Vehicle transaction evaluation information processing method, computer equipment and readable storage medium
By combining multi-level mapping rules with manual data entry, the problem of low efficiency in vehicle transaction assessment information entry was solved, automated and standardized processing was achieved, the accuracy of data entry and the adaptability of the system were improved, and the reliability of the approval process was ensured.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- PING AN INT FINANCIAL LEASING CO LTD
- Filing Date
- 2026-01-26
- Publication Date
- 2026-05-01
AI Technical Summary
In existing technologies, the difference in data format between vehicle transaction evaluation information provided by third parties and the data format of the management's own system leads to low efficiency and insufficient accuracy of manual data entry, affecting data processing efficiency and approval reliability.
A multi-level mapping rule is used to match vehicle transaction evaluation information in the vehicle information database, automatically enter the matched content, and introduce manual supplementation when no match is found, combined with the database self-learning mechanism to improve the database.
It has achieved a standardized and automated processing flow for vehicle transaction evaluation information, reducing time costs, improving data entry accuracy, enhancing the system's adaptability to non-standard data, and providing reliable data support for the approval process.
Smart Images
Figure CN121961490A_ABST
Abstract
Description
Vehicle transaction assessment information processing methods, computer equipment, and readable storage media Technical Field
[0001] This application relates to the field of data standardization technology, applicable to the automotive and financial sectors, and particularly to a method for processing vehicle transaction evaluation information, a computer device, and a readable storage medium. Background Technology
[0002] In vehicle transaction management, the vehicle transaction assessment information provided by third-party assessment systems differs significantly in data format from the management's own vehicle information database. The vehicle characteristic information in the third-party vehicle transaction assessment information is often not directly applicable to the management's subsequent approval process.
[0003] Currently, the approval of vehicle transaction assessment information provided by third parties still relies on manual review by vehicle appraisers. This process requires appraisers to manually enter the third-party vehicle transaction assessment information into the corresponding location in the management system. This process is not only labor-intensive, but also suffers from a high error rate due to the limitations of manual operation and the non-standard nature of third-party data. It frequently results in situations where assessment forms cannot be used properly, affecting the management's approval process. This severely impacts data processing efficiency, slows down business workflow, and has a significant negative impact on data statistics and the reliability of backend approvals.
[0004] Therefore, how to efficiently and accurately map the vehicle transaction evaluation information provided by third parties into standard data acceptable to the management has become an urgent technical problem to be solved. Summary of the Invention
[0005] This application provides a method for processing vehicle transaction assessment information, a computer device, and a readable storage medium, aiming to solve the technical problem in the related art where the inefficiency and inaccuracy of manually entering vehicle transaction assessment information provided by third parties affects the work of the management.
[0006] In a first aspect, embodiments of this application provide a method for processing vehicle transaction assessment information, comprising: acquiring vehicle transaction assessment information to be entered; determining, based on a predetermined multi-level mapping rule, whether a vehicle information database contains vehicle feature information matching the vehicle transaction assessment information, wherein the multi-level mapping rule is used to progressively reduce the mapping range of the vehicle transaction assessment information in the vehicle information database; if the vehicle information database contains vehicle feature information matching the vehicle transaction assessment information, filling the vehicle feature information into the entry position of the vehicle transaction assessment information; if the vehicle information database does not contain vehicle feature information matching the vehicle transaction assessment information, entering a manual entry mode, and storing the manually entered vehicle feature information in the vehicle information database.
[0007] In one embodiment of this application, optionally, determining whether the vehicle information database contains vehicle feature information matching the vehicle transaction evaluation information based on a predetermined multi-level mapping rule includes: determining the vehicle announcement model corresponding to the vehicle brand and model in the vehicle transaction evaluation information, and determining whether the vehicle information database stores first vehicle feature information with the vehicle announcement model, wherein if the vehicle information database stores the first vehicle feature information, all the first vehicle feature information is determined as a first screening object set; determining whether the first screening object set contains second vehicle feature information whose vehicle type is consistent with the vehicle type shown in the vehicle transaction evaluation information, wherein if the first screening object set contains second vehicle feature information... The system firstly identifies vehicle feature information and then uses all of the second vehicle feature information as a second set of filtering objects. Secondly, it uses keyword matching to determine whether the second set of filtering objects contains third vehicle feature information whose vehicle series matches the series shown in the vehicle transaction evaluation information. If the second set of filtering objects contains the third vehicle feature information, all of the third vehicle feature information is identified as a third set of filtering objects. Thirdly, it uses fuzzy matching to determine whether the third set of filtering objects contains fourth vehicle feature information whose model name matches the model shown in the vehicle transaction evaluation information. If the third set of filtering objects contains the fourth vehicle feature information, the required fourth vehicle feature information is identified from all the fourth vehicle feature information according to a predetermined result filtering rule.
[0008] Optionally, in one embodiment of this application, the method further includes: if the vehicle information database does not store the first vehicle feature information, determining the vehicle information database as the first screening object set; if the first screening object set does not have the second vehicle feature information, determining the first screening object set as the second screening object set; if the second screening object set does not have the third vehicle feature information, determining the second screening object set as the third screening object set; if the third screening object set does not have the fourth vehicle feature information, determining that the vehicle information database does not have vehicle feature information matching the vehicle transaction evaluation information.
[0009] In one embodiment of this application, the method may optionally further include: if the vehicle brand and model in the vehicle transaction evaluation information is empty, determining the vehicle information database as the first set of filtering objects; if the vehicle type shown in the vehicle transaction evaluation information is empty, determining the first set of filtering objects as the second set of filtering objects; if the vehicle series shown in the vehicle transaction evaluation information is empty, determining the second set of filtering objects as the third set of filtering objects; if the vehicle model shown in the vehicle transaction evaluation information is empty, determining that the vehicle information database does not have vehicle feature information matching the vehicle transaction evaluation information.
[0010] In one embodiment of this application, optionally, determining the required fourth vehicle feature information from all the fourth vehicle feature information according to a predetermined result filtering rule includes: selecting the fourth vehicle feature information with the shortest interval between the latest maintenance time and the current time from all the fourth vehicle feature information as the required fourth vehicle feature information.
[0011] Optionally, in one embodiment of this application, determining whether the vehicle information database stores first vehicle characteristic information having the vehicle announcement model includes: performing multi-level joins on multi-level data tables storing vehicle announcement models in the vehicle information database using conditional indexes to identify the first vehicle characteristic information in the vehicle information database; determining whether the first set of filtering objects contains second vehicle characteristic information whose vehicle type is consistent with the vehicle type shown in the vehicle transaction evaluation information includes: performing multi-level joins on multi-level data tables storing vehicle types in the first set of filtering objects using conditional indexes to identify the second vehicle characteristic information in the first set of filtering objects.
[0012] In one embodiment of this application, optionally, determining whether the third set of filtering objects contains fourth vehicle feature information whose vehicle model name matches the vehicle model shown in the vehicle transaction evaluation information through fuzzy matching includes: for the vehicle model shown in the vehicle transaction evaluation information, splitting the vehicle model into multiple segments using a preset word segmentation rule; reordering the multiple segments based on their respective weights in the string processing library to obtain a recombined word sequence; determining the similarity between the recombined word sequence and the word sequence corresponding to each vehicle model name in the third set of filtering objects; and determining the third vehicle feature information corresponding to the vehicle model name whose similarity to the recombined word sequence is greater than or equal to a predetermined similarity threshold as the fourth vehicle feature information.
[0013] In one embodiment of this application, optionally, storing the manually added vehicle feature information into the vehicle information database includes: monitoring the process of the manual addition through a first hook function, and triggering a storage operation to store the manually added vehicle feature information into the vehicle information database when the process of the manual addition is detected to be finished.
[0014] Secondly, embodiments of this application provide a vehicle transaction evaluation information processing device, comprising: a vehicle transaction evaluation information acquisition unit, used to acquire vehicle transaction evaluation information to be entered; a multi-level mapping unit, used to determine whether there is vehicle feature information in the vehicle information database that matches the vehicle transaction evaluation information based on a predetermined multi-level mapping rule, wherein the multi-level mapping rule is used to progressively reduce the mapping range of the vehicle transaction evaluation information in the vehicle information database; a first entry execution unit, used to fill the vehicle feature information into the entry position of the vehicle transaction evaluation information if there is vehicle feature information in the vehicle information database that matches the vehicle transaction evaluation information; and a second entry execution unit, used to enter a manual entry mode for feature information and store the manually entered vehicle feature information in the vehicle information database.
[0015] In one embodiment of this application, optionally, the multi-level mapping unit includes: a first-level mapping unit, configured to determine the vehicle announcement model corresponding to the vehicle brand model in the vehicle transaction evaluation information, and determine whether the vehicle information database stores first vehicle feature information having the vehicle announcement model, wherein if the vehicle information database stores the first vehicle feature information, all the first vehicle feature information is determined as a first screening object set; a second-level mapping unit, configured to determine whether the first screening object set contains second vehicle feature information whose vehicle type is consistent with the vehicle type shown in the vehicle transaction evaluation information, wherein if the first screening object set contains second vehicle feature information, all the second vehicle feature information is determined as a first screening object set. The system comprises: a second set of filtering objects; a third-level mapping unit, used to determine, through keyword matching, whether the second set of filtering objects contains third vehicle feature information whose vehicle series matches the vehicle series shown in the vehicle transaction evaluation information; wherein, if the second set of filtering objects contains the third vehicle feature information, all of the third vehicle feature information is determined as the third set of filtering objects; and a fourth-level mapping unit, used to determine, through fuzzy matching, whether the third set of filtering objects contains fourth vehicle feature information whose model name matches the model shown in the vehicle transaction evaluation information; wherein, if the third set of filtering objects contains the fourth vehicle feature information, the required fourth vehicle feature information is determined from all of the fourth vehicle feature information according to a predetermined result filtering rule.
[0016] Optionally, in one embodiment of this application, the first-level mapping unit is further configured to: if the vehicle information database does not store the first vehicle feature information, determine the vehicle information database as the first set of filtering objects; the second-level mapping unit is further configured to: if the first set of filtering objects does not have the second vehicle feature information, determine the first set of filtering objects as the second set of filtering objects; the third-level mapping unit is further configured to: if the second set of filtering objects does not have the third vehicle feature information, determine the second set of filtering objects as the third set of filtering objects; the fourth-level mapping unit is further configured to: if the third set of filtering objects does not have the fourth vehicle feature information, determine that the vehicle information database does not have vehicle feature information that matches the vehicle transaction evaluation information.
[0017] In one embodiment of this application, optionally, the first-level mapping unit is further configured to: if the vehicle brand and model in the vehicle transaction evaluation information is empty, determine the vehicle information database as the first set of filtering objects; the second-level mapping unit is further configured to: if the vehicle type shown in the vehicle transaction evaluation information is empty, determine the first set of filtering objects as the second set of filtering objects; the third-level mapping unit is further configured to: if the vehicle series shown in the vehicle transaction evaluation information is empty, determine the second set of filtering objects as the third set of filtering objects; the fourth-level mapping unit is further configured to: if the vehicle model shown in the vehicle transaction evaluation information is empty, determine that the vehicle information database does not have vehicle feature information matching the vehicle transaction evaluation information.
[0018] In one embodiment of this application, optionally, the fourth-level mapping unit includes: a result filtering unit, used to select the fourth vehicle feature information with the shortest interval between the latest maintenance time and the current time from all the fourth vehicle feature information, as the required fourth vehicle feature information.
[0019] In one embodiment of this application, optionally, the first-level mapping unit is configured to: perform multi-level joint queries on the multi-level data tables storing vehicle announcement models in the vehicle information database through conditional indexes, so as to identify the first vehicle feature information in the vehicle information database; the second-level mapping unit is configured to: perform multi-level joint queries on the multi-level data tables storing vehicle types in the first set of filtering objects through conditional indexes, so as to identify the second vehicle feature information in the first set of filtering objects.
[0020] In one embodiment of this application, optionally, the fourth-level mapping unit is configured to: for the vehicle models shown in the vehicle transaction evaluation information, split the vehicle models into multiple segments using a preset segmentation rule; reorder the multiple segments based on their respective weights in the string processing library to obtain a recombined word sequence; determine the similarity between the recombined word sequence and the word sequence corresponding to each vehicle model name in the third filtering object set; and determine the third vehicle feature information corresponding to the vehicle model name whose similarity to the recombined word sequence is greater than or equal to a predetermined similarity threshold as the fourth vehicle feature information.
[0021] In one embodiment of this application, optionally, the second input execution unit includes: a monitoring unit, configured to monitor the process of manual input through a first hook function, and when the process of manual input is monitored to end, trigger a storage operation to store the manually input vehicle feature information into the vehicle information database.
[0022] Thirdly, embodiments of this application provide a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in the first aspect above.
[0023] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the method described in the first aspect above.
[0024] The above technical solution addresses the technical problem of inefficiency and inaccuracy in manually entering vehicle transaction assessment information provided by third parties, which negatively impacts the management's work. Within the vehicle transaction management system's own vehicle information database, multi-level mapping rules are used to automatically match vehicle transaction assessment information provided by third-party assessment systems. When a valid mapping object is found, the matched content is automatically entered into the corresponding location in the vehicle transaction management system. When no valid mapping object is found, manual entry is introduced, and the manually entered content is stored in the vehicle information database to improve the database. This achieves a standardized and automated processing flow for vehicle transaction assessment information from extraction to entry, effectively reducing time costs compared to manual entry and avoiding the inaccuracies caused by manual entry. Furthermore, the database's self-learning mechanism continuously expands its coverage, enhancing the vehicle transaction management system's adaptability to external non-standard data and providing reliable data support for the post-entry vehicle transaction approval process. Attached Figure Description
[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0026] Figure 1 shows a flowchart of a vehicle transaction evaluation information processing method according to an embodiment of the present application; Figure 2 shows a flowchart of a multi-level mapping method according to an embodiment of the present application; Figure 3 shows a block diagram of a computer device according to an embodiment of the present application; Figure 4 shows a block diagram of a computer device according to another embodiment of the present application. Detailed Implementation
[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0028] Figure 1 shows a flowchart of a vehicle transaction evaluation information processing method according to an embodiment of this application.
[0029] As shown in Figure 1, a vehicle transaction assessment information processing method according to an embodiment of this application includes: step 102, obtaining vehicle transaction assessment information to be entered.
[0030] The vehicle transaction assessment information to be entered refers to the original vehicle assessment data provided by the third-party assessment system to the vehicle transaction management party, including but not limited to specific information reflecting the characteristics of the vehicle being traded, such as vehicle announcement model, vehicle type, series, and model. Obtaining this vehicle transaction assessment information provides a data source for subsequent assessment information entry operations, ensuring that the management party's vehicle transaction management system can receive and recognize the input from the third-party assessment system, thus providing a data foundation for subsequent automated mapping.
[0031] In real-world scenarios, such as vehicle mortgage loans in the financial sector, banks or financial institutions need to assess the value of vehicles used as collateral to determine the loan amount for borrowers. Vehicle transaction assessment information provided by third-party assessment systems is a crucial basis for this valuation. Banks or financial institutions must accurately input this vehicle transaction assessment information into their own vehicle transaction management systems for risk control, thereby completing the collateral registration, loan approval, and subsequent asset monitoring processes.
[0032] Furthermore, in the used car market, trading platforms or dealers often need to conduct professional appraisals of the vehicles they acquire or sell to determine an appropriate price. Third-party appraisal systems provide detailed records of the vehicle's technical condition, repair history, and market value, serving as a crucial basis for pricing. Therefore, vehicle transaction managers need to fully input this appraisal information into their vehicle transaction management system for generating sales lists, pricing strategies, and vehicle file management, thereby ensuring the rationality and compliance of vehicle transactions.
[0033] Step 104: Based on predetermined multi-level mapping rules, determine whether the vehicle information database contains vehicle feature information that matches the vehicle transaction evaluation information.
[0034] The multi-level mapping rule is used to progressively reduce the mapping range of the vehicle transaction evaluation information in the vehicle information database. It adopts a hierarchical and progressive approach, filtering from the announcement model to the vehicle name, and then to the series and model, thereby gradually narrowing the matching range of the vehicle transaction evaluation information in the vehicle information database. Thus, through a structured matching logic, the database query efficiency is improved, the complexity of the retrieval process is reduced, and the system's adaptability to diverse and non-standard data is enhanced.
[0035] Step 106: If the vehicle information database contains vehicle feature information that matches the vehicle transaction evaluation information, fill the vehicle feature information into the entry position of the vehicle transaction evaluation information.
[0036] If the vehicle information database contains vehicle feature information that matches the vehicle transaction assessment information, it indicates that the vehicle transaction assessment information provided by the third-party assessment system has an accurate mapping object in the vehicle information database. At this point, the information from the vehicle transaction assessment information that successfully retrieves the mapping object can be automatically filled into the system's input field. This achieves seamless data integration between the third-party assessment system and the vehicle transaction management system, effectively improving the input speed of vehicle transaction assessment information and reducing the possibility of input errors due to human intervention.
[0037] Step 108: If the vehicle information database does not contain vehicle feature information that matches the vehicle transaction evaluation information, the manual supplementation mode is entered, and the manually supplemented vehicle feature information is stored in the vehicle information database.
[0038] If the vehicle information database does not contain vehicle feature information that matches the vehicle transaction evaluation information, it indicates that no valid mapping object has been found for the vehicle transaction evaluation information. In this case, the manual entry function can be triggered to manually determine the mapping object of the vehicle transaction evaluation information in the vehicle transaction management system. The manual entry mode allows operators to manually input missing or abnormal vehicle feature information, such as adding new vehicle models or correcting existing models.
[0039] Simultaneously, the supplementary data entry results are saved to the vehicle information database. This allows the database to match the supplementary data entry results as mapping objects when similar vehicle transaction evaluation information reappears in subsequent data entry operations. Thus, mapping objects not present in the vehicle information database can be supplemented through manual intervention, expanding the database while completing the current data entry task.
[0040] The above technical solution, within the vehicle transaction management system's own vehicle information database, automatically matches vehicle transaction assessment information provided by third-party assessment systems using multi-level mapping rules. When a valid mapping object is found, the matched content is automatically entered into the corresponding location in the vehicle transaction management system. When no valid mapping object is found, manual entry is introduced, and the manually entered content is stored in the vehicle information database to improve the database. This achieves a standardized and automated processing flow for vehicle transaction assessment information from extraction to entry, effectively reducing time costs compared to manual entry and avoiding the inaccuracies caused by manual entry. Furthermore, the database's self-learning mechanism continuously expands its coverage, enhancing the vehicle transaction management system's adaptability to external non-standard data and providing reliable data support for the post-entry vehicle transaction approval process.
[0041] In the context of vehicle mortgage loan business in the financial sector, the technical solution of this application is exemplified as follows.
[0042] Customer A applied for a mortgage loan from a bank using a "Brand X, Series Y, 300L Luxury" SUV (Sport Utility Vehicle) registered under their name. The bank commissioned a third-party appraisal agency to appraise the vehicle, and the report generated by the third-party appraisal system showed the following: the vehicle's make and model is "Brand X, Series Y, 300L", the vehicle type is "passenger vehicle", the series is "Y", and the model is "Series Y, 300L Luxury".
[0043] First, the bank's vehicle transaction management system obtains this assessment information containing the aforementioned characteristics.
[0044] Next, the system uses a multi-level mapping rule. First, it searches the vehicle information database for vehicles with the announcement model "X brand Y series 300L" for the first level of mapping. Then, it filters for vehicles with the vehicle type "passenger car" for the second level of mapping. Next, it matches vehicles with the series "y" to complete the third level of mapping. Finally, it uses fuzzy matching to find records in the model name similar to "Y series 300L luxury model" for the fourth level of mapping.
[0045] Furthermore, once the system successfully finds a perfectly matching vehicle feature in the vehicle information database, it can automatically fill the vehicle's standard model, configuration code, valuation benchmark, and other feature information into the corresponding fields of the loan approval system.
[0046] It should be added that in this scenario, the vehicle information database contained complete and valid matching information, and manual data entry was not triggered.
[0047] In the context of used car transactions in the vehicle sector, the technical solution of this application is illustrated below.
[0048] The buyer acquired a "Z brand Q series sedan 1.5L automatic flagship model" vehicle on a used car trading platform. The evaluation information from the third-party evaluation system shows that the car's brand and model are "Z brand", the vehicle type is "sedan", and the series is "Q". However, due to historical inconsistencies, the model name is only marked as "1.5L automatic top-of-the-line".
[0049] First, the vehicle transaction management system of the vehicle transaction manager or the acquirer obtains this assessment information.
[0050] Next, the system performs multi-level mapping. First, it matches the announcement model "Z brand" as the first-level mapping. Then, it filters the vehicle type "sedan" to achieve the second-level mapping, and then matches the car series "Q" to achieve the third-level mapping. However, when performing fuzzy matching of car models at the fourth level, the system cannot accurately associate the colloquial phrase "1.5L automatic top-of-the-line" with the standard "Q car series sedan 1.5L automatic flagship model" model name in the database.
[0051] At this point, due to the failure of automatic matching, the system triggered manual data entry. Based on the vehicle nameplate and detailed configuration, the staff of the vehicle transaction management system manually selected and confirmed the correct standard model for the vehicle as "Z brand Q series sedan 1.5L automatic flagship model".
[0052] Finally, after manual data entry is completed, the system not only uses this information to generate the vehicle's sales list and pricing, but also associates the colloquial description "1.5L automatic top-of-the-line" with the standard model "Z brand Q series sedan 1.5L automatic flagship" in the vehicle information database. This way, the system can automatically match the description when it is encountered again in the future.
[0053] Based on the embodiment shown in FIG1, FIG2 shows a flowchart of a multi-level mapping method according to an embodiment of the present application.
[0054] As shown in Figure 2, a multi-level mapping method according to an embodiment of this application includes: step 202, determining the vehicle announcement model corresponding to the vehicle brand model in the vehicle transaction evaluation information, and determining whether the vehicle information database stores first vehicle feature information with the vehicle announcement model, wherein, if the vehicle information database stores the first vehicle feature information, all the first vehicle feature information is determined as a first set of filtering objects.
[0055] Vehicle make and model number reflect the vehicle's manufacturer, brand, and specific product model identifier. By searching the vehicle information database for vehicle feature information that matches the vehicle make and model number in the vehicle transaction evaluation information, a set of vehicle feature information with the same make and model number as the vehicle transaction evaluation information can be obtained, which is the first set of screening targets.
[0056] In one possible design, if the vehicle information database does not store the first vehicle feature information, the vehicle information database is determined as the first set of filtering objects.
[0057] In another possible design, if the vehicle brand and model in the vehicle transaction evaluation information are empty, the vehicle information database is determined as the first set of filtering objects.
[0058] In other words, if the vehicle brand and model in the vehicle transaction evaluation information are empty and cannot be matched in the vehicle information database, or if the vehicle information database cannot match the vehicle feature information that is consistent with the vehicle brand and model in the vehicle transaction evaluation information, the mapping at this level can be skipped directly, and the vehicle information database can be directly determined as the first set of filtering objects. The next level of mapping can be carried out using the first set of filtering objects as the retrieval range.
[0059] Specifically, step 202 includes: performing multi-level lookup on the multi-level data tables storing vehicle announcement models in the vehicle information database through conditional indexing, in order to identify the first vehicle feature information in the vehicle information database.
[0060] Among these features, conditional indexes can accelerate the query speed for vehicle announcement models, quickly locating target records in a database containing massive amounts of vehicle model data. Multi-level joins can link vehicle feature information scattered across different data tables, avoiding the waste of system resources caused by direct full table scans. Combining conditional indexes with multi-level joins effectively reduces the complexity of database queries and minimizes system resource consumption, helping to improve data retrieval efficiency. By optimizing data access paths, this ensures the accuracy and real-time nature of the initial vehicle feature information identification, laying a solid foundation for subsequent multi-level mapping.
[0061] It should be added that, in this application, the vehicle transaction evaluation information corresponding to multiple vehicle transactions can be entered automatically at the same time. The vehicle transaction management system can further improve the efficiency of data matching through this parallel automated data standard entry.
[0062] Step 204: Determine whether the first set of filtered objects contains second vehicle feature information whose vehicle type is consistent with the vehicle type shown in the vehicle transaction evaluation information. If the first set of filtered objects contains the second vehicle feature information, then all the second vehicle feature information is determined as the second set of filtered objects.
[0063] Vehicle type reflects the vehicle's purpose and function, such as truck, tractor, and passenger car. By retrieving vehicle feature information from the first set of screening targets that matches the vehicle type in the vehicle transaction evaluation information, a set of vehicle feature information with the same purpose and function as the vehicle transaction evaluation information can be obtained, which is the second set of screening targets.
[0064] In one possible design, if the first set of filtered objects does not have the second vehicle feature information, the first set of filtered objects is determined as the second set of filtered objects.
[0065] In another possible design, if the vehicle type shown in the vehicle transaction evaluation information is empty, the first set of filtering objects is determined as the second set of filtering objects.
[0066] In other words, if the vehicle type in the vehicle transaction assessment information is empty and cannot be matched in the vehicle information database, or if the vehicle information database cannot match the vehicle feature information that is consistent with the vehicle type in the vehicle transaction assessment information, the mapping at this level can be skipped directly, and the first set of filtering objects can be directly determined as the second set of filtering objects, and the next level of mapping can be carried out using the second set of filtering objects as the retrieval range.
[0067] Specifically, step 204 includes: performing multi-level lookups on the multi-level data tables storing vehicle types within the first set of filtered objects using conditional indexes, in order to identify the second vehicle feature information in the first set of filtered objects.
[0068] Conditional indexes accelerate vehicle type queries, enabling rapid location of target records in databases containing massive amounts of vehicle types. Multi-level joins connect vehicle feature information scattered across different tables, avoiding the waste of system resources caused by full table scans. Combining conditional indexes with multi-level joins effectively reduces database query complexity and system resource consumption, improving data retrieval efficiency. This, in turn, optimizes data access paths, ensuring the accuracy and real-time nature of secondary vehicle feature information identification, laying a solid foundation for subsequent multi-level mapping.
[0069] Step 206: Using keyword matching, determine whether the second set of filtered objects contains third vehicle feature information that matches the vehicle series shown in the vehicle transaction evaluation information. If the second set of filtered objects contains the third vehicle feature information, then all the third vehicle feature information is determined as the third set of filtered objects.
[0070] The vehicle series reflects a product sequence of a series of models developed based on the same platform or technology under the same car brand. By searching the second set of screening objects for vehicle feature information that is consistent with the vehicle series in the vehicle transaction evaluation information, a set of vehicle feature information of the same vehicle series as the vehicle transaction evaluation information can be obtained, which is the third set of screening objects.
[0071] In one possible design, if the second set of filtered objects does not have the third vehicle feature information, the second set of filtered objects is determined as the third set of filtered objects.
[0072] In another possible design, if the vehicle series shown in the vehicle transaction evaluation information is empty, the second set of filtering objects will be determined as the third set of filtering objects.
[0073] In other words, if the vehicle series in the vehicle transaction evaluation information is empty and cannot be matched in the vehicle information database, or if the vehicle information database cannot match the vehicle feature information that is consistent with the vehicle series in the vehicle transaction evaluation information, the mapping at this level can be skipped directly, and the second set of filtering objects can be directly determined as the third set of filtering objects, and the next level of mapping can be carried out using the third set of filtering objects as the retrieval interval.
[0074] Using keyword matching to match vehicle series involves using the vehicle series names provided in the vehicle transaction evaluation information as key query terms for precise string comparison within the vehicle feature information of the second filtering object set. First, the vehicle series fields in the vehicle transaction evaluation information and the vehicle information database are standardized and preprocessed to ensure consistency in the comparison basis; for example, they are uniformly converted to uppercase letters and irrelevant symbols such as spaces and hyphens are removed. Next, the processed query terms are matched against the pre-stored vehicle series names in the vehicle information database. If they match completely, the vehicle feature information corresponding to that pre-stored vehicle series name is considered a match between the pre-stored vehicle series name and the vehicle transaction evaluation information. This allows for the rapid filtering of vehicle feature information belonging to the same product series from a large amount of data, efficiently constructing the third filtering object set and laying a precise data foundation for subsequent fuzzy matching.
[0075] Step 208: Determine whether the third set of filtering objects contains fourth vehicle feature information whose vehicle model name matches the vehicle model shown in the vehicle transaction evaluation information by using fuzzy matching. If the third set of filtering objects contains the fourth vehicle feature information, determine the required fourth vehicle feature information from all the fourth vehicle feature information according to a predetermined result filtering rule.
[0076] The vehicle model name reflects fine-grained product model information such as the vehicle's specific configuration, power system, drive type, or market positioning. By searching for vehicle feature information in the third set of filtering objects that matches the vehicle model name in the vehicle transaction evaluation information, a set of vehicle feature information for the same model as the vehicle transaction evaluation information can be obtained, which is the retrieved fourth vehicle feature information.
[0077] In one possible design, if the third set of filtered objects does not have the fourth vehicle feature information, it is determined that the vehicle information database does not have vehicle feature information that matches the vehicle transaction evaluation information.
[0078] In another possible design, if the vehicle model shown in the vehicle transaction evaluation information is empty, it is determined that the vehicle information database does not have vehicle feature information that matches the vehicle transaction evaluation information.
[0079] In other words, if the vehicle model in the vehicle transaction evaluation information is empty and cannot be matched in the vehicle information database, or if the vehicle information database cannot match the vehicle feature information that is consistent with the vehicle model in the vehicle transaction evaluation information, this level of mapping can be skipped directly, and the conclusion can be drawn that the vehicle information database does not have vehicle feature information that matches the vehicle transaction evaluation information.
[0080] Specifically, step 208 includes: for the vehicle models shown in the vehicle transaction evaluation information, splitting the vehicle models into multiple segments using preset word segmentation rules; reordering the multiple segments based on their respective weights in the string processing library to obtain a recombined word sequence; determining the similarity between the recombined word sequence and the word sequence corresponding to each vehicle model name in the third filtering object set; and determining the third vehicle feature information corresponding to the vehicle model name whose similarity to the recombined word sequence is greater than or equal to a predetermined similarity threshold as the fourth vehicle feature information.
[0081] The above technical solution processes vehicle model names through preset word segmentation rules, splitting complex vehicle model strings into word units with independent semantics. For example, "brand A x series heavy truck" can be split into "brand A", "x series", and "heavy truck", thus providing a clearly structured data foundation for subsequent similarity calculations. The weight of each word segment in the string processing library reflects its importance in the vehicle model identifier. Through recombination, a standard word sequence that can more deeply mine the core features of the vehicle model can be obtained. Then, the recombined word sequence is compared with the candidate vehicle model names in the vehicle model library. The vehicle model names that reach the minimum similarity level required for matching with the recombined word sequence are determined as the matching content, and the vehicle feature information to which the vehicle model name belongs is determined as the fourth vehicle feature information. Thus, by combining text segmentation and similarity comparison, semantically similar vehicle model variants can be accurately identified, improving the accuracy of fuzzy matching.
[0082] The above technical solution employs a multi-level progressive mapping from vehicle announcement model, vehicle type, vehicle series to model name. It performs precise matching of third-party vehicle transaction evaluation information with its own database at each level, and can flexibly skip the rules of the current level when any mapping condition is missing or the match fails. Overall, it achieves efficient, accurate, and fault-tolerant automated and standardized entry of external non-standard vehicle evaluation information, improves data entry efficiency and accuracy, significantly reduces the need for manual intervention, enhances the system's ability to process complex heterogeneous data, and provides reliable data assurance for subsequent vehicle transaction approval.
[0083] In step 208, determining the required fourth vehicle feature information from all the fourth vehicle feature information according to the predetermined result filtering rules includes: selecting the fourth vehicle feature information with the shortest interval between the latest maintenance time and the current time from all the fourth vehicle feature information as the required fourth vehicle feature information.
[0084] If the final selected fourth vehicle feature information is a single item, it can be directly used as the mapping result. However, if the final selected fourth vehicle feature information is multiple items, one of the four fourth vehicle feature information items needs to be selected as the mapping result. Furthermore, the fourth vehicle feature information with the shortest interval between the latest maintenance time and the current time can be selected as the required fourth vehicle feature information, thereby ensuring that the selected mapping result is the latest data version and effectively avoiding mapping deviations caused by outdated information.
[0085] In addition, the process of storing the manually added vehicle feature information into the vehicle information database includes: monitoring the process of the manual addition through a first hook function, and triggering a storage operation to store the manually added vehicle feature information into the vehicle information database when the process of the manual addition is detected to be finished.
[0086] The hook function, acting as an event listening mechanism, can automatically execute the operation of storing the manually added vehicle feature information into the vehicle information database at the node where the supplementary data entry operation is completed, thereby achieving automated supervision of the manual intervention process. Triggering the storage operation immediately upon monitoring the process's completion ensures that the supplementary data can be synchronized to the vehicle information database in a timely manner, avoiding data loss due to human error or system malfunctions, and improving the timeliness of data entry.
[0087] In addition, this application provides a vehicle transaction evaluation information processing device, including: a vehicle transaction evaluation information acquisition unit, used to acquire vehicle transaction evaluation information to be entered; a multi-level mapping unit, used to determine whether there is vehicle feature information in the vehicle information database that matches the vehicle transaction evaluation information based on a predetermined multi-level mapping rule, wherein the multi-level mapping rule is used to progressively reduce the mapping range of the vehicle transaction evaluation information in the vehicle information database; a first entry execution unit, used to fill the vehicle feature information into the entry position of the vehicle transaction evaluation information if there is vehicle feature information in the vehicle information database that matches the vehicle transaction evaluation information; and a second entry execution unit, used to enter a manual entry mode for feature information and store the manually entered vehicle feature information in the vehicle information database.
[0088] In one embodiment of this application, optionally, the multi-level mapping unit includes: a first-level mapping unit, configured to determine the vehicle announcement model corresponding to the vehicle brand model in the vehicle transaction evaluation information, and determine whether the vehicle information database stores first vehicle feature information having the vehicle announcement model, wherein if the vehicle information database stores the first vehicle feature information, all the first vehicle feature information is determined as a first screening object set; a second-level mapping unit, configured to determine whether the first screening object set contains second vehicle feature information whose vehicle type is consistent with the vehicle type shown in the vehicle transaction evaluation information, wherein if the first screening object set contains second vehicle feature information, all the second vehicle feature information is determined as a first screening object set. The system comprises: a second set of filtering objects; a third-level mapping unit, used to determine, through keyword matching, whether the second set of filtering objects contains third vehicle feature information whose vehicle series matches the vehicle series shown in the vehicle transaction evaluation information; wherein, if the second set of filtering objects contains the third vehicle feature information, all of the third vehicle feature information is determined as the third set of filtering objects; and a fourth-level mapping unit, used to determine, through fuzzy matching, whether the third set of filtering objects contains fourth vehicle feature information whose model name matches the model shown in the vehicle transaction evaluation information; wherein, if the third set of filtering objects contains the fourth vehicle feature information, the required fourth vehicle feature information is determined from all of the fourth vehicle feature information according to a predetermined result filtering rule.
[0089] Optionally, in one embodiment of this application, the first-level mapping unit is further configured to: if the vehicle information database does not store the first vehicle feature information, determine the vehicle information database as the first set of filtering objects; the second-level mapping unit is further configured to: if the first set of filtering objects does not have the second vehicle feature information, determine the first set of filtering objects as the second set of filtering objects; the third-level mapping unit is further configured to: if the second set of filtering objects does not have the third vehicle feature information, determine the second set of filtering objects as the third set of filtering objects; the fourth-level mapping unit is further configured to: if the third set of filtering objects does not have the fourth vehicle feature information, determine that the vehicle information database does not have vehicle feature information that matches the vehicle transaction evaluation information.
[0090] In one embodiment of this application, optionally, the first-level mapping unit is further configured to: if the vehicle brand and model in the vehicle transaction evaluation information is empty, determine the vehicle information database as the first set of filtering objects; the second-level mapping unit is further configured to: if the vehicle type shown in the vehicle transaction evaluation information is empty, determine the first set of filtering objects as the second set of filtering objects; the third-level mapping unit is further configured to: if the vehicle series shown in the vehicle transaction evaluation information is empty, determine the second set of filtering objects as the third set of filtering objects; the fourth-level mapping unit is further configured to: if the vehicle model shown in the vehicle transaction evaluation information is empty, determine that the vehicle information database does not have vehicle feature information matching the vehicle transaction evaluation information.
[0091] In one embodiment of this application, optionally, the fourth-level mapping unit includes: a result filtering unit, used to select the fourth vehicle feature information with the shortest interval between the latest maintenance time and the current time from all the fourth vehicle feature information, as the required fourth vehicle feature information.
[0092] In one embodiment of this application, optionally, the first-level mapping unit is configured to: perform multi-level joint queries on the multi-level data tables storing vehicle announcement models in the vehicle information database through conditional indexes, so as to identify the first vehicle feature information in the vehicle information database; the second-level mapping unit is configured to: perform multi-level joint queries on the multi-level data tables storing vehicle types in the first set of filtering objects through conditional indexes, so as to identify the second vehicle feature information in the first set of filtering objects.
[0093] In one embodiment of this application, optionally, the fourth-level mapping unit is configured to: for the vehicle models shown in the vehicle transaction evaluation information, split the vehicle models into multiple segments using a preset segmentation rule; reorder the multiple segments based on their respective weights in the string processing library to obtain a recombined word sequence; determine the similarity between the recombined word sequence and the word sequence corresponding to each vehicle model name in the third filtering object set; and determine the third vehicle feature information corresponding to the vehicle model name whose similarity to the recombined word sequence is greater than or equal to a predetermined similarity threshold as the fourth vehicle feature information.
[0094] In one embodiment of this application, optionally, the second input execution unit includes: a monitoring unit, configured to monitor the process of manual input through a first hook function, and when the process of manual input is monitored to end, trigger a storage operation to store the manually input vehicle feature information into the vehicle information database.
[0095] The device uses the solution described in any one of the above embodiments, and therefore has all the above-mentioned technical effects, which will not be repeated here.
[0096] In another embodiment, this application provides a computer device, which can be a server, and its internal structure diagram is shown in Figure 3. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it can implement the methods described in any of the above embodiments.
[0097] In one embodiment, this application also provides a computer device, which can be a client, and its internal structure diagram can be as shown in Figure 4. The computer device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an 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 medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it can implement the methods described in any of the above embodiments.
[0098] The computer devices described in the embodiments of this application exist in various forms, including but not limited to: (1) mobile communication devices: These devices are characterized by having mobile communication functions and are primarily designed to provide voice and data communication. These terminals include: smartphones, multimedia phones, feature phones, and low-end phones, etc.
[0099] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access features. These terminals include PDAs, MIDs, and UMPCs, etc.
[0100] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes: audio and video players, handheld game consoles, e-books, as well as smart toys, wearable devices, and portable car navigation devices.
[0101] (4) Server: A device that provides computing services. The components of a server include a processor, hard disk, memory, system bus, etc. Servers are similar to general computer architectures, but because they need to provide highly reliable services, they have higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.
[0102] (5) Other electronic devices with data interaction functions.
[0103] In addition, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which are used to perform the following steps: obtaining vehicle transaction evaluation information to be entered; determining, based on a predetermined multi-level mapping rule, whether there is vehicle feature information in the vehicle information database that matches the vehicle transaction evaluation information, wherein the multi-level mapping rule is used to progressively reduce the mapping range of the vehicle transaction evaluation information in the vehicle information database; if there is vehicle feature information in the vehicle information database that matches the vehicle transaction evaluation information, filling the vehicle feature information into the entry position of the vehicle transaction evaluation information; if there is no vehicle feature information in the vehicle information database that matches the vehicle transaction evaluation information, entering a manual entry mode, and storing the manually entered vehicle feature information in the vehicle information database.
[0104] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.
[0105] The technical solution of this application has been described in detail above with reference to the accompanying drawings. This technical solution achieves a standardized and automated processing flow for vehicle transaction evaluation information, from extraction to entry. Compared to manual entry, this effectively reduces time costs and avoids the inaccuracies caused by manual entry. Furthermore, the database's self-learning mechanism continuously expands its coverage, enhancing the vehicle transaction management system's adaptability to external non-standard data and providing reliable data support for the post-entry vehicle transaction approval process.
[0106] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0107] It should be understood that although the terms "first," "second," etc., may be used to describe vehicle feature information in the embodiments of this application, such vehicle feature information should not be limited to these terms. These terms are only used to distinguish vehicle feature information from one another. For example, without departing from the scope of the embodiments of this application, first vehicle feature information may also be referred to as second vehicle feature information, and similarly, second vehicle feature information may also be referred to as first vehicle feature information.
[0108] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."
[0109] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.
[0110] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0111] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.
[0112] Those skilled in the art will understand that all or part of the processes in the methods of 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 of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0113] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.
Claims
1. A method for processing vehicle transaction evaluation information, characterized in that, include: Obtain the vehicle transaction evaluation information to be entered; based on a predetermined multi-level mapping rule, determine whether there is vehicle feature information in the vehicle information database that matches the vehicle transaction evaluation information, wherein the multi-level mapping rule is used to progressively reduce the mapping range of the vehicle transaction evaluation information in the vehicle information database; if there is vehicle feature information in the vehicle information database that matches the vehicle transaction evaluation information, fill the vehicle feature information into the entry position of the vehicle transaction evaluation information; if there is no vehicle feature information in the vehicle information database that matches the vehicle transaction evaluation information, enter the manual entry mode, and store the manually entered vehicle feature information in the vehicle information database.
2. The method according to claim 1, characterized in that, The determination of whether the vehicle information database contains vehicle feature information matching the vehicle transaction evaluation information, based on a predetermined multi-level mapping rule, includes: determining the vehicle announcement model corresponding to the vehicle brand and model in the vehicle transaction evaluation information, and determining whether the vehicle information database stores first vehicle feature information with the vehicle announcement model. If the vehicle information database stores the first vehicle feature information, all of the first vehicle feature information is determined as a first screening object set. The determination of whether the first screening object set contains second vehicle feature information whose vehicle type matches the vehicle type shown in the vehicle transaction evaluation information is also included. If the first screening object set contains the second vehicle feature information, all of the first vehicle feature information is determined as a first screening object set. The second vehicle feature information is determined as the second set of screening objects. Using keyword matching, it is determined whether the second set of screening objects contains third vehicle feature information whose vehicle series matches the vehicle series shown in the vehicle transaction evaluation information. If the second set of screening objects contains the third vehicle feature information, all of the third vehicle feature information is determined as the third set of screening objects. Using fuzzy matching, it is determined whether the third set of screening objects contains fourth vehicle feature information whose model name matches the model shown in the vehicle transaction evaluation information. If the third set of screening objects contains the fourth vehicle feature information, the required fourth vehicle feature information is determined from all the fourth vehicle feature information according to a predetermined result screening rule.
3. The method according to claim 2, characterized in that, Also includes: If the vehicle information database does not store the first vehicle feature information, the vehicle information database will be determined as the first set of filtering objects. If the first set of filtered objects does not have the second vehicle feature information, the first set of filtered objects is determined as the second set of filtered objects; if the second set of filtered objects does not have the third vehicle feature information, the second set of filtered objects is determined as the third set of filtered objects; if the third set of filtered objects does not have the fourth vehicle feature information, it is determined that the vehicle information database does not have vehicle feature information that matches the vehicle transaction evaluation information.
4. The method according to claim 2, characterized in that, Also includes: If the vehicle brand and model in the vehicle transaction evaluation information is empty, the vehicle information database will be determined as the first set of filtering objects; If the vehicle type shown in the vehicle transaction evaluation information is empty, the first set of filtering objects will be determined as the second set of filtering objects. If the vehicle series shown in the vehicle transaction evaluation information is empty, the second set of filtering objects is determined as the third set of filtering objects; if the vehicle model shown in the vehicle transaction evaluation information is empty, it is determined that the vehicle information database does not have vehicle feature information that matches the vehicle transaction evaluation information.
5. The method according to claim 2, characterized in that, The step of determining the required fourth vehicle feature information from all the fourth vehicle feature information according to the predetermined result filtering rules includes: selecting the fourth vehicle feature information with the shortest interval between the latest maintenance time and the current time from all the fourth vehicle feature information as the required fourth vehicle feature information.
6. The method according to any one of claims 2 to 5, characterized in that, Determining whether the vehicle information database stores first vehicle characteristic information with the vehicle announcement model includes: performing multi-level joins on the multi-level data tables storing vehicle announcement models in the vehicle information database using conditional indexes to identify the first vehicle characteristic information in the vehicle information database; determining whether the first set of filtering objects contains second vehicle characteristic information whose vehicle type is consistent with the vehicle type shown in the vehicle transaction evaluation information includes: performing multi-level joins on the multi-level data tables storing vehicle types in the first set of filtering objects using conditional indexes to identify the second vehicle characteristic information in the first set of filtering objects.
7. The method according to claim 6, characterized in that, The step of determining whether the third set of filtering objects contains fourth vehicle feature information whose vehicle model name matches the vehicle model shown in the vehicle transaction evaluation information through fuzzy matching includes: for the vehicle model shown in the vehicle transaction evaluation information, splitting the vehicle model into multiple segments using a preset word segmentation rule; reordering the multiple segments based on their respective weights in the string processing library to obtain a recombined word sequence; determining the similarity between the recombined word sequence and the word sequence corresponding to each vehicle model name in the third set of filtering objects; and determining the third vehicle feature information corresponding to the vehicle model name whose similarity to the recombined word sequence is greater than or equal to a predetermined similarity threshold as the fourth vehicle feature information.
8. The method according to claim 6, characterized in that, The step of storing manually added vehicle feature information into the vehicle information database includes: monitoring the process of manual addition through a first hook function, and triggering a storage operation to store the manually added vehicle feature information into the vehicle information database when the process of manual addition is detected to be finished.
9. A computer device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to cause the processor to perform the method of any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that, The device stores computer-executable instructions configured to perform the method as described in any one of claims 1 to 8.