Data processing method and device applied to vehicle knowledge tracking, computer equipment and storage medium

Abstract

The invention relates to a data processing method and device applied to vehicle knowledge tracking, computer equipment and a computer readable storage medium. The method comprises the steps of obtaining a to-be-processed vehicle knowledge text from a vehicle management server; obtaining a plurality of knowledge element points of the to-be-processed vehicle knowledge text; analyzing the plurality of knowledge element points to obtain a group of evaluation criteria corresponding to the plurality of knowledge element points, and obtaining first weight coefficients corresponding to different knowledge element points by using the group of evaluation criteria; determining the weighted evaluation value of each knowledge element point according to each evaluation standard in the group of evaluation standards and the corresponding first weight coefficient; based on the weighted evaluation value of each knowledge element point, determining a core element point from each knowledge element point; and determining a storage node corresponding to the core element point in the storage library, and storing the to-be-processed vehicle knowledge text into the corresponding storage node in the storage library. By adopting the method, the accuracy of classified storage of the vehicle knowledge texts can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data processing method, device, computer equipment and computer-readable storage medium for vehicle knowledge tracking. Background Art

[0002] With the rapid development of the automotive industry, a large amount of information data is generated during the driving and maintenance of vehicles in the market. These data cover a wide range of content, from vehicle performance, maintenance records to user feedback, and are of great value in improving product quality, optimizing service processes, and enhancing user experience. However, due to the large amount of data and high complexity, how to effectively manage and utilize knowledge has become an urgent problem to be solved.

[0003] In traditional technology, knowledge base management tools or assistants are used to process this type of information. However, when faced with ambiguous information data in a field, existing methods often find it difficult to accurately classify and store this knowledge. Simply relying on the classification and storage method of the field in which the knowledge is located often fails to deeply track the core elements that constitute the knowledge. Summary of the invention

[0004] Based on this, it is necessary to provide a data processing method, device, computer equipment and computer-readable storage medium for vehicle knowledge tracking that can accurately classify and store vehicle knowledge in order to address the above technical problems.

[0005] In a first aspect, the present application provides a data processing method for vehicle knowledge tracking, comprising:

[0006] Acquire the vehicle knowledge text to be processed from the vehicle management server; the vehicle management server stores the vehicle knowledge text related to market vehicle information, manufacturer information, after-sales service store information, network public opinion information and news media information;

[0007] Acquire a plurality of knowledge element points of the vehicle knowledge text to be processed, wherein the knowledge element points include at least one of vehicle basic data, regional characteristic data, and weather public data;

[0008] Analyze the multiple knowledge element points to obtain a set of evaluation criteria corresponding to the multiple knowledge element points, and use the set of evaluation criteria to obtain first weight coefficients corresponding to different knowledge element points; wherein the first weight coefficient of the knowledge element point is positively correlated with the degree of relevance between the knowledge element point and the vehicle, and the sum of the first weight coefficients corresponding to the knowledge element points is one;

[0009] Determining a weighted evaluation value of each of the knowledge element points according to each evaluation criterion in the set of evaluation criteria and a corresponding first weight coefficient;

[0010] Determining core elements from among the knowledge elements based on the weighted evaluation values ​​of the knowledge elements;

[0011] Determine the storage node corresponding to the core element point in the repository, and store the to-be-processed vehicle knowledge text in the corresponding storage node in the repository.

[0012] In one embodiment, determining the weighted evaluation value of each of the knowledge element points according to each evaluation criterion in the set of evaluation criteria and the corresponding first weight coefficient includes:

[0013] Determining a target evaluation standard corresponding to the knowledge element point and a corresponding first weight coefficient from the set of evaluation standards; the target evaluation standard includes at least two sub-evaluation standards, and different sub-evaluation standards are used to evaluate the knowledge element point from different angles;

[0014] Determine the evaluation value corresponding to the knowledge element point according to each of the sub-evaluation criteria in the target evaluation criteria and the corresponding second weight coefficient; the sum of the second weight coefficients corresponding to each of the sub-evaluation criteria in the target evaluation criteria is one;

[0015] According to the evaluation value corresponding to the knowledge element point and the first weight coefficient corresponding to the knowledge element point, a weighted evaluation value of the knowledge element point is obtained.

[0016] In one of the embodiments, the storage library includes a plurality of the storage nodes and a search node, wherein the search node includes a first association table for storing the correspondence between the plurality of storage nodes and the plurality of element points;

[0017] The step of determining a storage node corresponding to the core element point in the repository and storing the to-be-processed vehicle knowledge text in the corresponding storage node in the repository includes:

[0018] Obtaining element information corresponding to each of the core element points of the vehicle knowledge text to be processed, and obtaining text information corresponding to the core element points of the vehicle knowledge text to be processed; the element information at least includes element identification, element name, and element description information; the text information at least includes text identification, text title, text content, and text creation date; the text identification is used to indicate that the text information belongs to the vehicle knowledge text to be processed;

[0019] According to the core element point and the first association table, determine the storage node corresponding to the core element point in the storage library, and package and store the text information of the to-be-processed vehicle knowledge text corresponding to the core element point and the element information into the corresponding storage node in the storage library;

[0020] The association relationship between the plurality of element identifiers and the text identifiers corresponding to the vehicle knowledge text to be processed is stored in a second association table in the search node.

[0021] In one embodiment, the step of obtaining a plurality of knowledge elements of the vehicle knowledge text to be processed includes:

[0022] Obtaining preset classification element points; determining element points in the vehicle knowledge text to be processed that match the preset classification element points as the knowledge element points.

[0023] In one embodiment, the target evaluation criteria include an urgency evaluation criterion, a stored times evaluation criterion, and an associated countermeasure quantity evaluation criterion;

[0024] Determining the evaluation value corresponding to the knowledge element point according to each of the sub-evaluation criteria in the target evaluation criteria and the corresponding second weight coefficient thereof includes:

[0025] According to the urgency evaluation standard and the corresponding second weight coefficient, a first sub-evaluation value for the knowledge element point is obtained;

[0026] Obtaining a second sub-evaluation value for the knowledge element point according to the stored number of evaluation criteria and the corresponding second weight coefficient;

[0027] Obtaining a third sub-evaluation value for the knowledge element point according to the associated countermeasure quantity evaluation standard and the corresponding second weight coefficient;

[0028] An evaluation value corresponding to the knowledge element point is obtained according to the first sub-evaluation value, the second sub-evaluation value and the third sub-evaluation value.

[0029] In one embodiment, the method further comprises:

[0030] In response to a search request for a target knowledge element point, determining a storage node matching the target knowledge element point using a first association table of search nodes in the storage repository;

[0031] The text information and element information packaged in the matching storage node are used as the first search result corresponding to the search request.

[0032] In one embodiment, after taking the text information and element information packaged in the matching storage node as the first search result corresponding to the search request, the method further includes:

[0033] Determine the target text information and the corresponding packaged element information from the matched storage node, and obtain the text identifier in the target text information and the element identifier in the corresponding packaged element information;

[0034] Based on the text identifier in the target text information and the element identifier in the corresponding packaged element information, using the second association table to determine other element identifiers corresponding to the text identifier in the target text information, except for the element identifier in the corresponding packaged element information;

[0035] Determine the corresponding other knowledge element points according to the other element identifiers, use the first association table to determine other storage nodes corresponding to the other knowledge element points, and use the corresponding text information and element information in the other storage nodes as the second search result corresponding to the search request.

[0036] In a second aspect, the present application further provides a data processing device for vehicle knowledge tracking, comprising:

[0037] The vehicle knowledge text acquisition module is used to acquire the vehicle knowledge text to be processed from the vehicle management server; the vehicle management server stores vehicle knowledge text related to market vehicle information, manufacturer information, after-sales service store information, network public opinion information and news media information;

[0038] A core element point determination module is used to obtain multiple knowledge element points of the vehicle knowledge text to be processed, wherein the knowledge element points include at least one of vehicle basic data, regional characteristic data and weather public data; analyze the multiple knowledge element points to obtain a set of evaluation criteria corresponding to the multiple knowledge element points, and use the set of evaluation criteria to obtain first weight coefficients corresponding to different knowledge element points; wherein the first weight coefficient of the knowledge element point is positively correlated with the degree of relevance between the knowledge element point and the vehicle, and the sum of the first weight coefficients corresponding to each of the knowledge element points is one; determine the weighted evaluation value of each of the knowledge element points according to each evaluation criterion in the set of evaluation criteria and the corresponding first weight coefficient; and determine the core element point from each of the knowledge element points based on the weighted evaluation value of each of the knowledge element points;

[0039] The vehicle knowledge text storage module is used to determine the storage node corresponding to the core element point in the storage repository, and store the vehicle knowledge text to be processed in the corresponding storage node in the storage repository.

[0040] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in the first aspect when executing the computer program.

[0041] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in the first aspect.

[0042] The above-mentioned data processing method, device, computer equipment and computer-readable storage medium for vehicle knowledge tracking first obtain the vehicle knowledge text to be processed from the vehicle management server; the above-mentioned vehicle management server stores vehicle knowledge text related to market vehicle information, manufacturer information, after-sales service store information, network public opinion information and news media information; then obtain multiple knowledge element points of the above-mentioned vehicle knowledge text to be processed; the above-mentioned knowledge element points include at least one of vehicle basic data, regional characteristic data and weather public data; then analyze the above-mentioned multiple knowledge element points to obtain a set of evaluation criteria corresponding to the above-mentioned multiple knowledge element points, and use the above-mentioned one The first weight coefficient corresponding to different knowledge element points is obtained by group evaluation criteria; wherein, the first weight coefficient of the above knowledge element point is positively correlated with the relevance of the above knowledge element point to the vehicle, and the sum of the first weight coefficients corresponding to each of the above knowledge element points is one; then, according to each evaluation criterion in the above group of evaluation criteria and the corresponding first weight coefficient, the weighted evaluation value of each of the above knowledge element points is determined; then, based on the weighted evaluation value of each of the above knowledge element points, the core element point is determined from each of the above knowledge element points; finally, the storage node corresponding to the core element point in the storage library is determined, and the above vehicle knowledge text to be processed is stored in the corresponding storage node in the storage library. In this application, by analyzing the acquired knowledge element points and obtaining different first weight coefficients of different knowledge element points, and then obtaining the corresponding weighted evaluation value through the first weight coefficient and the evaluation criteria corresponding to the knowledge element point, it can ensure that the relevance of the core element point to the vehicle is high, and ensure that the core element point finally determined is the information that best reflects the characteristics of the vehicle, and can improve the accuracy of the classification storage of the vehicle knowledge text. In addition, the vehicle knowledge text stored by the above method can be quickly queried according to the core element point corresponding to the vehicle knowledge text for knowledge tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 An application environment diagram of a data processing method applied to vehicle knowledge tracking in one embodiment;

[0045] Figure 2 A schematic flow chart of a data processing method applied to vehicle knowledge tracking in one embodiment;

[0046] Figure 3 A schematic diagram of a flow chart of a step of determining a weighted evaluation value of each of the above-mentioned knowledge elements in one embodiment;

[0047] Figure 4 A structural block diagram of a data processing device applied to vehicle knowledge tracking in one embodiment;

[0048] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] The data processing method for vehicle knowledge tracking provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the server 102 communicates with the vehicle management server 104 through the network. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. The server 102 obtains the vehicle knowledge text to be processed from the vehicle management server; the above-mentioned vehicle management server stores vehicle knowledge text related to market vehicle information, manufacturer information, after-sales service store information, network public opinion information and news media information; the server 102 obtains multiple knowledge element points of the above-mentioned vehicle knowledge text to be processed, and the above-mentioned knowledge element points include at least one of vehicle basic data, regional characteristic data and weather public data; the server 102 analyzes the above-mentioned multiple knowledge element points to obtain a set of evaluation criteria corresponding to the above-mentioned multiple knowledge element points, and uses the above-mentioned set of evaluation criteria to obtain the first corresponding to different knowledge element points Weight coefficient; wherein the first weight coefficient of the above-mentioned knowledge element point is positively correlated with the relevance of the above-mentioned knowledge element point to the vehicle, and the sum of the first weight coefficients corresponding to each of the above-mentioned knowledge element points is one; the server 102 determines the weighted evaluation value of each of the above-mentioned knowledge element points according to each evaluation standard in the above-mentioned set of evaluation standards and the corresponding first weight coefficient; the server 102 determines the core element point from each of the above-mentioned knowledge element points based on the weighted evaluation value of each of the above-mentioned knowledge element points; the server 102 determines the storage node corresponding to the above-mentioned core element point in the repository, and stores the above-mentioned vehicle knowledge text to be processed in the corresponding storage node in the above-mentioned repository. Among them, the server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. Among them, the vehicle management server 104 is a server for managing vehicle-related data, which can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0051] In an exemplary embodiment, Figure 2 As shown, a data processing method for vehicle knowledge tracking is provided, and the method is applied to Figure 1 The server 102 in the example is used as an example to illustrate the method, which includes the following steps S201 to S206. Among them:

[0052] Step S201, obtaining the vehicle knowledge text to be processed from the vehicle management server. The vehicle management server stores vehicle knowledge text related to market vehicle information, manufacturer information, after-sales service store information, network public opinion information and news media information.

[0053] Among them, the vehicle knowledge text may refer to information related to the vehicle.

[0054] Exemplarily, the server sends a request to the vehicle management server to obtain the vehicle knowledge text that needs to be processed. Specifically, the vehicle knowledge text can be obtained from the vehicle management server by means of retrieval, server data reading, etc.

[0055] Step S202, obtaining a plurality of knowledge elements of the vehicle knowledge text to be processed, wherein the knowledge elements at least include one of vehicle basic data, regional characteristic data and weather public data.

[0056] Among them, knowledge element points may refer to key information points extracted from the vehicle knowledge text, and these information points can reflect different features or attributes related to the vehicle.

[0057] Exemplarily, based on the acquisition of the vehicle knowledge text to be processed, the server can identify and extract knowledge element points in the vehicle knowledge text to be processed. For example, the server can use natural language processing technology or other parsing algorithms to identify the vehicle knowledge text to be processed; for example, the server can use keyword matching, entity recognition and other methods to determine which parts are about vehicle basic data, regional characteristic data or weather public data.

[0058] Step S203: Analyze the above-mentioned multiple knowledge element points to obtain a set of evaluation criteria corresponding to the above-mentioned multiple knowledge element points, and use the above-mentioned set of evaluation criteria to obtain the first weight coefficients corresponding to different knowledge element points. The first weight coefficients of the above-mentioned knowledge element points are positively correlated with the degree of relevance between the above-mentioned knowledge element points and the vehicle, and the sum of the first weight coefficients corresponding to the above-mentioned knowledge element points is one.

[0059] The evaluation criteria may be specific indicators used to evaluate knowledge elements. A set of evaluation criteria may refer to multiple evaluation criteria corresponding to multiple knowledge elements for evaluation.

[0060] The first weight coefficient may refer to a weight value assigned to each knowledge element point according to its relevance in the multiple index analysis, indicating the relative importance of the knowledge element point in the overall evaluation.

[0061] Exemplarily, for each knowledge element point, the server will evaluate it according to a predefined set of evaluation criteria; the evaluation criteria can be formulated based on historical data analysis, expert opinions or other relevant factors; based on these evaluation criteria, the server can calculate the first weight coefficient of each knowledge element point, and ensure that the sum of all coefficients is equal to 1, and the size of the coefficient reflects the importance of the knowledge element point.

[0062] Step S204: Determine the weighted evaluation value of each of the above-mentioned knowledge element points according to each evaluation criterion in the above-mentioned set of evaluation criteria and the corresponding first weight coefficient.

[0063] Exemplarily, the server evaluates the knowledge element point according to an evaluation criterion corresponding to the knowledge element point in a set of evaluation criteria to obtain an initial evaluation value, and then uses the first weight coefficient determined in step S203 to perform weighted calculation on the initial evaluation value of each knowledge element point to obtain a weighted evaluation value for each knowledge element point.

[0064] Step S205: determining core elements from the above-mentioned knowledge elements based on the weighted evaluation values ​​of the above-mentioned knowledge elements.

[0065] Among them, the core elements may refer to the knowledge elements that are identified as the most critical after weighted evaluation.

[0066] Exemplarily, the server may compare the weighted evaluation value of each knowledge element point with a preset evaluation value threshold value to select one or more knowledge element points exceeding the evaluation value threshold value as core element points.

[0067] Step S206, determining the storage node corresponding to the core element point in the storage repository, and storing the to-be-processed vehicle knowledge text in the corresponding storage node in the storage repository.

[0068] The storage node may refer to a specific storage location in the repository for storing a specific type of vehicle knowledge text. The repository may be a system for centralized management and storage of data, such as a database, file system, or cloud storage service. The repository includes multiple storage nodes and search nodes to achieve efficient data management and retrieval.

[0069] Exemplarily, the server may find a suitable storage node in the repository according to the core element points, and store the original vehicle knowledge text to be processed in the storage node.

[0070] In the above data processing method applied to vehicle knowledge tracking, by analyzing the acquired knowledge element points and obtaining different first weight coefficients for different knowledge element points, and then obtaining the corresponding weighted evaluation value through the first weight coefficient and the evaluation criteria corresponding to the knowledge element points, it can be ensured that the core element points have a high degree of relevance to the vehicle, and that the core element points finally determined are the information that best reflects the characteristics of the vehicle, which can improve the accuracy of the classification and storage of vehicle knowledge texts. In addition, the vehicle knowledge texts stored by the above method can be quickly queried based on the core element points corresponding to the vehicle knowledge texts for knowledge tracking.

[0071] In an exemplary embodiment, the storage node may be used to store information corresponding to knowledge element points. The above step S206 may also include: after the server finds a suitable storage node in the repository according to the core element point, it may store the information corresponding to the core element point in the storage node, and save the association between the core element point and the vehicle knowledge text to be processed in the storage node, and store the vehicle knowledge text to be processed in a basic storage node specifically used to store vehicle knowledge texts.

[0072] In this embodiment, the core element points and their related information are stored separately from the vehicle knowledge text to be processed, and the association between the two is preserved, which can significantly improve the data retrieval efficiency. When the user or system needs to find specific information, it can directly locate the relevant storage node without traversing the entire knowledge text library. Storing the core element points and their corresponding information separately can reduce the storage of redundant data and avoid loading unnecessary information every time the vehicle knowledge text is accessed, thereby saving storage space and improving the response speed of the server.

[0073] In an exemplary embodiment, Figure 3 As shown, the step S204 above determines the weighted evaluation value of each of the above knowledge elements according to each evaluation criterion in the above set of evaluation criteria and the corresponding first weight coefficient, which may specifically include steps S302 to S306. Among them:

[0074] Step S302, determining a target evaluation standard corresponding to the knowledge element point and a corresponding first weight coefficient from the above set of evaluation standards, wherein the above target evaluation standard includes at least two sub-evaluation standards, and different sub-evaluation standards are used to evaluate the above knowledge element point from different angles.

[0075] The target evaluation criteria may refer to a set of evaluation criteria set for a specific knowledge element point, and the target evaluation criteria may include multiple sub-evaluation criteria for comprehensively evaluating the knowledge element point from different angles.

[0076] Among them, the sub-evaluation criteria can refer to the specific criteria that constitute the target evaluation criteria, which are used to evaluate a specific aspect of the knowledge element point.

[0077] Step S304, determining the evaluation value corresponding to the knowledge element point according to each of the above sub-evaluation criteria in the above target evaluation criteria and their corresponding second weight coefficients, wherein the sum of the second weight coefficients corresponding to each of the above sub-evaluation criteria in the above target evaluation criteria is one.

[0078] The second weight coefficient may refer to the importance of each sub-evaluation criterion relative to the knowledge element point within the target evaluation criterion, and the sum of the second weight coefficients of all sub-evaluation criteria is also one.

[0079] Step S306, obtaining a weighted evaluation value of the knowledge element point according to the evaluation value corresponding to the knowledge element point and the first weight coefficient corresponding to the knowledge element point.

[0080] Exemplarily, the server can determine the target evaluation standard corresponding to the above-mentioned knowledge element point from the above-mentioned set of evaluation standards according to the nature and content of the knowledge element point, and obtain the first weight coefficient corresponding to the knowledge element point based on the target evaluation standard through a pre-set algorithm; for each sub-evaluation standard in the target evaluation standard, the server assigns a second weight coefficient to it according to a pre-defined algorithm, and the server uses these sub-evaluation standards to specifically evaluate different aspects of the knowledge element point, and gives a sub-evaluation value through each sub-evaluation standard, and then multiplies each sub-evaluation value by the second weight coefficient corresponding to its corresponding sub-evaluation standard, and then adds the results to obtain the evaluation value corresponding to the knowledge element point, and the process can be as follows: evaluation value = ∑ (sub-evaluation value × second weight coefficient). After obtaining the evaluation value corresponding to the knowledge element point, the evaluation value is multiplied by its corresponding first weight coefficient to obtain the final weighted evaluation value of the knowledge element point, and the process can be as follows: weighted evaluation value = evaluation value × first weight coefficient.

[0081] In this embodiment, multiple sub-evaluation criteria are used to evaluate knowledge element points from different angles, and then a second weight coefficient corresponding to the sub-evaluation criteria can be used to conduct a comprehensive and detailed evaluation of the knowledge element points, so that the final weighted evaluation value can more accurately reflect the actual situation of the knowledge element point, thereby improving the evaluation accuracy.

[0082] In an exemplary embodiment, the above-mentioned storage library includes a plurality of the above-mentioned storage nodes, and also includes a search node, and the above-mentioned search node includes a first association table for storing the correspondence between the plurality of storage nodes and the plurality of element points; the above-mentioned step S206 performs the above-mentioned determination of the storage node corresponding to the above-mentioned core element point in the storage library, and storing the above-mentioned vehicle knowledge text to be processed in the corresponding storage node in the above-mentioned storage library, which may specifically include:

[0083] Step S41, obtaining element information corresponding to each of the core element points of the vehicle knowledge text to be processed, and obtaining text information corresponding to the core element points of the vehicle knowledge text to be processed.

[0084] Among them, the element information can be metadata used to describe the element points, and the above element information at least includes the element identifier, element name, and element description information; the text information can be metadata used to describe the vehicle knowledge text to be processed, and the above text information at least includes the text identifier, text title, text content, and text creation date; the above text identifier is used to characterize that the above text information belongs to the above vehicle knowledge text to be processed.

[0085] The search node can be used in the storage library to search for data in the storage node. The search node can quickly locate the storage node where the core element point is located through the first association table. The first association table can be set in the search node or in the storage node, and is used to record the correspondence between multiple storage nodes and multiple element points. The first association table can be used to quickly find the corresponding storage node according to the element point.

[0086] Exemplarily, the server extracts element information corresponding to each core element point from the vehicle knowledge text to be processed, and at the same time, collects text information corresponding to each core element point from the vehicle knowledge text to be processed.

[0087] Step S42, based on the core element points and the first association table, determine the storage node corresponding to the core element points in the repository, and package the text information of the vehicle knowledge text to be processed corresponding to the core element points and the element information into the corresponding storage node in the repository.

[0088] Exemplarily, the server uses the first association table to find out in which specific storage node each core element point should be stored, and then packages the element information of the core element point and the corresponding text information together, and then sends the packaged information to the corresponding storage node for storage. In this way, the relevant element information and text information are packaged together for storage, which can ensure the consistency and integrity of the relevant information and improve the convenience of subsequent reading and analysis of data.

[0089] Step S43, storing the association relationship between the plurality of element identifiers and the text identifiers corresponding to the vehicle knowledge text to be processed into the second association table in the search node.

[0090] The text identifier may be a code or number used to identify a specific piece of text information.

[0091] Exemplarily, the server obtains the element identifier from the element information of multiple core element points, obtains the text identifier from the text information corresponding to the above core element points of multiple vehicle knowledge texts to be processed, obtains the association relationship between multiple element identifiers and multiple text identifiers through the correspondence between the aforementioned element information and the aforementioned text information, and then stores the association relationship between the element identifier and the text identifier in the second association table in the search node. Among them, a text identifier can be set for a vehicle knowledge text to be processed, that is, the text identifiers in the aforementioned multiple text information can be the same text identifier to indicate that multiple text information belongs to the same vehicle knowledge text to be processed. In this way, the text identifier of the text information corresponding to the knowledge element point can be queried using the second association table, and then the vehicle knowledge text corresponding to the text identifier can be further searched through the text identifier, and the other knowledge element points corresponding to the text identifier can also be searched through the text identifier, thereby realizing the search for the knowledge element point and the corresponding text information of the entire vehicle knowledge text associated with it through one knowledge element point.

[0092] In this embodiment, through the predefined first association table, the system can directly locate the appropriate storage node, reducing unnecessary traversal operations and improving the speed and efficiency of the storage process. The second association table enhances the system's retrieval capability, and users can quickly find the other party through any one party (element identifier or text identifier), improving the speed and accuracy of information retrieval, and can implement complex queries involving multiple element points or multiple texts, thereby better serving the needs of data analysis and decision support.

[0093] In an exemplary embodiment, the acquisition of multiple knowledge element points of the above-mentioned vehicle knowledge text to be processed performed in the above-mentioned step S202 may specifically include: acquiring preset classification element points; determining element points in the above-mentioned vehicle knowledge text to be processed that match the above-mentioned preset classification element points as the above-mentioned knowledge element points.

[0094] Among them, the preset classification element points may refer to a set of predefined standard element points used to represent important information categories that may be contained in the vehicle knowledge text. Matching element points may refer to actual existing information points extracted from the vehicle knowledge text to be processed. These information points are consistent with or associated with the preset classification element points. By identifying and extracting these matching element points, it can be ensured that the most relevant data is obtained from the vehicle knowledge text. Knowledge element points may refer to information points that are ultimately determined to be of important value. These information points can be screened out from the vehicle knowledge text to be processed based on preset classification elements and are considered to be key components for building a vehicle knowledge system.

[0095] In this embodiment, the information extraction process is guided by preset classification element points to ensure that only truly important information is obtained, and then the knowledge element points are obtained, which can avoid the interference of irrelevant data and improve the accuracy of knowledge element point extraction.

[0096] In an exemplary embodiment, the target evaluation criteria include an urgency evaluation criterion, a stored times evaluation criterion, and an associated countermeasure quantity evaluation criterion; the step S304 performs the step of determining the evaluation value corresponding to the knowledge element point according to each of the sub-evaluation criteria in the target evaluation criteria and the corresponding second weight coefficient, which may specifically include:

[0097] According to the above urgency evaluation standard and the corresponding second weight coefficient, a first sub-evaluation value for the above knowledge element point is obtained.

[0098] According to the above-mentioned stored number of evaluation criteria and the corresponding second weight coefficient, a second sub-evaluation value for the above-mentioned knowledge element point is obtained.

[0099] According to the above-mentioned associated countermeasure quantity evaluation standard and the corresponding second weight coefficient, a third sub-evaluation value for the above-mentioned knowledge element point is obtained.

[0100] The evaluation value corresponding to the knowledge element point is obtained according to the first sub-evaluation value, the second sub-evaluation value and the third sub-evaluation value.

[0101] Among them, the urgency evaluation standard can be used to measure the importance of whether a certain knowledge element point needs to be processed immediately or whether its information is timely. For example, if a problem or situation requires a quick response, the urgency of the element point is higher. Among them, the number of stored times evaluation standard can refer to the frequency with which a certain knowledge element point has been stored in the past. Frequently appearing information may indicate that it is of high importance or attention, but it may also be duplicate data, so it needs to be considered comprehensively. Among them, the associated countermeasure quantity evaluation standard can refer to the number of solutions or countermeasures related to the knowledge element point. It can be seen from the above-mentioned embodiments that the sum of the second weight coefficient corresponding to the urgency evaluation standard, the second weight coefficient corresponding to the number of stored times evaluation standard, and the second weight coefficient corresponding to the associated countermeasure quantity evaluation standard is one.

[0102] Exemplarily, the server scores the urgency of the knowledge element point according to the urgency evaluation standard, and then multiplies this score by the second weight coefficient corresponding to the urgency evaluation standard to obtain a first sub-evaluation value; the server counts the number of times the knowledge element point has been stored from the repository, and then scores the number of times it has been stored according to the above-mentioned stored number evaluation standard, and then multiplies this score by the second weight coefficient corresponding to the stored number evaluation standard to obtain a second sub-evaluation value for the above-mentioned knowledge element point; the server can search for countermeasures or solutions associated with the knowledge element point from the database, and then score the number of countermeasures or solutions found using the associated countermeasure quantity evaluation standard, and then multiply this score by the second weight coefficient corresponding to the above-mentioned associated countermeasure quantity evaluation standard to obtain a third sub-evaluation value for the above-mentioned knowledge element point; finally, the first sub-evaluation value, the above-mentioned second sub-evaluation value and the above-mentioned third sub-evaluation value are added together to obtain the evaluation value corresponding to the above-mentioned knowledge element point.

[0103] In this embodiment, by introducing evaluation criteria of multiple dimensions (i.e., urgency, number of stored times, and number of associated countermeasures), the value of knowledge element points can be evaluated more comprehensively, avoiding the deviation caused by a single standard, and improving the accuracy of the evaluation of knowledge element points.

[0104] In an exemplary embodiment, the above method may also include the following steps: in response to a search request for a target knowledge element point, using the first association table of search nodes in the above storage repository to determine a storage node that matches the above target knowledge element point; and using the text information and element information packaged in the above matching storage node as the first search result corresponding to the above search request.

[0105] The target knowledge element point may refer to a specific knowledge element point specified by the user in the search request, and the target knowledge element point may be used as the starting point of the search.

[0106] Exemplarily, a user submits a search request for a target knowledge element point, and the server uses the first association table in the search node in the repository to find a storage node that matches the target knowledge element point, and then returns the packaged data (packaged text information and element information) stored in the matching storage node to the user as the first search result of the search request.

[0107] In this embodiment, by using the first association table, the server can quickly locate the storage node related to the target knowledge element point, reducing the time cost of traversing the entire repository and improving retrieval efficiency; the packaged storage method in the storage node ensures that the relevant text information and element information can be retrieved together, avoiding the problem of data fragmentation.

[0108] In an exemplary embodiment, after the above step of using the text information and element information packaged in the above matching storage node as the first search result corresponding to the above search request is performed, the following steps may also be included:

[0109] Step S51, determining the target text information and the corresponding packaged element information from the above-mentioned matched storage nodes, and obtaining the text identifier in the above-mentioned target text information and the element identifier in the above-mentioned corresponding packaged element information.

[0110] The target text information may refer to specific text information determined from the matching storage nodes.

[0111] Step S52, based on the text identifier in the target text information and the element identifier in the corresponding packaged element information, use the second association table to determine other element identifiers corresponding to the text identifier in the target text information, except the element identifier in the corresponding packaged element information.

[0112] Among them, other element identifiers may refer to other element identifiers related to the same text identifier in addition to the currently determined element information, and these other element identifiers point to other related knowledge element points in the same vehicle knowledge text.

[0113] Step S53, determine the corresponding other knowledge element points according to the above other element identifiers, use the above first association table to determine the other storage nodes corresponding to the above other knowledge element points, and use the corresponding text information and element information in the above other storage nodes as the second search result corresponding to the above search request.

[0114] Among them, other knowledge element points may refer to knowledge element points determined by other element identifiers.

[0115] Exemplarily, the server can determine a specific target text information and its corresponding packaged element information from the matching storage node. For example, the storage node includes three text information A, B, and C. The B text information can be used as the target text information to further view the information related to the B text information. The server extracts the text identifier and the element identifier from the target text information and its corresponding element information; then the server uses the second association table to query and determine other element identifiers associated with the text identifier. For example, the text identifier a is associated with 4 element identifiers, including element identifier m (currently extracted element identifier), element identifier n, element identifier p, and element identifier q. Therefore, the other element identifiers determined include element identifier n, element identifier p, and element identifier q. Then, the server uses the first association table to query according to other element identifiers to find the corresponding other knowledge element points and their corresponding other storage nodes. The server then returns the relevant information and element information found in other storage nodes to the user as the second search result after the extended search.

[0116] In this embodiment, after a target text information is determined in a matching storage node, other knowledge element points corresponding to the target text information are further queried by searching for other element identifiers corresponding to the text identifier in the target text information, except for the element identifier in the corresponding packaged element information. Then, the first association table is used to determine other storage nodes corresponding to the other knowledge element points. The corresponding text information and element information in the other storage nodes are used as the second search result, thereby achieving an expanded retrieval of other knowledge element points closely related to the search request, as well as text information and element information in storage nodes corresponding to other knowledge element points, thereby improving the depth of the retrieval information and improving the comprehensiveness of the retrieval for the search request.

[0117] In an exemplary embodiment, the present application also provides a data processing method for vehicle knowledge tracking, which may specifically include:

[0118] Obtaining the vehicle knowledge text to be processed from the vehicle management server; the vehicle management server stores the vehicle knowledge text related to market vehicle information, manufacturer information, after-sales service store information, network public opinion information and news media information;

[0119] Obtaining preset classification element points; determining element points in the vehicle knowledge text to be processed that match the preset classification element points as the knowledge element points. The knowledge element points include at least one of vehicle basic data, regional characteristic data, and weather public data;

[0120] Analyze the plurality of knowledge elements to obtain a set of evaluation criteria corresponding to the plurality of knowledge elements, and use the set of evaluation criteria to obtain first weight coefficients corresponding to different knowledge elements; wherein the first weight coefficients of the knowledge elements are positively correlated to the degree of relevance between the knowledge elements and the vehicle, and the sum of the first weight coefficients corresponding to the knowledge elements is one;

[0121] Determine the target evaluation standard and the corresponding first weight coefficient corresponding to the above-mentioned knowledge element point from the above-mentioned set of evaluation standards; the above-mentioned target evaluation standard contains at least two sub-evaluation standards, and different sub-evaluation standards are used to evaluate the above-mentioned knowledge element point from different angles; determine the evaluation value corresponding to the above-mentioned knowledge element point according to each of the above-mentioned sub-evaluation standards in the above-mentioned target evaluation standard and their corresponding second weight coefficients; the sum of the second weight coefficients corresponding to each of the above-mentioned sub-evaluation standards in the above-mentioned target evaluation standard is one; obtain the weighted evaluation value of the above-mentioned knowledge element point according to the evaluation value corresponding to the above-mentioned knowledge element point and the first weight coefficient corresponding to the above-mentioned knowledge element point.

[0122] By way of example, the target evaluation criteria include an urgency evaluation criterion, a stored times evaluation criterion, and an associated countermeasure quantity evaluation criterion; the evaluation value corresponding to the knowledge element point is determined according to each of the sub-evaluation criteria in the target evaluation criteria and the corresponding second weight coefficient, and specifically may include: obtaining a first sub-evaluation value for the knowledge element point according to the urgency evaluation criterion and the corresponding second weight coefficient; obtaining a second sub-evaluation value for the knowledge element point according to the stored times evaluation criterion and the corresponding second weight coefficient; obtaining a third sub-evaluation value for the knowledge element point according to the associated countermeasure quantity evaluation criterion and the corresponding second weight coefficient; obtaining the evaluation value corresponding to the knowledge element point according to the first sub-evaluation value, the second sub-evaluation value, and the third sub-evaluation value.

[0123] Based on the weighted evaluation values ​​of the above knowledge elements, a core element is determined from the above knowledge elements;

[0124] Obtaining element information corresponding to each of the core element points of the vehicle knowledge text to be processed, and obtaining text information corresponding to the core element points of the vehicle knowledge text to be processed; the element information at least includes element identification, element name, and element description information; the text information at least includes text identification, text title, text content, and text creation date; the text identification is used to indicate that the text information belongs to the vehicle knowledge text to be processed;

[0125] According to the core element point and the first association table, determine the storage node corresponding to the core element point in the storage library, and package and store the text information of the to-be-processed vehicle knowledge text corresponding to the core element point and the element information into the corresponding storage node in the storage library;

[0126] The association relationship between the plurality of element identifiers and the text identifiers corresponding to the vehicle knowledge text to be processed is stored in the second association table in the search node.

[0127] The storage library includes the plurality of storage nodes and a search node. The search node includes a first association table for storing the correspondence between the plurality of storage nodes and the plurality of element points.

[0128] Exemplarily, on the basis of the above method, in response to a search request for a target knowledge element point, the first association table of the search node in the above storage repository can be used to determine a storage node that matches the above target knowledge element point; and the text information and element information packaged in the above matching storage node can be used as the first search result corresponding to the above search request.

[0129] After obtaining the first search result, the target text information and the corresponding packaged element information can also be determined from the above-mentioned matching storage nodes, and the text identifier in the above-mentioned target text information and the element identifier in the above-mentioned corresponding packaged element information can be obtained; based on the text identifier in the above-mentioned target text information and the element identifier in the above-mentioned corresponding packaged element information, other element identifiers corresponding to the text identifier in the above-mentioned target text information, except for the element identifier in the above-mentioned corresponding packaged element information, can be determined using the above-mentioned second association table; other corresponding knowledge element points can be determined based on the above-mentioned other element identifiers, and other storage nodes corresponding to the above-mentioned other knowledge element points can be determined using the above-mentioned first association table, and the corresponding text information and element information in the above-mentioned other storage nodes can be used as the second search result corresponding to the above-mentioned search request.

[0130] The above-mentioned data processing method applied to vehicle knowledge tracking uses a weighted scoring method to perform a specific score on each knowledge element point of the vehicle knowledge text, assigns a corresponding weight through a set scoring standard, obtains the score of each knowledge element point based on the standard, calculates the total score of each element, and sorts the elements based on the total score. The most important elements in the vehicle knowledge text are determined and classified, so that the knowledge repository can be classified according to the type of elements, and the text in the corresponding repository can be found according to the important elements in the knowledge text, so as to accurately track vehicle knowledge and improve the management of vehicle knowledge.

[0131] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0132] Based on the same inventive concept, the embodiment of the present application also provides a data processing device for vehicle knowledge tracking for implementing the data processing method for vehicle knowledge tracking involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the data processing device for vehicle knowledge tracking provided below can refer to the limitations of the data processing method for vehicle knowledge tracking above, and will not be repeated here.

[0133] In an exemplary embodiment, Figure 4 As shown, a data processing device 900 applied to vehicle knowledge tracking is provided, comprising: a vehicle knowledge text acquisition module 901, a core element point determination module 902 and a vehicle knowledge text storage module 903, wherein:

[0134] The vehicle knowledge text acquisition module 901 is used to acquire the vehicle knowledge text to be processed from the vehicle management server; the vehicle management server stores vehicle knowledge text related to market vehicle information, manufacturer information, after-sales service store information, network public opinion information and news media information;

[0135] The core element point determination module 902 is used to obtain multiple knowledge element points of the above-mentioned vehicle knowledge text to be processed, and the above-mentioned knowledge element points include at least one of vehicle basic data, regional characteristic data and weather public data; analyze the above-mentioned multiple knowledge element points to obtain a set of evaluation criteria corresponding to the above-mentioned multiple knowledge element points, and use the above-mentioned set of evaluation criteria to obtain the first weight coefficients corresponding to different knowledge element points; wherein the first weight coefficient of the above-mentioned knowledge element point is positively correlated with the degree of relevance between the above-mentioned knowledge element point and the vehicle, and the sum of the first weight coefficients corresponding to each of the above-mentioned knowledge element points is one; according to each evaluation criterion in the above-mentioned set of evaluation criteria and the corresponding first weight coefficient, determine the weighted evaluation value of each of the above-mentioned knowledge element points; based on the weighted evaluation value of each of the above-mentioned knowledge element points, determine the core element point from each of the above-mentioned knowledge element points;

[0136] The vehicle knowledge text storage module 903 is used to determine the storage node corresponding to the above core element point in the storage library, and store the above vehicle knowledge text to be processed in the corresponding storage node in the above storage library.

[0137] In an exemplary embodiment, the core element point determination module 902 is also used to determine the target evaluation criteria and the corresponding first weight coefficient corresponding to the above-mentioned knowledge element point from the above-mentioned set of evaluation criteria; the above-mentioned target evaluation criteria includes at least two sub-evaluation criteria, and different sub-evaluation criteria are used to evaluate the above-mentioned knowledge element point from different angles; according to each of the above-mentioned sub-evaluation criteria in the above-mentioned target evaluation criteria and their corresponding second weight coefficients, the evaluation value corresponding to the above-mentioned knowledge element point is determined; the sum of the second weight coefficients corresponding to each of the above-mentioned sub-evaluation criteria in the above-mentioned target evaluation criteria is one; according to the evaluation value corresponding to the above-mentioned knowledge element point and the first weight coefficient corresponding to the above-mentioned knowledge element point, the weighted evaluation value of the above-mentioned knowledge element point is obtained.

[0138] In an exemplary embodiment, the above-mentioned storage repository includes multiple storage nodes and a search node, and the above-mentioned search node includes a first association table for storing the correspondence between multiple storage nodes and multiple element points; the above-mentioned vehicle knowledge text storage module 903 is also used to obtain the element information corresponding to each of the above-mentioned core element points of the above-mentioned vehicle knowledge text to be processed, and obtain the text information of the above-mentioned vehicle knowledge text to be processed corresponding to the above-mentioned core element points; the above-mentioned element information at least includes element identification, element name, and element description information; the above-mentioned text information at least includes text identification, text title, text content, and text creation date; the above-mentioned text identification is used to characterize that the above-mentioned text information belongs to the above-mentioned vehicle knowledge text to be processed; according to the above-mentioned core element points and the above-mentioned first association table, the storage node corresponding to the above-mentioned core element point in the above-mentioned storage repository is determined, and the text information of the above-mentioned vehicle knowledge text to be processed corresponding to the above-mentioned core element point and the above-mentioned element information are packaged and stored in the corresponding storage node in the above-mentioned storage repository; the association relationship between multiple above-mentioned element identifications and the text identification corresponding to the above-mentioned vehicle knowledge text to be processed is stored in the second association table in the above-mentioned search node.

[0139] In an exemplary embodiment, the core element point determination module 902 is further used to obtain preset classification element points; determine the element points in the to-be-processed vehicle knowledge text that match the preset classification element points as the knowledge element points.

[0140] In an exemplary embodiment, the above-mentioned target evaluation criteria include an urgency evaluation criteria, a stored times evaluation criteria and an associated countermeasure quantity evaluation criteria; the above-mentioned core element point determination module 902 is also used to obtain a first sub-evaluation value for the above-mentioned knowledge element point according to the above-mentioned urgency evaluation criteria and the corresponding second weight coefficient; obtain a second sub-evaluation value for the above-mentioned knowledge element point according to the above-mentioned stored times evaluation criteria and the corresponding second weight coefficient; obtain a third sub-evaluation value for the above-mentioned knowledge element point according to the above-mentioned associated countermeasure quantity evaluation criteria and the corresponding second weight coefficient; obtain the evaluation value corresponding to the above-mentioned knowledge element point according to the above-mentioned first sub-evaluation value, the above-mentioned second sub-evaluation value and the above-mentioned third sub-evaluation value.

[0141] In an exemplary embodiment, the data processing device applied to vehicle knowledge tracking also includes an element search module, which is used to respond to a search request for a target knowledge element point, use the first association table of the search node in the above-mentioned repository to determine the storage node that matches the above-mentioned target knowledge element point; and use the text information and element information packaged in the above-mentioned matching storage node as the first search result corresponding to the above-mentioned search request.

[0142] In an exemplary embodiment, the above-mentioned element search module is also used to determine the target text information and the corresponding packaged element information from the above-mentioned matching storage nodes, obtain the text identifier in the above-mentioned target text information and the element identifier in the above-mentioned corresponding packaged element information; based on the text identifier in the above-mentioned target text information and the element identifier in the above-mentioned corresponding packaged element information, use the above-mentioned second association table to determine other element identifiers corresponding to the text identifier in the above-mentioned target text information, except for the element identifier in the above-mentioned corresponding packaged element information; determine the corresponding other knowledge element points according to the above-mentioned other element identifiers, use the above-mentioned first association table to determine the other storage nodes corresponding to the above-mentioned other knowledge element points, and use the corresponding text information and element information in the above-mentioned other storage nodes as the second search result corresponding to the above-mentioned search request.

[0143] Each module in the data processing device for vehicle knowledge tracking can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0144] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a data processing method applied to vehicle knowledge tracking is implemented.

[0145] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0147] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0148] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0149] It should be noted that the user information (including but not limited to user vehicle information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, 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 relevant data must comply with relevant regulations.

[0150] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0151] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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 application.

[0152] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A data processing method for vehicle knowledge tracking, characterized in that: The method comprises: Acquire the vehicle knowledge text to be processed from the vehicle management server; the vehicle management server stores the vehicle knowledge text related to market vehicle information, manufacturer information, after-sales service store information, network public opinion information and news media information; Acquire a plurality of knowledge element points of the vehicle knowledge text to be processed; the knowledge element points include at least one of vehicle basic data, regional characteristic data and weather public data; Analyze the multiple knowledge element points to obtain a set of evaluation criteria corresponding to the multiple knowledge element points, and use the set of evaluation criteria to obtain first weight coefficients corresponding to different knowledge element points; wherein the first weight coefficient of the knowledge element point is positively correlated with the degree of relevance between the knowledge element point and the vehicle, and the sum of the first weight coefficients corresponding to the knowledge element points is one; Determining a weighted evaluation value of each of the knowledge element points according to each evaluation criterion in the set of evaluation criteria and a corresponding first weight coefficient; Determining core elements from among the knowledge elements based on the weighted evaluation values ​​of the knowledge elements; Determine the storage node corresponding to the core element point in the repository, and store the to-be-processed vehicle knowledge text in the corresponding storage node in the repository.

2. The method according to claim 1, characterized in that Determining the weighted evaluation value of each of the knowledge element points according to each evaluation standard in the set of evaluation standards and the corresponding first weight coefficient includes: Determining a target evaluation standard corresponding to the knowledge element point and a corresponding first weight coefficient from the set of evaluation standards; the target evaluation standard includes at least two sub-evaluation standards, and different sub-evaluation standards are used to evaluate the knowledge element point from different angles; Determine the evaluation value corresponding to the knowledge element point according to each of the sub-evaluation criteria in the target evaluation criteria and the corresponding second weight coefficient; the sum of the second weight coefficients corresponding to each of the sub-evaluation criteria in the target evaluation criteria is one; According to the evaluation value corresponding to the knowledge element point and the first weight coefficient corresponding to the knowledge element point, a weighted evaluation value of the knowledge element point is obtained.

3. The method according to claim 1, characterized in that The storage library includes a plurality of the storage nodes and a search node, wherein the search node includes a first association table for storing the correspondence between the plurality of storage nodes and the plurality of element points; The step of determining a storage node corresponding to the core element point in the repository and storing the to-be-processed vehicle knowledge text in the corresponding storage node in the repository includes: Obtaining element information corresponding to each of the core element points of the vehicle knowledge text to be processed, and obtaining text information corresponding to the core element points of the vehicle knowledge text to be processed; the element information at least includes element identification, element name, and element description information; the text information at least includes text identification, text title, text content, and text creation date; the text identification is used to indicate that the text information belongs to the vehicle knowledge text to be processed; According to the core element point and the first association table, determine the storage node corresponding to the core element point in the storage library, and package and store the text information of the to-be-processed vehicle knowledge text corresponding to the core element point and the element information into the corresponding storage node in the storage library; The association relationship between the plurality of element identifiers and the text identifiers corresponding to the vehicle knowledge text to be processed is stored in a second association table in the search node.

4. The method according to claim 1, characterized in that: The step of obtaining a plurality of knowledge elements of the vehicle knowledge text to be processed includes: Obtaining preset classification element points; determining element points in the vehicle knowledge text to be processed that match the preset classification element points as the knowledge element points.

5. The method according to claim 2, characterized in that: The target evaluation criteria include an urgency evaluation criterion, a stored times evaluation criterion, and an associated countermeasure quantity evaluation criterion; Determining the evaluation value corresponding to the knowledge element point according to each of the sub-evaluation criteria in the target evaluation criteria and the corresponding second weight coefficient thereof includes: According to the urgency evaluation standard and the corresponding second weight coefficient, a first sub-evaluation value for the knowledge element point is obtained; Obtaining a second sub-evaluation value for the knowledge element point according to the stored number of evaluation criteria and the corresponding second weight coefficient; Obtaining a third sub-evaluation value for the knowledge element point according to the associated countermeasure quantity evaluation standard and the corresponding second weight coefficient; An evaluation value corresponding to the knowledge element point is obtained according to the first sub-evaluation value, the second sub-evaluation value and the third sub-evaluation value.

6. The method according to claim 3, characterized in that: The method further comprises: In response to a search request for a target knowledge element point, determining a storage node matching the target knowledge element point using a first association table of search nodes in the storage repository; The text information and element information packaged in the matching storage node are used as the first search result corresponding to the search request.

7. The method according to claim 6, characterized in that After taking the text information and element information packaged in the matched storage node as the first search result corresponding to the search request, the method further includes: Determine the target text information and the corresponding packaged element information from the matched storage node, and obtain the text identifier in the target text information and the element identifier in the corresponding packaged element information; Based on the text identifier in the target text information and the element identifier in the corresponding packaged element information, using the second association table to determine other element identifiers corresponding to the text identifier in the target text information, except for the element identifier in the corresponding packaged element information; Determine the corresponding other knowledge element points according to the other element identifiers, use the first association table to determine other storage nodes corresponding to the other knowledge element points, and use the corresponding text information and element information in the other storage nodes as the second search result corresponding to the search request.

8. A data processing device for vehicle knowledge tracking, characterized in that: The device comprises: A vehicle knowledge text acquisition module is used to acquire the vehicle knowledge text to be processed from a vehicle management server; the vehicle management server stores vehicle knowledge text related to market vehicle information, manufacturer information, after-sales service store information, network public opinion information and news media information; A core element point determination module is used to obtain multiple knowledge element points of the vehicle knowledge text to be processed, wherein the knowledge element points include at least one of vehicle basic data, regional characteristic data and weather public data; analyze the multiple knowledge element points to obtain a set of evaluation criteria corresponding to the multiple knowledge element points, and use the set of evaluation criteria to obtain first weight coefficients corresponding to different knowledge element points; wherein the first weight coefficient of the knowledge element point is positively correlated with the degree of relevance between the knowledge element point and the vehicle, and the sum of the first weight coefficients corresponding to each of the knowledge element points is one; determine the weighted evaluation value of each of the knowledge element points according to each evaluation criterion in the set of evaluation criteria and the corresponding first weight coefficient; and determine the core element point from each of the knowledge element points based on the weighted evaluation value of each of the knowledge element points; The vehicle knowledge text storage module is used to determine the storage node corresponding to the core element point in the storage repository, and store the vehicle knowledge text to be processed in the corresponding storage node in the storage repository.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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