A method for intelligent adaptation and smooth transition based on on-board network department standard protocol

Abstract

The application discloses a kind of based on vehicle-mounted network department mark protocol intelligent adaptation and smooth transition method, it is related to communication protocol technical field, method includes: S1, NLP protocol knowledge automation construction: NLP module parses department mark document and terminal message annotation, extracts version difference, constructs the knowledge base containing field type, bit sequence, constraint, is synchronized to rule engine;S2, AI model training, initialization and strategy library construction;S3, rule check and AI mapping: rule engine checks message format and realizes source protocol message legality check, AI module is dynamically adjusted according to scene mapping rule;S4, protocol conversion;S5, synchronous compliance inspection;S6, data reporting: data after compliance inspection is reported.It realizes protocol automatic analysis, dynamic field mapping, version smooth transition and compliance check integration, reduces adaptation cost, improves access efficiency and data accuracy.

Description

Technical Field

[0001] This invention relates to the field of communication protocol technology, and in particular to a method for intelligent adaptation and smooth transition of vehicle network standard protocols. Background Technology

[0002] With the digital transformation of transportation, the Internet of Things, and other fields, national standard protocols (such as JT / T 808 "Communication Protocol and Data Format for Road Transport Vehicle Satellite Positioning System Terminals", JT / T 809 "Data Exchange Protocol for Road Transport Vehicle Satellite Positioning System Platform", and JT / T 1078 "Video Communication Protocol for Road Transport Vehicle Satellite Positioning System") have become core specifications for data interaction between terminal devices and regulatory platforms, and between platforms. They are widely used in key scenarios such as monitoring operating vehicles, tracking hazardous materials transportation, and freight dispatching. The adaptability and compatibility of these national standard protocols directly determine the access efficiency of terminal devices, the accuracy of data transmission, and the operational stability of the entire regulatory system.

[0003] In the practical application of national standard protocols, protocol adaptation and version transition are core aspects of ensuring system compatibility. On the one hand, national standard protocols need to be upgraded and iterated with industry needs. For example, JT / T 808 has been iterated from the 2011 version to the 2019 version, adding functions such as video data reporting and multi-terminal collaboration, with significant changes in field definitions and interaction logic. On the other hand, there are a large number of terminal devices from different manufacturers and from different eras on the market, each adapting to different versions of national standard protocols, forming a fragmented pattern of "new terminals, new protocols; old terminals, old protocols." At the same time, the demand for cross-scenario data interaction is increasing. For example, vehicle terminals need to report data to both regulatory platforms (following JT / T 808) and logistics scheduling platforms (following custom protocols) simultaneously, further increasing the complexity of protocol adaptation.

[0004] Existing solutions for adapting and transitioning to national standards and protocols mainly revolve around three core elements: "static configuration," "rule solidification," and "manual intervention." Typical technical paths and their current applications are as follows: 1. Fixed Parser Solution: A dedicated parsing module is developed for specific standard versions (such as JT / T 808-2019), defining parsing rules such as protocol headers, field lengths, and encoding formats through hard-coding. This solution offers high parsing efficiency for a single version and scenario, and is currently the mainstream choice for small and medium-sized platforms. For example, the road transport vehicle terminal communication method disclosed in patent CN106209306B achieves 808 protocol access through fixed checksum processing and field parsing logic.

[0005] 2. Protocol Pool Adaptation Solution: A protocol pool containing parsing rules for various national standards versions is constructed. By manually configuring the mapping relationship between terminal models and protocol versions, access for terminals of different versions is achieved. For example, some platforms use pre-set parsing scripts for versions such as 808-2011 and 808-2019, calling the corresponding rules based on the version identifier reported by the terminal. This is exemplified by the vehicle-to-everything (V2X) communication protocol adaptation method disclosed in patent CN116939066A, which uses a protocol pool to map and convert multiple version fields.

[0006] 3. Manual Intervention Transition Solution: During protocol version upgrades or cross-platform integration, technical personnel manually identify the field differences between the old and new protocols (such as the correspondence between the newly added "Video Channel Status" field and the old version's "Location Status" field), write adaptation scripts, or modify the parsing logic. This method is still widely used when handling personalized requirements (such as custom extended fields for a particular manufacturer's terminal).

[0007] However, the existing solutions mentioned above reveal significant technical shortcomings when facing large-scale, highly dynamic scenarios of adapting to national standards protocols, making it difficult to meet the industry's needs for efficient access and smooth transition. Specifically, these shortcomings are reflected in the following five aspects: First, the version adaptation flexibility is insufficient, failing to cope with protocol iterations and the fragmented terminal landscape. Fixed parser solutions only support a single protocol version. When the national standard protocol is upgraded or new terminals are added to adapt to the old version, a new parsing module needs to be redeveloped and deployed, with development cycles lasting weeks or even months. Furthermore, compatibility issues such as "upgrading one version affects a batch of terminals" are prone to occur. While protocol pool solutions support multiple versions, they rely on manually preset parsing rules. For custom extended fields not included in the pool or for less common terminal versions, manual expansion is still required, failing to achieve "plug and play." For example, when a provincial transportation supervision platform connected over 2,000 terminals from different manufacturers, 12 dedicated parsing modules needed to be developed due to protocol version differences, with adaptation costs accounting for over 35% of the total platform construction cost.

[0008] Second, field mapping is inefficient, and manual intervention leads to adaptation errors and delays. In existing solutions, the mapping of fields between old and new protocols (such as the correspondence between the "speed" field in the 808-2011 version and the "instantaneous speed" and "average speed" fields in the 2019 version), and the conversion of cross-protocol fields (such as aligning the formats of "location time" in the 808 protocol and "data upload time" in the 809 protocol) all rely on manual processing. This is not only time-consuming and labor-intensive, but also prone to mapping errors due to differences in personnel experience. Data from a logistics platform shows that the error rate of manually configured field mapping is approximately 2.3%, and a single error correction takes an average of 4 hours, directly affecting the accuracy of regulatory data.

[0009] Third, the disconnect between protocol parsing and compliance verification makes it easy for adapted data to fail to meet regulatory requirements. National standards have strict requirements on the range of field values ​​and format specifications (e.g., JT / T 808-2019 stipulates that the "longitude" field must be accurate to 0.000001 degrees). Existing solutions typically complete adaptation first and then conduct compliance verification. If field format conversion errors occur during adaptation (e.g., anomalies occur when converting string-type speed values ​​to numerical values), the adaptation logic needs to be backtracked and modified, leading to redundant processes. Due to this disconnect between adaptation and verification, some platforms have a regulatory data compliance pass rate of only 88%, facing the risk of regulatory penalties.

[0010] Fourth, it lacks dynamic optimization capabilities and cannot adapt to protocol interaction fluctuations in complex environments. In complex scenarios such as weak networks and high concurrency, terminals may report incomplete messages (such as missing protocol tail identifiers) or duplicate messages. Existing solutions can only handle this through fixed fault-tolerance rules (such as discarding incomplete messages) and cannot dynamically adjust adaptation strategies according to the scenario. For example, in a tunnel with a weak network environment, a vehicle terminal reported an 808 protocol message that was missing the "number of satellites" field. The existing solution directly discarded the message, resulting in a positioning data loss rate of up to 15%.

[0011] Fifth, the cross-scenario adaptability is weak, making it difficult to support the needs of multi-platform collaboration. In current transportation scenarios, terminals need to simultaneously connect to regulatory platforms (808 / 809 protocol), enterprise dispatch platforms (custom protocol), and third-party payment platforms (HTTPS protocol). Existing solutions require developing independent adaptation modules for each platform, resulting in a complex "many-to-many" adaptation relationship between terminals and platforms, leading to an exponential increase in system maintenance costs. Data from a freight company shows that its terminals need to develop 5 sets of adaptation modules to connect to 3 platforms, with annual maintenance costs exceeding 2 million yuan.

[0012] In recent years, technologies such as Natural Language Processing (NLP), Artificial Intelligence (AI), and rule engines have been increasingly applied in protocol parsing and data mapping, providing technological possibilities for addressing the aforementioned pain points. For example, NLP technology can automatically extract semantics from protocol documents, rule engines can solidify compliance verification logic, and AI models can optimize field mapping strategies by learning from historical adaptation data. However, existing technologies are mostly applied to the conversion of general industrial protocols (such as Modbus and OPC UA), and have not been customized for the structured characteristics of national standard protocols (such as fixed protocol headers and field order definitions), version iteration patterns (such as field addition / deprecation logic), and regulatory compliance requirements, making them unsuitable for direct migration to national standard protocol adaptation scenarios.

[0013] In summary, existing solutions for adapting and smoothly transitioning national standard protocols suffer from shortcomings such as insufficient flexibility, high reliance on manual intervention, poor compliance, and weak dynamic adaptation capabilities, making it difficult to meet the demands of large-scale, highly dynamic transportation scenarios. Therefore, developing a technical solution that integrates NLP, rule engines, and AI technologies to achieve automatic parsing of national standard protocols, dynamic field mapping, smooth version transition, and compliance verification has become a pressing technical challenge in this field. Summary of the Invention

[0014] This invention provides a method for intelligent adaptation and smooth transition of vehicle network standard protocols, which solves the problems of existing standard protocol adaptation relying on manual configuration, poor version compatibility and flexibility, low field mapping efficiency, and disconnection from compliance verification. It provides an intelligent adaptation solution that integrates NLP, rule engine and AI to achieve automatic protocol parsing, dynamic field mapping, smooth version transition and compliance verification, thereby reducing adaptation costs and improving access efficiency and data accuracy.

[0015] According to one aspect of this disclosure, a method for intelligent adaptation and smooth transition based on vehicle network standard protocols is provided, the method comprising: S1, Automated Construction of NLP Protocol Knowledge: The NLP module parses the national standard documents and terminal message annotations, extracts version differences, builds a knowledge base containing field types, order, and constraints, and synchronizes it to the rule engine; S2, AI model training, initialization and policy library construction: Input historical adaptation data to train the model and initialize the version adaptation policy library; S3, Rule Validation and AI Mapping: The rule engine validates the message format to verify the legality of the source protocol message, and the AI ​​module dynamically adjusts the mapping rules according to the scenario; S4, Protocol Conversion: The protocol conversion unit of the adaptation execution module receives the mapping rule table and core field data output by the AI ​​module, and completes field replacement, format conversion, and frame structure reassembly according to the requirements of the new protocol format to generate a draft message; S5, Synchronous Compliance Verification: The compliance verification module and the protocol conversion unit interact in real time through shared memory. Verification is initiated within 10ms after the initial draft of the message is generated. The new version of the compliance rules in the structured knowledge base is directly called to achieve seamless connection between "conversion and verification". S6, Data Reporting: Report the data after compliance verification.

[0016] In one possible implementation, S1, the automated construction of NLP protocol knowledge, includes: S11: Load the official PDF documents and extended field TXT descriptions of different versions of the protocol, and extract the protocol text and annotations through OCR + semantic segmentation technology; S12 uses the BERT model to identify the five-tuple "field name-data type-bit length-value range-semantic description" and generate structured tags; S13, automatically locate core differences through comparative learning: compare the field ID, number of bytes or numerical range of "speed", "instantaneous speed", "average speed" or "mileage" in different versions; S14 outputs a machine-readable structured knowledge base, and the entire process requires no human intervention.

[0017] In one possible implementation, S2, AI model training, initialization, and policy library construction, includes: Input historical adaptation data and train an LSTM-based dynamic mapping model: The feature input is "source field value + terminal model + network status", and the output is the target field mapping rule; The loss function is optimized through gradient descent, and the version negotiation strategy library is initialized. Pre-set basic strategies include "mapping of core fields for old terminals + simplification of redundant fields" and "full field adaptation for new terminals" (the core content of the AI ​​model initialization phase, and also the "initial execution criterion" for achieving intelligent adaptation of the vehicle network standard protocol). Essentially, this involves pre-determining adaptation rules for different versions of vehicle terminals that conform to the differences in the standard protocol (808-2011 / 2019) and the terminal capabilities, avoiding the inefficiency of manual terminal-by-terminal configuration in traditional solutions), supporting subsequent dynamic adjustments.

[0018] In one possible implementation, S3, rule validation, and AI mapping include: outputting information from a structured knowledge base built using NLP to achieve "intelligent mapping triggered upon successful validation": S31, Rule Engine Pre-validation Based on Structured Knowledge Base: The rule engine module calls the XML structured knowledge base generated by NLP through the data interface, loads the exclusive validation rules of the old protocol, and performs three-step validation: Check code verification: According to the "Old Version Location Message Check Code Calculation Rules" in the knowledge base, the check code is calculated for the core data segment, and the result is 0x00, which is consistent with the check code that comes with the message. S32, Field Range Validation: Extract the latitude and longitude fields “0x3045.1234” and “0x11630.5678”, convert them to decimal 30.451234° and 116.305678°, and verify that they meet the range constraints of “longitude ±180°, latitude ±90°”. S33, Field format verification: It is confirmed that the speed field "0x50" is a 1-byte unsigned integer and the positioning time "0x20250601100010" is a 14-bit BCD code, both of which conform to the old version of the field format definition in the knowledge base; S34, AI Dynamic Mapping: The AI ​​module receives field data output by the rule engine and obtains two types of real-time parameters through the scene perception module: ① Network status: the gateway detects network latency of 30ms≤50ms and marks it as "network stable"; ② Terminal historical running data: retrieves the speed values ​​reported in the last 10 times: calculates the fluctuation range ≤5km / h and marks it as "speed stable". Perform LSTM model inference: Model input layer: Convert "speed 80km / h + terminal model VT300 + network stability + speed stability" into a feature vector; the terminal model in the feature vector is mapped to the code "001" through the knowledge base, and the scene state is mapped to "1" to represent stability; S35 calls the pre-trained LSTM dynamic mapping model and outputs personalized mapping coefficients for the VT300 terminal: "Instantaneous speed = actual speed, average speed = actual speed × 0.975", where 0.975 is the historical fitting coefficient of the terminal's speed fluctuation characteristics, obtained through model iteration optimization; S36: Substitute the speed 80km / h into the coefficients to obtain the instantaneous speed 80km / h (hexadecimal 0x0050) and the average speed 78km / h (hexadecimal 0x004E). Generate the structured mapping rule table: {Old version speed: 80km / h → New version instantaneous speed: 0x0050, 2019 version average speed: 0x004E; other fields are mapped one-to-one by field ID}, and a negotiated scheme constraint of "retaining custom alarm codes and removing redundant mileage fields" is attached.

[0019] In one possible implementation, S4, protocol conversion, includes: S41, Format adaptation adjustment: According to the 2019 version of the field format requirements in the knowledge base, the instantaneous speed and average speed fields are converted into 2-byte unsigned integers to meet the bit length requirements of the "0-200km / h" value range. Other fields maintain format compatibility and the field length identifier is updated synchronously. S42, Frame Structure Reassembly: Reassemble the data according to the 2019 version of the location message frame structure. The message frame structure is: frame header + instruction identifier + data length + terminal ID + data segment + checksum + frame trailer. The instruction identifier adopts the 2019 version location message identifier 0x0200, and the data length is calculated as 0x0020 based on the total number of replaced fields. Generate the initial message draft: 0x7E 0x0200 0x0020 0x1234567890ABCDEF 0x20250601100010 0x3045.12340x11630.5678 0x0050 0x004E 0x00 [Verification code to be filled] 0x7E, synchronously triggers the synchronous compliance verification module.

[0020] Compared with the prior art, the beneficial effects of the present invention are: Significantly improved adaptability: Compared to fixed parsers that only support a single version and protocol pools that rely on manual expansion, this solution uses NLP to automatically parse protocols to generate a knowledge base, and AI dynamically adapts to multiple versions and custom fields. Version upgrades do not require redevelopment, and the adaptation cycle is reduced from weeks to hours.

[0021] Improved mapping efficiency and accuracy: Compared to the 2.3% error rate of manual mapping, the AI ​​model learns from historical data to optimize the mapping strategy, reducing the error rate to below 0.5%, improving adaptation efficiency by 60%, and reducing the cost of manual intervention.

[0022] Enhanced compliance and robustness: Unlike the traditional "adapt first, then verify" approach, this solution performs verification and adaptation simultaneously, increasing the compliance pass rate from 88% to 99%. Attached Figure Description

[0023] Figure 1 The diagram shows a flowchart of a method for intelligent adaptation and smooth transition based on vehicle network standard protocols according to an embodiment of the present disclosure. Detailed Implementation

[0024] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0025] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0026] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0027] This implementation method uses "JT / T 808-2011 version vehicle-mounted terminal accessing JT / T 808-2019 version provincial road transport supervision platform" as a typical scenario.

[0028] This invention focuses on the core concepts of "automatic construction of NLP protocol knowledge, AI dynamic field mapping, and simultaneous adaptation and verification." Through an edge gateway deployment example, it details the technical implementation logic, highlighting the fundamental differences from traditional manual adaptation and fixed parsing solutions.

[0029] I. System Initialization This phase addresses the pain points of traditional solutions, such as "protocol parsing relying on manual sorting and low efficiency in knowledge base construction," by using NLP to automate the extraction and structured construction of ministry standard protocol knowledge.

[0030] 1. Automated Construction of NLP Protocol Knowledge: Traditional solutions require 3 engineers to spend 5 days sorting out the differences between the two versions of the 808 protocol. This solution's NLP parsing module automatically completes the following operations: Load the official PDF documents of JT / T 808-2011 and 2019, as well as the TXT descriptions of extended fields from three manufacturers, and extract the protocol text and annotations using OCR and semantic segmentation technology; The BERT model is used to identify the five-tuple "field name-data type-length-value range-semantic description" and generate structured tags. By comparing and learning, the core differences are automatically located: the 2011 version's "Speed" field (field ID: 0x0020, 1 byte, range 0-255km / h) corresponds to the 2019 version's "Instantaneous Speed" (ID: 0x0021, 2 bytes, 0-200km / h) + "Average Speed" (ID: 0x0022, 2 bytes), and the field attributes such as "Mileage" (required in the 2011 version, redundant in the 2019 version) are marked; The final output is a machine-readable structured knowledge base (XML format). The entire process takes only 2 hours and requires no human intervention. 2. AI Model Initialization and Policy Library Construction: To address the issues of low efficiency and high error in traditional manual mapping, 5000 historical adaptation data points (including 300 vendor-defined field mapping examples) are input to train an LSTM-based dynamic mapping model. ① The feature input is "source field value + terminal model + network status", and the output is the target field mapping rule; ② Optimize the loss function through gradient descent (mapping error ≤ 2%), initialize the version negotiation strategy library, and preset basic strategies such as "mapping core fields of old terminals + simplifying redundant fields" and "adapting all fields of new terminals", which can be dynamically adjusted later.

[0031] Pre-setting basic strategies such as "mapping core fields of old terminals + simplifying redundant fields" and "adapting all fields of new terminals" are the core content of the AI ​​model initialization phase, and also the "initial execution criteria" for achieving intelligent adaptation of vehicle network standard protocols. Essentially, this involves pre-determining adaptation rules for different versions of vehicle terminals that conform to the differences in standard protocols (808-2011 / 2013 / 2019) and the capabilities of the terminals, avoiding the inefficiency of manual configuration for each terminal in traditional solutions.

[0032] II. Specific Implementation Steps I. Rule Validation and AI Mapping This stage completes the "source protocol message legality verification + AI dynamic mapping rule generation". The core relies on the structured knowledge base built by NLP to output XML information, so as to "trigger intelligent mapping as soon as the verification is passed", avoiding the logical redundancy of the traditional "mapping first and then verification".

[0033] 1. Rule Engine Pre-validation (based on structured knowledge base): The rule engine module calls the XML structured knowledge base generated by NLP through the data interface, loads the JT / T 808-2011 version of the dedicated validation rules, and performs three-step validation: Check code verification: According to the "2011 version of the location message check code calculation rules (XOR operation)" in the knowledge base, the check code is calculated on the core data segment, and the result is 0x00, which is consistent with the check code of the message itself; 2. Field range validation: Extract the latitude and longitude fields “0x3045.1234” and “0x11630.5678”, convert them to decimal 30.451234° and 116.305678°, and validate that they meet the range constraints of “longitude ±180°, latitude ±90°”. 3. Field format verification: It was confirmed that the speed field "0x50" is a 1-byte unsigned integer and the positioning time "0x20250601100010" is a 14-bit BCD code, both of which conform to the 2011 version of the field format definition in the knowledge base.

[0034] 4. AI Dynamic Mapping: The AI ​​module receives field data output by the rule engine and simultaneously obtains two types of real-time parameters through the scene perception module: ① Network status (the gateway detects network latency of 30ms≤50ms, which is marked as "network stable"); ② Terminal historical operating data (retrieving the speed values ​​reported in the last 10 times: 78km / h, 82km / h, 79km / h..., calculating the fluctuation range ≤5km / h, which is marked as "speed stable"), and executes LSTM model inference: Model input layer: Convert "speed 80km / h + terminal model VT300 + network stability + speed stability" into feature vectors (the terminal model is mapped to the code "001" through the knowledge base, and the scene state is mapped to "1" to represent stability). 5. Model Inference: Call the pre-trained LSTM dynamic mapping model (training data includes 5000 historical adaptation cases, mapping error ≤2%), and output personalized mapping coefficients for the VT300 terminal: "Instantaneous speed = actual speed, average speed = actual speed × 0.975" (0.975 is the historical fitting coefficient of the terminal's speed fluctuation characteristics, obtained through model iteration optimization); 6. Mapping rule generation: Substitute the speed of 80km / h into the coefficients to obtain the instantaneous speed of 80km / h (hexadecimal 0x0050) and the average speed of 78km / h (hexadecimal 0x004E). Generate a structured mapping rule table: {2011 version speed: 80km / h → 2019 version instantaneous speed: 0x0050, 2019 version average speed: 0x004E; other fields are mapped one-to-one according to "field ID"}, and attach a negotiated scheme constraint of "retaining custom alarm codes and removing redundant fields such as mileage".

[0035] II. Protocol Conversion The protocol conversion unit of the adaptation execution module receives the mapping rule table and core field data output by the AI ​​module, and completes "field replacement, format conversion, and frame structure reassembly" according to the JT / T 808-2019 protocol format requirements to generate a draft message. The core execution logic is as follows: 1. Format adaptation adjustment: Based on the 2019 version of the field format requirements in the knowledge base, the instantaneous speed and average speed fields are converted to 2-byte unsigned integers (meeting the bit length requirement of the value range of "0-200km / h"). Other fields maintain format compatibility and the field length identifier is updated synchronously.

[0036] 2. Frame Structure Reassembly: Reassemble the data according to the 2019 version of the location message frame structure (frame header + instruction identifier + data length + terminal ID + data segment + checksum + frame tail). The instruction identifier adopts the 2019 version of the location message identifier 0x0200. The data length is calculated based on the total number of replaced fields to be 0x0020 (32 bytes). The initial draft of the generated message is: 0x7E 0x0200 0x00200x1234567890ABCDEF 0x20250601100010 0x3045.1234 0x11630.5678 0x0050 0x004E0x00 [Checksum to be filled] 0x7E, and the synchronous compliance verification module is triggered synchronously.

[0037] III. Synchronous Compliance Inspection The compliance verification module and the protocol conversion unit interact in real time via shared memory. Verification is initiated within 10ms of the initial message draft being generated, directly calling the 2019 version of the compliance rules from the structured knowledge base to achieve seamless "conversion-verification" integration. Specific verification logic is as follows: 1. Target Protocol Format Verification: Verify the frame structure of the initial message draft (frame header and trailer integrity, instruction identifier correctness), field length (instantaneous speed and average speed are both 2 bytes, in line with the 2019 version requirements), and data length identifier (0x0020 is consistent with the actual data length) to confirm the format is valid.

[0038] 2. Field value compliance verification: The instantaneous speed of 80km / h is ≤200km / h upper limit as specified in the 2019 version, and the average speed of 78km / h is ≤200km / h upper limit. The values ​​of fields such as latitude and longitude and positioning time are all within the constraints, and there is no data exceeding the regulations.

[0039] IV. Data Reporting The data after compliance verification is reported.

[0040] The main steps for solving the problem are as follows: • Automated NLP Protocol Knowledge Construction: Load the 808-2011 / 2019 version protocol documents and vendor annotations, extract the content through OCR + semantic segmentation, identify field quintuples through the BERT model, compare and learn to locate version differences, and generate an XML structured knowledge base. The entire process takes 2 hours without manual intervention.

[0041] • AI initialization and strategy construction: Train the LSTM model by inputting 5000 historical adaptation data, optimize the loss function to make the mapping error ≤2%, initialize the version adaptation strategy library, preset the basic adaptation strategies for new and old terminals, and support subsequent dynamic adjustments.

[0042] • Rule validation and AI mapping: The rule engine calls the knowledge base to validate the message checksum, field range and format; the AI ​​module combines scenario parameters such as terminal model and network status to generate a personalized field mapping rule table, including coefficients and redundant field handling constraints.

[0043] • Protocol Conversion: Adapt the mapping rules received by the execution module, complete the field format conversion and frame structure reorganization according to the 2019 version of the protocol, update the field length identifier, generate a draft message containing core data, and trigger compliance verification simultaneously.

[0044] • Synchronous compliance verification: The verification module interacts in real time through shared memory, verifying the frame structure, field lengths and values ​​of the initial message draft within 10ms to confirm whether it complies with the 2019 version of the protocol compliance rules, achieving seamless connection between conversion and verification.

[0045] • Data reporting: After synchronous compliance verification confirms the legality of the messages, the final data encapsulation is completed and reported in accordance with the access specifications of the provincial road transport supervision platform to ensure the compliance and effectiveness of data transmission.

[0046] Significantly improved adaptability: Compared to fixed parsers that only support a single version and protocol pools that rely on manual expansion, this solution uses NLP to automatically parse protocols to generate a knowledge base, and AI dynamically adapts to multiple versions and custom fields. Version upgrades do not require redevelopment, and the adaptation cycle is reduced from weeks to hours.

[0047] Improved mapping efficiency and accuracy: Compared to the 2.3% error rate of manual mapping, the AI ​​model learns from historical data to optimize the mapping strategy, reducing the error rate to below 0.5%, improving adaptation efficiency by 60%, and reducing the cost of manual intervention.

[0048] Enhanced compliance and robustness: Unlike the traditional "adapt first, verify later" approach, this solution performs verification and adaptation simultaneously, increasing the compliance pass rate from 88% to 99%. The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for intelligent adaptation and smooth transition based on vehicle network standard protocols, characterized in that, The method includes: S1, Automated Construction of NLP Protocol Knowledge: The NLP module parses the standard documents and terminal message annotations, extracts version differences, builds a knowledge base containing field types, order, and constraints, and synchronizes it to the rule engine; S2, AI model training, initialization and policy library construction: Input historical adaptation data to train the model and initialize the version adaptation policy library; S3, Rule Validation and AI Mapping: The rule engine validates the message format to verify the legality of the source protocol message, and the AI ​​module dynamically adjusts the mapping rules according to the scenario; S4, Protocol Conversion: The protocol conversion unit of the adaptation execution module receives the mapping rule table and core field data output by the AI ​​module, and completes field replacement, format conversion, and frame structure reassembly according to the requirements of the new protocol format to generate a draft message; S5, Synchronous Compliance Verification: The compliance verification module and the protocol conversion unit interact in real time through shared memory. Verification is initiated within 10ms after the initial draft of the message is generated. The new version of the compliance rules in the structured knowledge base is directly called to achieve seamless connection between "conversion and verification". S6, Data Reporting: Report the data after compliance verification.

2. The method for intelligent adaptation and smooth transition based on vehicle network standard protocols according to claim 1, characterized in that, S1, Automated Construction of NLP Protocol Knowledge, includes: S11: Load the official PDF documents and extended field TXT descriptions of different versions of the protocol, and extract the protocol text and annotations through OCR + semantic segmentation technology; S12 uses the BERT model to identify the five-tuple "field name-data type-bit length-value range-semantic description" and generates structured tags; S13, automatically locates core differences through comparative learning: compares the field ID, number of bytes or numerical range of "speed", "instantaneous speed", "average speed" or "mileage" in different versions; S14 outputs a machine-readable structured knowledge base, and the entire process requires no human intervention.

3. The method for intelligent adaptation and smooth transition based on vehicle network standard protocols according to claim 1, characterized in that, S2, AI model training, initialization, and policy library construction, includes: Input historical adaptation data and train an LSTM-based dynamic mapping model: The feature input is "source field value + terminal model + network status", and the output is the target field mapping rule; The loss function is optimized by gradient descent, and the version negotiation strategy library is initialized. The basic strategies of "mapping core fields of old terminals + simplifying redundant fields" and "adapting all fields of new terminals" are preset to support subsequent dynamic adjustments.

4. The method for intelligent adaptation and smooth transition based on vehicle network standard protocols according to claim 1, characterized in that, S3, rule validation and AI mapping, includes: outputting information from a structured knowledge base built on NLP to achieve "intelligent mapping triggered upon successful validation": S31, Rule Engine Pre-validation Based on Structured Knowledge Base: The rule engine module calls the XML structured knowledge base generated by NLP through the data interface, loads the exclusive validation rules of the old protocol, and performs three-step validation: Check code verification: According to the "Old Version Location Message Check Code Calculation Rules" in the knowledge base, the check code is calculated for the core data segment, and the result is 0x00, which is consistent with the check code that comes with the message. S32, Field Range Validation: Extract the latitude and longitude fields "0x3045.1234" and "0x11630.5678", convert them to decimal 30.451234° and 116.305678°, and verify that they meet the range constraints of "longitude ±180°, latitude ±90°". S33, Field format verification: It is confirmed that the speed field "0x50" is a 1-byte unsigned integer and the positioning time "0x20250601100010" is a 14-bit BCD code, both of which conform to the old version of the field format definition in the knowledge base; S34, AI Dynamic Mapping: The AI ​​module receives field data output by the rule engine and obtains two types of real-time parameters through the scene perception module: ① Network status: the gateway detects network latency of 30ms≤50ms and marks it as "network stable"; ② Terminal historical running data: retrieves the speed values ​​reported in the last 10 times: calculates the fluctuation range ≤5km / h and marks it as "speed stable". Perform LSTM model inference: Model input layer: Convert "speed 80km / h + terminal model VT300 + network stability + speed stability" into feature vectors; S35 calls the pre-trained LSTM dynamic mapping model and outputs personalized mapping coefficients for the VT300 terminal: "Instantaneous speed = actual speed, average speed = actual speed × 0.975", where 0.975 is the historical fitting coefficient of the terminal's speed fluctuation characteristics, obtained through model iteration optimization; S36: Substitute the speed 80km / h into the coefficients to obtain the instantaneous speed 80km / h (hexadecimal 0x0050) and the average speed 78km / h (hexadecimal 0x004E). Generate the structured mapping rule table: {Old version speed: 80km / h → New version instantaneous speed: 0x0050, 2019 version average speed: 0x004E; other fields are mapped one-to-one by field ID}, and the negotiated scheme constraint of "retaining custom alarm codes and removing redundant mileage fields" is attached.

5. The method for intelligent adaptation and smooth transition based on vehicle network standard protocols according to claim 1, characterized in that, S4, protocol conversion, includes: S41, Format adaptation adjustment: According to the 2019 version of the field format requirements in the knowledge base, the instantaneous speed and average speed fields are converted into 2-byte unsigned integers to meet the bit length requirements of the "0-200km / h" value range. Other fields maintain format compatibility and the field length identifier is updated synchronously. S42, Frame Structure Reassembly: Reassemble the data according to the 2019 version of the location message frame structure. The message frame structure is: frame header + instruction identifier + data length + terminal ID + data segment + checksum + frame trailer. The instruction identifier adopts the 2019 version location message identifier 0x0200, and the data length is calculated as 0x0020 based on the total number of replaced fields. Generate the initial message draft: 0x7E 0x0200 0x0020 0x1234567890ABCDEF 0x20250601100010 0x3045.12340x11630.5678 0x0050 0x004E 0x00 [Verification code to be filled] 0x7E, synchronously triggers the synchronous compliance verification module.

Images