Protocol conversion method

Abstract

The invention discloses a protocol conversion method, which comprises the steps of receiving an original data frame from a charging power supply, extracting a protocol feature vector of the original data frame through a pre-trained machine learning model, and matching a first protocol type of the original data frame from a protocol feature library based on the protocol feature vector; calling an analysis rule base corresponding to the first protocol type to deconstruct an original data frame into intermediate layer data irrelevant to the protocol; and calling a field conversion rule from a protocol mapping table associated with the second protocol type, dynamically recombining the standardized field label in the intermediate layer data into a target data frame structure, and injecting a protocol control character originating from a target protocol specification to generate a target data frame conforming to the second protocol type. The method has the capability of adaptively learning a new protocol, reduces the manual intervention and maintenance cost, and improves the compatibility, communication reliability and overall operation efficiency of a battery replacement system for vehicles of different brands.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a protocol conversion method. Background Technology

[0002] In the actual operation of electric bus battery swapping services, a prominent technical challenge exists: electric buses from different manufacturers often use their own proprietary communication protocols for their onboard battery management systems and vehicle control systems. This protocol heterogeneity makes it difficult to directly establish a data communication link between the battery pack (as the power supply unit) and the vehicle (as the electrical appliance) during the battery swapping process. This manifests as differences in data frame structures, field definitions, and control commands, leading to communication mismatches, data parsing errors, and even affecting the normal operation of the vehicle.

[0003] To address the aforementioned protocol incompatibility issues, existing technologies typically employ dedicated protocol converters. These converters parse and reconstruct data frames for specific protocols to achieve conversion between different protocols. However, existing protocol conversion schemes largely rely on pre-defined mapping rule bases for a limited number of known protocol types. This approach lacks sufficient flexibility and adaptability when faced with new or non-standard protocols, often requiring manual intervention and hard-coded updates to the rule base. This results in high system maintenance costs, poor scalability, and difficulty in handling complex and ever-changing real-world application scenarios. Furthermore, the accuracy and robustness of existing solutions in the protocol identification stage need improvement, especially when processing noisy or non-standard data frames, which are prone to misidentification, thus affecting the reliability and efficiency of the entire battery swapping process. Summary of the Invention

[0004] To address the technical problems existing in the background art, this invention proposes a protocol conversion method, comprising: S1. Receive raw data frames from the charging power source, extract the protocol feature vector of the raw data frames through a pre-trained machine learning model, and match the first protocol type of the raw data frames from the protocol feature library based on the protocol feature vector. The protocol feature library is derived from industry standard protocol documents or historical communication data. S2. Call the parsing rule base corresponding to the first protocol type to deconstruct the original data frame into intermediate layer data that is independent of the protocol. The intermediate layer data includes standardized field labels and payload content. The parsing rule base is derived from industry standard protocol documents or historical communication data. S3. Retrieve field conversion rules from the protocol mapping table associated with the second protocol type, dynamically reorganize the standardized field labels in the intermediate layer data into the target data frame structure, and inject protocol control characters from the target protocol specification to generate a target data frame that conforms to the second protocol type.

[0005] Furthermore, the protocol feature vector of the original data frame is extracted through a pre-trained machine learning model, specifically including: byte alignment and noise filtering of the original data frame to obtain a pre-processed data frame; the pre-trained machine learning model calculates the attention score of each byte in the pre-processed data frame and sums them by weight to obtain the protocol feature vector, wherein the machine learning model is specifically an attention neural network model.

[0006] Furthermore, the process of matching the first protocol type of the original data frame from the protocol feature library based on the protocol feature vector specifically includes: calculating the similarity between the protocol feature vector and the feature vectors of all protocol types in the protocol feature library, and the protocol type with the maximum similarity and the maximum similarity being greater than or equal to a preset similarity threshold is the first protocol type.

[0007] Furthermore, if the maximum similarity is less than the preset similarity threshold, it is determined that the protocol feature vector does not match any protocol type in the protocol feature library. In this case, the original data frame is marked as an unknown protocol type, the protocol feature vector is added to the pending queue of the protocol feature library, and the protocol feature library update process is triggered.

[0008] Furthermore, the protocol feature library update process specifically includes: determining whether the number of protocol feature vectors in the queue to be processed reaches the minimum set size required for cluster analysis; if the minimum set size is reached, then performing cluster analysis on the protocol feature vectors in the queue to be processed to form at least one candidate protocol cluster for manual verification; if the minimum set size is not reached, then directly making the protocol feature vectors in the queue to be processed into a set of vectors to be labeled for manual verification; in response to the confirmation and labeling instructions for the candidate protocol clusters or the set of vectors to be labeled through the manual verification interface, generating new feature vectors and corresponding parsing rules, adding the new feature vectors to the protocol feature library, and adding the corresponding parsing rules to the parsing rule library.

[0009] This invention uses a machine learning model to adaptively extract features and match protocol types, which can significantly improve the accuracy and robustness of protocol identification. Even when facing unknown or non-standard protocols, it can quickly integrate new knowledge through an adaptive learning mechanism, effectively overcoming the limitations of traditional methods that rely on fixed rule bases. This greatly improves the battery swapping system's protocol adaptability to multiple vehicle brands and overall communication reliability.

[0010] Furthermore, in S2, calling the parsing rule library corresponding to the first protocol type to deconstruct the original data frame into protocol-independent intermediate layer data specifically includes: loading parsing rules corresponding to the first protocol type from the parsing rule library corresponding to the first protocol type; performing syntax parsing on the original data frame based on the parsing rules to generate an abstract syntax tree; and extracting standardized field tags and payload content based on the abstract syntax tree to form intermediate layer data.

[0011] Furthermore, based on the parsing rules, the original data frame is parsed to generate an abstract syntax tree. Specifically, this includes: formalizing the parsing rules into a protocol syntax paradigm, which defines the legal structure of a protocol data frame of a first protocol type through multiple production rules, and binding a corresponding semantic rule to each production rule. The semantic rule defines how to map the protocol syntax structure described by the production rule to one or more standardized field labels. Using a bottom-up parser, the byte stream of the original data frame is shifted and reduced based on the protocol syntax paradigm. During the reduction operation, non-leaf nodes of the abstract syntax tree are created based on the production rules in the protocol syntax paradigm. Each non-leaf node represents a protocol syntax structure, and the original byte sequence corresponding to the non-leaf node is created as a leaf node. All nodes together constitute the abstract syntax tree.

[0012] Furthermore, the extraction of standardized field labels and payload content based on the abstract syntax tree to form intermediate layer data specifically includes: performing a depth-first traversal of the abstract syntax tree, visiting each node sequentially; for each non-leaf node, executing the semantic rule bound to its corresponding production rule, mapping the protocol syntax structure represented by the node to one or more standardized field labels; extracting the original byte sequence carried by the leaf node corresponding to the current non-leaf node as the payload content corresponding to the standardized field label; and binding the standardized field label with the corresponding payload content to jointly constitute the intermediate layer data.

[0013] This invention standardizes data formats by calling a parsing rule base to deconstruct the original data frame into protocol-independent intermediate layer data. This standardization enables data from different protocols to be parsed and processed uniformly, avoiding data parsing errors caused by protocol differences and improving the accuracy and reliability of protocol conversion. Simultaneously, the intermediate layer data provides a universal foundation for subsequent dynamic reassembly into target protocol data frames, enhancing the system's flexibility and maintainability and reducing the need for manual intervention.

[0014] Furthermore, S3 specifically includes: S31. Retrieve field conversion rules and target data frame structure template from the protocol mapping table associated with the second protocol type; S32. Apply field conversion rules to map standardized field labels in intermediate layer data to target protocol field sequences; S33. Based on the target data frame structure template, dynamically assemble the target protocol field sequence and the payload content in the intermediate layer data into the initial byte sequence of the target data frame; S34. Based on the protocol specification of the second protocol type, inject protocol control characters into the initial byte sequence of the target data frame to generate a target data frame that conforms to the second protocol type.

[0015] This invention converts standardized information into data frames that the target device can recognize by dynamically recombining intermediate layer data and injecting target protocol control characters. This step ensures that the data semantics between different protocols can be correctly mapped, achieving high accuracy and reliability in protocol conversion. It enables the system to flexibly adapt to various target protocols without requiring separate hard-coded conversion programs for each protocol, thus significantly improving the system's adaptability and maintenance efficiency, and ensuring smooth and stable communication.

[0016] The protocol conversion method proposed in this invention identifies the protocol type of the original data frame based on a pre-trained machine learning model. Even when encountering unknown protocols, it can achieve adaptive learning through cluster analysis and manual verification, continuously expanding the protocol feature library and parsing rule library. This method uses syntax analysis to parse the original data frame into protocol-independent intermediate layer data, and then dynamically reassembles the data frame structure according to the target protocol, injecting protocol control characters to achieve accurate conversion. This significantly improves the compatibility of the battery swapping system with different brands and models of vehicles, avoids data parsing errors and communication interruptions, and improves the stability and efficiency of the battery swapping process. Simultaneously, the adaptive update mechanism reduces manual maintenance costs, enhances the system's intelligence and reliability, and provides efficient support for electric bus battery swapping services. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the protocol conversion method proposed in this invention; Figure 2 This is a flowchart illustrating the protocol conversion method proposed in this invention. Detailed Implementation

[0018] refer to Figure 1 and Figure 2 The protocol conversion method proposed in this invention includes the following steps: S1. Receive raw data frames from the charging power source, extract the protocol feature vector of the raw data frames through a pre-trained machine learning model, and match the first protocol type of the raw data frames from the protocol feature library based on the protocol feature vector. The protocol feature library is derived from industry standard protocol documents or historical communication data.

[0019] In this embodiment, the protocol feature vector of the original data frame is extracted using a pre-trained machine learning model. Specifically, this includes: byte alignment and noise filtering of the original data frame to obtain a pre-processed data frame; the pre-trained machine learning model calculates the attention score of each byte in the pre-processed data frame and performs a weighted summation to obtain the protocol feature vector. The machine learning model is specifically an attention neural network model. In this embodiment, the pre-processed data frame is analyzed based on the pre-trained attention neural network model. The byte sequence of the pre-processed data frame is input into an embedding layer, where each byte is mapped to a high-dimensional embedding vector, forming an embedding vector sequence. The embedding vector sequence is input into a multi-head attention layer, where each attention head calculates the attention score between bytes and generates a preliminary feature vector through weighted summation. Different attention heads focus on different protocol features, such as frame header structure, payload data distribution, and control character sequences. The preliminary feature vectors output by all attention heads are concatenated and subjected to nonlinear transformation and dimensionality regularization through a feedforward neural network layer to generate the protocol feature vector.

[0020] The first protocol type for matching the original data frame from the protocol feature library based on the protocol feature vector specifically includes: calculating the similarity between the protocol feature vector and the feature vectors of all protocol types in the protocol feature library, and the protocol type with the maximum similarity and the maximum similarity being greater than or equal to a preset similarity threshold is the first protocol type.

[0021] If the maximum similarity is less than the preset similarity threshold, it is determined that the protocol feature vector does not match any protocol type in the protocol feature library. In this case, the original data frame is marked as an unknown protocol type, the protocol feature vector is added to the pending queue of the protocol feature library, and the protocol feature library update process is triggered.

[0022] The protocol feature library update process specifically includes: determining whether the number of protocol feature vectors in the queue to be processed has reached the minimum set size required for cluster analysis; if the minimum set size has been reached, then performing cluster analysis on the protocol feature vectors in the queue to be processed to form at least one candidate protocol cluster for manual verification; if the minimum set size has not been reached, then directly forming a set of vectors to be labeled from the protocol feature vectors in the queue to be processed for manual verification; in response to the confirmation and labeling instructions of the candidate protocol clusters or the set of vectors to be labeled through the manual verification interface, generating new feature vectors and corresponding parsing rules, adding the new feature vectors to the protocol feature library, and adding the corresponding parsing rules to the parsing rule library.

[0023] In this embodiment, raw data frames from the charging power supply are received and byte-aligned: the start and end characters of the raw data frames are detected, and the boundaries of the byte sequence are adjusted to ensure that the data frames are aligned to the byte length specified by the protocol, avoiding misalignment during parsing; then noise filtering is performed to remove irrelevant bytes and error signals, resulting in clean preprocessed data frames, providing a foundation for subsequent feature extraction; the preprocessed data frames are analyzed based on a pre-trained attention neural network model to generate protocol feature vectors. These protocol feature vectors comprehensively characterize key protocol features such as the frame header pattern, data length distribution, and checksum type of the data frames. By focusing on key bytes through an attention mechanism, the targeting and robustness of feature extraction can be improved, avoiding misjudgments caused by noise interference; the cosine similarity between the protocol feature vectors and the feature vectors of all known protocol types in the protocol feature library is calculated, and the protocol type with the highest similarity is selected. If the highest similarity is greater than or equal to a preset similarity threshold, the selection is considered successful. If the maximum similarity is less than a preset similarity threshold, the original data frame is determined to be of an unknown protocol type. Its protocol feature vector is added to the processing queue, and the protocol feature library update process is triggered. Specifically, the system periodically checks the number of protocol feature vectors in the processing queue. If the number reaches the minimum scale required for cluster analysis, the K-means clustering algorithm is used to cluster the protocol feature vectors in the processing queue to form candidate protocol clusters. If the minimum scale is not reached, the protocol feature vectors in the processing queue are directly made into a set of vectors to be labeled. The operation and maintenance personnel can view, confirm and label these candidate protocol clusters or sets of vectors to be labeled through the manual verification interface. The system generates new feature vectors and their corresponding parsing rules based on the labels and adds them to the protocol feature library and parsing rule library respectively, thereby realizing the adaptive expansion of the protocol library.

[0024] S2. Call the parsing rule base corresponding to the first protocol type to deconstruct the original data frame into intermediate layer data that is independent of the protocol. The intermediate layer data includes standardized field labels and payload content. The parsing rule base is derived from industry standard protocol documents or historical communication data.

[0025] The process of deconstructing the original data frame into protocol-independent intermediate layer data by calling the parsing rule library corresponding to the first protocol type specifically includes: loading parsing rules corresponding to the first protocol type from the parsing rule library corresponding to the first protocol type; performing syntax parsing on the original data frame based on the parsing rules to generate an abstract syntax tree; and extracting standardized field tags and payload content based on the abstract syntax tree to form intermediate layer data.

[0026] The process of parsing the original data frame based on parsing rules to generate an abstract syntax tree includes: formalizing the parsing rules into a protocol syntax paradigm, which defines the legal structure of a protocol data frame of a first protocol type through multiple production rules, and binding a corresponding semantic rule to each production rule. The semantic rule defines how to map the protocol syntax structure described by the production rule to one or more standardized field labels. A bottom-up parser is used to perform shift and reduce parsing on the byte stream of the original data frame based on the protocol syntax paradigm. During the reduce operation, non-leaf nodes of the abstract syntax tree are created based on the production rules in the protocol syntax paradigm. Each non-leaf node represents a protocol syntax structure, and the original byte sequence corresponding to the non-leaf node is created as a leaf node. All nodes together constitute the abstract syntax tree.

[0027] Extracting standardized field tags and payload content from an abstract syntax tree to form intermediate layer data specifically includes: performing a depth-first traversal of the abstract syntax tree, visiting each node sequentially; for each non-leaf node, executing the semantic rules bound to its corresponding production rule, mapping the protocol syntax structure represented by the node to one or more standardized field tags; extracting the original byte sequence carried by the leaf node corresponding to the current non-leaf node as the payload content corresponding to the standardized field tags; and binding the standardized field tags with the corresponding payload content to jointly constitute the intermediate layer data.

[0028] In this embodiment, parsing rules for the first protocol type are loaded from the parsing rule base, and the parsing rules are formalized into a protocol syntax paradigm. The protocol syntax paradigm consists of a set of production rules, which are used to define the legal structure of the protocol data frame of the first protocol type. Each production rule describes a legal syntax structure and is bound to a corresponding semantic rule. Based on the terminal symbol definition in the protocol syntax paradigm, the byte stream of the original data frame is converted into a symbol sequence. Each symbol contains its type and corresponding byte value. The LR parser reads one symbol from the symbol sequence in turn and pushes it onto the parsing stack. When the symbol sequence at the top of the stack matches the right-hand side of a production rule, a reduction operation is triggered. Based on this production rule, a non-leaf node of the abstract syntax tree is created. This non-leaf node represents a specific protocol syntax structure. At the same time, the original byte sequence corresponding to the symbol sequence covered by this reduction is created as a leaf node and attached to the non-leaf node. Through continuous shift and reduction operations, a complete abstract syntax tree is constructed. The abstract syntax tree is then traversed in a depth-first manner. When visiting each non-leaf node, its bound semantic rule is executed, mapping the protocol syntax structure represented by the node to a normalized field label. At the same time, the original byte sequences carried by all leaf nodes under the non-leaf node are extracted as payload content. The normalized field labels are bound to the corresponding payload content, ultimately forming intermediate layer data containing complete semantic information. This formal syntax-based parsing method transforms protocol-specific structures into generic field labels through semantic rules, overcoming the limitations of traditional hard-coded parsers' poor adaptability to non-standard protocols. The generated intermediate-layer data provides standardized input for subsequent protocol conversions. The entire parsing process ensures that data from different protocols can be uniformly represented in a standard format, laying a solid foundation for dynamic protocol conversion.

[0029] S3. Retrieve field conversion rules from the protocol mapping table associated with the second protocol type, dynamically reorganize the standardized field labels in the intermediate layer data into the target data frame structure, and inject protocol control characters derived from the target protocol specification to generate a target data frame conforming to the second protocol type. Specifically, this includes: S31. Retrieve field conversion rules and target data frame structure template from the protocol mapping table associated with the second protocol type; S32. Apply field conversion rules to map standardized field labels in intermediate layer data to target protocol field sequences; S33. Based on the target data frame structure template, dynamically assemble the target protocol field sequence and the payload content in the intermediate layer data into the initial byte sequence of the target data frame; S34. Based on the protocol specification of the second protocol type, inject protocol control characters into the initial byte sequence of the target data frame to generate a target data frame that conforms to the second protocol type.

[0030] In this embodiment, the system retrieves field conversion rules and a target data frame structure template from the protocol mapping table associated with the second protocol type. The field conversion rules define the mapping relationship from standardized field labels to target protocol field sequences, and the target data frame structure template describes the overall byte layout of the target data frame. Applying the field conversion rules, the standardized field labels in the intermediate layer data are mapped one by one to the target protocol field sequence. This mapping process ensures that the data semantics remain consistent across different protocols, achieving semantic-level conversion. Based on the target data frame structure template, the converted target protocol field sequence is dynamically assembled with the payload content in the intermediate layer data. The assembly process follows the field order and length specifications defined by the second protocol type, arranging the identifiers, length information, and payload content of each field sequentially to generate the initial byte sequence of the target data frame. This initial byte sequence contains complete core data content but does not yet contain the complete control information required by the second protocol type. Finally, based on the protocol specifications of the second protocol type, necessary protocol control characters are injected into the initial byte sequence, including: inserting a start identifier and an end identifier specific to the second protocol type at the start and end positions of the sequence, respectively; calculating a checksum based on the content of the initial byte sequence according to the checksum algorithm specified by the second protocol type; and inserting the calculation result into a specified position in the data frame. After injecting all protocol control characters, a final target data frame conforming to the second protocol type specification is generated. This dynamic reassembly mechanism based on the protocol mapping table significantly reduces the complexity and error probability of protocol conversion by separating data conversion from protocol control character injection. This allows the system to flexibly adapt to various target protocol formats, effectively solving the problems of poor flexibility and maintenance difficulties in traditional hard-coded conversion methods. The generated target data frame can be directly sent to the target device, completing the entire protocol conversion process.

[0031] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A protocol conversion method characterized by, The method comprises the following steps: S1, receiving an original data frame from a charging power supply, extracting a protocol feature vector of the original data frame through a pre-trained machine learning model, and matching a first protocol type of the original data frame from a protocol feature library based on the protocol feature vector, wherein the protocol feature library is derived from an industry standard protocol document or historical communication data; S2, calling a parsing rule library corresponding to the first protocol type to deconstruct the original data frame into protocol-independent intermediate layer data, wherein the intermediate layer data comprises standardized field labels and payload content, and wherein the parsing rule library is derived from an industry standard protocol document or historical communication data; S3, calling a field conversion rule from a protocol mapping table associated with a second protocol type to dynamically recombine the standardized field labels in the intermediate layer data into a target data frame structure, and injecting a protocol control symbol derived from a target protocol specification to generate a target data frame conforming to the second protocol type.

2. The protocol conversion method of claim 1, wherein, The method of extracting the protocol feature vector of the original data frame through the pre-trained machine learning model specifically comprises: performing byte alignment and noise filtering on the original data frame to obtain a preprocessed data frame; the pre-trained machine learning model calculates an attention score of each byte in the preprocessed data frame and performs weighted summation to obtain the protocol feature vector, and the machine learning model is specifically an attention neural network model.

3. The protocol conversion method of claim 1, wherein, The method of matching the first protocol type of the original data frame from the protocol feature library based on the protocol feature vector specifically comprises: calculating the similarity of the protocol feature vector with the feature vectors of all protocol types in the protocol feature library, and the protocol type with the maximum similarity and greater than or equal to a preset similarity threshold is the first protocol type.

4. The protocol conversion method of claim 3, wherein, If the maximum similarity is less than the preset similarity threshold, it is determined that the protocol feature vector does not match all protocol types in the protocol feature library, the original data frame is marked as an unknown protocol type, the protocol feature vector is added to a to-be-processed queue of the protocol feature library, and a protocol feature library updating process is triggered.

5. The protocol conversion method of claim 4, wherein, The protocol feature library updating process specifically comprises: determining whether the number of protocol feature vectors in the to-be-processed queue reaches a minimum set size required for cluster analysis; if the minimum set size is reached, performing cluster analysis on the protocol feature vectors in the to-be-processed queue to form at least one candidate protocol cluster for manual verification; if the minimum set size is not reached, directly forming a to-be-labeled vector set from the protocol feature vectors in the to-be-processed queue for manual verification; in response to a confirmation and labeling instruction for the candidate protocol cluster or the to-be-labeled vector set through a manual verification interface, generating a new feature vector and corresponding parsing rules, adding the feature vector to the protocol feature library, and adding the corresponding parsing rules to the parsing rule library.

6. The protocol conversion method of claim 1, wherein, In S2, the method of calling the parsing rule library corresponding to the first protocol type to deconstruct the original data frame into protocol-independent intermediate layer data specifically comprises: loading the parsing rules corresponding to the first protocol type from the parsing rule library corresponding to the first protocol type; performing syntax analysis on the original data frame based on the parsing rules to generate an abstract syntax tree; and extracting standardized field labels and payload content based on the abstract syntax tree to form the intermediate layer data.

7. The protocol conversion method of claim 6, wherein, The original data frame is parsed based on the parsing rule to generate an abstract syntax tree, specifically including: formalizing the parsing rule into a protocol syntax paradigm, the protocol syntax paradigm defining a legal structure of a protocol data frame of a first protocol type through a plurality of production rules, and binding a corresponding semantic rule to each production rule, the semantic rule defining how to map the protocol syntax structure described by the production rule to one or more standardized field tags; using a bottom-up syntax analyzer, based on the protocol syntax paradigm, performing shift and reduction analysis on the byte stream of the original data frame, and when performing a reduction action, creating a non-leaf node of the abstract syntax tree based on the production rule in the protocol syntax paradigm, the non-leaf node representing a protocol syntax structure, and creating a leaf node for the original byte sequence corresponding to the non-leaf node, all nodes together constituting the abstract syntax tree.

8. The protocol conversion method of claim 6, wherein, The standardized field tags and payload contents are extracted based on the abstract syntax tree to form intermediate layer data, specifically including: performing depth-first traversal on the abstract syntax tree, and sequentially accessing each node; for each non-leaf node, executing the semantic rule bound to the production rule corresponding to the non-leaf node to map the protocol syntax structure represented by the node to one or more standardized field tags; extracting the original byte sequence carried by the leaf node corresponding to the current non-leaf node as the payload content corresponding to the standardized field tag; and binding the standardized field tag and the corresponding payload content to jointly constitute the intermediate layer data.

9. The protocol conversion method of claim 1, wherein, S3 specifically includes: S31, retrieving a field conversion rule and a target data frame structure template from a protocol mapping table associated with the second protocol type; S32, applying the field conversion rule to map the standardized field tags in the intermediate layer data to a target protocol field sequence; S33, based on the target data frame structure template, dynamically assembling the target protocol field sequence and the payload content in the intermediate layer data into an initial byte sequence of the target data frame; S34, based on the protocol specification of the second protocol type, injecting a protocol control symbol into the initial byte sequence of the target data frame to generate a target data frame conforming to the second protocol type.

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