target historical project data;

[0148] S614, Define the preset rules for the data to be calibrated. The preset rules are multiple statistical methods corresponding to the data type. For example, for single values, the statistical methods of outputting fixed values, mode, median and mean are corresponding to output; for two-dimensional data, the statistical methods of outputting fixed values, mean, median and fitted curve values ​​are corresponding to output; and for three-dimensional data, the statistical methods of outputting fixed values, mean and median are corresponding to output.

[0149] S616, Select the statistical method corresponding to the data type. First, determine the data type of the target historical item, such as: single-value type, two-dimensional type, three-dimensional type. Then, determine the statistical method of the target historical item. Based on the data type of the target historical item, select the corresponding statistical method, such as: single value corresponds to outputting fixed value, mode, median, and average. Finally, select the statistical value that meets the range based on the data range.

[0150] S618, determine whether the above statistical value meets the predetermined verification conditions. If the determination result is that the predetermined verification conditions are not met, proceed to S614 and re-perform statistical processing; if the determination result is that the predetermined verification conditions are met, proceed to S620.

[0151] S620, calibration data is automatically output. Statistical values ​​that meet the predetermined inspection conditions can be used as target calibration data. There are multiple output methods, such as batch output of DCM files.

[0152] (9) Effect verification: Taking the starting fuel injection quantity calibration as an example, the horizontal axis is water temperature and the vertical axis is rail pressure. The calibration requires 16 points, as shown in Table 10. Table 10 is a data table of the number of starting fuel injection quantity calibration tests under the traditional method used for comparison in the optional embodiments of the present invention. As shown in Table 10, the traditional calibration method: even if the data is transplanted from similar projects, for each point, there are no less than 2 optimizations on average, for a total of 44 tests.

[0153] Table 10

[0154]

[0155] Table 11 is a data table of the number of starting fuel injection quantity calibration tests after automatic preset in the optional embodiment of the present invention. As shown in Table 11, the starting fuel injection quantity data of all high-pressure direct injection engines are statistically analyzed using the data preset method based on the batch production database. Under the premise of meeting the same calibration development goal, the actual calibration optimization is performed 34 times, reducing the number of tests by 10, improving calibration efficiency, and in particular saving valuable environmental storage resources.

[0156] Table 11

[0157]

[0158] Based on the above optional implementation methods, the following effects can be effectively achieved:

[0159] (1) Based on various historical project data, realize centralized management and value reconstruction of all calibration data, formulate data attributes related to calibration, including multiple data attributes closely related to calibration such as engine, transmission, whole vehicle, and parts, and establish extraction and indexing rules corresponding to data attributes and calibration data, such as functional type, system constant, sensor type, etc.

[0160] (2) Formulate data preset rules, establish the correlation and mapping relationship between the target project and historical data, and set preset rules for each parameter based on expert experience by statistical results of all historical data in the database. For selected calibration parameters or calibration modules, preset and output the data using expert-defined rules, such as: set value, average value, mode, median, fitted value, etc., to improve the quality of matching data and realize the automation and intelligence of data preset. Improve the work efficiency of engineers.

[0161] (3) Establish historical statistical rules and determine the optimization boundary of the data. This can be achieved through the historical data statistical function. Single value type: normal distribution chart, histogram; two-dimensional data and three-dimensional type: curve box plot. All of these can realize the maximum, minimum and normal distribution of each value in the historical projects, clarify the data range of each data to be calibrated, and further ensure the safety and rationality of the data.

[0162] (4) Formulate data rationality check rules, combine trend analysis algorithms and expert experience, develop standards, and formulate data check rules with various typical characteristics based on engine control models and physical experience. Perform rationality checks and judgments on target data, expand data recognition methods, reduce data release risks, and improve calibration efficiency and data quality.

[0163] It should be noted that the above optional implementation methods may involve processing scope and implementation effects including but not limited to the following.

[0164] By adopting the above optional implementation method, high-quality pre-set calibration data can be efficiently provided for target projects in the initial stage of the project. In related technologies, the setting usually relies on the subjective experience of calibration engineers, which has problems of instability and inaccuracy. However, the calibration data pre-set by this optional implementation method enables the target project to initially achieve the basic driving and testing functions of the vehicle, which is convenient for subsequent adjustments to the pre-set calibration data. In addition, the calibration data obtained by adopting the optional implementation method of this invention can improve the maturity of the data and reduce the resources required for later adjustment experiments.

[0165] Based on the calibration data obtained from the above optional implementation methods, a large amount of process calibration data and test file data are obtained by repeatedly testing and optimizing various performance aspects of the vehicle. This process data is the core data for obtaining the final calibration data. Using the highly mature calibration data obtained from the optional implementation methods of this invention can effectively save time, resources, and manpower costs in the performance optimization process.

[0166] By introducing manual inspection into key nodes of the calibration process, the calibration data obtained based on the above optional implementation methods is systematically checked throughout the calibration process, thereby improving the current calibration quality. Furthermore, a feedback mechanism is established with calibration engineers to conduct timely verification and adjustments, further enhancing data maturity.

[0167] The calibration data obtained by the above optional implementation method can be the calibration data of the entire vehicle or the calibration data of some functional modules of the vehicle. The data is integrated manually, and it is judged whether the quality of the integrated calibration data meets the mass production standard. If the judgment result is that it meets the mass production standard, it can be put into production.

[0168] In this embodiment of the invention, an electronic control calibration data processing device is also provided. Figure 8 This is a structural block diagram of an electronically controlled calibration data processing device according to an embodiment of the present invention, such as... Figure 8 As shown, the electronic control calibration data processing device includes: a first acquisition module 81, a second acquisition module 82, a third acquisition module 83, and a calibration module 84. The device will be described below.

[0169] The first acquisition module 81 is used to acquire historical project data corresponding to multiple historical projects, wherein the multiple historical projects correspond to multiple vehicle models, and the historical project data includes electronic control data of the attributes included in the corresponding vehicle model; the second acquisition module 82 is connected to the first acquisition module 81 and is used to acquire the data to be calibrated in the target project and determine the attributes of the data to be calibrated in the target project; the third acquisition module 83 is connected to the second acquisition module 82 and is used to select a target historical project from multiple historical projects based on the attributes of the data to be calibrated in the target project, and acquire the target historical project data corresponding to the target historical project; the calibration module 84 is connected to the third acquisition module 83 and is used to calibrate the data to be calibrated in the target project based on the target historical project data to obtain the target calibration data of the target project.

[0170] In this embodiment of the invention, a computer-readable storage medium is also provided, which includes a stored program, wherein the program controls the device where the computer-readable storage medium is located to execute any of the above-mentioned electronic calibration data processing methods when it is running.

[0171] In this embodiment of the invention, a computer device is also provided, including: a memory and a processor, wherein the memory stores a computer program; and the processor is configured to execute the computer program stored in the memory, wherein the computer program, when running, causes the processor to execute any of the above-mentioned electronic control calibration data processing methods.

[0172] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0173] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0175] The units described above 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0176] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0177] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0178] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for processing electronic control calibration data, characterized in that, include: Acquire historical project data corresponding to multiple historical projects, wherein the multiple historical projects correspond to multiple vehicle models, and the historical project data includes electronic control data of the attributes included in the corresponding vehicle model; Obtain the data to be calibrated in the target project, and determine the attributes of the data to be calibrated in the target project; The historical project data corresponding to the various historical projects is parsed, and the attributes of the parsed historical project data corresponding to the various historical projects are set to obtain the attributes of the historical project data corresponding to the various historical projects. The attributes include at least one of the following: engine attributes, transmission attributes, vehicle attributes, and component attributes. The attributes of the data to be calibrated in the target project are compared with the attributes of the historical project data corresponding to the various historical projects to obtain the comparison results; Based on the comparison results, the target historical item is determined, and the target historical item data corresponding to the target historical item is obtained; Based on the target historical project data, the data to be calibrated in the target project is calibrated to obtain the target calibration data of the target project. Based on the target historical project data, the data to be calibrated in the target project is calibrated to obtain the target calibration data of the target project, including: When there are multiple target historical projects, the number of associated parameters of the target historical project data is determined, wherein the associated parameters are parameters that affect the target historical project data; Based on the number of the associated parameters, the data type of the target historical project data is determined, wherein the data type includes at least a single-value type that is not affected by other parameters, a two-dimensional data type that changes with the value of one parameter, and a three-dimensional data type that changes with the values ​​of two parameters. Based on the distribution of the target historical project data, the target data statistical method corresponding to the data type is determined according to preset rules. Different data types correspond to different target data statistical methods. The preset rules include at least the statistical methods for outputting fixed values, mode, median, and average values ​​for single-value types, the statistical methods for outputting fixed values, average values, median, and fitted curve values ​​for two-dimensional data types, and the statistical methods for outputting fixed values, average values, and median values ​​for three-dimensional data types. Using the aforementioned target data statistical method, statistical values ​​are obtained by statistically analyzing the target historical data of multiple target historical items. The data to be calibrated is calibrated based on the statistical values ​​and data range to obtain the target calibration data of the target project. The data range is used to represent the data range corresponding to the data to be calibrated. The data range is obtained in different ways for different numerical types. For the single-value type of the numerical type, the data range is obtained by plotting a normal distribution plot or a histogram. For the two-dimensional data type and the three-dimensional data type of the numerical type, the data range is obtained by plotting a box plot. After calibrating the data to be calibrated in the target project based on the target historical project data to obtain the target calibration data of the target project, the process further includes: The target calibration data is checked according to predetermined check conditions, wherein the predetermined check conditions are used to indicate that the data curves corresponding to the target calibration data are checked according to the following criteria: left high and right low, left low and right high, top high and bottom low, top low and bottom high, U-shaped, parabolic, vertical equal, horizontal equal, mutual reversal check, coordinate axis trend, and fixed value data trend. If the target calibration data meets the predetermined verification conditions, the target calibration data will be sent to the vehicle controller corresponding to the target item.

2. The method according to claim 1, characterized in that, The step of determining the target historical item based on the comparison result and obtaining the target historical item data corresponding to the target historical item includes: Based on the comparison results, historical projects that have corresponding attributes to the data to be calibrated in the target project are identified as preliminary target historical projects. The attributes included in the preliminary target historical project are compared with the attributes of the data to be calibrated in the target project to obtain the quantity values ​​corresponding to the preliminary target historical project, wherein the quantity value is the number of attributes included in the preliminary target historical project and the number of attributes of the data to be calibrated. Select historical projects whose quantity value is greater than a predetermined quantity threshold from the initial target historical projects as the target historical projects; Obtain the target historical project data corresponding to the target historical project.

3. The method according to claim 1, characterized in that, Before calibrating the data to be calibrated based on the statistical values ​​and data range to obtain the target calibration data for the target project, the method further includes: Based on the historical project data corresponding to the various historical projects, the data range of the data to be calibrated is determined.

4. An electronically controlled calibration data processing device, characterized in that, include: The first acquisition module is used to acquire historical project data corresponding to multiple historical projects, wherein the multiple historical projects correspond to multiple vehicle models, and the historical project data includes electronic control data of the attributes included in the corresponding vehicle model; The second acquisition module is used to acquire the data to be calibrated in the target project and determine the attributes of the data to be calibrated in the target project. The third acquisition module is used to parse the historical project data corresponding to the multiple historical projects, set attributes for the parsed historical project data corresponding to the multiple historical projects, and obtain the attributes of the historical project data corresponding to the multiple historical projects. The attributes include at least one of the following: engine attributes, transmission attributes, vehicle attributes, and component attributes. The module compares the attributes of the data to be calibrated in the target project with the attributes of the historical project data corresponding to the multiple historical projects to obtain a comparison result. Based on the comparison result, the module determines the target historical project and obtains the target historical project data corresponding to the target historical project. The calibration module is used to calibrate the data to be calibrated in the target project based on the target historical project data, so as to obtain the target calibration data of the target project; The device is used to calibrate the data to be calibrated in the target project based on the target historical project data through the following steps to obtain the target calibration data of the target project: When there are multiple target historical projects, the number of associated parameters of the target historical project data is determined, wherein the associated parameters are parameters that affect the target historical project data; Based on the number of the associated parameters, the data type of the target historical project data is determined, wherein the data type includes at least a single-value type that is not affected by other parameters, a two-dimensional data type that changes with the value of one parameter, and a three-dimensional data type that changes with the values ​​of two parameters. Based on the distribution of the target historical project data, the target data statistical method corresponding to the data type is determined according to preset rules. Different data types correspond to different target data statistical methods. The preset rules include at least the statistical methods for outputting fixed values, mode, median, and average values ​​for single-value types, the statistical methods for outputting fixed values, average values, median, and fitted curve values ​​for two-dimensional data types, and the statistical methods for outputting fixed values, average values, and median values ​​for three-dimensional data types. Using the aforementioned target data statistical method, statistical values ​​are obtained by statistically analyzing the target historical data of multiple target historical items. The data to be calibrated is calibrated based on the statistical values ​​and data range to obtain the target calibration data of the target project. The data range is used to represent the data range corresponding to the data to be calibrated. The data range is obtained in different ways for different numerical types. For the single-value type of the numerical type, the data range is obtained by plotting a normal distribution plot or a histogram. For the two-dimensional data type and the three-dimensional data type of the numerical type, the data range is obtained by plotting a box plot. The device is further configured to verify the target calibration data according to predetermined verification conditions, wherein the predetermined verification conditions are used to represent the verification based on the data curve corresponding to the target calibration data, according to the following data trends: left high right low, left low right high, top high bottom low, top low bottom high, U-shaped, parabolic, vertical equal, horizontal equal, mutual reversal check, coordinate axis trend, and fixed value data trend; when the target calibration data meets the predetermined verification conditions, the target calibration data is sent to the vehicle controller corresponding to the target item.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the electronic calibration data processing method according to any one of claims 1 to 3.

6. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the electronic control calibration data processing method according to any one of claims 1 to 3.

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