Vehicle model data analysis method and device, computer equipment and storage medium

Abstract

The invention relates to a vehicle type data analysis method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring at least two groups of vehicle model data, and dividing the at least two groups of vehicle model data into main body data and reference data; performing hierarchical data comparison processing on the subject data and the reference data through a data comparison model to obtain a comparison information set between the subject data and the reference data; the data comparison model is used for converting the main body data and the reference data into hierarchical data structures and then carrying out data comparison processing; according to the contrast information set, performing annotation processing on the main body data to obtain annotated main body data; and outputting the contrast information set and the marked main body data as contrast analysis results of the at least two groups of vehicle model data. By adopting the method, the data comparison efficiency and accuracy of the vehicle model data can be improved.

Description

Technical Field

[0001] This application relates to the field of automotive technology, and in particular to a method, apparatus, computer equipment, storage medium, and computer program product for analyzing vehicle model data. Background Technology

[0002] During the engineering design phase of a car, as requirements are adjusted and designs are iterated and optimized, vehicle data will undergo multiple iterations and changes. Staff need to conduct rigorous verification and data difference comparisons on the vehicle data to determine the key data that affects the car's performance.

[0003] However, each iteration involves a huge amount of vehicle model data. Manually comparing the differences between different versions is inefficient and time-consuming, and comparing data one by one by computer also takes a lot of time, resulting in low efficiency of vehicle model data comparison and analysis. Summary of the Invention

[0004] Therefore, it is necessary to provide a vehicle model data analysis method, device, computer equipment, computer-readable storage medium, and computer program product that can improve the efficiency of comparative analysis of vehicle model data in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for analyzing vehicle model data. The method includes:

[0006] Acquire at least two sets of vehicle model data, and divide the at least two sets of vehicle model data into main data and reference data;

[0007] A hierarchical data comparison process is performed on the main data and the reference data using a data comparison model to obtain a set of comparison information between the main data and the reference data. The data comparison model is used to convert the main data and the reference data into a hierarchical data structure before performing the data comparison process.

[0008] Based on the aforementioned set of reference information, the main data is annotated to obtain annotated main data;

[0009] The comparison information set and the labeled main data are output as the comparison analysis results of the at least two sets of vehicle model data.

[0010] In one embodiment, a hierarchical data comparison process is performed on the subject data and the reference data using a data comparison model to obtain a set of comparison information between the subject data and the reference data, including:

[0011] Using the data comparison model, based on data association rule information, hierarchical relationship mapping is performed on the main data to obtain the sub-main data of the main data at each level; and hierarchical relationship mapping is performed on the reference data to obtain the sub-reference data of the reference data at each level; the data association rule information is used to characterize the connection relationship between data.

[0012] The sub-sub-sub data and the sub-reference data are compared at the same level layer by layer to obtain a set of comparison information between the sub-sub-sub-sub data and the sub-reference data at the same level.

[0013] In one embodiment, the data association rule information includes the data association rule information of the main data and the data association rule information of the reference data;

[0014] Using the data comparison model, based on data association rule information, hierarchical relationship mapping is performed on the main data to obtain sub-main data of the main data at each level; and hierarchical relationship mapping is performed on the reference data to obtain sub-reference data of the reference data at each level, including:

[0015] Using the data comparison model, based on the data association rules of the main data, the main data is mapped to a first product tree; the sub-main data at each level of the first product tree are refined layer by layer in the form of product type to unit type;

[0016] Using the data comparison model, the reference data is mapped to a second product tree based on the data association rule information of the reference data; the sub-reference data at each level of the second product tree are refined layer by layer in the form from the product type to the unit type.

[0017] In one embodiment, the sub-sub-data and the sub-reference data are compared at the same level, layer by layer, to obtain a set of comparison information between the sub-sub-data and the sub-reference data at the same level, including:

[0018] The sub-subject data of the main data at the first level is taken as the sub-subject data of the main data at the current level, and the sub-reference data of the reference data at the first level is taken as the sub-reference data of the reference data at the current level.

[0019] The sub-sub-subject data and the sub-reference data of the current level are compared to obtain the comparison information of the current level, and the comparison information is stored in the comparison information set.

[0020] If the reference information of the current level indicates that the sub-subject data of the current level is the same as the sub-reference data of the current level, then the reference information set is output.

[0021] If the comparison information of the current level indicates that the sub-sub-sub-data of the current level is different from the sub-reference data of the current level, and the sub-sub-sub-data of the current level and the sub-reference data of the current level do not belong to the unit type, then the current level is updated to the next level below the current level, and the process jumps to the step of performing data comparison processing on the sub-sub-sub-data of the current level and the sub-reference data of the current level to obtain the comparison information of the current level, and storing the comparison information in the comparison information set;

[0022] If the comparison information of the current level indicates that the sub-sub-sub-data of the current level is different from the sub-reference data of the current level, and the sub-sub-sub-data of the current level and the sub-reference data of the current level belong to the unit type, then the comparison information set is output.

[0023] In one embodiment, the subject data is labeled according to the reference information set to obtain labeled subject data, including:

[0024] Based on the set of comparison information, target data that is identical to the reference data in the main data is determined;

[0025] The target data is labeled to obtain the labeled main data.

[0026] In one embodiment, at least two sets of vehicle model data are acquired, and the at least two sets of vehicle model data are divided into main data and reference data, including:

[0027] Obtain at least two sets of vehicle model data that differ in version information and / or configuration information and / or vehicle model information;

[0028] The vehicle data with the smallest time difference from the current time among the at least two sets of vehicle data is set as the main data, and the vehicle data with the largest time difference from the current time among the at least two sets of vehicle data is set as the reference data.

[0029] Secondly, this application also provides a vehicle model data analysis device. The device includes:

[0030] The data acquisition module is used to acquire at least two sets of vehicle model data and divide the at least two sets of vehicle model data into main data and reference data;

[0031] The data comparison module is used to perform hierarchical data comparison processing on the main data and the reference data through a data comparison model to obtain a set of comparison information between the main data and the reference data; the data comparison model is used to convert the main data and the reference data into a hierarchical data structure before performing data comparison processing;

[0032] The data annotation module is used to annotate the main data according to the reference information set to obtain the annotated main data;

[0033] The result output module is used to output the comparison information set and the labeled main data as the comparison analysis results of the at least two sets of vehicle model data.

[0034] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0035] Acquire at least two sets of vehicle model data, and divide the at least two sets of vehicle model data into main data and reference data;

[0036] A hierarchical data comparison process is performed on the main data and the reference data using a data comparison model to obtain a set of comparison information between the main data and the reference data. The data comparison model is used to convert the main data and the reference data into a hierarchical data structure before performing the data comparison process.

[0037] Based on the aforementioned set of reference information, the main data is annotated to obtain annotated main data;

[0038] The comparison information set and the labeled main data are output as the comparison analysis results of the at least two sets of vehicle model data.

[0039] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0040] Acquire at least two sets of vehicle model data, and divide the at least two sets of vehicle model data into main data and reference data;

[0041] A hierarchical data comparison process is performed on the main data and the reference data using a data comparison model to obtain a set of comparison information between the main data and the reference data. The data comparison model is used to convert the main data and the reference data into a hierarchical data structure before performing the data comparison process.

[0042] Based on the aforementioned set of reference information, the main data is annotated to obtain annotated main data;

[0043] The comparison information set and the labeled main data are output as the comparison analysis results of the at least two sets of vehicle model data.

[0044] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0045] Acquire at least two sets of vehicle model data, and divide the at least two sets of vehicle model data into main data and reference data;

[0046] A hierarchical data comparison process is performed on the main data and the reference data using a data comparison model to obtain a set of comparison information between the main data and the reference data. The data comparison model is used to convert the main data and the reference data into a hierarchical data structure before performing the data comparison process.

[0047] Based on the aforementioned set of reference information, the main data is annotated to obtain annotated main data;

[0048] The comparison information set and the labeled main data are output as the comparison analysis results of the at least two sets of vehicle model data.

[0049] The aforementioned vehicle model data analysis method, apparatus, computer equipment, storage medium, and computer program product acquire at least two sets of vehicle model data and divide these data into main data and reference data. Using a data comparison model, hierarchical data comparison processing is performed on the main data and reference data to obtain a set of comparison information between them. The data comparison model is used to convert the main data and reference data into a hierarchical data structure before data comparison processing. Based on the comparison information set, the main data is labeled to obtain labeled main data. The comparison information set and the labeled main data are output as the comparison analysis results for at least two sets of vehicle model data. This method enables hierarchical data comparison processing on at least two sets of vehicle model data, rather than comparing all data at once. This allows the terminal to gradually release computing resources, improving the efficiency of vehicle model data comparison analysis and effectively improving the utilization efficiency of computing resources. By labeling the main data, relevant personnel can quickly and clearly understand the similarities and differences between at least two sets of vehicle model data based on the labeled main data, improving the data comparison effect and laying the foundation for subsequent iterative optimization of vehicle model data. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a vehicle model data analysis method in one embodiment;

[0051] Figure 2 This is a schematic diagram of the data import window interface in one embodiment;

[0052] Figure 3 This is a flowchart illustrating the steps of hierarchical data comparison processing of subject data and reference data in one embodiment;

[0053] Figure 4 This is a flowchart illustrating the steps of performing hierarchical data comparison between sub-subject data and sub-reference data in one embodiment.

[0054] Figure 5 This is a flowchart illustrating the vehicle model data analysis method in another embodiment;

[0055] Figure 6 This is a flowchart illustrating the vehicle model data analysis method in yet another embodiment;

[0056] Figure 7 This is a structural block diagram of a vehicle model data analysis device in one embodiment;

[0057] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0060] In one embodiment, such as Figure 1 As shown, a vehicle model data analysis method is provided. This embodiment illustrates the method's application to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0061] Step S101: Obtain at least two sets of vehicle model data and divide the at least two sets of vehicle model data into main data and reference data.

[0062] Vehicle model data refers to data describing various attributes and characteristics of a car. It encompasses multiple dimensions, including the assembly methods of automotive products and components, technological feasibility, and manufacturing complexity. For example, vehicle model data may contain data on multiple (e.g., 2000) component units for a specific car model.

[0063] Specifically, the terminal determines the range of vehicle model data that needs to be compared, and can obtain at least two sets of vehicle model data for subsequent data comparison processing, and divide the vehicle model data used for data comparison processing into main data and reference data.

[0064] The main data refers to the core data used for data comparison processing. The reference data refers to the data that provides the comparison standard.

[0065] Step S102: Using a data comparison model, hierarchical data comparison processing is performed on the main data and reference data to obtain a set of comparison information between the main data and the reference data. The data comparison model is used to convert the main data and reference data into a hierarchical data structure before performing data comparison processing.

[0066] In this context, a data comparison model refers to a model used to perform data comparison processing on input vehicle model data. For example, a data comparison model could be 3D design software used in the automotive manufacturing industry, such as CATIA software, a computer-aided 3D interactive application.

[0067] Specifically, the terminal can also display a data import window linked to the data comparison model, in which the main data and reference data are imported respectively. Figure 2 This is a schematic diagram of the data import window interface. In response to the triggering of the target button displayed in the data import window, the main data and reference data are input into the data comparison model. The data comparison model first converts the main data and reference data into a hierarchical structure, then performs data comparison processing according to the hierarchy to obtain hierarchical comparison information, stores the hierarchical comparison information in the comparison information set, and finally outputs the comparison information set.

[0068] The comparison information set integrates comparison information of the subject data and reference data that have already been compared at different levels. The comparison information set can be in tabular form; for example, the subject data and reference data that have already been compared at different levels can be imported into the corresponding positions in the tabular comparison information set.

[0069] Step S103: Based on the reference information set, the main data is labeled to obtain the labeled main data.

[0070] Specifically, by comparing the information set, the terminal can identify the data in the main data that is the same as the reference data, and then mark the data in the main data that is the same as the reference data, such as by using labels or color marking, to obtain the marked main data.

[0071] Step S104: Output the comparison analysis results of the comparison information set and the labeled main data as at least two sets of vehicle model data.

[0072] The comparative analysis results are used to demonstrate the similarities and differences between at least two sets of vehicle model data.

[0073] Specifically, the terminal can output the comparison analysis results of at least two sets of vehicle model data as the comparison information set and the labeled main data. It can also generate more detailed data analysis reports based on the comparison information set and the labeled main data. The terminal can display the comparison analysis results on the interface, and can also send the comparison analysis results to other terminals (such as the terminals of project personnel) for display, so that project personnel can quickly view the similarities and differences between at least two sets of vehicle model data.

[0074] The aforementioned vehicle model data analysis method enables hierarchical data comparison processing of at least two sets of vehicle model data, rather than comparing all data at once. This allows the terminal to gradually release computing resources, improving both the efficiency of vehicle model data comparison and analysis and the utilization efficiency of computing resources. By labeling the main data, relevant personnel can quickly and clearly understand the similarities and differences between at least two sets of vehicle model data based on the labeled main data. This not only improves the data comparison effect of vehicle model data but also lays the foundation for subsequent iterative optimization of vehicle model data.

[0075] In one embodiment, such as Figure 3 As shown, step S102 above uses a data comparison model to perform hierarchical data comparison processing on the main data and the reference data, obtaining a set of comparison information between the main data and the reference data, specifically including the following:

[0076] Step S301: Using a data comparison model and based on data association rule information, perform hierarchical relationship mapping processing on the main data to obtain the sub-main data of the main data at each level, and perform hierarchical relationship mapping processing on the reference data to obtain the sub-reference data of the reference data at each level; the data association rule information is used to characterize the connection relationship between data.

[0077] Data association rule information refers to information describing the connections between data. For example, data association rule information can describe which part unit belongs to which product.

[0078] Here, sub-sub-subject data refers to the data at each level after the subject data has been hierarchically mapped. Sub-reference data refers to the data at each level after the reference data has been hierarchically mapped.

[0079] Specifically, the terminal can build a data comparison model using Python (a widely used high-level programming language), and apply the pycatia module (a Python module capable of CATIA automated processing) within the data comparison model to perform hierarchical data comparison processing between the main data and reference data, from Product (a whole composed of multiple part units) to CATPart (the smallest unit of a Product). Based on the data relationships between the data in the main data, the data comparison model performs hierarchical mapping processing on the main data, obtaining sub-main data at each level from the Product layer to the CATPart layer; similarly, based on the data relationships between the data in the reference data, the data comparison model performs hierarchical mapping processing on the reference data, obtaining sub-reference data at each level from the Product layer to the CATPart layer.

[0080] The product tree can contain multiple Product layers and at least one CATPart layer.

[0081] Step S302: Perform data comparison processing on the same level for the sub-sub-sub-data and sub-reference data at each level to obtain a set of comparison information for the sub-sub-sub-data and sub-reference data at the same level.

[0082] The reference information set refers to the data set used to store reference information. In practical applications, the reference information set can be an information summary table that summarizes all reference information.

[0083] Specifically, the terminal can perform data comparison processing on the sub-sub-subject data and sub-reference data at the same level in the order from the product layer to the unit layer, obtain the comparison information of the sub-sub-subject data and sub-reference data at the same level, and store the comparison information at this level in the comparison information set; after completing the data comparison processing, the comparison information set can be output.

[0084] It should be noted that this application may not necessarily perform data comparison processing at all levels. If the comparison information at the first level or the comparison information at the intermediate level reaches the set termination comparison condition, the terminal will terminate the data comparison processing and output a comparison information set. At this time, the output comparison information set only contains the comparison information at the first level or the comparison information from the first level to the intermediate level. That is, the comparison information set only contains the comparison information of the levels that have been compared and does not contain the comparison information of the levels that have not been compared.

[0085] In this embodiment, a data comparison model is used to map the main data to sub-main data at each level and the reference data to sub-reference data at each level based on data association rules. The terminal can then perform data comparison processing on the sub-main data and sub-reference data at each level to obtain a set of comparison information for the sub-main data and sub-reference data at the same level. This effectively saves the amount of data for data comparison processing, thereby improving the efficiency of data comparison and reducing the time consumption and manpower cost of data comparison processing for vehicle model data.

[0086] In one embodiment, the data association rule information includes data association rule information for the subject data and data association rule information for the reference data.

[0087] Step S301 above, through a data comparison model and based on data association rule information, performs hierarchical relationship mapping processing on the main data to obtain the sub-main data of the main data at each level, and performs hierarchical relationship mapping processing on the reference data to obtain the sub-reference data of the reference data at each level. Specifically, it includes the following: through a data comparison model and based on the data association rule information of the main data, the main data is mapped to a first product tree; the sub-main data at each level of the first product tree is refined layer by layer in the form from product type to unit type; through a data comparison model and based on the data association rule information of the reference data, the reference data is mapped to a second product tree; the sub-reference data at each level of the second product tree is refined layer by layer in the form from product type to unit type.

[0088] Specifically, the terminal inputs main data and reference data into the data comparison model. Based on the data association rules of the main data, the data comparison model organizes the main data hierarchically, resulting in sub-main data that is progressively refined from product type (e.g., car model, version, name) to unit type (e.g., engine, tire, car interior, etc.). Then, the sub-main data is deployed at each level in order from product type to unit type, forming a tree-structured product tree, thus the terminal obtains the first product tree of the main data. Similarly, based on the data association rules of the reference data, the comparison model organizes the reference data hierarchically, resulting in sub-reference data that is progressively refined from product type (e.g., car model, version, name) to unit type (e.g., engine, tire, car interior, etc.). Then, the sub-reference data is deployed at each level in order from product type to unit type, forming a tree-structured product tree, thus the terminal obtains the second product tree of the reference data.

[0089] In this embodiment, based on data association rule information, the main data and reference data are mapped to a first product tree and a second product tree, respectively. The sub-data at each level in the first and second product trees are refined layer by layer from product type to unit type. Therefore, when performing data comparison processing, the data comparison processing can be performed first on the sub-main data and sub-reference data of the highest level product type, and then the data comparison processing can be performed on the more refined sub-main data and sub-reference data step by step, which effectively improves the accuracy and efficiency of data comparison processing.

[0090] In one embodiment, step S302 above involves performing a hierarchical data comparison process between the sub-subject data and the sub-reference data to obtain a set of comparison information for the sub-subject data and the sub-reference data at the same level. This specifically includes the following:

[0091] The main data at the first level is used as the sub-main data at the current level, and the reference data at the first level is used as the sub-reference data at the current level. Data comparison processing is performed on the sub-main data and sub-reference data at the current level to obtain comparison information at the current level, and this comparison information is stored in a comparison information set. If the comparison information at the current level indicates that the sub-main data at the current level is the same as the sub-reference data at the current level, then the comparison information set is output. If the comparison information at the current level indicates that the sub-main data at the current level is the same as the sub-reference data at the current level, then the comparison information set is output. If the reference data differs, and the sub-sub-body data and the sub-reference data of the current level do not belong to the unit type, then the current level is updated to the next level below the current level, and the process jumps to the step of performing data comparison processing on the sub-sub-body data and the sub-reference data of the current level to obtain the comparison information of the current level, and storing the comparison information in the comparison information set; if the comparison information of the current level indicates that the sub-sub-body data and the sub-reference data of the current level are different, and the sub-sub-body data and the sub-reference data of the current level belong to the unit type, then the comparison information set is output.

[0092] In the product tree, the first level refers to the highest level, and the sub-subject data and sub-reference data at the first level belong to the product type. The last level refers to the lowest level, and the sub-subject data and sub-reference data at the last level belong to the unit type.

[0093] The terminal can perform data comparison processing layer by layer from the highest level of the product tree (e.g., the first product tree, the second product tree), that is, it starts by comparing data of the most basic product types, and then performs data comparison of more detailed unit types. Specifically, Figure 4The flowchart illustrates the process of performing hierarchical data comparison between sub-subject data and sub-reference data. The terminal uses the sub-subject data of the subject data at the first level as the sub-subject data of the subject data at the current level, and uses the sub-reference data of the reference data at the first level as the sub-reference data of the reference data at the current level. Then, the terminal performs data comparison processing on the sub-subject data and the sub-reference data at the current level to obtain the comparison information at the current level, and stores the comparison information in the comparison information set. As shown in Table 1, the comparison information set can be in tabular form.

[0094] Table 1. Comparison Information Set

[0095]

[0096] The system checks whether the sub-sub-matter data and sub-reference data in the comparison information are inconsistent. If the sub-matter data at the current level is the same as the sub-reference data at the current level, the data comparison process ends, and a comparison information set is output. If the sub-matter data at the current level differs from the sub-reference data at the current level, and neither the sub-matter data nor the sub-reference data at the current level belongs to a unit type, the data comparison process continues to the next level. The terminal can update the current level to the next level below the current level, and then jump to the steps described above for comparing the sub-matter data and sub-reference data at the current level to obtain the comparison information at the current level and storing the comparison information in the comparison information set. If the sub-matter data at the current level differs from the sub-reference data at the current level, and it is detected that the sub-matter data and sub-reference data at the current level belong to a unit type, or in other words, it is detected that different data in the comparison information belongs to the unit level, indicating that there are no more refined sub-matter data and sub-reference data that differ, the terminal then outputs the comparison information set. Therefore, the comparison information set stores comparison information between sub-subject data and sub-reference data at the hierarchical level that has undergone data comparison processing.

[0097] As can be seen from the above, there are two ways to end the data comparison process: First, if the sub-sub-main data of the current level is the same as the sub-reference data of the current level, the data comparison process ends immediately and no further data comparison processes are performed at subsequent levels; Second, even if the sub-sub-main data of the current level is different from the sub-reference data of the current level, if both the sub-main data and the sub-reference data of the current level belong to the unit type, the data comparison process can also be ended.

[0098] In this embodiment, by comparing the sub-sub-main data and sub-reference data of the current level starting from the product type level, the comparison information of the current level is obtained and stored in the comparison information set. This allows for an overall comparison of whether the main data and reference data are the same, effectively reducing the amount of data to be compared and improving the efficiency and accuracy of data comparison. If differences are detected between the sub-sub-main data and the sub-reference data of the current level, further refined data comparison processing is performed to accurately locate the level at which the main data and reference data differ. This eliminates the need to analyze all data each time, significantly improving the efficiency of data comparison processing for vehicle model data.

[0099] In one embodiment, step S103 above, which involves annotating the subject data according to the reference information set to obtain annotated subject data, specifically includes the following: determining the target data in the subject data that is the same as the reference data according to the reference information set; and annotating the target data to obtain annotated subject data.

[0100] Specifically, as shown in Table 1, the terminal can identify data in the main data that is identical to the reference data from the comparison information set. To facilitate differentiation, the data in the main data that is identical to the reference data can be set as the target data. The terminal can also mark the target data in the main data to more quickly and clearly understand the differences between the main data and the reference data. For example, the target data can be marked in the main data using colors (such as bright silver, yellow, red, etc.) or labels (such as numbers, characters, text, etc.). The terminal then obtains the marked main data.

[0101] In practical applications, after obtaining the set of reference information, the terminal can annotate the target data in the main data. Furthermore, after obtaining the reference information for the current level, the terminal can also annotate data in the main data that is identical to the sub-reference data at the current level, based on the reference information for that level. In other words, the timing of reference information annotation can be adaptively selected.

[0102] It is understandable that, since the comparison information set stores the comparison information between one or more levels of sub-sub-subject data and sub-reference data that have undergone data comparison processing, the labeled subject data contains the data that has undergone data comparison processing.

[0103] In this embodiment, target data that is the same as the sub-reference data in the main data is labeled, so that relevant analysts of vehicle model data can quickly understand the similarities and differences between the main data and the reference data through the labeled main data, effectively improving the visualization effect of the comparative analysis results of vehicle model data.

[0104] In one embodiment, step S101 above, which involves acquiring at least two sets of vehicle model data and dividing the at least two sets of vehicle model data into main data and reference data, specifically includes the following: acquiring at least two sets of vehicle model data with different version information and / or configuration information and / or vehicle model information; setting the vehicle model data with the smallest time difference from the current time among the at least two sets of vehicle model data as the main data, and setting the vehicle model data with the largest time difference from the current time among the at least two sets of vehicle model data as the reference data.

[0105] Version information refers to information describing vehicle model data or the version of the car. For example, version information could be version 1, version 2, etc.

[0106] Configuration information refers to information describing the configuration details of internal and external parts, units, and functions. For example, configuration information can be luxury configuration information or basic configuration information.

[0107] Vehicle model information refers to information describing the type of vehicle. For example, vehicle model information could include the brand and model number of the vehicle.

[0108] The terminal determines the vehicle model data for comparison. Specifically, the terminal can acquire at least two sets of vehicle model data, including but not limited to: different version information and / or different configuration information and / or different vehicle model information. For example, at least two sets of vehicle model data may include, but are not limited to:

[0109] (1) Two sets of vehicle data with the same model, different versions, and the same configuration, such as the first version of vehicle process data and the second version of vehicle process data;

[0110] (2) Two sets of vehicle data with the same model, the same version, but different configurations, such as EV (Electric Vehicle) and EVR (Electrified Vehicle Revision).

[0111] (3) Two sets of vehicle data with the same model, different versions, and different configurations, such as the first version of EV and the second version of EVR;

[0112] (4) Two sets of vehicle model data with the same parts units in different models and some areas, such as M1A (previous model model) and M1L (current model model).

[0113] The terminal explicitly defines the primary data and reference data. Specifically, the terminal can use the iteration time of vehicle model data as the basis for setting the primary and reference data. For example, it can set the vehicle model data most recent to the current time as the primary data (i.e., the latest vehicle model data) and the vehicle model data older than the current time (i.e., previous vehicle model data) as the reference data. The terminal can also set at least two sets of data as each other's primary and reference data. The terminal can also use the version number of the vehicle model data as the basis for setting the primary and reference data. For example, it can set previous vehicle model versions as reference data and the current vehicle model version as the primary data.

[0114] For example, in examples (1) and (3) above, the latest vehicle model data (second version of vehicle process data and first version of EV) can be set as the main data, and the previous vehicle model data (first version of vehicle process data and second version of EVR) can be set as the reference data. For another example, in example (2) above, EV and EVR can be set as each other as the main data and reference data. For yet another example, in example (4) above, the previous model (M1A) can be set as the reference data, and the current model (M1L) can be set as the main data.

[0115] In this embodiment, acquiring multiple sets of vehicle data for different versions, configurations, and / or models helps to analyze and compare trends and differences in vehicle model changes. By setting recent vehicle data as the primary data and past vehicle data as the reference data, relevant personnel can clearly understand the characteristic changes and configuration adjustments of the latest vehicle data, thereby improving the accuracy of subsequent automotive R&D decisions, optimizing vehicle development and improvement plans, and promoting continuous progress in automotive R&D to meet needs.

[0116] In one embodiment, such as Figure 5 As shown, another method for analyzing vehicle model data is provided. Taking the application of this method to a terminal as an example, the following steps are included:

[0117] Step S501: Obtain at least two sets of vehicle model data and divide the at least two sets of vehicle model data into main data and reference data.

[0118] Step S502: Using a data comparison model, the main data is mapped to a first product tree based on the data association rule information of the main data; the sub-main data of each level in the first product tree are refined layer by layer in the form of product type to unit type.

[0119] Step S503: Using the data comparison model, the reference data is mapped to a second product tree based on the data association rule information of the reference data; the sub-reference data at each level of the second product tree are refined layer by layer in the form of product type to unit type.

[0120] Step S504: The sub-subject data of the main data at the first level is taken as the sub-subject data of the main data at the current level, and the sub-reference data of the reference data at the first level is taken as the sub-reference data of the reference data at the current level.

[0121] Step S505: Perform data comparison processing on the sub-sub-subject data and the sub-reference data of the current level to obtain the comparison information of the current level, and store the comparison information in the comparison information set.

[0122] Determine whether there are differences between the sub-sub-matter data and the sub-reference data in the current level's comparison information. If the comparison information at the current level indicates that the sub-sub-matter data and the sub-reference data at the current level are the same, then proceed to step S506. If the comparison information at the current level indicates that the sub-matter data and the sub-reference data at the current level are different, then determine whether the sub-matter data and the sub-reference data at the current level belong to a unit type. If the sub-matter data and the sub-reference data at the current level belong to a unit type, then proceed to step S506. If the sub-matter data and the sub-reference data at the current level do not belong to a unit type, then update the current level to the next level below the current level, and then proceed to step S505.

[0123] Step S506: Output the set of comparison information.

[0124] Step S507: Based on the comparison information set, identify the target data in the main data that is the same as the reference data; perform annotation processing on the target data to obtain the annotated main data.

[0125] Step S508: Output the comparison analysis results of at least two sets of vehicle model data, using the comparison information set and the labeled main data.

[0126] The aforementioned vehicle model data analysis method achieves the following beneficial effects: it enables hierarchical data comparison processing of at least two sets of vehicle model data, rather than comparing all data at once, allowing the terminal to gradually release computing resources. This improves the efficiency of vehicle model data comparison and analysis while also effectively increasing the utilization efficiency of computing resources. By annotating the main data, relevant personnel can quickly and clearly understand the similarities and differences between at least two sets of vehicle model data based on the annotated main data. This not only improves the data comparison effect of vehicle model data but also lays the foundation for subsequent iterative optimization of vehicle model data.

[0127] To more clearly illustrate the vehicle model data analysis method provided in this disclosure, a specific embodiment will be used to specifically describe the above-mentioned vehicle model data analysis method. For example... Figure 6 As shown, another method for analyzing vehicle model data is provided, which can be applied to terminals, and specifically includes the following:

[0128] Step S601, confirm the data range to be compared: Four types of vehicle model data can be set for comparison:

[0129] The first category consists of two sets of vehicle data for the same model, different versions, and the same configuration, such as the first version of vehicle process data and the second version of vehicle process data.

[0130] The second category consists of two sets of vehicle data that are the same model, the same version, but different configurations, such as EV (Electric Vehicle) and EVR (Electrified Vehicle Revision).

[0131] The third category consists of two sets of vehicle data for the same model but different versions and configurations, such as the first version of EV and the second version of EVR.

[0132] The fourth category consists of two sets of vehicle model data that are the same for different models and some parts in certain areas, such as the M1A (previous model model) and the M1L (current model model).

[0133] Step S602: Set the main data and reference data: For the first and third categories, the latest data is used as the main data and the previous data is used as the reference data; for the second category, the main data and reference data can be used for each other; for the fourth category, the current vehicle model data is used as the main data and the previous vehicle model data is used as the reference data.

[0134] Step S603, independently develop data comparison model linking data: import the main data and reference data into the data comparison model through the data import window displayed on the terminal.

[0135] Step S604: Use a data comparison model to compare data. The data comparison model constructs a product tree from the main data and reference data, and then performs data comparison processing layer by layer from the product layer down to obtain comparison information. The comparison information is stored in a comparison table. Based on the comparison information, the parts of the main data that are the same as the reference data are changed to bright silver. If the main data and reference data in the comparison information of the current level are the same, proceed to step S605; otherwise, continue to determine whether the main data and reference data in the comparison information of the current level belong to the unit type. If the main data and reference data in the comparison information of the current level do not belong to the unit type, continue to perform data comparison processing at the next level; if the main data and reference data in the comparison information of the current level belong to the unit type, or in other words, there are different data belonging to the unit layer in the comparison information of the current level, proceed to step S605.

[0136] Step S605: Stop, output the comparison table and the main data after annotation.

[0137] In this embodiment, the following beneficial effects can be achieved: First, it can automatically find differences in part units in different versions of vehicle model data without manual searching; second, it effectively shortens the manual time spent comparing vehicle model data differences from 10 hours to 20 minutes, accelerates the vehicle model data review process, effectively reduces the project's labor costs, and saves working hours; third, it can gradually release the terminal's computing resources according to the data comparison level, improves the utilization rate of computing resources, and promotes the reallocation of computing resources to other projects.

[0138] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0139] Based on the same inventive concept, this application also provides a vehicle model data analysis device for implementing the vehicle model data analysis method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more vehicle model data analysis device embodiments provided below can be found in the limitations of the vehicle model data analysis method described above, and will not be repeated here.

[0140] In one embodiment, such as Figure 7 As shown, a vehicle model data analysis device 700 is provided, including: a data acquisition module 701, a data comparison module 702, a data annotation module 703, and a result output module 704, wherein:

[0141] The data acquisition module 701 is used to acquire at least two sets of vehicle model data and divide the at least two sets of vehicle model data into main data and reference data.

[0142] The data comparison module 702 is used to perform hierarchical data comparison processing on the main data and the reference data through the data comparison model to obtain a set of comparison information between the main data and the reference data; the data comparison model is used to convert the main data and the reference data into a hierarchical data structure before performing data comparison processing.

[0143] The data annotation module 703 is used to annotate the main data according to the reference information set to obtain the annotated main data.

[0144] The result output module 704 is used to output the comparison analysis results of the comparison information set and the labeled main data as at least two sets of vehicle model data.

[0145] In one embodiment, the data comparison module 702 is further configured to perform hierarchical relationship mapping processing on the main data based on data association rule information through a data comparison model to obtain the sub-main data of the main data at each level, and to perform hierarchical relationship mapping processing on the reference data to obtain the sub-reference data of the reference data at each level; the data association rule information is used to characterize the connection relationship between data; and the sub-main data and sub-reference data are compared at the same level layer by layer to obtain a set of comparison information of sub-main data and sub-reference data at the same level.

[0146] In one embodiment, the data association rule information includes the data association rule information of the main data and the data association rule information of the reference data. The vehicle model data analysis device 700 further includes a hierarchical mapping module, used to map the main data to a first product tree based on the data association rule information of the main data using a data comparison model; the sub-main data at each level of the first product tree are refined layer by layer from product type to unit type; the reference data is mapped to a second product tree based on the data association rule information of the reference data using a data comparison model; the sub-reference data at each level of the second product tree are refined layer by layer from product type to unit type.

[0147] In one embodiment, the vehicle model data analysis device 700 further includes a comparison processing module, used to take the sub-sub-subject data of the main data at the first level as the sub-sub-subject data of the main data at the current level, and to take the sub-reference data of the reference data at the first level as the sub-reference data of the reference data at the current level; to perform data comparison processing on the sub-sub-subject data and the sub-reference data at the current level to obtain comparison information at the current level, and to store the comparison information in a comparison information set; if the comparison information at the current level indicates that the sub-sub-subject data at the current level is the same as the sub-reference data at the current level, then the comparison information set is output; if the comparison information at the current level indicates that the sub-subject data at the current level is the same as the sub-reference data at the current level, then the comparison information set is output; if the comparison information at the current level indicates that the sub-subject data at the current level is the same as the sub-reference data at the current level, then the comparison information set is output. If the sub-sub-sub-data of the current level differs from the sub-reference data of the current level, and neither the sub-sub-sub-data nor the sub-reference data belongs to a unit type, then the current level is updated to the next level below the current level. The process then proceeds to the step of performing data comparison processing on the sub-sub-sub-data and sub-reference data of the current level to obtain the comparison information for the current level, and storing the comparison information in the comparison information set. If the comparison information of the current level indicates that the sub-sub-sub-data and sub-reference data of the current level differ, and both the sub-sub-sub-data and sub-reference data belong to a unit type, then the comparison information set is output.

[0148] In one embodiment, the data annotation module 703 is further configured to determine the target data in the main data that is the same as the reference data based on the comparison information set; and to perform annotation processing on the target data to obtain the annotated main data.

[0149] In one embodiment, the data acquisition module 701 is further configured to acquire at least two sets of vehicle model data that differ in version information and / or configuration information and / or vehicle model information; set the vehicle model data with the smallest time difference from the current time among the at least two sets of vehicle model data as the main data, and set the vehicle model data with the largest time difference from the current time among the at least two sets of vehicle model data as the reference data.

[0150] Each module in the aforementioned vehicle model data analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0151] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a vehicle model data analysis method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0152] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0153] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0154] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0155] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0156] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0157] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0158] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for analyzing vehicle model data, characterized in that, The method includes: Acquire at least two sets of vehicle model data, and divide the at least two sets of vehicle model data into main data and reference data; A hierarchical data comparison process is performed on the main data and the reference data using a data comparison model to obtain a set of comparison information between the main data and the reference data. The data comparison model is used to convert the main data and the reference data into a hierarchical data structure before performing the data comparison process. Based on the aforementioned set of reference information, the main data is annotated to obtain annotated main data; The comparison information set and the labeled main data are output as the comparison analysis results of the at least two sets of vehicle model data.

2. The method according to claim 1, characterized in that, The step involves using a data comparison model to perform hierarchical data comparison processing on the main data and the reference data, resulting in a set of comparison information between the main data and the reference data, including: Using the data comparison model, based on data association rule information, hierarchical relationship mapping is performed on the main data to obtain the sub-main data of the main data at each level; and hierarchical relationship mapping is performed on the reference data to obtain the sub-reference data of the reference data at each level; the data association rule information is used to characterize the connection relationship between data. The sub-sub-sub data and the sub-reference data are compared at the same level layer by layer to obtain a set of comparison information between the sub-sub-sub-sub data and the sub-reference data at the same level.

3. The method according to claim 2, characterized in that, The data association rule information includes the data association rule information of the main data and the data association rule information of the reference data; The process of mapping the main data according to the data association rule information using the data comparison model to obtain the sub-main data of the main data at each level, and mapping the reference data according to the reference data to obtain the sub-reference data of the reference data at each level, includes: Using the data comparison model, based on the data association rules of the main data, the main data is mapped to a first product tree; the sub-main data at each level of the first product tree are refined layer by layer in the form of product type to unit type; Using the data comparison model, the reference data is mapped to a second product tree based on the data association rule information of the reference data; the sub-reference data at each level of the second product tree are refined layer by layer in the form from the product type to the unit type.

4. The method according to claim 3, characterized in that, The step of performing hierarchical data comparison processing on the sub-sub-main data and the sub-reference data to obtain a set of comparison information for the sub-sub-main data and the sub-reference data at the same level includes: The sub-subject data of the main data at the first level is taken as the sub-subject data of the main data at the current level, and the sub-reference data of the reference data at the first level is taken as the sub-reference data of the reference data at the current level. The sub-sub-subject data and the sub-reference data of the current level are compared to obtain the comparison information of the current level, and the comparison information is stored in the comparison information set. If the reference information of the current level indicates that the sub-subject data of the current level is the same as the sub-reference data of the current level, then the reference information set is output. If the comparison information of the current level indicates that the sub-sub-sub-data of the current level is different from the sub-reference data of the current level, and the sub-sub-sub-data of the current level and the sub-reference data of the current level do not belong to the unit type, then the current level is updated to the next level below the current level, and the process jumps to the step of performing data comparison processing on the sub-sub-sub-data of the current level and the sub-reference data of the current level to obtain the comparison information of the current level, and storing the comparison information in the comparison information set; If the comparison information of the current level indicates that the sub-sub-sub-data of the current level is different from the sub-reference data of the current level, and the sub-sub-sub-data of the current level and the sub-reference data of the current level belong to the unit type, then the comparison information set is output.

5. The method according to claim 1, characterized in that, The step of annotating the subject data according to the reference information set to obtain annotated subject data includes: Based on the set of comparison information, target data that is identical to the reference data in the main data is determined; The target data is labeled to obtain the labeled main data.

6. The method according to claim 1, characterized in that, The process of acquiring at least two sets of vehicle model data and dividing the at least two sets of vehicle model data into main data and reference data includes: Obtain at least two sets of vehicle model data that differ in version information and / or configuration information and / or vehicle model information; The vehicle data with the smallest time difference from the current time among the at least two sets of vehicle data is set as the main data, and the vehicle data with the largest time difference from the current time among the at least two sets of vehicle data is set as the reference data.

7. A vehicle model data analysis device, characterized in that, The device includes: The data acquisition module is used to acquire at least two sets of vehicle model data and divide the at least two sets of vehicle model data into main data and reference data; The data comparison module is used to perform hierarchical data comparison processing on the main data and the reference data through a data comparison model to obtain a set of comparison information between the main data and the reference data; the data comparison model is used to convert the main data and the reference data into a hierarchical data structure before performing data comparison processing; The data annotation module is used to annotate the main data according to the reference information set to obtain the annotated main data; The result output module is used to output the comparison information set and the labeled main data as the comparison analysis results of the at least two sets of vehicle model data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images