Dynamic receiving and processing system for electric detection data

Abstract

The invention discloses an electric detection data dynamic receiving and processing system, and belongs to the field of electric detection. The system comprises an electric detection original data source, an electric detection operation model and a dynamic semantic data model, wherein the electric detection original data source is used for acquiring electric detection original data from different data sources and classifying the electric detection original data into a plurality of data units; the electric detection operation model is used for converting electric detection original data into structured electric detection data; and the dynamic semantic data model is used for matching the required structured electric detection data according to the vehicle type and converting the structured electric detection data into a standardized XML electric detection file. According to the method, the full-process automatic processing from the original disordered electric detection data to the available XML file is realized.

Description

Technical Field

[0001] This invention belongs to the field of electrical inspection, specifically, it relates to a dynamic receiving and processing system for electrical inspection data. Background Technology

[0002] Driven by the "new four modernizations" of the automotive industry, the number of ECUs in new energy vehicles has surged (especially BMS, VCU, MCU, etc.), with software complexity and reprogramming requirements far exceeding those of traditional gasoline vehicles. Meanwhile, advanced driver assistance systems (ADAS) require frequent sensor calibration and software upgrades, placing extremely high demands on diagnostic and calibration tools. Traditional systems and manual intervention methods are insufficient to meet the complex maintenance scenarios and rapid response requirements of electronic diagnostic data. Therefore, standardized management tools to support unified and efficient detection and maintenance of vehicle status are becoming an essential requirement.

[0003] The existing technology has the following problems: 1. The sources of electrical inspection data are scattered, and there are serious data silos across modules. The correlation relationship relies on manual maintenance, which is prone to mismatch and omission, and the data consistency cannot be guaranteed. 2. The original electrical inspection data is disorganized, and the entire process of cleaning, integrating, and updating requires a high degree of manual intervention. The data quality is uncontrollable, real-time closed-loop processing cannot be achieved, and the production line turnover efficiency is low. 3. There are no standardized rules for distinguishing between the electrical test parameters of mass production vehicles and experimental vehicles. They rely on manual configuration, which makes it very easy for the test parameters to be mistakenly printed as mass production parameters, resulting in high quality risks. 4. The XML files used for electronic inspection and flashing rely on manual writing or fixed script generation, which is inflexible, cannot be quickly adapted to new models or engineering changes, and has insufficient multi-source data fusion capabilities, resulting in poor equipment compatibility.

[0004] Therefore, this invention proposes a dynamic receiving and processing system for electrical inspection data. Summary of the Invention

[0005] This invention aims to overcome the shortcomings of existing technologies and proposes a dynamic receiving and processing system for electrical inspection data to achieve the following objectives: realize fully automated processing from raw, messy electrical inspection data to usable XML files.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A dynamic receiving and processing system for electronic inspection data, the system comprising an original electronic inspection data source, an electronic inspection calculation model, and a dynamic semantic data model: The original data source for electrical inspection is used to obtain original electrical inspection data from different data sources and classify it into multiple data units. The aforementioned electrical inspection calculation model is used to transform raw electrical inspection data into structured electrical inspection data. The dynamic semantic data model is used to match the required structured electronic inspection data according to the vehicle type and convert the structured electronic inspection data into standardized XML electronic inspection files.

[0007] Preferably, the data units of the original data source for the electrical inspection include engineering vehicle number and vehicle material number units, ewo and mwo relationship units, module and material units, configuration word units, module and parameter units, and parameter pool units.

[0008] Preferably, the electronic inspection calculation model includes a rule engine and a dynamic parsing model. The rule engine is used to define the attribute association rules and data status processing rules for each data unit in the original electronic inspection data source. The dynamic parsing model is used to dynamically parse the original electronic inspection data source to generate structured electronic inspection data.

[0009] Preferably, the dynamic semantic data model includes a vehicle trial assembly rule base and an electronic inspection XML dynamic generation module. The vehicle trial assembly rule base is used to define the structured electronic inspection data acquisition rules for experimental vehicles and mass-produced vehicles, and the electronic inspection XML dynamic generation module is used to generate standardized XML electronic inspection files based on the structured electronic inspection data.

[0010] Preferably, the workflow of the electrical inspection calculation model includes the following steps: Step S11: Define attribute association rules and status processing rules for each data unit of the original data source of the electronic inspection based on the rule engine; Step S12: The dynamic parsing model acquires the data of each data unit and extracts features. Based on the extracted features, the data of each data unit is compared with the existing data in the system. Based on the comparison results, the data of each data unit is added, discarded, or updated. Step S13: The dynamic parsing model takes modules and material units as the core, and uses a rule engine to dynamically associate each data unit, extracting the core parameters of each unit after association and displaying them uniformly in the modules and material units. Step S14: Extract the key attributes of the data processed by the module and material, and the module and parameter unit, compare and calculate them with the existing key attributes in the system, integrate the matching data into core electrical inspection data, and set its status. Step S15: Based on the status processing rules defined by the rule engine, and combined with the status of the core electrical inspection data, compare and verify the status of the existing electrical inspection data of the same dimension in the system to achieve status diagnosis and update.

[0011] Preferably, the state processing rules defined by the rule engine include: Define four categories of electrical inspection data statuses: pending release, released, trial installation, and closed. Set the status priority as: Release > Trial Installation > Pending Release > Closed; Set the uniqueness rule for status: When the associated attributes of each unit are the same, there is only one valid status for the release and trial installation status, while multiple statuses are allowed for the pending release and closed statuses.

[0012] Preferably, in step S14, if the key attributes of the extracted module and material, and the module and parameter unit processed data overlap with the existing key attributes in the system, then the data corresponding to the overlapping key attributes shall be used as the core electrical inspection data.

[0013] Preferably, the workflow of the dynamic semantic data model includes: Step S21: During the production process, at the electrical inspection and writing station, the vehicle defines the vehicle's request data based on the preset vehicle trial assembly rule library. Step S22: Based on the vehicle's request data, extract the required structured electronic inspection data from the electronic inspection calculation model; Step S23: Map all extracted structured data to the corresponding node attributes of the XML file according to the industry standard XML format; Step S24: After completing the data node mapping, a standardized XML electrical inspection file is generated and output to the electrical inspection writing station for writing vehicle electrical inspection parameters.

[0014] Preferably, the vehicle trial assembly rule library distinguishes experimental vehicles from mass production vehicles based on whether the vehicle chassis number is maintained, whether a trial assembly module exists, whether trial assembly parameters exist, whether it is a material module trial assembly, and whether it is a shortcode trial assembly, as well as the corresponding structured electrical inspection data extraction target.

[0015] Preferably, the trial assembly rules include: Module release & parameter release: When the vehicle chassis number is not maintained, it is defined as a mass-produced vehicle. At this time, the module release data and associated parameter release data corresponding to the chassis number are extracted from the electronic inspection calculation model. Module trial installation & parameter release: When the vehicle chassis number has been maintained and a trial module exists, it is defined as an experimental vehicle. At this time, the module trial installation data and associated parameter release data corresponding to the chassis number are extracted from the electronic inspection calculation model. Module Release & Parameter Trial Installation: When the vehicle chassis number has been maintained and there is a trial installation module and trial installation parameters, and it is a material module trial installation, it is defined as an experimental vehicle. At this time, the module release data and associated parameter trial installation data corresponding to the chassis number are extracted from the electrical inspection calculation model. Module trial assembly & parameter trial assembly: When the vehicle chassis number has been maintained and there are trial assembly modules and trial assembly parameters but it is not a material module trial assembly, it is defined as an experimental vehicle. At this time, the module trial assembly data and associated parameter trial assembly data corresponding to the chassis number are extracted from the electrical inspection calculation model. Shortcode trial installation: When the vehicle chassis number maintenance status has been maintained and there is a trial installation module and trial installation parameters, and it is a Shortcode trial installation, it is defined as an experimental vehicle. At this time, the configuration word data corresponding to the chassis number is extracted from the electronic inspection calculation model.

[0016] The technical effects of this invention are as follows: This invention achieves fully automated processing of multi-source heterogeneous data into structured core data through an electrical inspection computation model. It leverages a dynamic semantic data model to generate standardized XML files on demand, ultimately outputting high-quality, writable electrical inspection parameters. This fully automated process significantly reduces reliance on manual labor and errors. Furthermore, when the original electrical inspection data changes (e.g., due to engineering modifications), the model can instantly respond and generate the correct writable parameters, eliminating downtime caused by manual verification or configuration modifications in traditional models, directly improving production line efficiency. This invention transforms raw, disorganized electrical inspection data into self-describing XML files. This means that this data is no longer a machine-readable "black box," but a strategic asset that can be directly parsed and utilized by enterprise-level big data platforms and IoT platforms. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a dynamic receiving and processing system for electrical inspection data provided in an embodiment of the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. This is to help those skilled in the art to have a more complete, accurate, and in-depth understanding of the inventive concept and technical solutions of the present invention, and to facilitate its implementation. It should be noted that the terms "first," "second," etc., used in this application are only for the convenience of describing the technical solutions and to distinguish components; the corresponding component configurations may be the same or different, and are not intended to limit the scope of this application. To make the technical solutions of the present invention clearer, the present invention will be explained and illustrated through the following embodiments.

[0019] This invention uses an electronic inspection (EI) calculation model as its core support. It achieves dynamic reception, calculation, and parsing of raw EI data through a rule engine and a dynamic parsing model. Then, relying on a dynamic semantic data model, it completes the real-time generation of structured EI data into standardized, self-describing XML files. Finally, it outputs EI parameters that conform to industry standards and can be written. This realizes the fully automated processing of raw, messy EI data into enterprise-level usable strategic assets. It is suitable for EI data processing and EI parameter writing generation scenarios in vehicle production. It solves the problems of excessive manual intervention, mismatched data associations, chaotic status, and non-standard output in traditional EI data processing.

[0020] Specifically, this embodiment provides a dynamic receiving and processing system for electrical inspection data, such as... Figure 1 As shown, the system includes an original data source for electronic inspection, an electronic inspection calculation model, and a dynamic semantic data model, wherein: The original data source for electrical inspection is used to obtain original electrical inspection data from different data sources and classify it into multiple data units. The aforementioned electrical inspection calculation model is used to transform raw electrical inspection data into structured electrical inspection data. The dynamic semantic data model is used to match the required structured electronic inspection data according to the vehicle type and convert the structured electronic inspection data into standardized XML electronic inspection files.

[0021] Electronic inspection data refers to all relevant data used for vehicle electronic testing and parameter rewriting during the vehicle manufacturing process. It is core data ensuring the normal operation of the vehicle's electronic control system (ECU / HCU, etc.), compliance of vehicle electronic inspections, and matching of electronic configurations with the vehicle as a whole. It is also the core object processed by the electronic inspection calculation model. The original data source for electronic inspection obtains data from different sources and then organizes it into units to obtain multiple data units.

[0022] The data units in this embodiment include the engineering vehicle number and vehicle material number unit, the ewo and mwo relationship unit, the module and material unit, the configuration word unit, the module and parameter unit, and the parameter pool unit. The engineering vehicle number and vehicle material number unit are basic identifiers for vehicle production and electrical inspection, used to uniquely identify the engineering design number and material matching number of the vehicle, serving as the core benchmark for electrical inspection data traceability and vehicle attribute matching. The ewo and mwo relationship unit contains the associated data of engineering changes and manufacturing changes during vehicle production, recording the engineering change order number, change content, effective time, and the corresponding manufacturing change order number and manufacturing execution requirements. The module and material unit contains matching data between various vehicle electronic modules (HCU, BCM, ECU, etc.) and their corresponding materials, recording the material number, module model, accessory package information, and attributes such as the module's factory, vehicle model, and whether it is a test vehicle. The configuration word unit records the vehicle / module configuration requirements corresponding to different configuration word short codes, as well as the electrical inspection parameter configuration rules associated with the short codes. The module and parameter unit contains matching data for each electronic module of the vehicle and its corresponding electrical testing parameters. This data records the electrical testing parameter items, values, versions, and statuses for each electronic module. The parameter pool unit is the basic database of vehicle electrical testing parameters, used to store all electrical testing parameters for all electronic modules of the vehicle.

[0023] In this embodiment, the electronic inspection calculation model includes a rule engine and a dynamic parsing model. The rule engine defines the attribute association rules and data state processing rules for each data unit in the original electronic inspection data source. The dynamic parsing model dynamically parses the original electronic inspection data source to generate structured electronic inspection data. The electronic inspection calculation model is the core of data processing. By defining data association and state rules through the rule engine and then transforming the data into structured data through the dynamic parsing model, a closed loop is ultimately achieved from receiving the original electronic inspection data to structured processing.

[0024] Specifically, the workflow of the electrical detection calculation model in this embodiment includes the following steps: Step S11: Define attribute association rules and status processing rules for each data unit of the original data source of the electronic inspection based on the rule engine. In this embodiment, the status processing rules defined by the rule engine include: Define four categories of electrical inspection data statuses: pending release, released, trial installation, and closed. Set the status priority as: Release > Trial Installation > Pending Release > Closed; Set the uniqueness rule for status: When the associated attributes of each unit are the same, there is only one valid status for the release and trial installation status, while multiple statuses are allowed for the pending release and closed statuses.

[0025] Step S12: The dynamic analysis model acquires data from each data unit and extracts features (factory, vehicle model, module, software assembly number, PKG number, whether it is an experimental vehicle, vehicle platform, etc.). Based on the extracted features, the data of each data unit is compared with the existing data in the system. Based on the comparison results, data processing is performed on each data unit: adding (no matching data), discarding (data redundancy / error), or updating (data version iteration). In step S12, the model automatically breaks down and extracts features from the original electrical inspection data, compares it with existing data, and performs addition, discarding, or updating operations based on the identifier. This process requires no manual intervention and effectively removes redundant, erroneous, or duplicate data, ensuring that the data entering subsequent stages is clean, accurate, and up-to-date, thus improving the overall data quality from the source.

[0026] Step S13: The dynamic parsing model takes modules and material units as its core. Through a rule engine, it dynamically associates various data units, extracting the core parameters of each associated unit and displaying them uniformly in the modules and material units. This breaks down data silos and achieves data alignment. For example, the engineering vehicle number and vehicle material number units, the ewo and mwo relationship units, the module and material units, the configuration word unit, the module and parameter unit, and the parameter pool unit form data linkages through the attribute association rules defined by the rule engine. In this embodiment, the engineering vehicle number and vehicle material number unit are associated with the material unit through material_no (vehicle material number), vehicle_code (engineering vehicle number) and module; the ewo and mwo relationship unit are associated with the material unit through ewo_no (engineering change order number), mwo_no (manufacturing change order number) and module; the configuration word unit is associated with the material unit through short_code (configuration word short code) and module; the module and parameter unit are associated with the material unit through factory, car_type (vehicle model), ecu_name (ECU module name), ecu_no (ECU module number), pkg_no (parts package number), is_test_car (whether it is an experimental vehicle), eea_type (electronic and electrical architecture type) and module; the module and parameter unit are associated with the parameter pool unit through hash (hash value, used for parameter uniqueness verification).

[0027] In step S13, the model uses modules and material modules as its core, employing a rule engine to dynamically associate the various modules and extract core parameters for unified presentation. This approach breaks down data silos, achieves automatic alignment of cross-module data, avoids mismatches and omissions caused by manual maintenance of relationships, and ensures a high degree of consistency between vehicle configuration and electrical inspection parameters.

[0028] Step S14: Extract the key attributes of the data processed by the module and material, and the module and parameter unit, compare them with the existing key attributes in the system, integrate the matching data into core electrical inspection data, and set its status. If the key attributes of the extracted data overlap with existing key attributes in the system, the data corresponding to the overlapping key attributes will be used as core electrical inspection data, forming the basic data pool for electrical inspection writing.

[0029] Step S15: Based on the status processing rules defined by the rule engine, and combined with the status of the current core electrical inspection data, compare and verify the status of existing electrical inspection data of the same dimension in the system to achieve status diagnosis and update. Specifically, based on the four data status categories (released, trial installation, pending release, closed) and status priority (released > trial installation > pending release > closed) defined in the rule engine, the model automatically diagnoses and updates the status of the current core electrical inspection data. For example, when the status of the current core electrical inspection data is released, the status of existing electrical inspection data of the same dimension in the system is automatically updated to closed, ensuring the uniqueness and validity of the data status. Existing electrical inspection data of the same dimension in the system refers to data that overlaps with the newly added core electrical inspection data in terms of core related attributes (such as the same factory, same vehicle model, same ECU module, same vehicle part number), and is a historical version of the same dimension as the new data; therefore, status comparison and update are required.

[0030] Step S15 automatically compares key attributes after data processing, determines the data status (such as release, trial installation, obsolescence, etc.), and updates the storage. This mechanism enables the model to perceive data validity in real time, automatically identify abnormal states (such as using an old version that has been closed), and drive subsequent processes (such as alarms or triggering re-inspections), forming a closed-loop quality control from data reception to final storage, significantly enhancing the system's reliability and traceability.

[0031] Ultimately, the entire processing flow completes a closed loop of raw electronic inspection data reception, parsing, matching, fusion, and status updating, generating standardized core electronic inspection data for the vehicle model. The structured data processed through the above steps is output as the first-level sorting result, containing vehicle attributes, type, organization, manufacturing characteristics, and integrated core electronic inspection parameters, providing a structured data source for subsequent XML file generation.

[0032] In this embodiment, the dynamic semantic data model includes a vehicle trial assembly rule base and an electronic inspection XML dynamic generation module. The vehicle trial assembly rule base is used to define the structured electronic inspection data acquisition rules for experimental vehicles and mass-produced vehicles, and the electronic inspection XML dynamic generation module is used to generate standardized XML electronic inspection files based on the structured electronic inspection data.

[0033] Specifically, the workflow of the dynamic semantic data model includes: Step S21: During the production process, at the electrical inspection and writing station, the vehicle defines the vehicle's request data based on the preset vehicle trial assembly rule library. Step S22: Based on the vehicle's request data, extract the required structured electronic inspection data from the electronic inspection calculation model; Step S23: Map all extracted structured data to the corresponding node attributes of the XML file according to the industry standard XML format; Step S24: After completing the data node mapping, a standardized XML electrical inspection file is generated and output to the electrical inspection writing station for writing vehicle electrical inspection parameters.

[0034] The vehicle trial assembly rule library distinguishes between experimental vehicles and mass-production vehicles based on factors such as whether the vehicle chassis number is maintained, whether a trial assembly module exists, whether trial assembly parameters exist, whether it is a material module trial assembly, and whether it is a shortcode trial assembly, as well as the corresponding structured electrical inspection data extraction targets. For example, the trial assembly rules in this embodiment include: Module release & parameter release: When the vehicle chassis number is not maintained, it is defined as a mass-produced vehicle. At this time, the module release data and associated parameter release data corresponding to the chassis number are extracted from the electronic inspection calculation model. Module trial installation & parameter release: When the vehicle chassis number has been maintained and a trial module exists, it is defined as an experimental vehicle. At this time, the module trial installation data and associated parameter release data corresponding to the chassis number are extracted from the electronic inspection calculation model. Module Release & Parameter Trial Installation: When the vehicle chassis number has been maintained and there is a trial installation module and trial installation parameters, and it is a material module trial installation, it is defined as an experimental vehicle. At this time, the module release data and associated parameter trial installation data corresponding to the chassis number are extracted from the electrical inspection calculation model. Module trial assembly & parameter trial assembly: When the vehicle chassis number has been maintained and there are trial assembly modules and trial assembly parameters but it is not a material module trial assembly, it is defined as an experimental vehicle. At this time, the module trial assembly data and associated parameter trial assembly data corresponding to the chassis number are extracted from the electrical inspection calculation model. Shortcode trial installation: When the vehicle chassis number maintenance status has been maintained and there is a trial installation module and trial installation parameters, and it is a Shortcode trial installation, it is defined as an experimental vehicle. At this time, the configuration word data corresponding to the chassis number is extracted from the electronic inspection calculation model.

[0035] Among them, module release / trial installation data refers to the electronic module status in the structured electrical inspection data, which is the electrical inspection data where the electronic module status is released / trial installation, and parameter release / trial installation data refers to the electronic module corresponding parameter status in the structured electrical inspection data, which is the electrical inspection data where the parameter status is released / trial installation.

[0036] This trial assembly rule ensures that the experimental vehicle accurately obtains the parameters of the trial assembly parts, and the mass production vehicle uses the officially released data, preventing incorrect electrical testing due to confusion of status from the source and significantly reducing quality accidents.

[0037] The dynamic semantic data model uses the vehicle identification number (VIN) as the entry point, dynamically parsing request data and extracting structured data in parallel from multiple modules (modules and materials, modules and parameters, processes and parameters, configuration words, etc.), automatically mapping it to XML nodes. The entire process requires no manual intervention, responds rapidly, and completely eliminates the time-consuming process of traditional manual configuration or script modification, significantly improving production line efficiency. When extracting structured electrical inspection data, the model strictly filters request data according to the rule base definition, selecting only data rows that meet the requirements of the current vehicle. This state-aware capability ensures that the final generated XML always contains the latest and most valid electrical inspection parameters, improving data reliability and compliance. The final generated XML file is structured and self-descriptive, conforming to the industry-standard parsing format for electrical inspection equipment. Standardized output reduces the adaptation workload of downstream equipment, supports seamless integration with different brands or models of electrical inspection equipment, and reduces the complexity of production line integration.

[0038] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution; or the direct application of the inventive concept and technical solution to other situations without modification, are all within the protection scope of the present invention.

Claims

1. A dynamic receiving and processing system for electrical inspection data, characterized in that: The system includes a raw data source for electronic inspection, an electronic inspection calculation model, and a dynamic semantic data model. The original data source for electrical inspection is used to obtain original electrical inspection data from different data sources and classify it into multiple data units. The aforementioned electrical inspection calculation model is used to transform raw electrical inspection data into structured electrical inspection data. The dynamic semantic data model is used to match the required structured electronic inspection data according to the vehicle type and convert the structured electronic inspection data into standardized XML electronic inspection files.

2. The electronic inspection data dynamic receiving and processing system according to claim 1, characterized in that: The data units of the original data source for the electrical inspection include the engineering vehicle number and vehicle material number unit, the ewo and mwo relationship unit, the module and material unit, the configuration word unit, the module and parameter unit, and the parameter pool unit.

3. The dynamic receiving and processing system for electrical inspection data according to claim 2, characterized in that: The electronic inspection calculation model includes a rule engine and a dynamic parsing model. The rule engine is used to define the attribute association rules and data status processing rules for each data unit in the original electronic inspection data source. The dynamic parsing model is used to dynamically parse the original electronic inspection data source to generate structured electronic inspection data.

4. The dynamic receiving and processing system for electrical inspection data according to claim 3, characterized in that: The dynamic semantic data model includes a vehicle trial assembly rule base and an electronic inspection XML dynamic generation module. The vehicle trial assembly rule base is used to define the structured electronic inspection data acquisition rules for experimental vehicles and mass-produced vehicles. The electronic inspection XML dynamic generation module is used to generate standardized XML electronic inspection files based on the structured electronic inspection data.

5. The dynamic receiving and processing system for electrical inspection data according to claim 3, characterized in that: The workflow of the electrical inspection calculation model includes the following steps: Step S11: Define attribute association rules and status processing rules for each data unit of the original data source of the electronic inspection based on the rule engine; Step S12: The dynamic parsing model acquires the data of each data unit and extracts features. Based on the extracted features, the data of each data unit is compared with the existing data in the system. Based on the comparison results, the data of each data unit is added, discarded, or updated. Step S13: The dynamic parsing model takes modules and material units as the core, and uses a rule engine to dynamically associate each data unit, extracting the core parameters of each unit after association and displaying them uniformly in the modules and material units. Step S14: Extract the key attributes of the data processed by the module and material, and the module and parameter unit, compare and calculate them with the existing key attributes in the system, integrate the matching data into core electrical inspection data, and set its status. Step S15: Based on the status processing rules defined by the rule engine, and combined with the status of the core electrical inspection data, compare and verify the status of the existing electrical inspection data of the same dimension in the system to achieve status diagnosis and update.

6. The dynamic receiving and processing system for electrical inspection data according to claim 5, characterized in that: The state processing rules defined by the rule engine include: Define four categories of electrical inspection data statuses: pending release, released, trial installation, and closed. Set the status priority as: Release > Trial Installation > Pending Release > Closed; Set the uniqueness rule for status: When the associated attributes of each unit are the same, there is only one valid status for the release and trial installation status, while multiple statuses are allowed for the pending release and closed statuses.

7. The dynamic receiving and processing system for electrical inspection data according to claim 5, characterized in that: In step S14, if the key attributes of the extracted module and material, and the module and parameter unit processed data overlap with the existing key attributes in the system, then the data corresponding to the overlapping key attributes will be used as the core electrical inspection data.

8. The electronic inspection data dynamic receiving and processing system according to claim 4, characterized in that: The workflow of the dynamic semantic data model includes: Step S21: During the production process, at the electrical inspection and writing station, the vehicle defines the vehicle's request data based on the preset vehicle trial assembly rule library. Step S22: Based on the vehicle's request data, extract the required structured electronic inspection data from the electronic inspection calculation model; Step S23: Map all extracted structured data to the corresponding node attributes of the XML file according to the industry standard XML format; Step S24: After completing the data node mapping, a standardized XML electrical inspection file is generated and output to the electrical inspection writing station for writing vehicle electrical inspection parameters.

9. The dynamic receiving and processing system for electrical inspection data according to claim 8, characterized in that: The vehicle trial assembly rule library distinguishes experimental vehicles from mass production vehicles based on whether the vehicle chassis number is maintained, whether a trial assembly module exists, whether trial assembly parameters exist, whether it is a material module trial assembly, and whether it is a shortcode trial assembly, as well as the corresponding structured electrical inspection data extraction targets.

10. The electronic inspection data dynamic receiving and processing system according to claim 9, characterized in that: The trial assembly rules include: Module release & parameter release: When the vehicle chassis number is not maintained, it is defined as a mass-produced vehicle. At this time, the module release data and associated parameter release data corresponding to the chassis number are extracted from the electronic inspection calculation model. Module trial installation & parameter release: When the vehicle chassis number has been maintained and a trial module exists, it is defined as an experimental vehicle. At this time, the module trial installation data and associated parameter release data corresponding to the chassis number are extracted from the electronic inspection calculation model. Module Release & Parameter Trial Installation: When the vehicle chassis number has been maintained and there is a trial installation module and trial installation parameters, and it is a material module trial installation, it is defined as an experimental vehicle. At this time, the module release data and associated parameter trial installation data corresponding to the chassis number are extracted from the electrical inspection calculation model. Module trial assembly & parameter trial assembly: When the vehicle chassis number has been maintained and there are trial assembly modules and trial assembly parameters but it is not a material module trial assembly, it is defined as an experimental vehicle. At this time, the module trial assembly data and associated parameter trial assembly data corresponding to the chassis number are extracted from the electrical inspection calculation model. Shortcode trial installation: When the vehicle chassis number maintenance status has been maintained and there is a trial installation module and trial installation parameters, and it is a Shortcode trial installation, it is defined as an experimental vehicle. At this time, the configuration word data corresponding to the chassis number is extracted from the electronic inspection calculation model.

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