Cross-border logistics process automatic generation method and device based on natural language analysis

Abstract

This application relates to the field of artificial intelligence technology and discloses a method and apparatus for automatically generating cross-border logistics processes based on natural language parsing. The method includes: performing cross-language semantic parsing and task structuring on natural language instructions to generate a structured task description including logistics operation logic, target area information, and equipment interaction points; driving a large language model to perform logical reasoning and process construction based on the structured task description to generate an executable logistics process including equipment control nodes and compliance document generation nodes; dynamically mapping protocols for equipment control nodes based on an equipment function vector space alignment mechanism to generate equipment driving strategies; dynamically generating compliance content for compliance document generation nodes based on a multimodal rule base and real-time policy information to obtain formatted compliance documents; and finally integrating these into a logistics process execution package for deployment. This application can effectively improve the efficiency, adaptability, and compliance of cross-border logistics process customization.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, specifically to a method and apparatus for automatically generating cross-border logistics processes based on natural language parsing. Background Technology

[0002] Against the backdrop of rapid development of global trade, cross-border logistics scenarios encompass core needs such as multi-regional collaboration, multi-device linkage, and adaptation to multiple compliance requirements. Enterprises are increasingly demanding the rapid construction, flexible adaptation, and compliant implementation of logistics processes.

[0003] Currently, most aspects of the existing logistics process rely on manual labor. Natural language requests from business personnel must be manually translated into technical solutions and coded by technical experts, a time-consuming process prone to misunderstandings. Furthermore, the communication protocols of various devices involved in logistics operations differ, requiring the development of customizable code, resulting in poor system scalability and lengthy deployment cycles for new processes. In addition, the complex and dynamically changing regulations, cultures, and customs requirements of different countries and regions make ensuring full compliance manually extremely difficult and risky. Therefore, existing technologies struggle to quickly and accurately transform unstructured business instructions into readily deployable, regionally compliant, end-to-end executable logistics processes, leading to inefficiencies, lack of flexibility, and poor adaptability in cross-border logistics process customization.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] This application provides a method and apparatus for automatically generating cross-border logistics processes based on natural language parsing. It can automatically convert natural language instructions related to cross-border logistics into end-to-end executable logistics processes and complete their deployment, solving the problems of low efficiency in process customization, insufficient equipment adaptability and compliance assurance in the prior art.

[0006] In a first aspect, embodiments of this application provide a method for automatically generating cross-border logistics processes based on natural language parsing, including:

[0007] The received natural language instructions are subjected to cross-language semantic parsing and task structuring to generate a structured task description that includes logistics operation logic, target area information and equipment interaction points.

[0008] Based on the structured task description, the large language model is driven to perform logical reasoning and process construction, generating an executable logistics process including device control nodes and compliance document generation nodes.

[0009] For the device control nodes in the executable logistics process, dynamic protocol mapping is performed based on the device function vector space alignment mechanism to generate corresponding device driving strategies;

[0010] For the compliance document generation node in the executable logistics process, the compliance content is dynamically generated based on the multimodal rule base and real-time policy information to obtain a formatted compliance document that meets the requirements of the target area.

[0011] The executable logistics process, the device driver strategy, and the formatted compliance documents are integrated to form a logistics process execution package and then deployed.

[0012] Furthermore, in some embodiments of this application, the step of performing cross-language semantic parsing and task structuring on the received natural language instructions to generate a structured task description including logistics operation logic, target area information, and equipment interaction points includes:

[0013] The received multilingual natural language instructions are input into a pre-trained multilingual semantic model to obtain a preliminary semantic encoding sequence;

[0014] Based on the attention mechanism, the preliminary semantic encoding sequence and the pre-set regional cultural feature knowledge are interacted to calculate the feature enhancement vector that integrates the regional cultural context.

[0015] Based on the feature enhancement vector, after joint decoding of entity relationships, a structured task description including logistics operation logic, target area information, and equipment interaction points is generated.

[0016] Furthermore, in some embodiments of this application, the step of interacting the preliminary semantic encoding sequence and pre-set regional cultural feature knowledge based on an attention mechanism to calculate a feature enhancement vector that integrates the regional cultural context includes:

[0017] Based on the regional keywords identified from the preliminary semantic coding sequence, the corresponding multidimensional cultural feature vectors are loaded from the regional cultural feature database.

[0018] The relevance weight between each position in the preliminary semantic encoding sequence and the multidimensional cultural feature vector is calculated through a cross-attention layer.

[0019] The multidimensional cultural feature vector is dynamically weighted and aggregated based on the relevance weights to generate the feature enhancement vector that is related to the semantics of the current instruction.

[0020] Furthermore, in some embodiments of this application, the step of driving a large language model to perform logical reasoning and process construction based on the structured task description to generate an executable logistics process including device control nodes and compliance document generation nodes includes:

[0021] The structured task description is converted into large language model prompt words that include context and constraints;

[0022] The large language model is driven to perform step-by-step reasoning based on the prompt words, generating an initial flow logic diagram represented by nodes and edges;

[0023] The initial process logic diagram is compiled into an executable form, each node is bound to a specific API call, data source or document template, and the device interaction points and compliance output points are identified as nodes of a specific type, thus forming the executable logistics process.

[0024] Furthermore, in some embodiments of this application, the step of dynamically mapping the device control nodes in the executable logistics process based on the device function vector space alignment mechanism to generate corresponding device driving strategies includes:

[0025] The device control node is analyzed to extract the corresponding functional requirements, and the functional requirements are transformed into a standardized requirement feature vector.

[0026] In the pre-constructed device protocol vector space, the requirement feature vector and the functional description vectors of multiple candidate protocols are compared for similarity and spatially aligned to determine the optimal matching target protocol.

[0027] Based on the communication specifications and data format of the optimal matching target protocol, the logical operations of the device control node are compiled into a plug-and-play device driver package.

[0028] The corresponding device driver strategy is constructed based on the device driver package.

[0029] Furthermore, in some embodiments of this application, the step of performing similarity comparison and spatial alignment processing on the requirement feature vector and the functional description vectors of multiple candidate protocols in a pre-constructed device protocol vector space to determine the optimal matching target protocol includes:

[0030] The requirement feature vector and the functional description vector of each candidate protocol are respectively input into a pre-trained feature encoder and mapped to a shared vector representation space.

[0031] Within the shared vector representation space, a similarity calculation function based on distance metric is used to calculate the matching score between the demand feature vector and each candidate protocol vector.

[0032] All candidate protocols are sorted according to the matching score, and the candidate protocol with the highest score is determined as the optimal matching target protocol.

[0033] Furthermore, in some embodiments of this application, the step of dynamically generating compliance content based on a multimodal rule base and real-time policy information for the compliance document generation node in the executable logistics process to obtain a formatted compliance document that meets the requirements of the target region includes:

[0034] Based on the target area and document type associated with the compliance document generation node, concurrently retrieve textual regulatory clauses, image form templates, and associated real-time policy update summaries from the multimodal compliance rule base;

[0035] The retrieved textual regulations are subjected to structured constraint extraction, key fields are located and format parsed from the image form templates, and the extracted structured constraints and key fields are verified for timeliness and conflict resolution in conjunction with the real-time policy update summary.

[0036] Based on the verified structured constraints and field formats, the business data required for the execution of the executable logistics process is automatically populated, and the formatted compliance file is generated.

[0037] Furthermore, in some embodiments of this application, the step of extracting structured constraints from the retrieved textual regulatory clauses, locating key fields and parsing the format of the image form template, and verifying the timeliness and resolving conflicts of the extracted structured constraints and key fields in conjunction with the real-time policy update summary includes:

[0038] Input the textual regulations and the real-time policy update summary into the text analysis model to extract the list of written constraints and their effective time range;

[0039] The image form template is input into the document understanding model to identify the corresponding fixed fields, variable fields, and layout specifications.

[0040] The constraint list and the variable fields are mapped and compliance checks are performed to ensure that the entered data meets all effective constraints and conforms to form layout specifications.

[0041] Furthermore, in some embodiments of this application, the method for updating the multimodal compliance rule base includes:

[0042] By connecting to multiple official information sources, we continuously monitor the release status and revision dynamics of policies, regulations, and standard forms in the target area;

[0043] When a change in the release status or revision dynamics is detected, the updated content is automatically captured, and the updated content is parsed into structured rule objects using a multimodal understanding model;

[0044] The structured rule object is associated with the version number, effective date and source region and stored in the multimodal compliance rule library, and the compliance reassessment of the relevant historical processes is automatically triggered.

[0045] Furthermore, in some embodiments of this application, the integration of the executable logistics process, the device driver strategy, and the formatted compliance file to form a logistics process execution package and its deployment includes:

[0046] The device driving strategy is encapsulated into a lightweight execution module that can run independently at the edge.

[0047] Bind the formatted compliance file to the logical path in the executable logistics process;

[0048] The executable logistics process, the lightweight execution module, and the bound compliance documents are packaged into a logistics process execution package and then deployed to the target execution environment through a deployment interface.

[0049] Secondly, embodiments of this application provide an automatic cross-border logistics process generation device based on natural language parsing, comprising:

[0050] The instruction parsing module is used to perform cross-language semantic parsing and task structuring on the received natural language instructions, and generate a structured task description that includes logistics operation logic, target area information and equipment interaction points.

[0051] The process construction module is used to drive the large language model to perform logical reasoning and process construction based on the structured task description, and generate an executable logistics process including equipment control nodes and compliance document generation nodes.

[0052] The strategy generation module is used to dynamically map the device control nodes in the executable logistics process according to the device function vector space alignment mechanism to generate corresponding device driving strategies.

[0053] The compliance document module is used to dynamically generate compliance content for the compliance document generation node in the executable logistics process based on a multimodal rule base and real-time policy information, so as to obtain a formatted compliance document that meets the requirements of the target region.

[0054] The integration and deployment module is used to integrate the executable logistics process, the device driver strategy, and the formatted compliance file to form a logistics process execution package and deploy it.

[0055] This application provides a method and apparatus for automatically generating cross-border logistics processes based on natural language parsing. First, by performing cross-language semantic parsing and task structuring on received natural language instructions, unstructured business requirements are transformed into structured task descriptions containing logistics operation logic, target area information, and equipment interaction points. This eliminates the need for manual requirement breakdown and technical transformation, reducing misunderstandings and communication costs from the outset. Next, based on this structured description, a large language model is used to generate an executable logistics process including equipment control nodes and compliance document generation nodes, achieving automated transformation from requirements to process logic and avoiding the tedious process of traditional manual coding development. Subsequently, an equipment-driven strategy is generated for the equipment control nodes through an equipment function vector space alignment mechanism, eliminating the need for manual development of protocol adaptation modules and significantly reducing the difficulty of adapting to multi-protocol devices. Simultaneously, based on a multimodal rule base and real-time policy information, formatted compliance documents conforming to the target area requirements are dynamically generated for the compliance document generation nodes, solving the timeliness and accuracy issues of manual regulation searching and document compilation. Finally, by integrating the executable process, equipment-driven strategy, and compliance documents into an execution package and deploying it, end-to-end automation from business requirement input to logistics process implementation is achieved. In summary, this application, through end-to-end automation, completely eliminates the traditional reliance on manual labor. It not only improves the customization efficiency of cross-border logistics processes and shortens the process launch cycle, but also reduces deviations caused by manual operation through automated mechanisms for equipment adaptation and compliance generation. This improves the equipment adaptability and regional compliance of the process, effectively meeting the core needs of cross-border logistics for multi-regional collaboration, multi-equipment linkage, and adaptation to multiple compliance requirements. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is an application environment diagram of the automatic generation method for cross-border logistics processes based on natural language parsing provided in the embodiments of this application;

[0058] Figure 2 This is a flowchart illustrating the automatic generation method for cross-border logistics processes based on natural language parsing provided in an embodiment of this application.

[0059] Figure 3 This is a schematic diagram of the structure of the cross-border logistics process automatic generation device based on natural language parsing provided in the embodiments of this application;

[0060] Figure 4This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0061] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with those detailed in the appended claims or with some aspects of this application.

[0062] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover descriptions such as non-exclusive inclusion, so that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0063] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0064] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0065] To address the aforementioned technical problems and overcome the shortcomings of existing technologies, this application provides a method and apparatus for automatically generating cross-border logistics processes based on natural language parsing. This method can automatically convert natural language instructions related to cross-border logistics into end-to-end executable logistics processes and complete their deployment, thus resolving the issues of low efficiency in process customization, insufficient equipment adaptability, and inadequate compliance guarantees in existing technologies.

[0066] Figure 1 This is a diagram illustrating the application environment of a method for automatically generating cross-border logistics processes based on natural language parsing, as described in one embodiment. (Refer to...) Figure 1This method for automatically generating cross-border logistics processes based on natural language parsing is applied to a system for automatically generating cross-border logistics processes based on natural language parsing. This system includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal; a mobile terminal can be at least one of a mobile phone, tablet, or laptop. The server 120 can be a standalone server or a server cluster consisting of multiple servers. Server 120 is configured to execute the aforementioned method for automatically generating cross-border logistics processes based on natural language parsing. This includes: performing cross-language semantic parsing and task structuring on received natural language instructions to generate a structured task description including logistics operation logic, target area information, and equipment interaction points; driving a large language model to perform logical reasoning and process construction based on the structured task description to generate an executable logistics process including equipment control nodes and compliance document generation nodes; dynamically mapping protocols for equipment control nodes in the executable logistics process based on the equipment function vector space alignment mechanism to generate corresponding equipment driving strategies; dynamically generating compliance content for compliance document generation nodes in the executable logistics process based on a multimodal rule base and real-time policy information to obtain formatted compliance documents that meet the requirements of the target area; and integrating the executable logistics process, equipment driving strategies, and formatted compliance documents to form a logistics process execution package and deploying it.

[0067] Please see Figure 2 , Figure 2 This is a flowchart illustrating an embodiment of the automatic generation method for cross-border logistics processes based on natural language parsing provided in this application. This embodiment primarily uses the application of this method to computer equipment as an example for illustration. Specifically, the automatic generation method for cross-border logistics processes based on natural language parsing provided in this application may include the following steps:

[0068] S1. Perform cross-language semantic parsing and task structuring on the received natural language instructions to generate a structured task description that includes logistics operation logic, target area information and equipment interaction points;

[0069] Specifically, for step S1, the system receives cross-border logistics business requests submitted by business personnel in natural language. This instruction supports multilingual input, including single-language instructions such as "Establish a cold chain logistics process for electronics from China to the Middle East" in Chinese and "Establish a cold chain logistics process for electronics from China to the Middle East" in English, as well as mixed multilingual instructions. Through cross-language semantic parsing technology, the system accurately understands the core business intent within the instruction and performs task structuring. Three types of key information are extracted from the instruction and organized into a standardized structured task description: first, the logistics operation logic, such as the core process requirements of "temperature control monitoring → anomaly handling → goods delivery," specific operational parameters such as the temperature control range of 2-8℃ and the monitoring frequency of 15 minutes / time; second, target area information, such as the country of origin and the country of destination; and third, equipment interaction points, i.e., the types of IoT devices that need to be linked in the process, such as temperature sensors, AGV handling equipment, and refrigeration units.

[0070] S2. Based on structured task description, drive the large language model to perform logical reasoning and process construction, and generate an executable logistics process including equipment control nodes and compliance document generation nodes;

[0071] Specifically, in step S2, the obtained structured task description is used as input to drive the large language model for logical reasoning and process construction. The large language model combines the general business logic of cross-border logistics with the specific constraints in the structured task to gradually derive a complete process framework, ultimately generating an executable logistics process. This process is presented in a node-based format, clearly including two types of core nodes: one is the equipment control node, corresponding to operational steps requiring interaction with IoT devices, such as "temperature sensor data acquisition" and "AGV equipment cargo transfer"; the other is the compliance document generation node, corresponding to the steps requiring the compilation of cross-border logistics compliance documents. Each node clearly defines its corresponding business purpose and execution sequence.

[0072] S3. For the equipment control nodes in the executable logistics process, perform dynamic protocol mapping based on the equipment function vector space alignment mechanism to generate the corresponding equipment driving strategy;

[0073] Specifically, for step S3, for each device control node in the generated executable logistics process, the functional requirements of the corresponding device are first clarified, such as "real-time acquisition of cargo temperature data" and "precise control of refrigeration unit operation status". Based on the device function vector space alignment mechanism, the device functional requirements are dynamically matched with the communication protocols of various IoT devices, and the optimal communication protocol (such as Zigbee, Modbus-TCP, etc.) for the device control node can be determined without manual intervention. According to the matching results, the corresponding device driver strategy is generated to ensure that the device control node in the logistics process can achieve seamless communication and collaborative work with the actual IoT devices. For example, a driver strategy adapted to the protocol of a specified model of temperature sensor is generated for the "temperature sensor data acquisition" node to ensure that sensor data is stably uploaded to the logistics system.

[0074] S4. For compliance document generation nodes in the executable logistics process, dynamically generate compliance content based on a multimodal rule base and real-time policy information to obtain formatted compliance documents that meet the requirements of the target region;

[0075] Specifically, for step S4, the compliance document generation node in the executable logistics process dynamically generates compliance content based on a multimodal rule base and real-time policy information. The multimodal rule base covers customs declaration rules, regulatory requirements, form formats, and other information from different countries (regions), while the real-time policy information synchronizes the latest compliance policy revisions in the target region, such as adjustments to customs declaration form fields and updates to certification requirements. Combining the target region and document type associated with the node, the corresponding compliance requirements and form templates are retrieved from the multimodal rule base, and the real-time policy information is integrated for content adaptation. The business data generated during the process (such as cargo name, quantity, temperature control records, etc.) is automatically populated, ultimately generating a standardized compliance document that meets the compliance requirements of the target region.

[0076] S5. Integrate executable logistics processes, equipment-driven strategies, and formatted compliance documents to form a logistics process execution package and deploy it;

[0077] Specifically, in step S5, the generated executable logistics process, device driver strategy, and formatted compliance documents are fully integrated. Following a unified packaging standard, these three are packaged into a complete logistics process execution package. This package contains all the logic required for process operation, device adaptation solutions, and compliance document resources. Through a pre-defined deployment interface, this execution package is distributed to the target execution environment for actual cross-border logistics operations, such as the logistics company's local server, cloud system, or edge computing device. Once deployment is complete, the automated operation of the logistics process can begin.

[0078] In specific embodiments, based on the application scenario of the logistics process and the type of the target execution environment, a matching interface is selected from a pre-set set of multiple deployment interfaces, and the delivery method is determined. If the target environment is a Kubernetes cluster (such as an enterprise cloud distributed cluster), the execution package is delivered through a REST API or message queue. For example, a comprehensive logistics execution package covering multiple regions in Southeast Asia is delivered to the enterprise Kubernetes cluster through a message queue to achieve highly available distributed deployment. If the target environment is an edge computing node, warehouse edge controller, vehicle terminal, or other edge device, a local network port, serial port, or OPC UA gateway interface is selected, and the execution package is delivered through short-range data transmission. For example, an execution package adapted to cold chain warehouse sensors is delivered to the edge computing node through a local network port. If the target environment is a cross-border logistics cloud management platform, the execution package can also be remotely transmitted through a REST API interface.

[0079] Verify the system compatibility and basic dependencies of the target execution environment to ensure that the environment meets deployment requirements. At the system level, it should be compatible with edge-side embedded operating systems (such as embedded Linux or RTOS), Kubernetes cluster runtime environments, and cloud-based distributed systems (supporting x86 / ARM architecture). At the dependency level, only basic container runtimes (such as Docker), container orchestration components required by the Kubernetes cluster, and standard communication protocol stacks (compatible with device protocols such as Modbus-TCP, Zigbee, and OPC UA) need to be pre-installed. No additional complex third-party components or dedicated runtime environments need to be installed. For example, in a Kubernetes cluster, only ensuring that container orchestration functions correctly and pre-installing lightweight Docker components on edge computing nodes is sufficient to meet the runtime requirements of the executable package.

[0080] After the execution package is delivered to the target environment, the built-in lightweight process parsing kernel automatically starts initialization. First, it scans the internal structure of the execution package to identify the logical links of the executable logistics process, the lightweight device driver module, and the bound compliance files, establishing the association mapping relationship between the three. Then, it dynamically adjusts the running parameters and adapts the resource configuration according to the resource characteristics of the target environment (such as the high concurrency scheduling capability of Kubernetes clusters and the low computing power constraints of edge computing nodes). For example, it optimizes the load balancing strategy of the engine in Kubernetes clusters and reduces the engine memory usage in edge computing nodes to ensure that the execution engine is efficiently adapted to the target environment.

[0081] After the execution engine completes initialization, it automatically triggers the component loading and deployment process. The logical rules for the executable logistics process, device driver modules, and associated compliance documents are loaded sequentially without manual configuration intervention. Upon deployment, the system automatically performs an initial runtime verification, checking the communication connectivity between process nodes and device drivers, the validity of compliance document calls, and the scheduling adaptability of the Kubernetes cluster or the reasonableness of resource usage by edge computing nodes. Once verification is successful, the automated execution of the logistics process is directly initiated. For example, after deployment, the system automatically verifies that the temperature sensor data acquisition function and customs declaration document call function are normal, the Kubernetes cluster scheduling response is timely, or the edge computing nodes are running stably, and then starts the entire cold chain transportation process.

[0082] This embodiment achieves end-to-end automation of the entire cross-border logistics process, from natural language business requirements to an executable cross-border logistics process. It significantly shortens the customization cycle of the logistics process, reduces the cost of manual intervention, and effectively improves the compatibility of equipment adaptation and the compliance of cross-border logistics. It solves the problems of low efficiency, poor adaptability and high compliance risks in the customization of traditional cross-border logistics processes, and meets the core demand of enterprises to respond quickly to market demands.

[0083] Furthermore, in some embodiments, step S1, "performing cross-language semantic parsing and task structuring of the received natural language instructions to generate a structured task description including logistics operation logic, target area information, and equipment interaction points," may specifically include:

[0084] S11. Input the received multilingual natural language instructions into the pre-trained multilingual semantic model to obtain a preliminary semantic encoding sequence;

[0085] Specifically, in step S11, the system receives cross-border logistics business requests from business personnel. These requests are presented in natural language and support single-language or multi-language input. These multi-language natural language instructions are then input into a pre-trained multi-language semantic model. This model can recognize the grammatical rules and semantic logic of different languages, transforming unstructured natural language instructions into preliminary semantic encoding sequences that the model can further process, thus achieving preliminary extraction and digital representation of the core information of the instructions.

[0086] In this embodiment, the multilingual semantic model is based on the XLM-RoBERTa architecture. This model natively possesses powerful cross-language semantic alignment capabilities and a strong foundation in multilingual text understanding, covering the grammatical rules and semantic logic of multiple mainstream languages ​​globally, making it naturally adaptable to multilingual interaction scenarios in cross-border logistics. To further enhance the model's professional adaptability in the cross-border logistics field, a dedicated corpus covering multiple commonly used languages ​​in cross-border trade is used for targeted fine-tuning. This corpus is derived from actual cross-border logistics business scenarios, specifically including multilingual logistics demand instructions (such as customized transportation process requests in different languages), regional compliance requirement descriptions (such as multilingual customs regulations in various countries), equipment interaction operation manuals (such as multilingual IoT device user guides), customs declaration business terminology sets (such as multilingual expressions of various trade documents), cross-border trade contract fragments, and logistics process description texts, etc. Through multiple rounds of supervised fine-tuning, the model's semantic representation capabilities are optimized, enabling it to accurately capture the meanings of professional terms, industry-specific expression logic, and semantic correspondences between multiple languages ​​in cross-border logistics scenarios. This effectively reduces the comprehension bias of multilingual instructions and provides reliable support for the stable transformation of unstructured multilingual natural language instructions into logically coherent and semantically accurate preliminary semantic encoding sequences.

[0087] S12. Based on the attention mechanism, the initial semantic encoding sequence and the pre-set regional cultural feature knowledge are interacted to calculate the feature enhancement vector that integrates the regional cultural context;

[0088] Specifically, for step S12, pre-set regional cultural characteristic knowledge is retrieved. This knowledge includes key information such as compliance requirements, cultural taboos, and business practices of different countries (regions). Through an attention mechanism, the preliminary semantic encoding sequence interacts deeply with this regional cultural characteristic knowledge. First, region-related keywords in the preliminary semantic encoding sequence are identified. Then, the relevance weights between the preliminary semantic encoding corresponding to each keyword and the relevant regional cultural characteristic vectors are calculated. Based on these weights, the regional cultural characteristic vectors are dynamically weighted and aggregated, ultimately generating a feature enhancement vector that incorporates the regional cultural context, making the instruction encoding more aligned with the actual business requirements of the target region.

[0089] S13. Based on the feature enhancement vector, after joint decoding of entity relationships, a structured task description including logistics operation logic, target area information and equipment interaction points is generated;

[0090] Specifically, in step S13, the obtained feature enhancement vector undergoes joint entity relationship decoding. This process accurately identifies the core entities contained in the vector (such as logistics cargo type, transportation mode, target country, required equipment, etc.) and their interrelationships. For example, from the feature enhancement vector corresponding to "establishing a China-Vietnam fresh food cold chain transportation process, monitoring temperature every 15 minutes," the core logistics operation of "fresh food cold chain transportation," the target area combination of "AA-BB," the operational requirement of "temperature monitoring every 15 minutes," and the key equipment interaction point of "temperature sensor" are decoded. Subsequently, according to a unified structured specification, the decoded core content such as logistics operation logic, target area information, and equipment interaction points are organized and summarized to generate a standardized structured task description, such as "Logistics operation logic: cold chain transportation → temperature monitoring every 15 minutes → anomaly warning; target area information: country of origin AA, country of destination BB; equipment interaction point: temperature sensor."

[0091] This embodiment can accurately parse multilingual natural language instructions, effectively integrate the cultural characteristics of the target region, generate accurate and standardized structured task descriptions, solve the problems of misunderstanding of multilingual instructions and insufficient adaptation to regional cultural context, and provide accurate and reliable input basis for the subsequent generation of executable logistics processes.

[0092] Furthermore, in some embodiments, step S12, "based on an attention mechanism, interacting the preliminary semantic encoding sequence and pre-set regional cultural feature knowledge to calculate a feature enhancement vector that integrates the regional cultural context," may specifically include:

[0093] S121. Based on the regional keywords identified from the preliminary semantic coding sequence, load the corresponding multidimensional cultural feature vectors from the regional cultural feature database;

[0094] Specifically, for step S121, keywords are first extracted from the acquired preliminary semantic encoding sequence, focusing on identifying core words directly related to the target region of cross-border logistics. These words can be explicit country or region names or region-specific identifiers. Based on the identified regional keywords, the corresponding multi-dimensional cultural feature vectors are accurately retrieved from a pre-set regional cultural feature database. This vector covers key information dimensions of the target region in terms of logistics compliance, cultural taboos, and business norms.

[0095] S122. Calculate the relevance weights of each position and the multidimensional cultural feature vector in the initial semantic encoding sequence through a cross-attention layer;

[0096] Specifically, in step S122, the obtained preliminary semantic encoding sequence and the loaded multidimensional cultural feature vector are input into a cross-attention layer, which is used to mine the semantic association between the two. For each position in the preliminary semantic encoding sequence, corresponding to a single semantic unit in the instruction, such as "fresh food transportation," "customs declaration," and "temperature monitoring," the correlation strength between it and each dimension in the multidimensional cultural feature vector is calculated, ultimately obtaining a set of correlation weights. For example, if the preliminary semantic encoding sequence contains the semantic unit "food transportation," and the multidimensional cultural feature vector contains the dimension of "certification requirements," then the correlation weight between this semantic unit and that dimension will be significantly higher than the weight with the dimension of "customs clearance process for electronic products"; if the encoding sequence contains the semantic unit "customs declaration document preparation," then its correlation weight with the dimension of "customs clearance form format" will be at a high level.

[0097] S123. Dynamically weight and aggregate the multidimensional cultural feature vectors based on relevance weights to generate feature enhancement vectors that are related to the semantics of the current instruction;

[0098] Specifically, in step S123, based on the calculated relevance weights, the dimensions of the multidimensional cultural feature vector are dynamically adjusted. Dimensions with high relevance weights are given greater importance to strengthen their representation in the vector; dimensions with low relevance weights are weakened to reduce their interference. Subsequently, the adjusted multidimensional cultural feature vector is aggregated to integrate the scattered feature dimensions into a unified vector representation, ultimately generating a feature enhancement vector highly relevant to the semantics of the current natural language instruction. For example, for the instruction "Establish a cold chain transportation process for fresh food in Indonesia," after weighted aggregation, the feature enhancement vector will emphasize features strongly related to the core semantics of the instruction, such as "certification compliance," "retention requirements for fresh food temperature control data," and "standard customs clearance forms for cold chain goods," while weakening regional features unrelated to fresh food transportation.

[0099] This embodiment can accurately capture the correlation between natural language instructions and target region cultural features, generate feature enhancement vectors that fit the semantics of instructions and regional needs, effectively improve the accuracy of cross-language semantic parsing, avoid the omission or mismatch of regional cultural information, and provide reliable support for the subsequent generation of accurate structured task descriptions.

[0100] Furthermore, in some embodiments, step S2, "based on the structured task description, driving the large language model to perform logical reasoning and process construction, generating an executable logistics process including device control nodes and compliance document generation nodes," may specifically include:

[0101] S21. Convert the structured task description into large language model prompts that include context and constraints;

[0102] Specifically, for step S21, a structured task description containing logistics operation logic, target area information, and equipment interaction points is obtained, such as "Logistics operation logic: Fresh food cold chain transportation (temperature control 2-8℃, monitoring every 15 minutes); Target area: AA-BB; Equipment interaction points: temperature sensor, refrigeration unit; Compliance requirements: generate BB customs declaration." Based on this structured information, a large language model prompt word containing contextual background, core constraints, and output requirements is constructed to ensure that the prompt word clearly conveys the task boundaries and key requirements. Example prompt word: Based on the fresh food cold chain transportation requirements from AA to BB, a complete process including temperature monitoring, anomaly handling, and customs declaration needs to be constructed. It needs to meet the 2-8℃ temperature control standard, a monitoring frequency of 15 minutes / time, associate the interaction between temperature sensor and refrigeration unit equipment, generate process logic including the BB customs declaration generation step, and output the process framework in the form of nodes and edges.

[0103] S22. Drive the large language model to perform step-by-step reasoning based on prompt words, and generate an initial flow logic diagram represented by nodes and edges;

[0104] Specifically, for step S22, the constructed prompt words are input into the large language model, driving the model to perform step-by-step reasoning based on cross-border logistics business logic, target area characteristics, and equipment interaction requirements. The model first clarifies the core main process, then breaks down the sub-steps, and finally generates an initial process logic diagram represented by nodes and edges. Nodes correspond to specific business operations or output steps, and edges represent the execution order and relationships of each step. For example, for the above prompt words, the initial process logic diagram generated by the model may be: main node "Fresh Food Cold Chain Transportation Process" → sub-node "Goods Outbound → Temperature Control Monitoring (Equipment Interaction: Temperature Sensor) → Refrigeration Unit Start-Stop Control (Equipment Interaction: Refrigeration Unit) → Abnormal Alarm (Trigger Condition: Temperature > 8℃) → Customs Declaration (Compliance Output: BB Customs Declaration) → Goods Inbound", with each node's execution order clearly defined by directed edges.

[0105] S23. Compile the initial process logic diagram into an executable form, bind specific API calls, data sources or document templates to each node, and identify device interaction points and compliance output points as specific types of nodes to form an executable logistics process;

[0106] Specifically, for step S23, the generated initial process logic diagram is made executable. First, specific execution resources are bound to each node, such as binding the corresponding device data API to the device interaction node, binding the customs declaration template or regulatory data source to the compliance output node, and binding the database query interface to the data processing node. Then, based on the device interaction points and compliance output points in the structured task description, the corresponding nodes are identified as specific types, such as device control nodes (e.g., "temperature control monitoring" and "refrigeration unit start / stop control") and compliance document generation nodes, clarifying the functional attributes and execution priorities of the nodes. This ultimately forms an executable logistics process that can directly connect to subsequent device adaptation and compliance document generation stages. For example, the "temperature control monitoring" node is bound to a temperature sensor data acquisition API and identified as a device control node; the "customs declaration" node is bound to a FORMX customs declaration template and identified as a compliance document generation node.

[0107] This embodiment of the method enables the automated generation of an executable logistics process from a structured task description, eliminating the need for manual coding and process design. This significantly improves the efficiency of logistics process construction while ensuring that the process logic aligns with business needs and regional characteristics, providing a clear and standardized execution framework for subsequent equipment adaptation and compliance document generation.

[0108] Furthermore, in some embodiments, step S3, "for the device control nodes in the executable logistics process, perform dynamic protocol mapping based on the device function vector space alignment mechanism to generate corresponding device driving strategies," may specifically include:

[0109] S31. Analyze the equipment control nodes, extract the corresponding functional requirements, and transform the functional requirements into a standardized requirement feature vector;

[0110] Specifically, for step S31, the equipment control nodes in the executable logistics process are first analyzed in depth to clarify the core functional requirements corresponding to the node, including key information such as the operational goals, performance parameters, and data interaction requirements that the equipment needs to achieve. For example, a certain equipment control node is "temperature data acquisition in fresh food cold chain transportation," and the functional requirement extracted after analysis is "to collect cargo temperature data every 15 minutes within the 2-8℃ temperature control range and upload the data to the logistics system in real time." Another example is the "AGV cargo transfer" node, whose functional requirement is "to transfer cargo from warehouse A area to the loading and unloading area according to a preset route, with a load capacity of not less than 500kg and a positioning accuracy error ≤5cm." Subsequently, these concrete functional requirements are transformed into standardized requirement feature vectors. The vector dimensions cover core indicators such as operation type, parameter range, and data transmission method, realizing the digital representation of functional requirements.

[0111] S32. In the pre-constructed device protocol vector space, the similarity comparison and spatial alignment of the requirement feature vector and the functional description vectors of multiple candidate protocols are performed to determine the optimal matching target protocol;

[0112] Specifically, in step S32, the pre-constructed device protocol vector space integrates the functional description vectors of various mainstream IoT device communication protocols (such as Zigbee 3.0, Modbus-TCP, OPCUA, Bluetooth Mesh, etc.). Each protocol's functional description vector includes features such as supported operation types, compatible device types, data transmission rates, and communication distances. The generated requirement feature vector is compared one by one with the functional description vectors of multiple candidate protocols in this space. Simultaneously, spatial alignment processing eliminates differences in different vector representation systems, accurately calculating the matching degree between the two. For example, for the requirement feature vector of "high-frequency temperature data acquisition," after comparing it with the functional description vectors of Zigbee 3.0 (low power consumption, high-frequency data transmission adaptation) and Modbus-TCP (long-distance, high-volume data transmission adaptation), it is determined that the Zigbee 3.0 protocol has a higher matching degree and is selected as the optimal matching target protocol.

[0113] In a specific embodiment, the construction and maintenance of the device protocol vector space first involves collecting core information such as official specification documents, functional parameter manuals, and data interaction protocol details of mainstream global IoT device communication protocols (e.g., Zigbee 3.0, Modbus-TCP, OPC UA, Bluetooth Mesh) through multi-source data acquisition technology to ensure coverage of all dimensions of device communication features. Then, using natural language processing and structured parsing techniques, key protocol feature dimensions are extracted from the collected data, including supported operation types, compatible device types, data transmission rates, communication distances, power consumption levels, verification methods, and instruction encoding rules, forming a standardized feature set. Finally, a pre-trained feature encoding model is used to transform the standardized feature set of each protocol into a fixed-dimensional functional description vector, which is then categorized by protocol type and stored in the vector space, completing the initial construction. During maintenance, the vector space is incrementally expanded by monitoring the release of new protocols and updates to existing protocols in real time, collecting data on newly added protocols, and repeating the feature extraction and vector generation process described above. Simultaneously, based on historical device compatibility data and matching feedback, the functional description vectors of existing protocols are periodically optimized and iterated, adjusting feature weight distribution to improve the accuracy of vector representation. Furthermore, a vector space version management mechanism is established to record the protocol type, vector adjustments, and effective time of each update, supporting historical version tracing and rollback to ensure the stability and reliability of the vector space. This vector space integrates functional description vectors of various mainstream IoT device communication protocols. Each protocol's functional description vector includes features such as supported operation types, compatible device types, data transmission rates, and communication distances. The generated requirement feature vectors are compared one-to-one with the functional description vectors of multiple candidate protocols in this space. Spatial alignment is used to eliminate differences in different vector representation systems, accurately calculating the matching degree between the two. For example, after comparing the feature vector of the requirement for "high-frequency temperature data acquisition" with the functional description vectors of Zigbee 3.0 (adapted to low power consumption and high-frequency data transmission) and Modbus-TCP (adapted to long-distance and high-volume data transmission), it was determined that the Zigbee 3.0 protocol had a higher matching degree and was selected as the optimal matching target protocol.

[0114] S33. Based on the communication specifications and data format of the optimal matching target protocol, compile the logical operations of the device control node into a plug-and-play device driver package;

[0115] Specifically, for step S33, the complete communication specifications and data format requirements of the optimal matching target protocol are obtained. These complete communication specifications include data frame format, instruction encoding rules, and verification methods. Combined with the logical operation content of the device control node, automated compilation processing is performed. For example, if the optimal protocol is Zigbee 3.0, and the logical operation of the device control node is "send temperature acquisition instructions every 15 minutes → receive sensor feedback data → encapsulate data in a specified format," then according to the Zigbee 3.0 communication specifications, this logical operation is converted into device-recognizable and executable code instructions, and encapsulated into a plug-and-play device driver package. This driver package requires no additional modification or adaptation and can directly establish a communication connection with the corresponding type of device, enabling instruction issuance and data interaction.

[0116] S34. Construct the corresponding device driver strategy based on the device driver package;

[0117] Specifically, for step S34, a complete device driver strategy is constructed based on the generated device driver package and combined with information such as the execution scenario and operating constraints (e.g., power supply mode, communication priority) of the device control node. For example, the device driver strategy for the "cold chain temperature sensor" includes not only the Zigbee 3.0 driver package but also specifies the driver package's startup timing (automatic loading after the logistics process starts), the retry mechanism after data transmission failure (3 retries with a 10-second interval), and the frequency reduction acquisition rules under low power conditions. This strategy ensures that the device driver package can operate stably and efficiently in real-world application scenarios, guaranteeing the consistency between the device control node and the physical device.

[0118] This embodiment realizes automated adaptation of device control nodes and communication protocols and generation of driving strategies, eliminating the need for manual development of adaptation modules, significantly shortening the device docking cycle, improving the compatibility and reliability of device adaptation, and effectively solving the problems of cumbersome and inefficient traditional device adaptation.

[0119] Furthermore, in some embodiments, step S32, "in the pre-constructed device protocol vector space, performing similarity comparison and spatial alignment processing on the requirement feature vector and the functional description vectors of multiple candidate protocols to determine the optimal matching target protocol," may specifically include:

[0120] S321. Input the requirement feature vector and the functional description vector of each candidate protocol into the pre-trained feature encoder, and map them to a shared vector representation space;

[0121] Specifically, for step S321, first obtain the generated requirement feature vector (this vector is a digital representation of the functional requirements of the device control node, such as the feature vector corresponding to "collecting cold chain cargo temperature every 15 minutes, low-power data transmission, and communication distance within 50 meters"), and the functional description vectors of multiple candidate protocols in the pre-constructed device protocol vector space (such as the functional description vectors of protocols like Zigbee 3.0, Modbus-TCP, and Bluetooth Mesh, each vector containing core feature dimensions such as the sampling frequency, power consumption level, communication distance, and data transmission rate supported by the protocol). Input the requirement feature vector and the functional description vectors of each candidate protocol into the pre-trained feature encoder. This encoder has the ability to unify vector representation, eliminating the original differences in representation systems between different types of vectors (requirement side and protocol side), mapping all input vectors to a shared vector representation space with unified dimensions and comparable semantics, ensuring the effectiveness of subsequent similarity calculations. For example, the requirement feature vector corresponding to "high-frequency temperature acquisition" and the functional description vector of the Zigbee 3.0 protocol are both mapped to 128-dimensional shared space vectors.

[0122] S322. Within the shared vector representation space, a similarity calculation function based on distance metric is used to calculate the matching score between the demand feature vector and each candidate protocol vector, respectively.

[0123] Specifically, in step S322, within the shared vector representation space, a similarity calculation function based on distance metrics is used to calculate the degree of matching between the requirement feature vector and the functional description vector of each candidate protocol, i.e., the matching score. Commonly used distance metrics include cosine similarity and Euclidean distance, where a higher cosine similarity or a smaller Euclidean distance indicates a higher degree of functional fit between the two. For example, for the requirement feature vector of "low power consumption, high frequency data acquisition", the cosine similarity calculated with the functional description vector of the Zigbee 3.0 protocol (adapted to low power consumption, high frequency acquisition scenarios) is 0.92, and the cosine similarity calculated with the functional description vector of the Modbus-TCP protocol (adapted to long distance, high traffic data transmission scenarios) is 0.45, with corresponding matching scores of 92 and 45 points respectively (a higher score indicates a better matching degree).

[0124] S323. Sort all candidate protocols according to their matching scores, and determine the candidate protocol with the highest score as the optimal matching target protocol;

[0125] Specifically, in step S323, all candidate protocols are sorted in descending order of their matching scores to form a clear list of protocol matching priorities. For example, the candidate protocols are ranked as follows: Zigbee 3.0 (92 points) > Bluetooth Mesh (78 points) > Modbus-TCP (45 points) > OPCUA (38 points). The candidate protocol with the highest score and the highest ranking is selected as the optimal matching target protocol. This protocol is the communication protocol that best matches the functional requirements of the current device control node. Using the above example, Zigbee 3.0 is ultimately determined as the optimal matching target protocol for the requirement of "low power consumption and high frequency data acquisition".

[0126] This embodiment achieves precise and quantitative matching of requirement characteristics and device protocols, which can efficiently screen out the optimal compatible protocol, avoid the subjectivity and blindness of protocol selection, thereby improving the accuracy and reliability of device protocol matching and providing a precise basis for the subsequent generation of device driver packages.

[0127] Furthermore, in some embodiments, step S4, "dynamically generating compliance content for the compliance document generation node in the executable logistics process based on a multimodal rule base and real-time policy information to obtain a formatted compliance document that meets the requirements of the target region," may specifically include:

[0128] S41. Based on the target area and document type associated with the compliance document generation node, concurrently retrieve textual regulatory clauses, image form templates, and associated real-time policy update summaries from the multimodal compliance rule base;

[0129] Specifically, for step S41, the core information corresponding to the compliance document generation node in the executable logistics process is clarified: first, the target region; second, the document type, such as food customs declarations, certificates of origin, and customs clearance documents for cross-border electronic products. Based on these two types of information, a search is simultaneously initiated from the multimodal compliance rule base to obtain three types of key resources: the first type is textual regulatory clauses, i.e., the hard compliance requirements of the target region for this type of document, such as import and export regulations and data entry specifications; the second type is image form templates, i.e., document format templates officially recognized by the target region, such as customs declarations with specific layouts and certification application forms with fixed columns; the third type is related real-time policy update summaries, i.e., dynamic information such as recent regulatory revisions, form field adjustments, and certification requirement changes issued by the target region.

[0130] In a specific embodiment, the multimodal rule base uses a graph database to achieve the associated storage of multiple types of information. Specifically, a structured storage architecture is constructed using the create command, with "compliance rule objects" as the core nodes. Each core node is associated with three types of key sub-nodes: text clause nodes (storing the specific clause content and constraints of cross-border logistics-related regulations in the target area), image template nodes (storing image parsing data such as the layout structure, field positions, and formatting specifications of official compliance forms), and policy version nodes (storing dynamic information such as policy effective time, revision records, and scope of application). At the same time, the association relationship between the core nodes and auxiliary nodes such as target areas, document types, and goods categories is established. The logical association between nodes is defined through the edges of the graph database (such as "core rule - corresponding text clause", "core rule - adapted image template", "core rule - policy version"), forming a network association structure to ensure efficient association query and dynamic update of text, image, and policy information. It also supports the rapid tracing of all associated compliance resources by dimensions such as target area and document type.

[0131] S42. Extract structured constraints from the retrieved textual regulations, locate key fields and parse the format of the image form templates, and verify the timeliness and resolve conflicts of the extracted structured constraints and key fields in conjunction with real-time policy update summaries;

[0132] Specifically, for step S42, the three types of retrieved resources are processed accordingly. For textual regulations, structured constraints are extracted, such as explicit requirements like "certification number must be indicated," "certificate of origin must be notarized," and "goods composition must list relevant raw materials." For image form templates, key fields (such as "certification number," "country of origin," and "goods composition") are located through parsing, and column positions and layout specifications (such as font, line spacing, and signature area) are fixed. Combined with real-time policy update summaries, the extracted structured constraints and key fields are double-checked: first, for timeliness verification, outdated regulations are removed, such as obsolete certification field requirements, and newly added compliance clauses are added, such as new requirements for the number of digits in the certification number; second, for conflict resolution, if there are inconsistencies between old regulations and new policies (e.g., a field in the original form is optional, but the new policy makes it mandatory), the latest real-time policy will be used to unify the constraint standards. For example, if the retrieved old regulations do not require the number of digits in the certification number, while the real-time policy explicitly requires a 10-digit number, then "10-digit certification number is mandatory" will be the final constraint.

[0133] S43. Based on the verified structured constraints and field formats, automatically populate the business data required for the execution of the logistics process and generate formatted compliance documents;

[0134] Specifically, for step S43, based on the validated and conflict-resolved structured constraints and field formats, business data generated during the logistics process is automatically extracted, including basic cargo information (name, quantity, specifications, ingredients), certification information (certification number, certificate of origin number), and transportation-related data (port of loading, port of destination, mode of transport). This business data is then accurately filled into the corresponding key fields of the image form template, ensuring that the data entry meets the constraints and the format conforms to the layout specifications, ultimately generating a standardized, formatted, compliant document. For example, business data such as "Goods Name: Frozen Chicken," "Certification Number: HL2024051234," and "Country of Origin: XXXX" are filled into the corresponding fields of the Malaysian food customs declaration form according to the template requirements, generating a complete customs declaration document that complies with the latest local regulations.

[0135] This embodiment enables the automated and accurate generation of compliance documents, eliminating the need for manual querying of regulations, designing forms, or filling in data. It ensures that the documents comply with the requirements and latest policies of the target region, improves the efficiency of compliance document generation, and reduces the risk of errors caused by manual operation.

[0136] Furthermore, in some embodiments, step S42, "extracting structured constraints from the retrieved textual regulatory clauses, locating key fields and parsing the format of the image form template, and verifying the timeliness and resolving conflicts of the extracted structured constraints and key fields in conjunction with real-time policy update summaries," may specifically include:

[0137] S421. Input the textual legal provisions and real-time policy update summaries into the text analysis model to extract the list of written constraints and their effective time range;

[0138] Specifically, for step S421, the retrieved textual regulations and related real-time policy update summaries from the target region are input into the text analysis model. The model performs in-depth analysis of the text content, accurately extracting the list of written constraints, and clearly defining the compliance requirements that must be met, such as "food products must be labeled with certification numbers and validity periods," "cross-border transportation data must not contain sensitive personal information," and "electronic products must provide CE certification numbers," etc. Simultaneously, the effective time range of each constraint is extracted, distinguishing between currently valid clauses and clauses that are about to take effect or have expired. For example, the new regulation requiring 12-digit certification numbers takes effect on March 1, 2024, while the old 8-digit certification number expires on February 28, 2024.

[0139] S422. Input the image form template into the document understanding model and identify the corresponding fixed fields, variable fields, and layout specifications;

[0140] Specifically, in step S422, the retrieved official image form template of the target area is input into the document understanding model. The model, through image recognition and format parsing, identifies the core components of the template: first, fixed fields, which are pre-defined, unmodifiable identifying elements such as "Customs Declaration Unit," "Form Number," and "Official Signature Area"; second, variable fields, which are areas that need to be filled in according to actual business needs, such as "Goods Name," "Quantity," "Certification Number," and "Ingredient Description"; and third, formatting specifications, including font type (e.g., SimSun, Arial), font size, field alignment, format requirements for the content (e.g., date format YYYY-MM-DD, number must be uppercase), and the position and size limitations of the signature area. For example, after parsing the food customs declaration template, it is determined that the variable field "Certification Number" is located in column 3 of the form, requiring a 12-digit number in SimSun font (small four), left-aligned.

[0141] S423. Map the constraint list and variable fields and perform compliance checks to ensure that the entered data meets all effective constraints and conforms to form formatting specifications;

[0142] Specifically, in step S423, the extracted list of constraints and the identified variable fields are precisely mapped to clarify the fields to be filled in for each constraint. For example, "mark a 12-digit certification number" corresponds to the "certification number" variable field in the template, and "must not contain sensitive personal information" corresponds to the filling restrictions for variable fields such as "recipient information" and "shipper information." Then, compliance verification is conducted: on the one hand, it checks whether the business data meets all effective constraints to ensure that the content complies with regulations, such as verifying whether the certification number is a 12-digit valid number and whether it contains sensitive information; on the other hand, it checks whether the data filling format conforms to the form layout specifications, such as whether the date filling conforms to the specified format and whether the font and alignment meet the standards. If data is found to be inconsistent with constraints or layout requirements, the problem items are automatically marked to ensure that the final content is both compliant and conforms to the template format.

[0143] This embodiment achieves precise correspondence and compliance verification between legal constraints, form fields, and formatting specifications, ensuring that the entered data not only meets the compliance requirements of the target area but also conforms to the form format standards, providing direct assurance for generating accurate and standardized formatted compliance documents.

[0144] Furthermore, in some embodiments, step S5, "integrating the executable logistics process, device-driven strategy, and formatted compliance documents to form a logistics process execution package and deploying it," may specifically include:

[0145] S51. Encapsulate the device driver strategy into a lightweight execution module that can run independently at the edge;

[0146] Specifically, in step S51, the generated device driver strategies, such as the Zigbee 3.0 driver strategy adapted to temperature sensors and the Modbus-TCP driver strategy for AGV devices, are lightweighted and encapsulated. Redundant code components in the driver strategies are removed, resource consumption is optimized, and the module size is ensured to be small and efficient, enabling it to run independently on edge devices (such as local controllers in logistics warehouses and vehicle terminals) or lightweight servers without relying on complex backend support systems. For example, the driver strategy for "cold chain temperature sensor data acquisition and anomaly feedback" is encapsulated into a lightweight module occupying only 500KB of storage space, supporting autonomous startup, data interaction, and fault retries on low-configuration edge controllers.

[0147] S52. Bind formatted compliance documents to logical paths in executable logistics processes;

[0148] Specifically, for step S52, the business scenario corresponding to the formatted compliance document is clearly defined, and a unique association is established between it and the specific logical path in the executable logistics process. This means determining when each compliance document is generated, the node it is used at, and its flow direction within the process, ensuring that the compliance document can be automatically retrieved, submitted, or retained when the process reaches the corresponding node. For example, the FORMX customs declaration in the "Fresh Food Cold Chain Process" is bound to the "Customs Declaration" node in the process's logical path. When the process reaches this node, the system automatically retrieves the corresponding formatted customs declaration for customs declaration operations. Similarly, certification documents are bound to the "Goods Outbound Verification" node to ensure that compliance certificates can be presented simultaneously upon outbound shipment.

[0149] S53. Package the executable logistics process, lightweight execution module, and bound compliance documents into a logistics process execution package, and distribute it to the target execution environment through the deployment interface;

[0150] Specifically, for step S53, following a unified packaging standard, the executable logistics process (including equipment control nodes, compliance document generation nodes, and execution sequence), the packaged lightweight execution module, and the bound compliance documents are integrated into a complete logistics process execution package. This execution package contains all the logical rules, device adaptation resources, and compliance documents required for process operation and can run independently without the need for additional components. Through preset deployment interfaces (such as cloud API interfaces and edge device local deployment interfaces), this execution package is distributed to the target execution environment, including the logistics company's cloud management system, warehouse local servers, and edge computing devices. For example, the execution package for the "AA to BB food cold chain logistics process" can be distributed to the logistics company's cross-border logistics operation platform via a cloud interface, and after deployment, the fully automated process can be started.

[0151] This embodiment achieves standardized integration and efficient deployment of logistics processes, equipment drivers, and compliance documents, simplifies deployment operations, ensures that the process execution package can be quickly deployed and run in the target environment, and improves the efficiency and stability of cross-border logistics processes.

[0152] Furthermore, in some embodiments, the method of updating the multimodal compliance rule base may specifically include:

[0153] S61. By connecting multiple official information sources, continuously monitor the release status and revision dynamics of policies, regulations, and standard forms in the target area;

[0154] Specifically, for step S61, stable connections are established with official information sources in multiple target regions. These sources include, but are not limited to, the official websites of the General Administration of Customs of various regions, the official platforms of cross-border trade regulatory agencies, and the release channels of industry standard-setting organizations, covering policies, regulations, and standard forms related to cross-border logistics. Through an automated monitoring mechanism, the release status (such as new policy documents) and revision dynamics (such as modifications to existing regulatory clauses and adjustments to form formats) of relevant content in these sources are tracked in real time to ensure that no key changes affecting the generation of compliance documents are missed.

[0155] S62. When a change in the release status or revision dynamics is detected, the updated content is automatically captured, and the updated content is parsed into structured rule objects using a multimodal understanding model;

[0156] Specifically, for step S62, when changes such as policy releases, regulatory revisions, or form updates are detected in official information sources, an update content capture mechanism is automatically triggered to fully capture the changed full text or the revised differences. The captured update content is input into a multimodal understanding model, which can identify information such as revisions to textual regulations, additions or deletions of fields in image forms, or format adjustments, and convert them into standardized structured rule objects. The structured rule object contains the following core elements: revised constraints, scope of application (e.g., "cross-border food customs declaration"), and key field descriptions (e.g., "certification number is 10 digits"). For example, after capturing a revision notice issued by a customs office in 2024 regarding the addition of a temperature control record field to the fresh food customs declaration, it is parsed into a structured rule object with the following constraints: "Fresh food customs declaration requires full temperature control record; Scope of application: Fresh cross-border logistics customs declaration documents; Field format: Temperature data is filled in chronological order every 2 hours."

[0157] S63. Store structured rule objects in the multimodal compliance rule library along with their version number, effective date, and source region, and automatically trigger compliance reassessment of relevant historical processes;

[0158] Specifically, in step S63, the parsed structured rule object is associated with a unique version number, a specific effective date, and a corresponding source region. For example, the aforementioned temperature control record-related rule object is assigned version number V1.2, with an effective date of June 1, 2024, and a source region of AA. This rule object is then stored in the multimodal compliance rule base, enabling incremental updates to the rule base. Simultaneously, the system automatically triggers a compliance reassessment of historically generated logistics processes, checking whether the compliance documents corresponding to these historical processes meet the requirements of the newly added or revised rules. For example, for previously generated fresh food cold chain logistics processes, the customs declaration documents are re-verified to ensure they contain the "full-process temperature control record" field. If not, they are marked as requiring optimization, ensuring that historical processes also adapt to the latest policy requirements.

[0159] In a specific embodiment, the relevant historical processes include various cross-border logistics processes that were previously generated and deployed by the system and are related to the target region of this policy update. These processes cover all historical logistics processes that have used old compliance clauses, form templates, or adapted to the compliance requirements of the corresponding region in relation to the updated rules.

[0160] When identifying relevant historical processes, the core tags corresponding to this policy update (including target area tags, document type tags, and goods category tags) are first extracted. Then, based on these tags, a precise match is performed with the pre-set "region-document type-goods category-process tag" correspondence in the regional association rule base. All historical processes with consistent or highly related tags are filtered out. Subsequently, an asynchronous compliance verification task is automatically triggered. This task runs independently in the background, does not occupy main thread resources, and does not affect the normal business processing of the current system. During the verification process, the compliance documents corresponding to the historical processes are retrieved one by one. The updated structured rule objects are compared to check whether the field filling, content description, and format specifications in the documents comply with the newly added or revised constraints. If compliance conflicts or non-compliance items are found, the corresponding process ID, problem type, and violation clause are automatically marked, and a detailed verification report is generated and stored for users to optimize in a targeted manner later.

[0161] This embodiment realizes the automated and real-time updating of the multimodal compliance rule base, ensuring that the content of the rule base is consistent with the latest policies and regulations in the target region, while ensuring the compliance adaptation of historical logistics processes, effectively reducing the compliance risks caused by policy changes.

[0162] In summary, compared with existing technologies, the cross-border logistics process automatic generation method based on natural language parsing provided in this embodiment completely eliminates the high dependence on manual labor in traditional methods through full-process automation. It not only improves the customization efficiency of cross-border logistics processes and shortens the process launch cycle, but also reduces the deviation caused by manual operation through the automated mechanism of equipment adaptation and compliance generation. This improves the equipment adaptability and regional compliance of the process, effectively meeting the core needs of cross-border logistics for multi-regional collaboration, multi-device linkage, and multi-compliance requirement adaptation.

[0163] To facilitate better implementation of the cross-border logistics process automatic generation method based on natural language parsing in this application, this application also provides a cross-border logistics process automatic generation device based on natural language parsing, which is based on the aforementioned method. The meanings of the terms used are the same as in the aforementioned cross-border logistics process automatic generation method based on natural language parsing; specific implementation details can be found in the descriptions within the method embodiments.

[0164] Please see Figure 3 , Figure 3 The schematic diagram of the automatic cross-border logistics process generation device based on natural language parsing provided in this application embodiment shows that the device may specifically include an instruction parsing module 201, a process construction module 202, a strategy generation module 203, a compliance document module 204, and an integration and deployment module 205, as follows:

[0165] The instruction parsing module 201 is used to perform cross-language semantic parsing and task structuring on the received natural language instructions, and generate a structured task description including logistics operation logic, target area information and equipment interaction points.

[0166] The process construction module 202 is used to drive the large language model to perform logical reasoning and process construction based on the structured task description, and generate an executable logistics process including equipment control nodes and compliance document generation nodes.

[0167] The strategy generation module 203 is used to dynamically map the protocol of the equipment control nodes in the executable logistics process based on the equipment function vector space alignment mechanism to generate corresponding equipment driving strategies.

[0168] The compliance document module 204 is used to dynamically generate compliance content based on a multimodal rule base and real-time policy information for the compliance document generation node in the executable logistics process, so as to obtain a formatted compliance document that meets the requirements of the target area.

[0169] The integration and deployment module 205 is used to integrate executable logistics processes, device driver policies, and formatted compliance documents to form a logistics process execution package and deploy it.

[0170] Furthermore, in some embodiments, the instruction parsing module 201 is specifically used for:

[0171] The received multilingual natural language instructions are input into a pre-trained multilingual semantic model to obtain a preliminary semantic encoding sequence;

[0172] Based on the attention mechanism, the initial semantic encoding sequence and the pre-set regional cultural feature knowledge are interacted to calculate the feature enhancement vector that integrates the regional cultural context.

[0173] Based on the feature enhancement vector, after joint decoding of entity relationships, a structured task description is generated, including logistics operation logic, target area information, and equipment interaction points.

[0174] Furthermore, in some embodiments, the instruction parsing module 201 is specifically used for:

[0175] Based on the regional keywords identified from the preliminary semantic coding sequence, the corresponding multidimensional cultural feature vectors are loaded from the regional cultural feature database.

[0176] The relevance weights between each position and the multidimensional cultural feature vector in the initial semantic encoding sequence are calculated through a cross-attention layer.

[0177] Based on relevance weights, multidimensional cultural feature vectors are dynamically weighted and aggregated to generate feature enhancement vectors that are related to the semantics of the current instruction.

[0178] Furthermore, in some embodiments, the process construction module 202 is specifically used for:

[0179] Transform structured task descriptions into large language model prompts that include context and constraints;

[0180] The large language model is driven to perform step-by-step reasoning based on prompt words, generating an initial flow logic graph represented by nodes and edges;

[0181] The initial process logic diagram is compiled into an executable form, each node is bound to a specific API call, data source or document template, and the device interaction points and compliance output points are identified as specific types of nodes, thus forming an executable logistics process.

[0182] Furthermore, in some embodiments, the policy generation module 203 is specifically used for:

[0183] Analyze the device control nodes, extract the corresponding functional requirements, and transform the functional requirements into standardized requirement feature vectors;

[0184] In the pre-built device protocol vector space, the requirement feature vector and the functional description vectors of multiple candidate protocols are compared for similarity and spatially aligned to determine the optimal matching target protocol.

[0185] Based on the communication specifications and data format of the optimal matching target protocol, the logical operations of the device control node are compiled into a plug-and-play device driver package.

[0186] The corresponding device driver strategy is constructed based on the device driver package.

[0187] Furthermore, in some embodiments, the policy generation module 203 is specifically used for:

[0188] The requirement feature vector and the functional description vector of each candidate protocol are respectively input into the pre-trained feature encoder and mapped to a shared vector representation space.

[0189] Within the shared vector representation space, a similarity calculation function based on distance metric is used to calculate the matching score between the demand feature vector and each candidate protocol vector.

[0190] All candidate protocols are sorted according to their matching scores, and the candidate protocol with the highest score is determined as the optimal matching target protocol.

[0191] Furthermore, in some embodiments, the compliance documentation module 204 is specifically used for:

[0192] Based on the target area and document type associated with the compliance document generation node, concurrently retrieve textual regulatory clauses, image form templates, and associated real-time policy update summaries from the multimodal compliance rule base;

[0193] The retrieved textual regulations are subjected to structured constraint extraction, key fields are located and format parsed from the image form templates, and the extracted structured constraints and key fields are verified for timeliness and conflict resolution by combining real-time policy update summaries.

[0194] Based on the validated structured constraints and field formats, the system automatically populates the business data required for the execution of the logistics process and generates formatted compliance documents.

[0195] Furthermore, in some embodiments, the compliance documentation module 204 is specifically used for:

[0196] Input the textual regulations and real-time policy update summaries into the text analysis model to extract the list of written constraints and their effective time range;

[0197] Input the image form template into the document understanding model to identify the corresponding fixed fields, variable fields, and layout specifications;

[0198] Map the constraint list and variable fields and perform compliance checks to ensure that the entered data meets all effective constraints and conforms to form formatting guidelines.

[0199] Furthermore, in some embodiments, the apparatus further includes a rule update module, specifically used for:

[0200] By connecting to multiple official information sources, we continuously monitor the release status and revision dynamics of policies, regulations, and standard forms in the target area;

[0201] When a change in the release status or revision dynamics is detected, the updated content is automatically captured, and the updated content is parsed into structured rule objects using a multimodal understanding model;

[0202] Structured rule objects are associated with version numbers, effective dates, and source regions and stored in the multimodal compliance rule library, automatically triggering compliance reassessments of relevant historical processes.

[0203] Furthermore, in some embodiments, the integration deployment module 205 is specifically used for:

[0204] The device driver strategy is encapsulated into a lightweight execution module that can run independently at the edge.

[0205] Bind formatted compliance documents to logical paths in executable logistics processes;

[0206] The executable logistics process, lightweight execution module, and bound compliance documents are packaged into a logistics process execution package and then deployed to the target execution environment through the deployment interface.

[0207] For specific limitations regarding the automatic generation device for cross-border logistics processes based on natural language parsing, please refer to the limitations of the automatic generation method for cross-border logistics processes based on natural language parsing mentioned above, which will not be repeated here. Each module in the aforementioned automatic generation device for cross-border logistics processes based on natural language parsing can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0208] The cross-border logistics process automatic generation device based on natural language parsing provided in this embodiment completely eliminates the high dependence on manual labor in traditional methods through full-process automation. It not only improves the customization efficiency of cross-border logistics processes and shortens the process launch cycle, but also reduces the deviation caused by manual operation through the automated mechanism of equipment adaptation and compliance generation. This improves the equipment adaptability and regional compliance of the process, effectively meeting the core needs of cross-border logistics for multi-regional collaboration, multi-device linkage and multi-compliance requirements adaptation.

[0209] Furthermore, embodiments of this application also provide an electronic device, such as... Figure 4 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically:

[0210] The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will understand that... Figure 4 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0211] The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 302, and by calling data stored in the memory 302, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0212] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and a method for automatically generating cross-border logistics processes based on natural language parsing by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0213] The electronic device also includes a power supply 303 that supplies power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0214] The electronic device may also include an input unit 304, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0215] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 302 according to the following instructions, and the processor 301 runs the applications stored in the memory 302 to realize various functions, as follows:

[0216] The system performs cross-language semantic parsing and task structuring on received natural language instructions, generating a structured task description that includes logistics operation logic, target area information, and equipment interaction points. Based on the structured task description, it drives a large language model to perform logical reasoning and process construction, generating an executable logistics process that includes equipment control nodes and compliance document generation nodes. For the equipment control nodes in the executable logistics process, it performs dynamic protocol mapping based on the equipment function vector space alignment mechanism to generate corresponding equipment driving strategies. For the compliance document generation nodes in the executable logistics process, it performs dynamic compliance content generation based on a multimodal rule base and real-time policy information to obtain formatted compliance documents that meet the requirements of the target area. Finally, it integrates the executable logistics process, equipment driving strategies, and formatted compliance documents to form a logistics process execution package and deploys it.

[0217] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0218] This application embodiment completely eliminates the traditional reliance on manual labor through full-process automation. It not only improves the customization efficiency of cross-border logistics processes and shortens the process launch cycle, but also reduces the deviation caused by manual operation through automated mechanisms for equipment adaptation and compliance generation. This improves the equipment adaptability and regional compliance of the process, effectively meeting the core needs of cross-border logistics for multi-regional collaboration, multi-device linkage, and multi-compliance requirements adaptation.

[0219] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0220] To this end, embodiments of this application provide a storage medium storing multiple instructions that can be loaded by a processor to execute steps in any of the natural language parsing-based automatic cross-border logistics process generation methods provided in embodiments of this application. For example, the instructions can execute the following steps:

[0221] The system performs cross-language semantic parsing and task structuring on received natural language instructions, generating a structured task description that includes logistics operation logic, target area information, and equipment interaction points. Based on the structured task description, it drives a large language model to perform logical reasoning and process construction, generating an executable logistics process that includes equipment control nodes and compliance document generation nodes. For the equipment control nodes in the executable logistics process, it performs dynamic protocol mapping based on the equipment function vector space alignment mechanism to generate corresponding equipment driving strategies. For the compliance document generation nodes in the executable logistics process, it performs dynamic compliance content generation based on a multimodal rule base and real-time policy information to obtain formatted compliance documents that meet the requirements of the target area. Finally, it integrates the executable logistics process, equipment driving strategies, and formatted compliance documents to form a logistics process execution package and deploys it.

[0222] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0223] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0224] Since the instructions stored in the storage medium can execute the steps in any of the automatic generation methods for cross-border logistics processes based on natural language parsing provided in the embodiments of this application, the beneficial effects that any of the automatic generation methods for cross-border logistics processes based on natural language parsing provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0225] The above provides a detailed description of a method and apparatus for automatically generating cross-border logistics processes based on natural language parsing, as provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for automatically generating cross-border logistics processes based on natural language parsing, characterized in that, include: The received natural language instructions are subjected to cross-language semantic parsing and task structuring to generate a structured task description that includes logistics operation logic, target area information and equipment interaction points. Based on the structured task description, the large language model is driven to perform logical reasoning and process construction, generating an executable logistics process including device control nodes and compliance document generation nodes. The device control node is parsed to extract the corresponding functional requirements, and the functional requirements are transformed into standardized requirement feature vectors. The requirement feature vectors and the functional description vectors of each candidate protocol are input into a pre-trained feature encoder and mapped to a shared vector representation space. Within the shared vector representation space, a similarity calculation function based on distance metric is used to calculate the matching score between the demand feature vector and each candidate protocol vector. All candidate protocols are sorted according to the matching score, and the candidate protocol with the highest score is determined as the optimal matching target protocol; according to the communication specifications and data format of the optimal matching target protocol, the logical operations of the device control node are compiled into a plug-and-play device driver package; The corresponding device driver strategy is constructed based on the aforementioned device driver package; For the compliance document generation node in the executable logistics process, the compliance content is dynamically generated based on the multimodal rule base and real-time policy information to obtain a formatted compliance document that meets the requirements of the target area. The executable logistics process, the device driver strategy, and the formatted compliance documents are integrated to form a logistics process execution package and then deployed.

2. The method for automatically generating cross-border logistics processes based on natural language parsing according to claim 1, characterized in that, The process of performing cross-language semantic parsing and task structuring on the received natural language instructions generates a structured task description that includes logistics operation logic, target area information, and equipment interaction points, including: The received multilingual natural language instructions are input into a pre-trained multilingual semantic model to obtain a preliminary semantic encoding sequence; Based on the attention mechanism, the preliminary semantic encoding sequence and the pre-set regional cultural feature knowledge are interacted to calculate the feature enhancement vector that integrates the regional cultural context. Based on the feature enhancement vector, after joint decoding of entity relationships, a structured task description including logistics operation logic, target area information, and equipment interaction points is generated.

3. The method for automatically generating cross-border logistics processes based on natural language parsing according to claim 2, characterized in that, The attention-based mechanism interacts the initial semantic encoding sequence with pre-set regional cultural feature knowledge to calculate a feature enhancement vector that integrates the regional cultural context, including: Based on the regional keywords identified from the preliminary semantic coding sequence, the corresponding multidimensional cultural feature vectors are loaded from the regional cultural feature database. The relevance weight between each position in the initial semantic encoding sequence and the multidimensional cultural feature vector is calculated through a cross-attention layer. The multidimensional cultural feature vector is dynamically weighted and aggregated based on the relevance weights to generate the feature enhancement vector that is related to the semantics of the current instruction.

4. The method for automatically generating cross-border logistics processes based on natural language parsing according to claim 1, characterized in that, The structured task description drives a large language model to perform logical reasoning and process construction, generating an executable logistics process including device control nodes and compliance document generation nodes, including: The structured task description is converted into large language model prompt words that include context and constraints; The large language model is driven to perform step-by-step reasoning based on the prompt words, generating an initial flow logic diagram represented by nodes and edges; The initial process logic diagram is compiled into an executable form, each node is bound to a specific API call, data source or document template, and the device interaction points and compliance output points are identified as nodes of a specific type, thus forming the executable logistics process.

5. The method for automatically generating cross-border logistics processes based on natural language parsing according to claim 1, characterized in that, The compliance document generation node in the executable logistics process dynamically generates compliance content based on a multimodal rule base and real-time policy information to obtain a formatted compliance document that meets the requirements of the target region, including: Based on the target area and document type associated with the compliance document generation node, concurrently retrieve textual regulatory clauses, image form templates, and associated real-time policy update summaries from the multimodal compliance rule base; The retrieved textual regulations are subjected to structured constraint extraction, key fields are located and format parsed from the image form templates, and the extracted structured constraints and key fields are verified for timeliness and conflict resolution in conjunction with the real-time policy update summary. Based on the verified structured constraints and field formats, the business data required for the execution of the executable logistics process is automatically populated, and the formatted compliance file is generated.

6. The method for automatically generating cross-border logistics processes based on natural language parsing according to claim 5, characterized in that, The process of extracting structured constraints from retrieved textual regulations, locating key fields and parsing the format of image form templates, and verifying the timeliness and resolving conflicts of the extracted structured constraints and key fields in conjunction with the real-time policy update summary includes: Input the textual regulations and the real-time policy update summary into the text analysis model to extract the list of written constraints and their effective time range; The image form template is input into the document understanding model to identify the corresponding fixed fields, variable fields, and layout specifications. The constraint list and the variable fields are mapped and compliance checks are performed to ensure that the entered data meets all effective constraints and conforms to form layout specifications.

7. The method for automatically generating cross-border logistics processes based on natural language parsing according to claim 6, characterized in that, The update methods for the multimodal compliance rule base include: By connecting to multiple official information sources, we continuously monitor the release status and revision dynamics of policies, regulations, and standard forms in the target area; When a change in the release status or revision dynamics is detected, the updated content is automatically captured, and the updated content is parsed into structured rule objects using a multimodal understanding model; The structured rule object is associated with the version number, effective date and source region and stored in the multimodal compliance rule library, and the compliance reassessment of the relevant historical processes is automatically triggered.

8. A device for automatically generating cross-border logistics processes based on natural language parsing, characterized in that, include: The instruction parsing module is used to perform cross-language semantic parsing and task structuring on the received natural language instructions, and generate a structured task description that includes logistics operation logic, target area information and equipment interaction points. The process construction module is used to drive the large language model to perform logical reasoning and process construction based on the structured task description, and generate an executable logistics process including equipment control nodes and compliance document generation nodes. The strategy generation module is used to parse the device control node, extract the corresponding functional requirements, and transform the functional requirements into a standardized requirement feature vector; the requirement feature vector and the functional description vector of each candidate protocol are respectively input into a pre-trained feature encoder and mapped to a shared vector representation space; Within the shared vector representation space, a similarity calculation function based on distance metric is used to calculate the matching score between the demand feature vector and each candidate protocol vector. All candidate protocols are sorted according to the matching score, and the candidate protocol with the highest score is determined as the optimal matching target protocol; according to the communication specifications and data format of the optimal matching target protocol, the logical operations of the device control node are compiled into a plug-and-play device driver package; The corresponding device driver strategy is constructed based on the aforementioned device driver package; The compliance document module is used to dynamically generate compliance content for the compliance document generation node in the executable logistics process based on a multimodal rule base and real-time policy information, so as to obtain a formatted compliance document that meets the requirements of the target region. The integration and deployment module is used to integrate the executable logistics process, the device driver strategy, and the formatted compliance file to form a logistics process execution package and deploy it.

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