Integrated design method for cross-domain adaptation of industrial system based on multi-dimensional industrial characteristic mapping

Abstract

The invention belongs to the technical field of industrial system integration, and provides a design method for rapid adaptive integration of a cross-domain system. Through the method, developers extract business logic rules of the industry, standardize characteristic mapping and select, develop and integrate functional modules, logic processes and scene components of the cross-domain system, so that the cross-domain system which conforms to industry characteristics and meets business circulation is formed, and meanwhile, the development efficiency of the cross-domain system is improved. The newly added industry logic component can be classified into each industry characteristic model based on the knowledge base, and a multi-dimensional characteristic vector is generated in the platform, so that cross-industry integrated component multiplexing is realized, and the cross-field applicability and flexibility of the platform are further improved. Modularized construction and configuration type development are supported, mapping and matching functions are carried out on various industrial manufacturing industry characteristics such as machining, electronic and electrical appliances and equipment sets, the requirements for subdivision production in the industrial field and rapid adaptation of business processes are met, the system integration cost is reduced, and the application development efficiency is greatly improved.

Description

TECHNICAL FIELD

[0001] The method described in the present application provides a convenient and efficient industry application development and integration environment for industrial manufacturing enterprises through a modular architecture and an open design. The present application relates to the technical field of industrial internet and system integration, in particular to a method for cross-domain adaptive integration design of industrial systems based on multi-dimensional industry characteristic mapping. The method provides a convenient and efficient cross-industry application development and integration environment for industrial manufacturing enterprises through a multi-layer modular architecture and an open design, which can support unified abstraction and rapid adaptation of various industry characteristics such as mechanical processing, electronic appliances, equipment complete sets, etc. BACKGROUND

[0002] Currently, the production process of industrial manufacturing enterprises usually involves equipment monitoring, production scheduling, quality management, logistics management and other business systems. These systems are mostly provided by different suppliers and use different protocols and data formats, and lack effective integration between them, forming data islands. Existing industrial internet platforms provide data collection and analysis functions, but still have deficiencies in cross-system integration and flexible application construction. More importantly, different industries (such as mechanical processing, electronic appliances, equipment complete sets, etc.) have their own unique production processes, equipment standards and data models. The existing platform lacks a unified abstraction and mapping mechanism for industry characteristics, making it difficult for the same platform to be directly reused in multiple fields. With the development of cloud computing and artificial intelligence, various industrial platforms have begun to gather computing power, data, algorithms and other elements to quickly build applications in a low-code manner, but still face problems such as insufficient multi-industry adaptation capability, low industry component reuse efficiency and long project implementation cycle. Therefore, an integration method is needed that can abstract, match and rapidly adapt the multi-dimensional characteristics of different manufacturing industries to improve the cross-field flexibility and intelligence level of the platform. SUMMARY

[0003] In view of the deficiencies in the prior art, the present application provides an industrial system cross-domain adaptation integrated design method based on multi-dimensional industry characteristic mapping. The method adopts a multi-layer modular architecture design, including function units such as a basic service module, an industry characteristic mapping module, an intelligent decision module, an application customization module, an integrated scheduling module, a connector module, a component market module, a deployment management module, a running monitoring module and an open platform module. The responsibilities of each functional module are clear: the basic service module provides core services such as system management, user authentication, data storage and security audit; the intelligent decision module provides rule reasoning and prediction analysis based on AI; the application customization module supports defining business processes and algorithm logic through a visual interface or configuration file; the integrated scheduling module is used to define and execute cross-module task flows; the connector module provides multi-protocol adaptation interfaces, facilitating unified access to field devices and business systems; the component market module collects industry algorithms, models and process components for developers to reuse and share; and the open platform module provides standardized API interfaces and access portals, supporting third-party application and service access and promoting the expansion of the platform ecosystem.

[0004] The present application adds an industry characteristic mapping module, which is used to abstractly analyze and match the key characteristics of various manufacturing industries (such as mechanical processing, electronic appliances, complete equipment, etc.), including production process, equipment parameters, data semantics, etc., and map them to the general components and processes of the platform, thereby realizing cross-industry model reuse and rapid adaptation. The industry characteristic mapping module is composed of an industry knowledge base and a mapping engine, maintains the feature dictionary and mapping rules of each industry, uses multi-dimensional feature vectors for matching calculation, and supports the abstraction and rapid migration of different industrial manufacturing field characteristics.

[0005] The method of the present application has the following core capabilities: - Multi-system integration service: through the connector module and the open platform module, unified access and management of various heterogeneous systems and devices such as PLC, SCADA, MES, ERP, etc. are realized, supporting cross-level data collection and business collaboration; - Industry component reuse: the component market module provides a rich set of reusable components, supporting rapid assembly of sensor data processing, production optimization, quality detection and other industry processes; - Open interface call: the open platform module provides a unified API, so that external software systems and cloud services can call platform functions to realize cross-organizational and cross-regional collaboration; - Task flow scheduling and intelligent early warning: the integrated scheduling module supports visual task flow arrangement, which can start tasks according to event triggers or timing strategies; the intelligent decision module provides real-time early warning of production abnormalities through built-in rules and machine learning models; System visualization management: The method has built-in large screen visualization and report tools, which provide intuitive display and alarm prompt for key indicators such as production running status and equipment health; The method of the application has the following core capabilities: Multi-system integration service: Through the connector module and the open platform module, various heterogeneous systems and devices such as PLC, SCADA, MES and ERP are uniformly accessed and managed, supporting cross-level data collection and business collaboration; Industry component reuse: The component market module provides a rich set of reusable industry components, supporting rapid assembly of sensor data processing, production optimization, quality detection and other industry processes; Open interface call: The open platform module provides a unified API, so that external software systems and cloud services can call platform functions to realize cross-organization and cross-regional collaboration; Task flow scheduling and intelligent early warning: The integrated scheduling module supports visual task flow arrangement, and can start tasks according to event triggers or timing strategies; The intelligent decision module can provide real-time early warning for production abnormalities through built-in rules and machine learning models; System visualization management: The method has built-in large screen visualization and report tools, which provide intuitive display and alarm prompt for key indicators such as production running status and equipment health; In addition, through the industry characteristic mapping mechanism, the platform can quickly create and migrate application models for different manufacturing fields, decoupling industry commonality and individuality during design, significantly improving the configurability and cross-industry reuse rate of the system.

[0006] The application adopts a modular and micro-service architecture, has good scalability, and can flexibly increase functional modules according to requirements. The platform provides rich visual configuration and low-code tools, reduces the development threshold of industrial applications; through a unified interface and multi-protocol adaptation, seamless integration of field devices, plant systems and enterprise systems is realized; the built-in intelligent analysis function can optimize and alarm the production process in real time, improve production efficiency and resource utilization; component-based design encourages algorithm and process reuse, forms a benign ecology, and greatly reduces development costs. The method design focuses on high availability and reliability, supports distributed deployment and multi-replica operation, realizes automatic switching and data protection in case of failure; supports multi-tenant isolation and strict access control to meet enterprise security and compliance requirements. Through the above design, platform developers can quickly build cross-industry applications through configuration based on pre-built components, greatly shortening the development cycle and reducing costs. For example, in the intelligent manufacturing scenario, operation and maintenance engineers can select industry components such as vibration monitoring, fault diagnosis and historical data analysis from the component market, and configure data processing processes combined with industry mapping rules, to deploy a multi-industry integrated device health prediction system in a short time, realize early warning of device failure and reduce maintenance costs.

[0007] Advantages The application realizes rapid adaptation and integration of industrial systems across industries by introducing a multi-dimensional industry characteristic mapping mechanism. Compared with the prior art, the method has the following advantages: - Improve cross-industry applicability: The industry characteristic mapping module abstracts and matches industry characteristics such as mechanical processing, electronic appliances, and complete equipment, enabling the platform to quickly reuse and migrate application components across different industry scenarios without the need to redesign the architecture for each industry.

[0008] - Enhance platform flexibility and intelligence: The multi-layer modular and micro-service architecture combined with the mapping mechanism supports multiple deployment modes such as edge gateway, private cloud or public cloud, and synchronizes data through a secure channel, balancing real-time performance and scalability. The industry mapping mechanism enables the platform to dynamically adjust the adaptation scheme, automatically load models and processes for the corresponding industry, and improve the intelligence level and flexibility of the system.

[0009] - Reduce development and integration costs: Based on the component market and open API, combined with industry mapping rules, developers can quickly build cross-industry applications through drag-and-drop configuration, reducing hand-written code and repeated development; the unified interface and multi-protocol adaptation function realize seamless connection between field devices and enterprise systems, significantly shortening the system integration cycle and reducing maintenance costs.

[0010] - Enhanced system reliability and security: The platform design supports distributed deployment and multi-tenant isolation, employing security technologies such as access control, audit logs, and encrypted communication to ensure data isolation and secure operation. The industry mapping module works in conjunction with basic services, improving data quality through standardized industry data models and providing secure verification and auditing for cross-domain access. Attached Figure Description

[0011] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the platform architecture of the present invention, illustrating the relationship between the device layer, gateway layer, platform layer and application layer, wherein the platform layer integrates various functional modules, including an industry characteristic mapping module. Figure 2 This is a schematic diagram for system integration across multiple industries, illustrating a scenario where data interaction and unified management of heterogeneous devices and platforms are achieved through a multi-dimensional industry characteristic mapping mechanism; Figure 3 This diagram illustrates the integration of big data processing and applications, showcasing the data flow and invocation relationships between the component marketplace module, intelligent decision-making module, and open platform module, as well as the role of industry characteristic mapping in this process. Detailed Implementation

[0012] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention. Example

[0013] This embodiment introduces the overall architecture and main module functions of the method. The method is divided into four layers: device layer, gateway layer, platform layer, and application layer. The device layer includes industrial equipment and sensors used to collect various data from the production site; the gateway layer is used to preprocess the data collected by the device layer and securely transmit it to the platform; the platform layer is the core of this invention, including a basic service module, an industry characteristic mapping module, an intelligent decision-making module, an application customization module, a connector module, a component marketplace module, a deployment management module, an operation monitoring module, and an open platform module. The basic service module provides the platform with infrastructure such as user management, permission authentication, message bus, and database services; specifically, the basic service module includes sub-modules such as multi-tenant management, permission control, and system logs to ensure the high availability and secure operation of the method. The intelligent decision-making module includes a knowledge-based rule engine and machine learning model, which can analyze data in the production process and generate early warnings; the application customization module provides users with a visual configuration interface for customizing business processes and algorithm logic; the deployment management module is responsible for deploying customized applications to the runtime environment and managing versions; the operation monitoring module collects the running status and performance indicators of each module in real time, supporting fault diagnosis and maintenance. The industry characteristic mapping module maintains characteristic models for various industries, including equipment models, process models, and data models. It maps business needs from different fields to the platform's common components and processes, enabling rapid cross-industry adaptation.

[0014] This approach employs a microservice architecture and containerized deployment, allowing each functional module to be independently expanded and deployed, while communicating with each other through a unified message bus or RESTful interface. The platform incorporates load balancing and elastic scaling mechanisms, supporting distributed deployment and online updates, and utilizes access control and audit logs to ensure system security. The basic service module also includes a time-series database for storing time-series data collected by devices, and supports external data querying and visualization services. The connector module provides a visual interface management interface, facilitating user registration and configuration of various system interfaces, and includes data encryption and flow control functions to ensure secure data access across different protocols. This approach supports edge or cloud deployment modes, allowing platform components to be deployed on industrial gateways or run in enterprise private or public clouds, synchronizing data through secure channels to balance real-time performance and scalability. The industry-specific mapping module can be deployed collaboratively with the basic service module at the edge or cloud, semantically annotating data during device access and performing preliminary transformations according to industry rules to improve the real-time performance of cross-industry data processing. The platform employs a unified identity authentication mechanism, setting access permission policies for each module and API to ensure the security of application data and algorithms. The basic service module has a built-in access control function, which can assign different access permissions to different roles and record operation audit logs to meet industrial information security requirements. Example

[0015] This embodiment relates to the integration of various devices in an industrial manufacturing site with the method of this invention, demonstrating an operational scenario where multi-source heterogeneous data access and process linkage are achieved through a connector module and an industry mapping mechanism. In this embodiment, various types of device terminals are deployed in the industrial site, including but not limited to PLC controllers, CNC machine tools, sensor nodes, vision inspection systems, and barcode acquisition devices. These devices, due to different manufacturers, use different communication protocols, such as Modbus, OPCUA, EtherNet / IP, HTTP, and MQTT, forming a multi-source heterogeneous data environment. To achieve unified integration and standardized data processing, the platform uses a connector module to facilitate data interoperability between the devices and the platform. The connector module has built-in multiple protocol adapters, which can perform protocol parsing, format standardization, and authentication of data collected by field devices, and transmit it to the platform's core processing layer via a data bus. Furthermore, the industry characteristic mapping module performs semantic annotation on the accessed data during this process, combining equipment type and process semantics to convert the data into a platform-wide format for subsequent component processing.

[0016] At the platform layer, the integrated scheduling module is responsible for defining the processing flow of device data. Through a graphical task flow configuration interface, users can sequentially connect the "Device Data Acquisition" node with modules such as "Data Cleaning," "Model Analysis," and "Early Warning Trigger" to build an automated processing chain. Each task node can be configured with trigger conditions (such as timed, event-driven, or device status change), supporting parallel tasks, multi-instance operation, and process interruption recovery mechanisms. The industry-specific mapping module plays a role in the process orchestration stage. It automatically selects or converts suitable processing components based on the business rules of different industrial sectors, enabling the unified process to flexibly adapt to different scenarios.

[0017] The intelligent decision-making module analyzes the incoming data in real time, judging the equipment's operating status based on a rule engine and machine learning model. For example, when equipment vibration exceeds a threshold, the platform can immediately trigger an early warning process. At this time, the operation monitoring module is responsible for continuously tracking the operating status of on-site equipment and various functional modules, displaying key indicators such as data upload frequency, task execution success rate, and average response time for each device. When an anomaly occurs, the system automatically calls the intelligent decision-making module to perform root cause analysis and output handling suggestions. The industry characteristic mapping module works in conjunction with monitoring and decision-making to mark abnormal events as key indicators specific to the industry, improving the accuracy and relevance of alarms. Through this embodiment, industrial enterprises can achieve rapid integration, centralized data management, and intelligent response for various workshop equipment; this solution improves the efficiency of equipment data utilization and system linkage, providing fundamental support for transparent production processes, visualized anomalies, and intelligent maintenance. Example

[0018] This embodiment provides a big data processing and system integration solution for industrial manufacturing scenarios. It demonstrates the entire process of calling various analytical components through a component marketplace module, combining data processing with intelligent decision-making and industry mapping modules, and then outputting the results as a service through an open platform module. In this embodiment, multi-source heterogeneous data from the production site (including equipment status data, process parameters, production logs, quality inspection results, etc.) is first connected to the platform through a connector module, uniformly converted to the platform's standard format, and written to the data bus. The industry characteristic mapping module performs unified label mapping and preprocessing on data from different sources during data entry, such as renaming or standardizing data fields according to industry characteristics, to ensure that subsequent modules can correctly understand the meaning of the data. The data bus, as the core channel for communication between modules within the platform, enables high-speed data transmission between multiple functional modules.

[0019] The component marketplace module acts as a resource scheduling center, providing multiple functional components including data preprocessing, feature extraction, model inference, and result write-back. Developers select the necessary data processing components from the marketplace based on business needs and combine them into standard processing flows through task orchestration. An industry-specific mapping mechanism plays a crucial role in this process: the system automatically matches standard process templates and data models for the corresponding industry based on the component types and business scenarios within the flow. For example, in a quality inspection scenario, the platform can recommend image processing and statistical analysis components and perform data calibration based on industry quality standards, thereby ensuring that the processing flow meets industry requirements.

[0020] After data flows into the platform, it undergoes real-time processing through the aforementioned component chain. Finally, the intelligent decision-making module judges and infers from the data, outputting prediction results and suggested operational information. The processing results can be service-encapsulated and exposed via a unified open platform module. This open platform module supports registering any processing flow as a callable API interface for external systems (such as MES, ERP, mobile apps, etc.) to access and call. The industry-specific mapping module also plays a role here: the platform can provide industry tags during service registration, and external systems can call the corresponding model version based on the industry tag, ensuring that the service interface provides a consistent return format and business semantics for system calls across different domains.

[0021] Furthermore, the platform supports access control, authentication, and access logging for open API calls, ensuring the security and traceability of data services. Service results can be pushed to external systems via REST API or returned to the platform's local visualization module for generating real-time reports and business dashboards. The solution provided in this embodiment achieves a complete closed loop from raw industrial data acquisition, componentized processing, intelligent inference to API service output, significantly improving data processing efficiency and application deployment speed, and providing manufacturing enterprises with an efficient and reusable intelligent service infrastructure.

[0022] The above embodiments are only used to illustrate the technical solutions of the present invention, and their scope is not limited thereto. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions described in the foregoing embodiments.

Claims

1. A cross-domain adaptive integration design method for industrial systems based on multi-dimensional industry characteristic mapping, characterized in that, include: The platform comprises four modules: Basic Services, Industry Feature Mapping, Intelligent Decision-Making, Application Customization, Integration Scheduling, Connector, Component Marketplace, Deployment Management, Operation Monitoring, and Open Platform. The Basic Services module provides fundamental services such as system management, user authentication, data storage, and logging. The Industry Feature Mapping module abstracts and analyzes the manufacturing characteristics of different industries (e.g., machining, electronics, equipment assembly) and maps them to platform components and business processes to support rapid cross-industry adaptation and integration. The Intelligent Decision-Making module provides predictive analysis and early warning based on artificial intelligence algorithms. The Application Customization module allows developers to define and build application business logic through configuration. The Integration Scheduling module orchestrates and schedules cross-module task flows. The Connector module provides interface adaptation for multiple heterogeneous business systems and devices. The Component Marketplace module stores and manages reusable industry components. The Deployment Management module is responsible for the deployment, version management, and release of application components. The Open Platform module provides open APIs for external system access, enabling ecosystem expansion and secondary development.

2. The method according to claim 1, characterized in that, The basic services module also includes multi-tenant management functionality to support resource isolation and access control between different organizations or departments; the intelligent decision-making module includes a rules engine and a machine learning engine to execute rule control and deep learning inference in the production process. The application customization module supports low-code configuration; the industry feature mapping module includes an industry knowledge base and a mapping engine, which are used to maintain the feature dictionary and mapping rules of each industry, and realize the abstract matching of different industry features through multi-dimensional feature vectors.

3. The method according to claim 1, characterized in that, The connector module includes various protocol adapters for data interconnection with different systems such as PLCs, sensors, monitoring systems, MES, and ERP. The integrated scheduling module has a task flow designer for defining data flow order and triggering conditions, and can realize parallel execution of multiple instances and timed or event-triggered process scheduling. The operation monitoring module monitors the operating status and performance indicators of each module and issues alarms for abnormal situations through an intelligent early warning mechanism. The industry characteristic mapping module works in conjunction with the connector module to achieve unified conversion of data formats and semantics of different industries by parsing and matching industry characteristic tags.

4. The method according to claim 1, characterized in that, The component marketplace module integrates industry algorithms and business logic components, and provides component version control and permission approval functions; the open platform module provides a unified service call interface, and manages the service addresses of each component through the registry center to achieve high availability and load balancing of services; this method provides software development kits and service documentation to support third-party developers to access and call platform services; The industry feature mapping module maintains the feature tags of industry components in the component market and supports cross-industry service discovery and invocation at the open platform level.

5. The method according to claim 1, characterized in that, This method enables the rapid generation of new cross-industry applications through configuration and combination: developers can select the required components from the component marketplace and connect them according to the task flow, combined with the cross-domain adaptation rules provided by the industry characteristic mapping mechanism, to quickly build application systems for specific production scenarios, significantly shortening the development cycle and reducing system integration costs.

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