A data communication method, device, storage medium and electronic device

Abstract

The specification discloses a data communication method, device, storage medium and electronic equipment, wherein the method comprises: when a service platform and an external application service perform service data communication, in a management state mode, an external service application communication protocol and a platform internal application communication protocol of the associated external application service are acquired, a data conversion template corresponding to a data conversion relationship is updated based on the external service application communication protocol and the platform internal application communication protocol, in a running state mode, source application communication data for a target external application service is monitored, a target data conversion relationship corresponding to the source application communication data is determined, the source application communication data is converted to obtain target application communication data by calling a target data conversion template from the data conversion template, and platform service data communication is performed based on the target application communication data and the target external application service, so that the access cost of a new external service and the long-term maintenance complexity of the platform can be reduced.

Description

Technical Field

[0001] This specification relates to the field of computer technology, and in particular to a data communication method, apparatus, storage medium and electronic device. Background Technology

[0002] In current application scenarios combining enterprise-level application and service platform technologies, the core service system of the service platform often needs to interact with numerous external third-party application services to expand its functionality. However, different external application services often have their own independently defined data communication protocols, while the core system within the enterprise typically uses a unified, standardized data model to ensure system stability and consistency. Therefore, it is necessary to efficiently and cost-effectively adapt and convert between the ever-changing external communication protocols and the unified internal standard communication model.

[0003] To efficiently and cost-effectively adapt and convert between diverse external communication protocols and a unified internal standard communication model, the industry commonly employs a dedicated data adapter for each external application. While this approach achieves data format conversion, its drawbacks are significant. Firstly, as the number of external applications increases, the number of adapters required also surges, leading to continuously rising R&D and maintenance costs. Secondly, the data conversion logic is tightly coupled with the transaction service code; any change in the protocol of either party necessitates modification, compilation, and redeployment of the adapter code, severely impacting system flexibility and service response speed. Summary of the Invention

[0004] This specification provides a data communication method, apparatus, storage medium, and electronic device, which solves the problem of difficult system expansion and soaring costs caused by the need to develop dedicated adapters for each external application. The technical solution is as follows:

[0005] Firstly, this specification provides a data communication method, the method comprising:

[0006] When the service platform communicates service data with external application services, it triggers management mode and runtime mode.

[0007] In management mode, the external service application communication protocol and the platform internal application communication protocol of at least one associated external application service are obtained. An external communication reference data template is determined based on the external service application communication protocol. An internal system standard communication model is determined based on the platform internal application communication protocol. External system data definition structure information corresponding to each external application service is extracted based on the external communication reference data template and the external service application communication protocol. Internal system data definition structure information is extracted based on the platform internal application communication protocol and the internal system standard communication model. Communication data matching is performed based on the external system data definition structure information and the internal system data definition structure information to obtain a data conversion template corresponding to at least one data conversion relationship.

[0008] In runtime mode, the source application communication data for the target external application service is monitored, the target data conversion relationship corresponding to the source application communication data is determined, the target data conversion template corresponding to the target data conversion relationship is called from the data conversion template to perform communication data conversion on the source application communication data to obtain the target application communication data, and platform service data communication is performed with the target external application service based on the target application communication data.

[0009] The external communication reference data template includes external communication data instances and external communication protocol specifications. The internal system standard communication model includes internal communication protocol specifications. The internal system data definition structure information is metadata information for internal service application communication protocols. The external system data definition structure information is metadata information for internal service application communication protocols.

[0010] Secondly, this specification provides a data communication device, the device comprising:

[0011] The mode triggering module is used to trigger the management mode and the running mode when the service platform communicates service data with external application services;

[0012] The template management module is used in management mode to obtain the external service application communication protocol and the platform internal application communication protocol of at least one associated external application service; determine the external communication reference data template based on the external service application communication protocol; determine the internal system standard communication model based on the platform internal application communication protocol; extract the external system data definition structure information corresponding to each external application service based on the external communication reference data template and the external service application communication protocol; extract the internal system data definition structure information based on the platform internal application communication protocol and the internal system standard communication model; and perform communication data matching based on the external system data definition structure information and the internal system data definition structure information to obtain a data conversion template corresponding to at least one data conversion relationship.

[0013] The data communication module is used to monitor source application communication data for a target external application service in runtime mode, determine the target data conversion relationship corresponding to the source application communication data, call the target data conversion template corresponding to the target data conversion relationship from the data conversion template to perform communication data conversion on the source application communication data to obtain target application communication data, and perform platform service data communication with the target external application service based on the target application communication data.

[0014] The external communication reference data template includes external communication data instances and external communication protocol specifications. The internal system standard communication model includes internal communication protocol specifications. The internal system data definition structure information is metadata information for internal service application communication protocols. The external system data definition structure information is metadata information for internal service application communication protocols.

[0015] Thirdly, this specification provides a computer storage medium storing at least one instruction adapted for loading by a processor and executing method steps of one or more embodiments of this specification.

[0016] Fourthly, this specification provides a computer program product storing at least one instruction adapted to be loaded by a processor and to execute the method steps of one or more embodiments of this specification.

[0017] Fifthly, this specification provides an electronic device that may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to execute the method steps of one or more embodiments of this specification.

[0018] The beneficial effects of the technical solutions provided in some embodiments of this specification include at least the following:

[0019] In one or more embodiments of this specification, by setting up a management mode and a runtime mode, the data protocol adaptation logic is successfully decoupled from the platform's real-time data processing flow. In related technologies, data conversion rules are tightly coupled with service data processing logic, resulting in high maintenance costs for changes in internal and external protocols. In this specification, all data conversion relationships are fixed in an external data conversion template in an association mode independent of the runtime mode. When the external protocol changes, only the corresponding template needs to be updated in the management mode, without modifying and redeploying the core service code, greatly improving the system's flexibility and transaction response speed. Secondly, by calling the target data conversion template in the runtime mode, the problem of difficult system expansion and soaring costs caused by developing dedicated adapters for each external application is solved. By using a set of universal runtime processing logic, any template configured in the management mode can be parsed. This allows the platform to add only a new template configuration when accessing new external application services, without any code development, significantly reducing the access cost of new external services and the long-term maintenance complexity of the platform. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of a data communication system provided in this specification;

[0022] Figure 2 This is a flowchart illustrating a data communication method provided in this specification;

[0023] Figure 3 This is a schematic diagram of a data communication scenario provided in this specification;

[0024] Figure 4 This is a flowchart illustrating the maintenance and updating process of a data conversion template provided in this manual;

[0025] Figure 5 This is a flowchart illustrating a communication data matching process provided in this specification;

[0026] Figure 6 This is a schematic diagram of a communication data conversion process provided in this manual;

[0027] Figure 7 This is a schematic diagram of the structure of a data communication device provided in this specification;

[0028] Figure 8 This is a schematic diagram of the structure of an electronic device provided in this specification. Detailed Implementation

[0029] The technical solutions in this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0030] In the description of this specification, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. In the description of this specification, it should be noted that, unless otherwise expressly specified and limited, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. Those skilled in the art can understand the specific meaning of the above terms in this specification based on the specific circumstances. Furthermore, in the description of this specification, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0031] In the relevant technologies, the scenarios involved in the background technology are very common in today's digital transactions and are not limited to a specific industry. For example, the core order system of a large e-commerce platform needs to simultaneously connect to dozens of payment gateways with different API protocols, conduct order placement and tracking with multiple logistics service providers (such as SF Express and EMS), and connect to electronic waybill services such as Cainiao. Similarly, in order to complete a loan approval, the core system of a fintech platform must interact with multiple credit reporting agencies, funding banks, and KYC (Know Your Customer) services, each with vastly different interface specifications. In addition, the search engines of travel booking platforms (OTAs) must aggregate massive and heterogeneous external data sources in real time from global distribution systems (GDS), hotel group inventory systems (CRS), and car rental service providers. These scenarios all highlight the same technical pain point: in order to build a rich service ecosystem to provide better services to users, a service platform must spend huge costs to develop and maintain a large number of dedicated data adapters for different external protocols.

[0032] The present specification will now be described in detail with reference to specific embodiments.

[0033] Please see Figure 1 This is a schematic diagram of a data communication system provided in this specification. Figure 1 As shown, the data communication system may include at least a client cluster and a service platform 100.

[0034] The client cluster may include at least one client, such as Figure 1 As shown, it specifically includes client 1 corresponding to user 1, client 2 corresponding to user 2, ..., client n corresponding to user n, where n is an integer greater than 0.

[0035] Each client in a client cluster can be an electronic device with communication capabilities, including but not limited to: wearable devices, handheld devices, personal computers, tablets, in-vehicle devices, smartphones, computing devices, or other processing devices connected to a wireless modem. Electronic devices may have different names in different networks, such as: user equipment, access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, electronic device, wireless communication device, user agent or user device, cellular phone, cordless phone, personal digital assistant (PDA), and electronic devices in 5G networks or future evolved networks.

[0036] The service platform 100 can be a standalone server device, such as a rack-mount, blade, tower, or cabinet-type server device, or a workstation, mainframe, or other hardware device with strong computing power; or it can be a server cluster composed of multiple servers. The servers in the service cluster can be composed in a symmetrical manner, wherein each server is functionally and hierarchically equivalent in the transaction chain, and each server can provide services independently. The independent provision of services can be understood as not requiring the assistance of other servers.

[0037] In one or more embodiments of this specification, the service platform 100 may establish a communication connection with at least one client in the client cluster, and complete data interaction during data communication, such as online transaction data interaction, based on the communication connection.

[0038] It should be noted that the service platform 100 establishes a communication connection with at least one client in the client cluster via a network for interactive communication. This network can be a wireless network or a wired network. Wireless networks include, but are not limited to, cellular networks, wireless LANs, infrared networks, or Bluetooth networks. Wired networks include, but are not limited to, Ethernet, universal serial bus (USB), or controller area networks. In one or more embodiments of the specification, technologies and / or formats including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network (such as target compressed packets). Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), and Internet Protocol Security (IPsec) can be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0039] The data communication system embodiments provided in this specification and the data communication methods described in one or more embodiments belong to the same concept. The execution entity corresponding to the data communication method involved in one or more embodiments of this specification can be the aforementioned service platform 100; the execution entity corresponding to the data communication method involved in one or more embodiments of this specification can also be the electronic device corresponding to the client, specifically determined based on the actual application environment. The implementation process of the data communication system embodiments can be detailed in the following method embodiments, and will not be repeated here.

[0040] based on Figure 1 The schematic diagram shown below illustrates the data communication method provided by one or more embodiments of this specification.

[0041] Please see Figure 2 This document provides a flowchart illustrating a data communication method according to one or more embodiments. This method can be implemented using a computer program and can run on a data communication device based on the von Neumann architecture. The computer program can be integrated into an application or run as a standalone utility application. The data communication device can be a service platform.

[0042] Specifically, the data communication method includes:

[0043] S102: When the service platform communicates service data with external application services, it triggers the management mode and the running mode.

[0044] Management Mode: This is an operating mode of the service platform described in this manual, which defines and manages the "rules" for data communication. Activation of this mode is associated with preparatory work such as system configuration and protocol adaptation. It is responsible for generating and maintaining data conversion templates to guide data format conversion, but it does not process real-time service data itself.

[0045] Runtime Mode: Another operating mode of the service platform described in this manual, which performs the "operation" of data communication. The activation of this mode is driven by a specific, real-time transaction service data exchange requirement, and is responsible for format conversion and sending / receiving of real-time data according to the preset rules (i.e., data conversion templates) under the "Management Mode".

[0046] In the embodiments of the present invention, S102 is intended to illustrate the two core operating modes included in the technical solution and the macroscopic conditions for their activation. It should be specifically noted that "triggering the management mode and the running mode" in step S102 does not mean that these two modes are activated simultaneously and synchronously at any moment during "service data communication between the service platform and external application services." Rather, those skilled in the art should understand that the triggering of these two modes is based on different stages and different needs within the overall context of "service data communication." They are logically independent and can be set as needed based on actual application conditions; in terms of time, they can occur sequentially, on demand, or in parallel.

[0047] The following explains the triggering conditions and process of the management mode:

[0048] The triggering of the management mode typically corresponds to the "establishment and maintenance" phase of "service data communication between the service platform and external application services". Specific triggering events may include, but are not limited to:

[0049] First Access Event: When the service platform needs to establish a data interaction relationship with a new external application service for the first time, it will trigger the management mode to complete the parsing, mapping and initial template creation of the communication protocol of the new service.

[0050] Protocol change event: When an external application service that has been connected upgrades or modifies its communication protocol, the management mode is triggered to update or generate a new data conversion template to adapt to the change.

[0051] Internal model adjustment event: When the service platform's own internal standard data model changes, it may also be necessary to trigger the management mode to update all related templates in batches.

[0052] Once triggered, the service platform will enter management mode, which allows for the definition and management of protocol mapping rules until the relevant template configuration is complete.

[0053] Furthermore, the triggering conditions and process of the runtime mode:

[0054] The triggering of runtime mode corresponds to the "execution and operation" phase of "service platform communicating service data with external application services". Specific triggering events may include, but are not limited to:

[0055] Incoming data request event: When the service platform receives a specific data request from an external application service, it triggers the runtime mode to convert the requested data into an internal standard format.

[0056] Outbound data push event: When the internal transaction logic of the service platform needs to push or request data to external application services, the runtime mode is triggered to convert the internal standard data into the format required by the external protocol.

[0057] Once triggered, the service platform will enter the running mode, in which a specific data conversion and communication task will be executed.

[0058] Example: Let's take an example of an "e-commerce platform connecting to the new payment channel 'PayTong'" to illustrate this.

[0059] Scenario: An e-commerce platform (service platform) plans to connect to a payment application (external application service) to achieve **service data communication** between the two.

[0060] Triggering of management mode:

[0061] During the project integration phase, the service platform needs to perform protocol integration to enable system support for the "payment application." At this point, the "New Channel Integration" process is initiated in the service platform's backend management system, triggering the management mode. In this mode, the service platform obtains the "payment application's" API documentation, configures the mapping relationships between internal and external fields for interfaces such as payment requests and refund notifications, and ultimately generates a data conversion template for the "payment application." Once the configuration is complete, the management mode work is finished.

[0062] Triggering of runtime mode:

[0063] After the service platform went live, a customer selected the "Payment Application" for payment at checkout. When the customer clicked the "Confirm Payment" button, the platform's payment system needed to initiate a specific payment request to the "Payment Application," triggering the runtime mode. In this mode, based on the target of the request being the "Payment Application," the system invoked a template previously configured in the management mode, converting the platform's internal order data into the encrypted message format required by "Payment Pass," and then sending it out. In this payment process, each real-time interaction with the "Payment Application" (such as creating an order or checking the payment status) independently triggers entry into the runtime mode.

[0064] S104: In management mode, obtain the external service application communication protocol and the platform internal application communication protocol of at least one associated external application service, and update the data conversion template corresponding to at least one data conversion relationship based on the external service application communication protocol and the platform internal application communication protocol.

[0065] External service application communication protocols can be understood as a set of specifications and conventions defined by the external application service provider for data exchange. Their content includes, for example, the structure of data objects, field names, data types, format requirements, and communication methods. This protocol is diverse and uncertain for the service platform.

[0066] The platform's internal application communication protocol can be understood as a unified and standardized data model and interface specification defined by the service platform itself and followed by all core application modules within it. This protocol is typically stable for the service platform.

[0067] A data conversion template can be understood as a structured rule file or data object. It declaratively describes the data field mapping relationships, format conversion logic, and structural reorganization rules between the "external service application communication protocol" and the "platform internal application communication protocol." This template is generated in management mode and serves as a reference for data conversion in runtime mode. This solution separates management and runtime modes, allowing for immediate adaptation by simply updating the data conversion template configuration when the communication protocol changes. This solves the problem in existing technologies using adapter mechanisms where any modification requires a system interruption and adapter updates.

[0068] As an illustration, after the management mode is activated, the system first acquires and checks whether the communication protocols between the two parties have been updated. Acquiring the "external service application communication protocol" can include, but is not limited to: acquiring the protocol definition file provided by the external service provider and uploaded by the system administrator; or reversing the protocol structure by parsing a specific external service message sample. Acquiring the "platform internal application communication protocol" can load its standardized data model definition from a preset internal code or configuration library.

[0069] Furthermore, after obtaining the agreements between the two parties, a data conversion relationship is established between them. First, structural parsing is performed: both the external and internal agreements are parsed to identify all data fields, data types, hierarchical structures, and related constraints. Then, relationship matching is performed: by comparing the parsing results, a one-to-one or many-to-one mapping relationship is established between the fields of the external and internal agreements. For example, the `cust_id` field of the external agreement is determined to correspond to the `customerIdentifier` field of the internal agreement. Optionally, this matching process can be assisted by an intelligent large-scale model engine, which recommends the most likely mapping relationship through semantic analysis, historical data learning, etc., to improve configuration efficiency.

[0070] Once the data transformation relationship is determined, a "data transformation template" is updated based on this relationship. Update operations include, but are not limited to:

[0071] Create a "Data Transformation Template": If a new external service is accessed, the service platform will generate a new template file.

[0072] Modify "Data Conversion Template": If a change in the protocol of an external / internal service is detected, the service platform will load the original template and modify it according to the new conversion relationship.

[0073] This "data transformation template" solidifies the established mapping relationship using a preset template syntax (such as Velocity syntax), forming a machine-readable instruction set. Finally, the generated template is stored and associated with the corresponding external application service, ready for runtime invocation.

[0074] S106: In the running mode, monitor the source application communication data for the target external application service, determine the target data conversion relationship corresponding to the source application communication data, call the target data conversion template corresponding to the target data conversion relationship from the data conversion template to perform communication data conversion on the source application communication data to obtain the target application communication data, and perform platform service data communication with the target external application service based on the target application communication data.

[0075] Source application communication data: Communication data in its original state before data conversion operations. Depending on the direction of communication, this data can be generated by an internal application of the platform and conform to internal protocols, or it can be sent by an external application service and conform to external protocols.

[0076] Target data conversion template: refers to the data conversion template that corresponds to the target data conversion relationship associated with the current source application communication data among many data conversion templates.

[0077] Target application communication data: This refers to the resulting data generated after the "source application communication data" has been processed by the "target data conversion template". The format and structure of this data fully conform to the protocol specifications of the communication receiver.

[0078] To illustrate, when a real-time data communication requirement arises, the runtime mode is activated, and the service platform monitors the "source application communication data" generated internally or externally. Simultaneously, it determines the "target data transformation relationship" corresponding to the "source application communication data." Specifically, this determination process is accomplished by identifying the context information of the data. For example, for an incoming data entry, its source IP address, the requested API endpoint, or an identifier in the message header (such as an AppKey) can be used to determine which external application service it originates from. For an outgoing data entry, the internal transaction logic explicitly specifies which external application service its communication target is.

[0079] Once the target data transformation relationship is determined (equivalent to identifying the communication counterpart), the service platform, based on this relationship, precisely retrieves and calls the corresponding "target data transformation template" from a pre-stored set of "data transformation templates." The "source application communication data" and the "target data transformation template" retrieved in the previous step are then fed into a general transformation engine. This engine parses the rules defined in the template and strictly performs transformation operations on the source data according to these rules. The transformation operations can be configured to include, depending on the specific circumstances:

[0080] Field mapping operation: Change the source field name to the target field name.

[0081] Structure transformation operation: flattening or reorganizing the hierarchical structure of data.

[0082] Formatting operations: Format data such as dates, currencies, and codes.

[0083] Value conversion operation: Calculate or translate data values ​​according to preset logic (e.g., convert status code 1 to SUCCESS).

[0084] After the conversion process, a brand new "target application communication data" is output. This "target application communication data" generated in the previous step is used to communicate and interact with the "target external application service". Since this data has been precisely converted using a template, its format and content fully conform to the target's receiving protocol. Therefore, it can be processed directly in an independent runtime mode without any additional adaptation logic, thus ensuring the success rate and efficiency of communication.

[0085] Example: See reference Figure 3 , Figure 3 This is a schematic diagram of a data communication scenario. Figure 3 The description demonstrates how the platform handles requests from multiple different external application services.

[0086] Execute S102, which defines the entire method as existing in the macro context of "service data communication" and includes two modes: "management mode" and "running mode". Figure 3 This describes the complete scenario of "service data communication": an end-to-end link that starts from the user, passes through external applications, and finally reaches the core application.

[0087] Execute S104, Figure 3 Its core outputs and objects of action are clearly visible in the diagram. The core task of the management mode is to generate "data transformation templates," which are used for... Figure 3The crucial step of "open platform template adaptation" is the core logic upon which subsequent work is based. Specifically, the S104 process involves analyzing the communication protocols of the upstream "external applications" and the downstream "core applications A, B, C" in the graph, and generating templates to connect them.

[0088] Execute S106, Figure 3 The data flow indicated by the arrow in the diagram corresponds to the real-time processing flow in the running state of S106. When an external application initiates a request, the data flow reaches the "Open Platform Template Adaptation" stage through the gateway, which corresponds to the process in S106. The relationship is determined and the template is called to perform data conversion and interactive communication.

[0089] In one or more embodiments of this specification, by setting up a management mode and a runtime mode, the data protocol adaptation logic is successfully decoupled from the platform's real-time data processing flow. In related technologies, data conversion rules are tightly coupled with service data processing logic, resulting in high maintenance costs for changes in internal and external protocols. In this specification, all data conversion relationships are fixed in an external data conversion template in an association mode independent of the runtime mode. When the external protocol changes, only the corresponding template needs to be updated in the management mode, without modifying and redeploying the core service code, greatly improving the system's flexibility and transaction response speed. Secondly, by calling the target data conversion template in the runtime mode, the problem of difficult system expansion and soaring costs caused by developing dedicated adapters for each external application is solved. By using a set of universal runtime processing logic, any template configured in the management mode can be parsed. This allows the platform to add only a new template configuration when accessing new external application services, without any code development, significantly reducing the access cost of new external services and the long-term maintenance complexity of the platform.

[0090] Optional, please see Figure 4 , Figure 4 This is a schematic diagram illustrating a data conversion template maintenance and update process proposed in one or more embodiments of this specification. Specifically, the process of updating the data conversion template corresponding to at least one data conversion relationship based on the external service application communication protocol and the platform internal application communication protocol can be referred to the following method:

[0091] S202: Determine the external communication reference data template based on the external service application communication protocol, and determine the internal system standard communication model based on the platform internal application communication protocol;

[0092] External communication reference data template: This does not refer to the "external service application communication protocol" itself, but rather to the external communication data instance and external communication protocol specification that can be parsed by a machine, as defined by the protocol. This template can be a structured data instance (such as a JSON or XML message) or a schema definition file (such as a JSONSchema or XSD file), which provides a clear operational blueprint for subsequent automated structure extraction.

[0093] Internal system standard communication model: refers to the communication system model within the service platform used by various parts for internal system communication, which can be understood as including internal communication protocol specifications.

[0094] This is an illustrative example of the specific implementation of external protocols. The service platform parses external service application communication protocols to extract their definitions and structures, identifying specific instances representing their core data structures as "external communication reference data templates." If the protocol document provides explicit request / response message examples, these examples can be directly selected as templates. If the protocol document is described using a standardized schema definition language, the schema definition document itself can be used as a template. If the document is merely a textual description, a structured data instance is automatically constructed based on the description as a template.

[0095] Furthermore, regarding the specification of internal protocols, since the internal protocols are defined by the platform itself, their implementation within the service platform is explicit and unique. Therefore, the process of determining the "internal system standard communication model" can be based on the definition and structure of the internal protocols.

[0096] S204: Extract the external system data definition structure information corresponding to each external application service based on the external communication reference data template and the external service application communication protocol; extract the internal system data definition structure information based on the platform internal application communication protocol and the internal system standard communication model.

[0097] Data definition structure information refers to metadata information obtained after parsing protocols, templates, or models, specifically for (external service application communication protocols or platform internal application communication protocols). This metadata information details the names, data types, hierarchical relationships, and format constraints of all data fields contained in the communication protocol.

[0098] Indicatively, S204 is the core analysis and parsing stage in the management configuration process. It "reads" and "understands" the physical objects representing the two communicating parties (i.e., the "external communication reference data template" and the "internal system standard communication model"), and "translates" their internal structure into standardized "data definition structure information" that can be compared and matched in subsequent steps.

[0099] S206: Based on the external system data definition structure information and the internal system data definition structure information, perform communication data matching to obtain at least one data conversion template corresponding to a data conversion relationship.

[0100] Communication data matching establishes correspondence and conversion rules at the field, structure, and even numerical levels between two different sets of "data definition structure information".

[0101] Data transformation relationship: Defines a set of rules for how to transform a source data element into a target data element.

[0102] Data transformation template: refers to the final output generated after the "data transformation relationship" is solidified and encoded. It is a rule file containing complete transformation logic that can be directly executed by the runtime transformation engine.

[0103] Indicatively, the process of synthesizing and linking the independent structural information parsed in the preceding steps to ultimately generate an executable template file can be broken down into the following two core stages:

[0104] The communication data matching phase to establish "data transformation relationships" takes two sets of "data definition structure information" (one external, one internal) as input. The goal is to establish a series of precise "data transformation relationships" by comparing these two sets of information. Possible transformation relationships include, but are not limited to:

[0105] Direct field mapping: This directly associates an external field name with an internal field name. For example, it maps the external `order_id` field to the internal `orderNumber` field.

[0106] Data type conversion: Define conversion rules for fields that need to have their data types converted. For example, convert the external string type date "2025-10-15" to the internal long integer timestamp 1760515200.

[0107] Fixed value / default value mapping: Sets the target field to a fixed constant value, or provides a default value if the source field does not exist.

[0108] Enumerated value mapping: Establishes value correspondences for fields such as status and type. For example, external status codes 1, 2, and 3 are mapped to internal statuses "PROCESSING", "SUCCESS", and "FAILED", respectively.

[0109] Structured transformation: Define more complex structure transformation rules, such as reorganizing a flat source structure into a nested target structure, or vice versa.

[0110] The "Data Transformation Template Generation" stage, based on "data transformation relationships": Once all "data transformation relationships" are determined and saved, the automatic template generation stage begins. Specifically, it iterates through each established data transformation relationship. For each relationship, a corresponding code snippet is generated according to a preset template syntax (e.g., Velocity template language). For example, a simple direct field mapping relationship will be converted into a single assignment statement.

[0111] The final output is to combine and encapsulate all the generated code snippets into a complete and syntactically correct "data transformation template" file. This file is then saved to the template library and associated with the corresponding external application service. At this point, the entire management configuration process is complete.

[0112] In this specification, the implementation methods described in S202-S206 provide a systematic and efficient template generation process. By automating the structural extraction of external data templates and internal standard models, the complex protocol adaptation logic is clearly decomposed into "parsing-matching-generation", making the data conversion relationship intuitive, transparent and easy to maintain.

[0113] Optional, please see Figure 5 , Figure 5 This is a schematic diagram of a communication data matching process proposed in one or more embodiments of this specification. Specifically, the process of matching communication data based on the external system data definition structure information and the internal system data definition structure information to obtain a data conversion template corresponding to at least one data conversion relationship can be performed as follows:

[0114] S302: Input the external system data definition structure information and the internal system data definition structure information into the large data conversion and processing model;

[0115] Large-scale data transformation and processing model: This refers to a large-scale multimodal language model (MLLM) with a large number of parameters, which has been pre-trained on sample data and specifically optimized for tasks such as data structure understanding, semantic analysis, and code generation. This large-scale data transformation and processing model is responsible for subsequent automated feature extraction, matching decisions, and template generation.

[0116] S304: Using the data transformation and processing model, extract semantic features of the first field, semantic features of the second field, structural features of the first field, and structural features of the second field from the first field in the external service communication data structure and the second field in the internal system data structure;

[0117] First field and second field: The first field refers to any field from the "External Service Communication Data Structure"; the second field refers to any field from the "Internal System Data Structure". This step will process all possible first and second fields.

[0118] Semantic features: These are mathematical representations that characterize the inherent transactional meaning of a data field, typically a high-dimensional numerical vector (Embedding). These features are generated by a large model through analysis of textual information such as field names and comments, ensuring that fields with similar transactional meanings (e.g., "order number" and "order_reference") have a higher mathematical similarity.

[0119] Structural features: These are mathematical representations that characterize the position and context of a data field within its hierarchical data structure. These features typically include the field's node path, depth, parent node information, etc., and are used to determine the structural correspondence between two fields.

[0120] To illustrate, for the first field in the external service communication data structure and the second field in the internal system data structure, the data transformation and processing model utilizes its natural language processing (NLP) capabilities to process the text information of each field (including the field name itself, such as order_ref, and any descriptive annotations that may exist). Through its internal word embedding layer, the model maps this text information into a high-dimensional semantic space and generates a first field semantic feature and a second field semantic feature that condenses the transactional meaning of the field.

[0121] Furthermore, for structural feature extraction, the large model traverses the entire data structure tree of each communication data structure through data transformation and processing, and extracts the structural context information for each field. This information may include: node path: the complete path from the root node to the current field; node depth: the level of the current field in the tree structure; and parent-child relationship: the name and type of the direct parent node of the current field.

[0122] At the same time, this structural information will be encoded to form a structural feature vector or a set of structured data that can represent the field position and hierarchical relationship.

[0123] In one feasible implementation, specifically performing the data transformation and processing model to extract semantic features of the first field, semantic features of the second field, structural features of the first field, and structural features of the second field from the first field in the external service communication data structure and the second field in the internal system data structure, can be done in the following manner:

[0124] Step A2: Extract first field parameters and first field context descriptions for each first field in the external service communication data structure using the data transformation and processing large model, and generate first field semantic features based on the first field parameters and first field context descriptions; and extract second field parameters and second field context descriptions for each second field in the internal system data structure, and generate second field semantic features based on the second field parameters and second field context descriptions.

[0125] Field parameters: These refer to the structured, machine-readable attribute information describing a field. This may include the field name, data type, format constraints, and whether it is required.

[0126] Field context description: Refers to the unstructured natural language text associated with a field in the protocol definition. For example, it can be annotation information or descriptive attributes of the field, designed to explain the field's transactional purpose and meaning.

[0127] Protocol data structure tree: refers to a tree-like or graph-like data structure built in memory that can completely represent all fields in a protocol and their hierarchical relationships (such as parent-child, sibling relationships). It is the foundation for structural feature analysis.

[0128] This illustration demonstrates how the large-scale data transformation and processing model comprehensively utilizes the structured parameters and unstructured descriptions of fields to generate a richer and more accurate semantic representation. Specifically, information extraction is performed first: for each field to be processed (whether it's the external "first field" or the internal "second field"), the large-scale model first extracts two parts from the input data definition structure information: field parameters and field context description. Subsequently, these two parts of information are fused and processed by the natural language understanding unit. For example, the model does not only process the isolated word "b_date," but comprehensively processes complete information such as "the field name is b_date, and the description is the user's birth date." In this way, the model can more accurately capture the true service transaction meaning of the field and generate a "semantic feature" vector that highly summarizes this meaning, resulting in the semantic features of the first field and the semantic features of the second field.

[0129] Step A4: Construct the external protocol data structure tree corresponding to the external service communication data structure and the internal protocol data structure tree corresponding to the internal system data structure through the data transformation and processing big model. Perform tree structure parsing on each first field node in the external protocol data structure tree to obtain the first field structure feature, and perform tree structure parsing on each second field node in the internal system data structure to obtain the second field structure feature.

[0130] Data structure tree construction process: The large model controls data transformation and processing. Based on the data definition structure information (which includes the hierarchical relationship of all fields), two "protocol data structure trees" are built in memory: the external protocol data structure tree and the internal protocol data structure tree.

[0131] Tree structure parsing process: After the tree is constructed, the large-scale data transformation and processing model traverses and performs "tree structure parsing" on each field node in the tree. This parsing process aims to extract a series of quantitative indicators that describe the structural position of each field. These indicators collectively constitute the "structural features" of the field, producing the first field structural features and the second field structural features. The extractable indicators include, but are not limited to:

[0132] Node path: The complete path from the root node of the tree to the current field node.

[0133] Node depth: The level of the current field node in the tree.

[0134] Parent node information: The name and type of the direct parent node of the current field node.

[0135] Sibling node information: The number and names of other sibling nodes that belong to the same parent node as the current field node.

[0136] S306: Perform field semantic matching based on the first field semantic features and the second field semantic features to obtain field semantic association information, and perform field result matching based on the first field structural features and the second field structural features to obtain field structural similarity information;

[0137] Field semantic association information is a quantified score or confidence level that represents the degree of semantic association between two data fields (one from an external protocol and the other from an internal model) in terms of transactional function and meaning. The higher the score, the more likely the two fields are to conceptually refer to the same thing.

[0138] Field structure similarity information is also a quantified score or confidence level, representing the degree of structural similarity between two data fields in their respective protocol hierarchies, depths, and contexts. A higher score indicates greater structural similarity between the two fields.

[0139] This illustration demonstrates how a large-scale data transformation and processing model performs pairwise, multi-dimensional comparisons of the features extracted in previous steps, providing quantitative and objective judgment criteria for the final decision. Specifically, the large-scale model performs field semantic matching: it extracts the "semantic features" (usually high-dimensional semantic vectors) of the "first field" and "second field." Then, a vector space similarity calculation method, such as cosine similarity, is used to calculate the similarity between these two vectors. Cosine similarity measures the angle between two vectors in multi-dimensional space to determine their similarity, with a value range of [-1, 1] (usually normalized to [0,1] in this scenario). The closer the directions of the two vectors are, the closer the cosine similarity value is to 1, indicating that the transactional meanings of the two fields are more similar. This calculated score is the "field semantic association information."

[0140] Simultaneously, a large-scale data transformation and processing model is used to perform field structure matching: the model extracts the "structural semantic features" (including node paths, depths, parent nodes, etc.) of two fields. A set of pre-defined or learned structured data comparison algorithms is then employed to obtain multifaceted structural comparison results. For example, string similarity algorithms (such as Jaccard similarity or edit distance) are used to compare the node paths of the two fields; and / or, the node depth difference between the two fields is calculated and compared to obtain a node depth difference comparison result, with a smaller difference indicating higher similarity; and / or, the parent nodes of the two fields are recursively compared semantically or structurally to obtain a parent node structure comparison result.

[0141] Ultimately, the large model will integrate these structural comparison results from different aspects through data transformation and processing, and then use weighted scoring to obtain a comprehensive score as "field structure similarity information".

[0142] In one feasible implementation, specifically performing field semantic matching based on the first field semantic features and the second field semantic features to obtain field semantic association information, and performing field result matching based on the first field structural features and the second field structural features to obtain field structural similarity information, can be done in the following manner:

[0143] Step B2: Determine the target service domain knowledge graph based on the semantic features of the first field and the semantic features of the second field, call the target service domain knowledge graph to vectorize the semantic features of the first field and the semantic features of the second field to obtain the semantic service vector of the first field and the semantic service vector of the second field, and perform bidirectional semantic matching based on the semantic service vector of the first field and the semantic service vector of the second field to obtain the field semantic association information between the first field and the second field.

[0144] Target service domain knowledge graph: This refers to a structured knowledge base containing a large number of entities, concepts, and their relationships within a specific service domain (e.g., finance, logistics, healthcare). This knowledge graph provides rich background knowledge for refining semantic features. It can be understood as a large model pre-associating service domain knowledge graphs from multiple domains, with each round of processing identifying the target service domain knowledge graph indicated by the semantics of the current field.

[0145] Semantic service vectors refer to the original "first / second field semantic feature" vectors, which, after being processed by a large data transformation model and augmented with knowledge from the "target service domain knowledge graph" and calibrated for context, become a more accurate semantic representation vector that better fits the specific transaction domain context. Compared to the original vectors, semantic service vectors can better reflect the true transactional meaning of fields within a specific domain.

[0146] Bidirectional semantic matching refers to a semantic similarity calculation strategy that calculates not only the unidirectional similarity from the first field to the second field, but also the reverse similarity from the second field to the first field. In some implementations, a strong semantic association between two fields is only determined when the similarity scores in both directions are high, such as exceeding a similarity threshold. This aims to effectively avoid misjudgments due to high unidirectional scores caused by the distinction between broad and narrow definitions of concepts (e.g., "address" and "home address").

[0147] To illustrate, prior to semantic matching, a large data transformation model identifies the domain context of the currently processed communication protocol by referencing the semantic features of the first and second fields. For example, by analyzing the protocol's metadata, title, or high-frequency words (such as "loan," "interest"), the model can determine that it belongs to the "financial services" domain. Based on this determination, the model selects and invokes a pre-built "target service domain knowledge graph" (e.g., a "financial services knowledge graph") corresponding to that domain from its knowledge base.

[0148] Then, the target service domain knowledge graph is used to "calibrate" and "sharpen" the "first / second field semantic features". The model interacts with the knowledge graph it generates for the first and second fields, and performs "vectorization" on the "first / second field semantic features" based on the entities and relationships stored within the knowledge graph. For example, if the first field is cust_id and the second field is client_no, and the knowledge graph stores the relationship "customer is a strong synonym of client", then the model will "pull" or "align" the original semantic vectors of these two fields with the vector representation of the core concept of "customer identifier" in the knowledge graph, thereby generating a more relevant "first field semantic service vector" and "second field semantic service vector".

[0149] After obtaining the enhanced "semantic service vector" in the previous step, the large model will execute a "bidirectional semantic matching" algorithm. Specifically:

[0150] Calculate the semantic similarity score S1 between the first field and the second field.

[0151] Calculate the semantic similarity score S2 between the second field and the first field.

[0152] A pre-defined fusion function (e.g., min(S1, S2) of S1 and S2, or sqrt(S1 * S2) of the two) is used to calculate the final composite score as "field semantic association information". This two-way verification mechanism ensures that the highest association score is obtained only when the two fields are the best match for each other.

[0153] Step B4: Based on the structural features of the first field, determine the basic path sequence of the first field, the parent node information of the first field, and the set of sibling nodes of the first field. Based on the structural features of the second field, determine the basic path sequence of the second field, the parent node information of the second field, and the set of sibling nodes of the second field. Based on the basic path sequences of the first and second fields, perform structural path matching to determine the structural association similarity of multiple potentially related field pairs. Based on the parent node information of the first and second fields, the parent node information of the second field, the set of sibling nodes of the first and second fields, and the set of sibling nodes of the second field corresponding to the potentially related field pairs, perform semantic weighting on the structural association similarity to obtain the potential structural association similarity of the potentially related field pairs. Based on the potential structural association similarity, generate field structural similarity information.

[0154] First / Second Basic Path Sequence: Refers to the serialized representation of the complete path of a field from the root node of its data structure tree to its own position, such as the string / order / details / item_id, which precisely describes the absolute position of the field in the hierarchical structure.

[0155] The first / second parent node field information and the first / second sibling node set information refer to the attribute information of a field's direct parent node, and the set information of all other fields (sibling nodes) that belong to the same parent node as that field. These two parts together constitute the field's direct structural context.

[0156] Structural path matching: refers to a preliminary similarity calculation process based on "basic path sequences", the purpose of which is to quickly filter out field pairs that may be related in structural position.

[0157] Semantic weighting refers to a mechanism for secondary correction and refinement of the initial matching score. It is not based on structure, but uses semantic analysis to evaluate the similarity of the context of two fields (i.e., parent node and sibling node) in terms of transaction service meaning, and uses this as a weight to adjust the initial structural matching score.

[0158] To illustrate, before matching, the large model first performs a fine decomposition of the "first / second field structural features" extracted for each first / second field through data transformation. Specifically, the model will clearly separate three key parts from them: a) "first / second basic path sequence"; b) "first / second parent node field information"; c) "first / second field sibling node set information".

[0159] Then, preliminary path-based matching and candidate filtering are performed: this stage aims to quickly narrow down the matching range. The large model traverses all combinations of the first and second fields, using only the "first / second basic path sequence" to perform fast "structural path matching" through string similarity calculation mechanisms (such as Jaccard similarity or edit distance), and calculates an initial "structural association similarity" score. Only when the "structural association similarity" score is higher than a preset threshold is the field pair considered a "potentially associated field pair" and enters the next stage of refinement.

[0160] Then, context-based semantic weighted refinement is performed on the "potentially related field pairs," aiming to conduct in-depth context validation for each "potentially related field pair." Specifically:

[0161] By performing context extraction, the large model will extract the "first / second parent node field information" and "first / second sibling node set information" for each "potentially related field pair".

[0162] Then, contextual semantic analysis is performed. The large model will call its semantic understanding ability (the mechanism of step B2 can be reused) to calculate the semantic similarity of the parent nodes of the two fields. At the same time, it will also evaluate the overall semantic similarity of the two "sibling node sets" to obtain the contextual semantic similarity.

[0163] Then, semantic weighting is performed. The large model uses the contextual semantic similarity calculated in the previous step as a weighting factor to perform semantic weighting calculations, thereby correcting the initial "structural association similarity" obtained in the second stage to obtain the "potential structural association similarity". If the parent nodes of a field pair are highly related semantically (e.g., one is a purchaser and the other is a buyer), its initial score will be significantly improved; conversely, if the parent nodes are completely unrelated semantically (e.g., one is a product and the other is a customer), even if the initial path similarity is high, its score will be greatly reduced.

[0164] The "potential structural association similarity" refined through "semantic weighting" is the final "field structural similarity information" that can accurately reflect the true structural similarity.

[0165] S308: Based on the semantic association information and structural similarity information of the fields, the data conversion processing model determines the field mapping relationship between the first field and the second field, determines the data conversion relationship between the external service application communication protocol and the platform internal application communication protocol based on all the field mapping relationships, and generates at least one data conversion template for the data conversion relationship, wherein the data conversion relationship is the communication data conversion relationship corresponding to the external service application communication protocol and the platform internal application communication protocol.

[0166] Data conversion relationships: In protocol interoperability scenarios, this refers to the overall set of all established "field mapping relationships." It is a complete mapping logic that describes how to completely convert one protocol into another.

[0167] Data transformation template: refers to the final physical file that can be executed by a machine after the "data transformation relationship" is encoded and implemented through a specific template syntax (such as Velocity syntax).

[0168] This illustration demonstrates how a large-scale data transformation and processing model employs a pre-defined decision model or fusion rule to comprehensively evaluate the "semantic association information" and "structural similarity information" of a field pair to determine the field mapping relationship between the first field and the second field. Examples of pre-defined decision models or fusion rules include:

[0169] High-confidence direct pass rule: If both the semantic score and the structural score are higher than a high threshold (such as 0.9), the mapping relationship is directly determined.

[0170] Semantic priority rejection rule: If the semantic score is lower than a low threshold (such as 0.4), the mapping relationship is directly rejected regardless of the structural score.

[0171] Fuzzy interval weighted judgment: If the score is in the middle range, the model may perform a weighted sum of the two scores based on the weights learned during training, and then compare it with a final judgment threshold. Through this process, the model determines whether there is a clear "field mapping relationship" for each possible field pair.

[0172] Furthermore, all field mappings are aggregated to construct a complete "data transformation relationship". This stage is an aggregation process that collects all the "field mapping relationships" that were determined to be "yes" in the previous stage and constructs a complete, global "data transformation relationship" object, which is then automatically converted into a "data transformation template".

[0173] For example, iterating through the complete "data transformation relationship" built in the previous stage, for each mapping rule, automatically generating one or more lines of code that conform to the target template syntax (such as Velocity syntax). For example, a direct mapping of a field will be generated as an assignment statement; a mapping that includes type or value conversion will be generated as a complex block of statements containing function calls or conditional judgments. Finally, all the generated code snippets are combined into a syntactically correct and logically complete "data transformation template" file and saved to the template library.

[0174] In one feasible implementation, the process of determining the field mapping relationship between the first field and the second field based on the field semantic association information and the field structural similarity information through the data transformation and processing model can be carried out in the following manner:

[0175] The large model for data transformation and processing constructs a first comprehensive feature vector for each of the first fields and a second comprehensive feature vector for each of the second fields based on the semantic association information and structural similarity information of the fields. Field mapping matching is then performed based on the first comprehensive feature vector and the second comprehensive feature vector to obtain the field mapping relationship between the first field and the second field.

[0176] First / Second Field Comprehensive Feature Vector: Refers to a numerical vector that integrates multi-dimensional information. It can be a "comprehensive feature vector" constructed by combining the "semantic feature" vector and the "structural feature" vector of a field. It can simultaneously represent the transactional meaning of a field ("what it is") and its positional context in the data structure ("where it is"), providing a more comprehensive and holistic mathematical description of the field.

[0177] Intuitively, the large model performs feature fusion operations on each "first field" in the external data structure and each "second field" in the internal data structure to construct a comprehensive feature vector for the first / second field. Then, it performs field mapping matching based on these comprehensive feature vectors. A vector space similarity search mechanism can be used to determine the mapping relationship. Specifically, the large model extracts the comprehensive feature vector of a given "first field" and calculates its similarity with the comprehensive feature vectors of all "second fields." Since the comprehensive feature vector already contains both semantic and structural information, the angle between these two vectors in multidimensional space can comprehensively reflect the overall similarity of the two fields in terms of transaction and structure. For a given first field, the large model selects the second field with the highest cosine similarity to its comprehensive feature vector, and this similarity value exceeds a preset high confidence threshold (e.g., 0.9), as its final matching object. This determined pairing is the field mapping relationship between the first field and the second field. The large model iterates through all first fields, repeating this process to find all mapping relationships.

[0178] This specification describes an implementation method driven by a large data transformation and processing model, which improves automation, accuracy, and efficiency. By inputting the structural information of the protocol into the large model, and leveraging its powerful feature extraction (S304), multi-dimensional matching (S306), and intelligent decision-making (S308) capabilities, the previously tedious and error-prone field mapping process, requiring significant manual intervention, is fully automated. In particular, by introducing mechanisms such as domain knowledge graphs and contextual semantic weighting, the large model can accurately understand complex and ambiguous scenarios, thereby generating transformation templates with higher quality and lower error rates than manual configuration. This fundamentally solves the integration problem caused by human misunderstanding of the protocol. This end-to-end automated process significantly shortens the integration cycle for new transactions. This enables the service platform to rapidly and massively expand its partner ecosystem at extremely low marginal costs, completely resolving the fundamental contradiction in the traditional adapter model where integration capabilities cannot keep pace with the speed of transaction development, providing strong technical support for the platform's rapid expansion and iteration.

[0179] Optional, the following is an example of a model training process for large-scale data transformation and processing:

[0180] In some embodiments, a pre-trained basic large language model can be obtained and adapted to the data conversion and processing scenario to obtain a large data conversion and processing model. However, directly applying the basic large language model to a data conversion and processing scenario is often difficult to adapt to new data conversion and processing scenarios. Therefore, the basic large language model is first obtained to create an initial large data conversion and processing model, and sample data for the new data conversion and processing scenario is obtained. This sample data consists of "external system data definition structure information and internal system data definition structure information". Since the basic large language model is usually a pre-trained open-source AIGC model with content generation capabilities, this specification only needs to adapt it to the data conversion and processing scenario. Specifically, the sample data can be used to fine-tune the initial large data conversion and processing model. After the model fine-tuning training is completed, a large data conversion and processing model adapted to the data conversion and processing scenario is obtained.

[0181] Model creation: Obtain the basic large language model, create an initial data transformation and processing scenario plugin model for the data transformation and processing scenario, and form an initial data transformation and processing large model based on the basic large language model and the initial data transformation and processing scenario plugin model; the basic large language model (MLLM) includes, but is not limited to, the DeepSeek large model, the GPT series large models, etc.

[0182] Sample data acquisition: Acquire sample data for the new data transformation and processing scenario. This sample data consists of "external system data definition structure information and internal system data definition structure information" for the data transformation and processing scenario.

[0183] Sample data annotation: The "data conversion template" label is used to annotate the data conversion and processing requirements based on the data conversion and processing scenario.

[0184] Model training process: Input the sample data into the initial data transformation and processing large model for at least one round of model training. During the forward training of the model: Based on the sample data, the initial data transformation and processing large model is used to obtain the prediction "data transformation template".

[0185] Understandably, the prediction "data transformation template" obtained by processing the initial data transformation large model based on the sample data can be referred to in steps S302-S308. The forward propagation process in the training phase will not be described in detail here. The only difference is that the data object is the sample data and the model is the initial data transformation large model in the training phase.

[0186] During the reverse training of the model, the model loss value is determined by the model loss function based on the predicted "data conversion template" and "data conversion template" label. Based on the model loss value, the model parameters of the initial data conversion processing scenario plugin model in the initial data conversion processing large model are adjusted to keep the model structure of the basic large language model unchanged until the model training termination condition is met, thus obtaining the basic large language model and the data conversion processing scenario plugin model. The model fusion of the basic large language model and the data conversion processing scenario plugin model is completed, resulting in the trained data conversion processing large model.

[0187] As an illustration, the initial data transformation and processing scenario plugin model can be created based on a machine learning model.

[0188] Model fusion of the basic large language model and the data conversion and processing scenario plugin model: This involves fusing the model structure layer weights of the data conversion and processing scenario plugin model with the weights of the basic large language model. By determining the target model structure layer corresponding to the model structure layer weights in the basic large language model, the parameters of the target model structure layer are fused with the model structure layer weights. The model structure layer weights of the data conversion and processing scenario plugin model may only correspond to a portion of all model structure layers in the basic large language model. By updating the parameters of the target model structure layer based on the model structure layer weights, and so on, the reference update process for the weights of all model structure layers is completed, thus obtaining the large data conversion and processing model.

[0189] Optionally, the model's training termination conditions may include, for example, the loss function value being less than or equal to a preset loss function threshold, or the number of iterations reaching a preset threshold. Specific training termination conditions can be determined based on actual circumstances and are not specifically limited here.

[0190] Optionally, the model loss function can be customized, such as cross-entropy loss function, contrastive loss function, Euclidean distance loss function, etc.

[0191] It should be noted that the machine learning models involved in one or more embodiments of this specification include, but are not limited to, fitting of one or more of the following machine learning models: Convolutional Neural Network (CNN) model, Deep Neural Network (DNN) model, Recurrent Neural Networks (RNN) model, embedding model, Gradient Boosting Decision Tree (GBDT) model, Logistic Regression (LR) model, etc.

[0192] In one possible implementation, please refer to Figure 6 , Figure 6 This is a schematic diagram of a communication data conversion process proposed in one or more embodiments of this specification. Specifically, the process involves determining the target data conversion relationship corresponding to the source application communication data, calling the target data conversion template corresponding to the target data conversion relationship from the data conversion template to perform communication data conversion on the source application communication data to obtain the target application communication data, and then performing platform service data communication with the target external application service based on the target application communication data. The following method can be used as a reference:

[0193] S402: Determine the data conversion type and communication conversion protocol corresponding to the source application communication data, and determine the target data conversion relationship based on the data conversion type and communication conversion protocol;

[0194] When the service platform detects "source application communication data," it first determines its "data conversion type." Specifically, by analyzing information such as the data's source IP and the request's gateway endpoint, it can determine whether the data originates from within or outside the platform, thereby identifying its "inbound" (corresponding to internal / external conversion type) or "outbound" attribute (corresponding to external / internal conversion type). Simultaneously, by analyzing the communication context (e.g., whether a session ID awaiting a response exists), it can determine whether the data is a "request" or a "response," thus determining whether the data conversion type corresponding to the source application communication data is external / internal or internal / external, and then identifying the specific "communication conversion protocol."

[0195] Furthermore, based on the data conversion type and communication conversion protocol, a target data conversion relationship is formed. Optionally, the target data conversion relationship can be in the form of a composite key.

[0196] S404: Query the target data conversion template corresponding to the target data conversion relationship from the data conversion template, use the target data conversion template and the data conversion type to perform communication data conversion on the source application communication data to obtain target application communication data, and perform platform service data communication with the target external application service based on the target application communication data and the data conversion type.

[0197] To illustrate, after obtaining the target data conversion template, a preset general conversion engine can be used. The general conversion engine will receive three inputs: a) "source application communication data"; b) the obtained "target data conversion template"; c) "data conversion type", and then perform communication data conversion to obtain the target application communication data.

[0198] The "data conversion type" serves as an important contextual parameter here, guiding the template to execute more complex logic. For example, the template can contain conditional logic, allowing the same template to execute one set of field mapping rules when processing "request" type data, and another set of rules when processing "response" type data, thereby achieving high template reusability and flexibility.

[0199] In one feasible implementation, after the data conversion is complete, the generated "target application communication data" is transmitted to the target party. Specifically:

[0200] If the "Data Conversion Type" is the external-to-internal conversion type corresponding to "Incoming Request", the converted data will be routed to the core service application within the platform for processing.

[0201] If the "Data Conversion Type" is the same as the "Outbound Response" type, the converted data will be used as the return value of a service call and sent to the external application service through the established connection.

[0202] If the "Data Conversion Type" is the inbound / outbound conversion type corresponding to "Outbound Request", it will proactively act as a client to initiate a new service call to the API endpoint of the target external application service.

[0203] In this way, the correct communication actions can be automatically executed according to different transaction scenarios.

[0204] In one feasible implementation, the specific execution of converting the source application communication data using the target data conversion template and the data conversion type to obtain target application communication data, and then conducting platform service data communication with the target external application service based on the target application communication data and the data conversion type, can refer to the following method:

[0205] If the data conversion type is an external-internal conversion type, then the source application communication data is converted using the target data conversion template to obtain internal application communication data, and the target external application service is processed as internal application data based on the internal application communication data.

[0206] If the data conversion type is an internal / external conversion type, then the source application communication data is converted using the target data conversion template to obtain external application communication data, and the external application communication data is sent to the target external application service for external application service data processing.

[0207] External-Internal Conversion Type: This indicates that the direction of data communication is from "external application services" to "internal platform applications". This type typically corresponds to scenarios where the platform receives external requests or responses.

[0208] Internal / External Conversion Type: This indicates that the direction of data communication is from "internal platform applications" to "external application services". This type typically corresponds to scenarios where the platform actively initiates requests or responses to external systems.

[0209] In one feasible implementation, for the processing flow of "external-internal conversion type," a "target data conversion template" is used to perform conversion operations on "source application communication data" from the external system. Since the target is an internal system, the output of the conversion is "internal application communication data." After the conversion is completed, "platform internal application data processing" is performed based on the internal application communication data. Specifically, this data can be routed or distributed to one or more core transaction applications within the platform (e.g., order processing center, risk control engine, user account service, etc.) for further service logic operations.

[0210] In one feasible implementation, regarding the processing flow of "internal / external conversion type," the "source application communication data" originates from an internal platform application. A "target data conversion template" is used to perform a conversion operation on this internal standard data, transforming it into "external application communication data" that conforms to the target external application service protocol specification, and then sending it to the designated interface address of the "target external application service" for "external application service data processing." This step completes a platform-to-external service call.

[0211] This specification defines unique data transformation relationships by combining "data transformation type" (e.g., internal / external transformation) with "communication transformation protocol" (e.g., specific interface version), enabling template location for complex, multi-scenario external services. Furthermore, by introducing "data transformation type" as a context parameter during transformation and communication, a single template can embed processing logic for different service processing scenarios (e.g., request or response), enhancing template reusability and processing capabilities. Ultimately, it can automatically determine whether the transformed data should be distributed internally for processing or sent externally as a response, improving automation levels.

[0212] The following will combine Figure 7 This manual provides a detailed description of the data communication device provided. It should be noted that... Figure 7 The data communication device shown is used to execute this specification. Figures 1-6 The methods of the embodiments shown are illustrated only in connection with this specification for ease of explanation. For specific technical details not disclosed, please refer to this specification. Figures 1-6 The example shown.

[0213] Please see Figure 7 This diagram illustrates the structure of the data communication device described in this specification. The data communication device 1 can be implemented as all or part of a user electronic device through software, hardware, or a combination of both. According to some embodiments, the data communication device 1 includes a mode triggering module 11, a template management module 12, and a data communication module 13, specifically used for:

[0214] The mode triggering module 11 is used to trigger the management mode and the running mode when the service platform communicates service data with external application services;

[0215] The template management module 12 is used to obtain the external service application communication protocol and the platform internal application communication protocol of at least one associated external application service in the management mode, and update the data conversion template corresponding to at least one data conversion relationship based on the external service application communication protocol and the platform internal application communication protocol.

[0216] Data communication module 13 is used to monitor source application communication data for target external application services in runtime mode, determine the target data conversion relationship corresponding to the source application communication data, call the target data conversion template corresponding to the target data conversion relationship from the data conversion template to perform communication data conversion on the source application communication data to obtain target application communication data, and perform platform service data communication with the target external application service based on the target application communication data.

[0217] In one feasible implementation, updating the data conversion template corresponding to at least one data conversion relationship based on the external service application communication protocol and the platform internal application communication protocol includes:

[0218] Based on the external service application communication protocol, an external communication reference data template is determined, and based on the platform's internal application communication protocol, an internal system standard communication model is determined.

[0219] Based on the external communication reference data template and the external service application communication protocol, extract the external system data definition structure information corresponding to each external application service; and extract the internal system data definition structure information based on the platform internal application communication protocol and the internal system standard communication model.

[0220] Based on the external system data definition structure information and the internal system data definition structure information, communication data matching is performed to obtain at least one data conversion template corresponding to a data conversion relationship.

[0221] In one feasible implementation, the step of matching communication data based on the external system data definition structure information and the internal system data definition structure information to obtain a data conversion template corresponding to at least one data conversion relationship includes:

[0222] The external system data definition structure information and the internal system data definition structure information are input into the large data transformation and processing model;

[0223] The large data transformation and processing model extracts semantic features, structural features, and structural features of the first and second fields from the first field in the external service communication data structure and the second field in the internal system data structure. Based on the first and second field semantic features, field semantic matching is performed to obtain field semantic association information, and based on the first and second field structural features, field result matching is performed to obtain field structural similarity information.

[0224] Based on the semantic association information and structural similarity information of the fields, the large data conversion processing model determines the field mapping relationship between the first field and the second field. Based on all the field mapping relationships, it determines the data conversion relationship between the external service application communication protocol and the platform internal application communication protocol, and generates at least one data conversion template for the data conversion relationship. The data conversion relationship is the communication data conversion relationship corresponding to the external service application communication protocol and the platform internal application communication protocol.

[0225] In one feasible implementation, the step of extracting semantic features of the first field, semantic features of the second field, structural features of the first field, and structural features of the second field from the first field in the external service communication data structure and the second field in the internal system data structure through the data transformation and processing model includes:

[0226] The large data transformation and processing model extracts first field parameters and first field context descriptions for each first field in the external service communication data structure, and generates first field semantic features based on the first field parameters and first field context descriptions. Similarly, it extracts second field parameters and second field context descriptions for each second field in the internal system data structure, and generates second field semantic features based on the second field parameters and second field context descriptions.

[0227] The large model for data transformation and processing constructs an external protocol data structure tree corresponding to the external service communication data structure and an internal protocol data structure tree corresponding to the internal system data structure. The tree structure is parsed for each first field node in the external protocol data structure tree to obtain the first field structure feature, and the tree structure is parsed for each second field node in the internal system data structure to obtain the second field structure feature.

[0228] The process of determining the field mapping relationship between the first field and the second field through the data transformation and processing model based on the field semantic association information and the field structural similarity information includes:

[0229] The large model for data transformation and processing constructs a first comprehensive feature vector for each of the first fields and a second comprehensive feature vector for each of the second fields based on the semantic association information and structural similarity information of the fields. Field mapping matching is then performed based on the first comprehensive feature vector and the second comprehensive feature vector to obtain the field mapping relationship between the first field and the second field.

[0230] In one feasible implementation, the step of obtaining field semantic association information by performing field semantic matching based on the first field semantic features and the second field semantic features, and obtaining field structure similarity information by performing field result matching based on the first field structure features and the second field structure features, includes:

[0231] Based on the semantic features of the first field and the semantic features of the second field, a target service domain knowledge graph is determined. The target service domain knowledge graph is called to vectorize the semantic features of the first field and the semantic features of the second field to obtain the semantic service vector of the first field and the semantic service vector of the second field. Based on the semantic service vector of the first field and the semantic service vector of the second field, bidirectional semantic matching is performed to obtain the semantic association information between the first field and the second field.

[0232] Based on the structural features of the first field, the basic path sequence of the first field, the parent node information of the first field, and the set of sibling nodes of the first field are determined. Based on the structural features of the second field, the basic path sequence of the second field, the parent node information of the second field, and the set of sibling nodes of the second field are determined. Based on the basic path sequence of the first field and the basic path sequence of the second field, structural path matching is performed to determine the structural association similarity of multiple potentially related field pairs. Based on the parent node information of the first field, the parent node information of the second field, the set of sibling nodes of the first field, and the set of sibling nodes of the second field corresponding to the potentially related field pairs, the structural association similarity is semantically weighted to obtain the potential structural association similarity of the potentially related field pairs. Based on the potential structural association similarity, field structural similarity information is generated.

[0233] In one feasible implementation, the step of determining the target data conversion relationship corresponding to the source application communication data, calling the target data conversion template corresponding to the target data conversion relationship from the data conversion template to perform communication data conversion on the source application communication data to obtain target application communication data, and performing platform service data communication with the target external application service based on the target application communication data includes:

[0234] Determine the data conversion type and communication conversion protocol corresponding to the source application communication data, and determine the target data conversion relationship based on the data conversion type and communication conversion protocol;

[0235] The target data conversion template corresponding to the target data conversion relationship is queried from the data conversion template. The target data conversion template and the data conversion type are used to convert the source application communication data to obtain the target application communication data. Based on the target application communication data and the data conversion type, platform service data communication is performed with the target external application service.

[0236] In one feasible implementation, the step of converting the source application communication data using the target data conversion template and the data conversion type to obtain target application communication data, and then performing platform service data communication with the target external application service based on the target application communication data and the data conversion type, includes:

[0237] If the data conversion type is an external-internal conversion type, then the source application communication data is converted using the target data conversion template to obtain internal application communication data, and the target external application service is processed as internal application data based on the internal application communication data.

[0238] If the data conversion type is an internal / external conversion type, then the source application communication data is converted using the target data conversion template to obtain external application communication data, and the external application communication data is sent to the target external application service for external application service data processing.

[0239] It should be noted that the data communication device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the data communication method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the data communication device and the data communication method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0240] The serial numbers in this specification are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0241] This specification also provides a computer storage medium capable of storing multiple instructions adapted to be loaded and executed by a processor as described above. Figures 1-6 The data communication method described in the illustrated embodiment can be found in the following document for a detailed execution process. Figures 1-6 The specific details of the illustrated embodiments will not be elaborated here.

[0242] This specification also provides a computer program product that stores at least one instruction, said at least one instruction being loaded and executed by the processor as described above. Figures 1-6 The data communication method described in the illustrated embodiment can be found in the following document for a detailed execution process. Figures 1-6 The specific details of the illustrated embodiments will not be elaborated here.

[0243] Please refer to Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of this specification. The electronic device in this specification may include one or more of the following components: a processor 1010, a memory 1020, an input device 1030, an output device 1040, and a bus 1050. The processor 1010, memory 1020, input device 1030, and output device 1040 may be connected to each other via the bus 1050.

[0244] Processor 1010 may include one or more processing cores. Processor 1010 connects to various parts of the electronic device using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 1020, and by calling data stored in memory 1020. Optionally, processor 1010 may be implemented using at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). Processor 1010 may integrate one or more of a central processing unit (CPU), graphics processing unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into processor 1010 and may be implemented separately through a communication chip.

[0245] The memory 1020 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 1020 may include non-transitory computer-readable storage medium. The memory 1020 may be used to store instructions, programs, code, code sets, or instruction sets.

[0246] The input device 1030 is used to receive input instructions or data, and includes, but is not limited to, a keyboard, mouse, camera, microphone, or touch device. The output device 1040 is used to output instructions or data, and includes, but is not limited to, a display device and a speaker. In this embodiment, the input device 1030 can be a temperature sensor for acquiring the operating temperature of the electronic device. The output device 1040 can be a speaker for outputting audio signals.

[0247] In addition, those skilled in the art will understand that the structure of the electronic device shown in the above figures does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the electronic device may also include radio frequency circuits, input units, sensors, audio circuits, wireless fidelity (WIFI) modules, power supplies, Bluetooth modules, etc., which will not be described in detail here.

[0248] In the embodiments of this specification, the executing entity for each step can be the electronic device described above. Optionally, the executing entity for each step can be the operating system of the electronic device. The operating system can be Android, iOS, or other operating systems; this specification does not limit this.

[0249] exist Figure 8 In the electronic device, the processor 1010 can be used to call a program stored in the memory 1020 and execute it to implement the data communication method as described in the various method embodiments of this specification.

[0250] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory, or random access memory, etc.

[0251] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this specification are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the service data involved in this specification were obtained under full authorization.

[0252] The above-disclosed embodiments are merely preferred embodiments of this specification and should not be construed as limiting the scope of this specification. Therefore, any equivalent variations made in accordance with the claims of this specification shall still fall within the scope of this specification.

Claims

1. A data communication method, characterized in that, Applied to a service platform, the method includes: When the service platform communicates service data with external application services, it triggers management mode and runtime mode. In management mode, the external service application communication protocol and the platform internal application communication protocol of at least one associated external application service are obtained. An external communication reference data template is determined based on the external service application communication protocol. An internal system standard communication model is determined based on the platform internal application communication protocol. External system data definition structure information corresponding to each external application service is extracted based on the external communication reference data template and the external service application communication protocol. Internal system data definition structure information is extracted based on the platform internal application communication protocol and the internal system standard communication model. Communication data matching is performed based on the external system data definition structure information and the internal system data definition structure information to obtain a data conversion template corresponding to at least one data conversion relationship. In runtime mode, the source application communication data for the target external application service is monitored, the target data conversion relationship corresponding to the source application communication data is determined, the target data conversion template corresponding to the target data conversion relationship is called from the data conversion template to perform communication data conversion on the source application communication data to obtain the target application communication data, and platform service data communication is performed with the target external application service based on the target application communication data. The external communication reference data template includes external communication data instances and external communication protocol specifications; the internal system standard communication model includes internal communication protocol specifications; the internal system data definition structure information is metadata information for internal service application communication protocols; and the external system data definition structure information is metadata information for internal service application communication protocols.

2. The method according to claim 1, characterized in that, The process of obtaining at least one data conversion template corresponding to a data conversion relationship by matching communication data based on the external system data definition structure information and the internal system data definition structure information includes: The external system data definition structure information and the internal system data definition structure information are input into the large data transformation and processing model; The large data transformation and processing model extracts semantic features, structural features, and structural features of the first and second fields from the first field in the external service communication data structure and the second field in the internal system data structure. Based on the first and second field semantic features, field semantic matching is performed to obtain field semantic association information, and based on the first and second field structural features, field result matching is performed to obtain field structural similarity information. Based on the semantic association information and structural similarity information of the fields, the large data conversion processing model determines the field mapping relationship between the first field and the second field. Based on all the field mapping relationships, it determines the data conversion relationship between the external service application communication protocol and the platform internal application communication protocol, and generates at least one data conversion template for the data conversion relationship. The data conversion relationship is the communication data conversion relationship corresponding to the external service application communication protocol and the platform internal application communication protocol.

3. The method according to claim 2, characterized in that, The data transformation and processing model extracts semantic features, structural features, and structural features of the first and second fields from the first field in the external service communication data structure and the second field in the internal system data structure, including: The large data transformation and processing model extracts first field parameters and first field context descriptions for each first field in the external service communication data structure, and generates first field semantic features based on the first field parameters and first field context descriptions. Similarly, it extracts second field parameters and second field context descriptions for each second field in the internal system data structure, and generates second field semantic features based on the second field parameters and second field context descriptions. The large model for data transformation and processing constructs an external protocol data structure tree corresponding to the external service communication data structure and an internal protocol data structure tree corresponding to the internal system data structure. The tree structure is parsed for each first field node in the external protocol data structure tree to obtain the first field structure feature, and the tree structure is parsed for each second field node in the internal system data structure to obtain the second field structure feature. The process of determining the field mapping relationship between the first field and the second field through the data transformation and processing model based on the field semantic association information and the field structural similarity information includes: The large model for data transformation and processing constructs a first comprehensive feature vector for each of the first fields and a second comprehensive feature vector for each of the second fields based on the semantic association information and structural similarity information of the fields. Field mapping matching is then performed based on the first comprehensive feature vector and the second comprehensive feature vector to obtain the field mapping relationship between the first field and the second field.

4. The method according to claim 2, characterized in that, The process of obtaining field semantic association information by performing field semantic matching based on the first field semantic features and the second field semantic features includes: A target service domain knowledge graph is determined based on the semantic features of the first field and the semantic features of the second field. The target service domain knowledge graph is invoked to vectorize the semantic features of the first field and the semantic features of the second field to obtain the semantic service vector of the first field and the semantic service vector of the second field. Based on the semantic service vector of the first field and the semantic service vector of the second field, bidirectional semantic matching is performed to obtain the semantic association information between the first field and the second field.

5. The method according to claim 2, characterized in that, The process of obtaining field structure similarity information by matching field results based on the first field structure feature and the second field structure feature includes: Based on the structural features of the first field, the basic path sequence of the first field, the parent node field information of the first field, and the sibling node set information of the first field are determined. Based on the structural features of the second field, the basic path sequence of the second field, the parent node field information of the second field, and the sibling node set information of the second field are determined. Based on the basic path sequence of the first field and the basic path sequence of the second field, structural path matching is performed to determine the structural association similarity of multiple potentially related field pairs. Based on the parent node information of the first field, the parent node information of the second field, the sibling node set information of the first field, and the sibling node set information of the second field corresponding to the potentially related field pairs, the structural association similarity is semantically weighted to obtain the potential structural association similarity of the potentially related field pairs. Based on the potential structural association similarity, field structural similarity information is generated.

6. The method according to claim 1, characterized in that, The step of determining the target data conversion relationship corresponding to the source application communication data, calling the target data conversion template corresponding to the target data conversion relationship from the data conversion template to perform communication data conversion on the source application communication data to obtain target application communication data, and performing platform service data communication with the target external application service based on the target application communication data includes: Determine the data conversion type and communication conversion protocol corresponding to the source application communication data, and determine the target data conversion relationship based on the data conversion type and communication conversion protocol; The target data conversion template corresponding to the target data conversion relationship is queried from the data conversion template. The target data conversion template and the data conversion type are used to convert the source application communication data to obtain the target application communication data. Based on the target application communication data and the data conversion type, platform service data communication is performed with the target external application service.

7. The method according to claim 6, characterized in that, The process of converting the source application communication data using the target data conversion template and the data conversion type to obtain target application communication data, and then communicating with the target external application service based on the target application communication data and the data conversion type, includes: If the data conversion type is an external-internal conversion type, then the source application communication data is converted using the target data conversion template to obtain internal application communication data, and the target external application service is processed as internal application data based on the internal application communication data. If the data conversion type is an internal / external conversion type, then the source application communication data is converted using the target data conversion template to obtain external application communication data, and the external application communication data is sent to the target external application service for external application service data processing.

8. A data communication device, characterized in that, The device, applied to a service platform, includes: The mode triggering module is used to trigger the management mode and the running mode when the service platform communicates service data with external application services; The template management module is used in management mode to obtain the external service application communication protocol and the platform internal application communication protocol of at least one associated external application service; determine the external communication reference data template based on the external service application communication protocol; determine the internal system standard communication model based on the platform internal application communication protocol; extract the external system data definition structure information corresponding to each external application service based on the external communication reference data template and the external service application communication protocol; extract the internal system data definition structure information based on the platform internal application communication protocol and the internal system standard communication model; and perform communication data matching based on the external system data definition structure information and the internal system data definition structure information to obtain a data conversion template corresponding to at least one data conversion relationship. The data communication module is used to monitor source application communication data for a target external application service in runtime mode, determine the target data conversion relationship corresponding to the source application communication data, call the target data conversion template corresponding to the target data conversion relationship from the data conversion template to perform communication data conversion on the source application communication data to obtain target application communication data, and perform platform service data communication with the target external application service based on the target application communication data. The external communication reference data template includes external communication data instances and external communication protocol specifications; the internal system standard communication model includes internal communication protocol specifications; the internal system data definition structure information is metadata information for internal service application communication protocols; and the external system data definition structure information is metadata information for internal service application communication protocols.

9. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions, which are adapted to be loaded by a processor and executed as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product stores at least one instruction, which is loaded by a processor and executed as described in any one of claims 1 to 7.

11. An electronic device, characterized in that, include: A processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and executed as described in any one of claims 1 to 7.

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