A full-company-level digital management platform for robot intelligent manufacturing

Abstract

This invention relates to the field of enterprise information management technology, and in particular to a company-wide digital management platform for intelligent robotic manufacturing. By breaking down data silos through a data management layer, it provides unified and reliable data to the business service layer, forming a closed loop with the real-time data generated by the business service layer and the intelligent analysis layer. This enables AI predictions to provide immediate feedback and optimize business decisions. All results are ultimately presented uniformly through the user interaction layer, transforming data-driven and intelligent empowerment into a convenient one-stop operation for users. This collaboration completely changes the fragmented nature of traditional systems, forming a complete closed loop from data perception to intelligent decision-making to business execution, thus improving overall enterprise operational efficiency and the level of intelligent decision-making.

Description

Technical Field

[0001] This invention relates to the field of enterprise information management technology, and in particular to a company-wide digital management platform for intelligent robotic manufacturing. Background Technology

[0002] With the rapid development of information technology, enterprise operations management is undergoing a profound digital transformation. In current practice, enterprise management typically relies on a series of independently built, heterogeneous information systems, such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Supply Chain Management (SCM), Product Lifecycle Management (PLM), Manufacturing Execution System (MES), and Human Resources (HR) systems. These systems are often developed by different vendors at different times, using their own independent technology stacks and data standards, inevitably leading to a serious "data silo" phenomenon within the enterprise.

[0003] Specifically, existing technologies suffer from the following problems: First, systems are severely fragmented, making integration difficult. Inconsistent data standards and complex interfaces hinder data flow, leading to inefficient cross-system business process collaboration and high integration and maintenance costs. Second, the value of data is difficult to effectively extract. Because data is scattered across various independent systems, the lack of unified data governance and powerful analytical tools makes it difficult to conduct comprehensive, cross-domain correlation analysis, weakening the enterprise's decision support capabilities. Third, systems lack flexibility and scalability. Traditional monolithic application architectures struggle to respond quickly to business changes. When enterprises need to add new features or adjust existing business processes, large-scale system overhauls are often required, which are not only time-consuming but also risky. Finally, user experience is inconsistent. Employees frequently switch between different systems in their daily work, resulting in complex operating procedures, high learning costs, and severely impacting work efficiency and satisfaction.

[0004] While various general-purpose digital management platforms exist in the market, the specific field of robotic intelligent manufacturing places extremely high demands on the precision of project processes, the synergy of software and hardware development, the agility of the supply chain, and the real-time nature of production equipment data. These unique industry characteristics make it difficult for general-purpose platforms to meet these requirements. Therefore, there is an urgent need in this field for an integrated solution that can completely break down data barriers, achieve business collaboration, and provide intelligent decision support. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a company-wide digital management platform for intelligent manufacturing of robots, in order to solve the problems of system fragmentation, data silos, difficulties in business collaboration, and low level of intelligent decision-making in the existing technology.

[0006] In a first aspect, embodiments of the present invention provide a company-wide digital management platform for intelligent manufacturing of robots, the management platform comprising: The data management layer is used to integrate data from multiple heterogeneous data sources inside and outside the robot manufacturing enterprise, and to collect, store, govern and encapsulate the integrated data in a service-oriented manner to provide a unified data service interface. The business service layer consists of multiple independent business microservices that are decoupled based on domain-driven design. These microservices handle the business logic in the robot's R&D, manufacturing, delivery, and operation and maintenance processes, and interact with each other through service communication interfaces. Specifically, the business microservices call the unified data service interface provided by the data management layer to obtain data. The intelligent analysis layer is used to perform machine learning and / or real-time streaming data processing and analysis on data related to robot manufacturing, and to feed the analysis results back to the business service layer through standard interfaces to optimize business execution; The user interaction layer provides a unified system access point and dynamically combines functional interfaces and data views from different business microservices based on user identity and permission information through a micro-frontend architecture.

[0007] In conjunction with the first aspect, heterogeneous data sources include operational data collected by industrial IoT sensing devices on robotic production lines, as well as business data from at least one of enterprise resource planning systems, customer relationship management systems, product lifecycle management systems, and manufacturing execution systems.

[0008] In conjunction with the first aspect, the business microservices in the business service layer include at least one core business microservice group from the customer service domain, product service domain, project service domain, supply chain domain, production service domain, and after-sales service domain, as well as at least one support management microservice group from the financial management domain, human resource management domain, and operations management domain.

[0009] In conjunction with the first aspect, the customer service domain includes customer management services, demand analysis services, or solution management services; The product service domain includes product management services, design management services, or R&D management services; Project service domains include project management services, task management services, or milestone services; The supply chain domain includes procurement services, inventory services, or supplier services; The production services domain includes production management services, quality inspection services, or equipment management services; After-sales service includes after-sales service, maintenance service, or warranty service; The financial management domain includes budget management, cost control, accounting, or asset management services; The human resources management domain includes organizational structure, talent management, performance management, and compensation and benefits services. The operations management domain includes process management, quality management, compliance management, or document management services.

[0010] In conjunction with the first aspect, the intelligent services provided by the intelligent analysis layer include at least one of BI analysis services, risk management services, performance analysis services, or AI prediction services.

[0011] In conjunction with the first aspect, the intelligent analysis layer includes: The model library unit stores machine learning models used for sales forecasting, equipment failure early warning, or quality analysis. The stream processing unit is configured to perform real-time analysis of sensor data streams from the robot production line.

[0012] In conjunction with the first aspect, the user interaction layer includes: A unified portal platform, employing a micro-frontend architecture to enable personalized workbench customization; The single sign-on system implements unified identity authentication based on the OAuth 2.0 and OpenID Connect protocols; A role-based view system is used to provide multi-dimensional data presentations for management, department managers, employees, and customers.

[0013] In conjunction with the first aspect, the management platform also includes a secure access layer, which includes: The API gateway is configured to achieve unified route distribution, request rate limiting, and security protection. The certification center supports multi-factor authentication and certificate authentication. A distributed session management system that enables session timeout control and single sign-out.

[0014] In conjunction with the first aspect, the management platform is built on cloud-native infrastructure; cloud-native infrastructure includes: Container platform, based on Kubernetes, implements container orchestration and scheduling; DevOps platform, providing continuous integration and continuous deployment pipelines; The monitoring and alarm system enables intelligent monitoring of infrastructure, application performance, and business metrics.

[0015] In conjunction with the first aspect, the data management layer includes: The data lake storage module is used to store the collected multi-source heterogeneous data in its raw format; The data warehouse storage module is used to store structured data that has been cleaned, transformed, and modeled by subject area; The metadata management module is used to manage the metadata of data assets and record the source and destination of the data.

[0016] Secondly, this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor runs the computer program to cause the electronic device to perform the above-described method.

[0017] Thirdly, this application provides a readable storage medium storing computer program instructions, which are read and executed by a processor to perform the above-described method.

[0018] The embodiments of this invention bring the following beneficial effects: This application provides a company-wide digital management platform for intelligent robot manufacturing. This enterprise digital intelligent management platform includes: a data management layer, used to integrate data from multiple heterogeneous data sources inside and outside the robot manufacturing enterprise, and to collect, store, process, and encapsulate the integrated data for data services to provide a unified data access interface; a business service layer, composed of multiple independent business processing modules, which are used to process the business logic in the robot R&D, manufacturing, delivery, and operation and maintenance processes, and interact through a service communication interface, wherein the business processing modules call the unified data access interface provided by the data management layer to obtain data; an intelligent analysis layer, used to perform machine learning, real-time streaming data processing and analysis operations on data related to robot manufacturing, and to provide the analysis results to the business service layer; and a user interaction layer, used to provide a unified system access entry point, and to dynamically combine functional interfaces and data views from different business processing modules according to user identity and permission information. This application breaks down data silos through a data management layer, providing unified and reliable data to the business service layer. This forms a closed loop with the real-time data generated by the business service layer and the intelligent analysis layer, enabling AI predictions to provide immediate feedback and optimize business decisions. All results are ultimately presented uniformly through the user interaction layer, transforming data-driven and intelligent empowerment into a convenient one-stop operation for users. This collaboration completely changes the fragmented situation of traditional systems, forming a complete closed loop from data perception to intelligent decision-making to business execution, thereby improving the overall operational efficiency and decision-making intelligence of enterprises.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 A schematic diagram of an architecture for a company-wide digital management platform for intelligent manufacturing of robots, provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of another architecture for a company-wide digital management platform for intelligent manufacturing of robots, provided in an embodiment of the present invention. Figure 3 A flowchart illustrating the robot manufacturing business data processing method provided in an embodiment of the present invention; Figure 4 This is a hierarchical architecture diagram of the strategic decision-making and management execution layer in an embodiment of the present invention; Figure 5 This is a schematic diagram of the core business process of the business operation layer in an embodiment of the present invention; Figure 6 This is a schematic diagram of the core architecture of microservices and data platform in an embodiment of the present invention; Figure 7 This is a schematic diagram of the external system integration architecture in an embodiment of the present invention; Figure 8 This is a schematic diagram of cloud infrastructure and technology stack in an embodiment of the present invention; Figure 9 This is a schematic diagram of the architecture of the unified portal and secure access layer in an embodiment of the present invention; Figure 10 This is a schematic diagram of the business microservice domain architecture (decoupled by DDD) in an embodiment of the present invention; Figure 11 This is a schematic diagram of the intelligent service and data storage architecture in an embodiment of the present invention; Figure 12 This is a schematic diagram of the external system single sign-on integration architecture in an embodiment of the present invention; Figure 13 This is a schematic diagram of the architecture of the external data source and the data acquisition layer in an embodiment of the present invention; Figure 14 This is a schematic diagram of the data platform storage and governance architecture in an embodiment of the present invention; Figure 15 This is a schematic diagram of the microservice architecture of the business middle platform in an embodiment of the present invention; Figure 16 This is a schematic diagram of the architecture of the intelligent engine and the user interface layer in an embodiment of the present invention; Figure 17 This is a schematic diagram of the cloud infrastructure support architecture in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] To facilitate understanding of this embodiment, the technical terms used in this application will be briefly introduced below.

[0025] Data middle platform: refers to a unified platform that realizes data collection, storage, governance and service. It provides standardized and consistent data services for upper-layer applications through distributed storage, ETL processes and API encapsulation.

[0026] Microservice architecture: Divide a single application into a set of small, independently deployed services, each running in its own process and collaborating through lightweight communication mechanisms such as REST APIs.

[0027] Domain-Driven Design: A software design methodology that achieves high cohesion and low coupling by explicitly modeling business concepts as domain models in the software and defining business boundaries.

[0028] Micro-frontend architecture: The frontend application is split into multiple small applications that can be developed and deployed independently. They are combined at runtime through the main application to achieve technology stack independence and independent iteration.

[0029] RSSI: Received Signal Strength Indicator, used to measure the strength of wireless signals and can be used for a rough estimate of the distance between devices.

[0030] Kubernetes: A container orchestration and management platform responsible for the automatic deployment, scaling, and maintenance of containers.

[0031] DevOps: A set of practices that combine software development and IT operations to shorten system development lifecycles and deliver high-frequency results.

[0032] To facilitate understanding of this embodiment, the application scenarios and design concepts of this application embodiment will be briefly introduced below.

[0033] Traditional manufacturing enterprises commonly employ multiple independent systems (such as ERP, MES, and CRM), resulting in data silos, fragmented business operations, and a lack of intelligent decision-making. This is particularly true in the robotics manufacturing sector, where the entire value chain demands extremely high levels of data real-time performance and business collaboration. Existing general-purpose platforms struggle to meet the integrated and sophisticated management needs from R&D to after-sales service, hindering the industry's digital transformation.

[0034] Based on this, the embodiments of this application provide a company-wide digital management platform for intelligent manufacturing of robots, which breaks down data and system barriers, realizes business collaboration across the entire value chain, and deeply integrates artificial intelligence into business processes, thereby improving enterprise operational efficiency and decision-making intelligence.

[0035] Example 1 This application provides a first aspect: embodiments of the present invention provide a company-wide digital management platform for intelligent robotic manufacturing, combined with... Figure 1 As shown, the enterprise's digital intelligent management platform includes: a data management layer, a business service layer, an intelligent analysis layer, and a user interaction layer.

[0036] Figure 1 This is a schematic diagram of an architecture for a company-wide digital management platform for intelligent manufacturing of robots, provided in an embodiment of the present invention. The diagram clearly shows four layers and their interactions: the bottom layer is the data management layer, followed by the business service layer, the intelligent analysis layer, and the top layer is the user interaction layer. The arrows indicate that business requests flow from top to bottom (from the user interaction layer to the data management layer), while processing results and data services return from bottom to top, reflecting the clarity of dependencies and the concept of data-driven intelligence.

[0037] The following is a detailed description of each layer: The data management layer, as the data core of the entire system, is responsible for breaking down data silos. It integrates data from multiple heterogeneous data sources both inside and outside the robot manufacturing company, and collects, stores, governs, and service-encapsulates the integrated data to provide a unified data service interface. Specifically, for example... Figure 13 As shown, this layer includes a data acquisition and integration module. Through API gateways, message queues (such as Kafka), and ETL tools, it seamlessly integrates heterogeneous data sources both internally and externally, such as office collaboration systems (DingTalk), R&D management tools (ZenTao, GitLab, JIRA), enterprise resource planning systems (SAP ERP), customer relationship management systems (Salesforce, Qianyun CRM), product lifecycle management systems (PLM), manufacturing execution systems (MES), and industrial Internet of Things (IoT) sensing devices on robotic production lines. Figure 14As shown, the data lake / warehouse storage module employs distributed storage technologies (such as HDFS and S3) to standardize, clean, label, and classify the collected raw data, forming unified and reliable data assets. The data service and API module encapsulates the cleaned data into standardized RESTful API services, providing a unified data supply for upper-layer applications. Through these measures, the data management layer fundamentally solves the data silo problem caused by traditional point-to-point interface integration.

[0038] The business service layer consists of multiple independent business microservices decoupled based on Domain-Driven Design (DDD), such as... Figure 15 As shown. Figure 6 As shown, these microservices constitute a business middle platform, decoupling the enterprise's core business capabilities into a set of microservice components that can be independently developed, deployed, and scaled. Combined with... Figure 10 As shown, each microservice is divided based on clearly defined business boundaries and accessed uniformly through an API gateway. Services collaborate through standard interfaces (such as REST API or gRPC). Each microservice is independently developed, deployed, and scaled, with its own data storage, achieving service autonomy. Specifically, business microservices call the unified data service interface provided by the data management layer to obtain data, rather than directly accessing the original database, ensuring the uniqueness and reliability of the data source. This business service layer specifically includes at least one core business microservice group from the following domains: customer service, product service, project service, supply chain, production service, and after-sales service. For example, the customer service domain can be further subdivided into customer management services (providing a 360-degree view of customers), requirements analysis services (intelligent requirements classification and priority assessment), and solution management services (solution templates and version control); the production service domain can be subdivided into production management services (production planning and scheduling), quality inspection services (quality standards and quality inspection process control), and equipment management services (equipment ledgers and preventative maintenance). This microservice design gives the platform extreme flexibility and high scalability, enabling it to respond quickly to business changes like building blocks.

[0039] The intelligent analytics layer, serving as a horizontal enabling layer, is deeply integrated into the platform. This layer performs machine learning and / or real-time streaming data processing and analysis on data related to robot manufacturing, and feeds the analysis results back to the business service layer through standard interfaces to optimize business execution. For example... Figure 11As shown, the intelligent analysis layer accesses business data through standard interfaces, providing intelligent capabilities such as BI analysis services (multi-dimensional data analysis, self-service report generation), risk management services (real-time risk indicator monitoring, risk threshold early warning), performance analysis services (automatic KPI calculation and trend analysis), and AI prediction services (sales forecasting, equipment failure early warning). Specifically, this layer includes: a model library unit storing machine learning models for sales forecasting, equipment failure early warning, or quality analysis; and a stream processing unit (such as Spark Streaming or Flink) configured to perform real-time analysis of sensor data streams from the robot production line. For example, when sensors on the production line detect an abnormal equipment condition, the data is transmitted to the data platform in real time via an IoT gateway. The stream processing unit of the intelligent analysis layer identifies this abnormal value and immediately triggers the equipment operation and maintenance microservice in the business service layer, achieving proactive and intelligent operation and maintenance management. Figure 16 It further demonstrates the interaction between the intelligent engine and the user interface layer. The BI tool visualizes the analysis results on a unified portal, forming a complete closed loop from data to insights to decision-making.

[0040] The user interaction layer, also known as the unified interaction portal, provides a unified system access point (such as a web browser or mobile app). Figure 9 As shown, this layer, through a micro-frontend architecture (such as Single-SPA), dynamically combines functional interfaces and data views from different business microservices based on user identity and permission information, providing users with a personalized workbench. For example, through a single sign-on system (based on OAuth 2.0 and OpenID Connect protocols), users can access all authorized modules with a single login; through configurable widgets (such as "My To-Dos," "My Projects," and "My Reports"), functional and data dashboards from different microservices are dynamically aggregated, achieving "one platform for all business operations." The role-based view system (RBAC+ABAC hybrid permission model) ensures that different roles, such as management, department managers, employees, and customers, can see multi-dimensional data displays that match their permissions. Figure 12 and Figure 7 The solution demonstrates a single sign-on integration with external systems. Through a unified single sign-on server, users can access all authorized external systems with a single login, solving the cumbersome problem of logging into multiple systems in traditional enterprises.

[0041] In this embodiment, business requests flow from top to bottom (from the user interaction layer to the data management layer), while processing results and data services return from bottom to top. This structure ensures clear dependencies and highlights the digital management concept of data-driven intelligence, intelligence-enabled business, and business serving users. The synergistic effect of this series of technical measures enables the present invention to maintain a high degree of integration while possessing the ability to continuously evolve and intelligently evolve, which is particularly in line with the stringent requirements of the robotic intelligent manufacturing industry for data fusion, business agility, intelligent decision-making, and operational efficiency.

[0042] like Figure 2 As shown, the system's hierarchical architecture for strategic decision-making and management execution is further demonstrated, reflecting a data-driven decision-making process from corporate strategic vision to specific management execution.

[0043] Figure 2 This diagram illustrates another architecture of a company-wide digital management platform for intelligent robotic manufacturing, as provided in this embodiment of the invention. The diagram employs a vertically integrated design of strategy-execution-operation-support, with the layers arranged from top to bottom as follows: strategic decision-making layer, management execution layer, business operation layer, and a bottom layer of technology and data support system. The layers are connected by bidirectional arrows, representing a closed-loop mechanism of data aggregation upwards and instructions transmission downwards.

[0044] The strategic decision-making layer, located at the top of the architecture, serves as the central hub of intelligence. Based on the data and analysis results from the lower layers, it focuses on the company's strategic vision, conducts overall monitoring through strategic data dashboards, makes decisions based on risk control and performance management systems, and integrates artificial intelligence throughout the process to achieve intelligent analysis and prediction, driving the dynamic adjustment and optimization of strategy.

[0045] The management execution layer is a crucial bridging level. It is used to break down the macro-level goals of the strategic decision-making layer into specific, actionable tasks and plans, distribute them downwards to various business operation domains, and supervise the execution process to ensure that corporate strategy is effectively implemented in operational activities.

[0046] The business operations layer is the entity responsible for the system's business operations. This layer horizontally covers key business dimensions such as market products, project execution, sales and service, and supply chain production, and serves as the concrete carrier of enterprise management practices.

[0047] The technology and data support system is located at the bottom layer of the architecture, including the data management layer, business service layer, intelligent analysis layer, and unified portal and single sign-on system, providing shared and integrated technical services and data support for all upper-layer business applications.

[0048] Figure 4The diagram further illustrates the hierarchical architecture of strategic decision-making and management execution. The left side of the diagram represents the company's strategic vision, while the right side represents the management execution layer (including financial management, human resource management, and operations management). The middle section connects these layers through strategic data dashboards (financial performance, operational efficiency, quality indicators, and innovation index dashboards), risk control systems, performance management systems, and AI-enabled technologies. This demonstrates how strategic goals are transformed into actionable management activities through data-driven approaches. The strategic data dashboards present key indicators in real time, the risk control system monitors and warns of potential risks, the performance management system tracks goal achievement, and AI-enabled technologies provide predictions and optimization suggestions for strategic decision-making.

[0049] Figure 5 The diagram illustrates the core business processes at the operational level. Using the end-to-end business processes of a robotics intelligent manufacturing company as the main thread, it shows a complete closed loop, from market research, product development, and solution design, to project planning, implementation, and delivery, and further to sales management, design management, after-sales service, as well as procurement management, production management, and logistics management. This diagram demonstrates how the business platform digitizes and standardizes core business processes and achieves cross-departmental collaboration through horizontal connections. For example, market demand directly drives product development, sales orders trigger project execution and supply chain production, and after-sales service feedback is used to improve product design, forming a data closed loop across the entire value chain.

[0050] Furthermore, in combination Figure 9 and Figure 8 As shown, the system also includes a secure access layer and cloud-native infrastructure.

[0051] The secure access layer is the first line of defense for the system, and specifically includes: API gateways (such as Spring Cloud Gateway + Nginx) are configured to implement unified route distribution, request rate limiting and circuit breaking, API version management, and provide security protection through mechanisms such as anti-replay attack, parameter validation, and SQL injection protection.

[0052] Certification Center: Supports multi-factor authentication (dynamic tokens, biometrics) and certificate authentication, and ensures data security through an enterprise-level multi-tenant isolation mechanism.

[0053] Distributed session management system (based on Redis cluster): Enables centralized management of session state, session timeout control, and single sign-out, effectively preventing session hijacking and improving system security.

[0054] Cloud-native infrastructure is the cornerstone of stable and efficient system operation, and specifically includes: Container platform (based on Kubernetes): Enables containerized deployment, orchestration, and scheduling of all microservices, data components, and AI services. Each service is packaged as a Docker container and managed uniformly by Kubernetes, enabling rapid service startup, fault self-healing, and automatic elastic scaling based on business pressure.

[0055] DevOps platform: Provides a continuous integration / continuous deployment (CI / CD) pipeline. When developers submit code, the DevOps platform automatically triggers the pipeline to complete code compilation, image building, automated testing, and finally automatically deploy the new version service to the production environment through Kubernetes, greatly reducing the launch cycle and risk of new features.

[0056] Monitoring and alarm system: Enables full-stack, intelligent monitoring and alarming from underlying infrastructure (nodes, containers), application performance (response time, error rate) to upper-layer business indicators, ensuring the observability and high availability of the system.

[0057] Figure 17 The demonstration showcased the comprehensive support relationship between cloud infrastructure and the data acquisition layer, data middle platform layer, business middle platform layer, intelligent engine layer, and user portal layer. Specifically, the container platform, based on Kubernetes, provides a unified containerized deployment environment for components at all layers, enabling dynamic resource scheduling and elastic scaling to ensure stable operation of each service under high load; the DevOps platform, through automated CI / CD pipelines, supports continuous integration and rapid iteration of services at all layers, significantly shortening the cycle from code submission to production deployment and making business responses more agile; the monitoring and alerting system provides full-stack real-time monitoring of infrastructure (such as nodes and containers), application performance (such as response time and error rate), and business metrics (such as order volume and equipment failure rate), providing data support for fault warnings, self-healing recovery, and capacity planning for services at all layers. In addition, the pooling capabilities of computing, storage, and network resources provided by the private cloud platform offer a reliable and flexible technical foundation for high-throughput data access in the data acquisition layer, large-scale storage and distributed computing in the data middleware layer, high-concurrency service calls in the business middleware layer, machine learning model training and inference computing power requirements in the intelligent engine layer, and multi-terminal adaptation and personalized rendering in the user portal layer. Together, these features ensure the high performance, high availability, and scalability of the entire system.

[0058] In conjunction with the first aspect, heterogeneous data sources include operational data collected by industrial IoT sensing devices on robotic production lines, as well as business data from at least one of enterprise resource planning systems, customer relationship management systems, product lifecycle management systems, and manufacturing execution systems.

[0059] Based on the first aspect of the company-wide digital management platform for intelligent robotic manufacturing, its data management layer serves as the core of the entire system's data integration, specifically designed to connect and integrate various heterogeneous data sources from both inside and outside the robotic manufacturing enterprise. These data sources mainly fall into two categories: first, real-time operational data collected by industrial IoT sensors on the robotic production line; and second, business data from at least one of the following systems: Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Product Lifecycle Management (PLM), and Manufacturing Execution System (MES).

[0060] like Figure 1 As shown, this invention deeply integrates these key business data from the traditional manufacturing system framework to construct the core data foundation for a company-wide digital management platform for robotic intelligent manufacturing. The data management layer achieves this goal through the collaborative work of its three core modules: the data acquisition and integration module seamlessly integrates various heterogeneous data sources, including ERP, MES, PLM, CRM, and IoT devices, using multiple technologies such as API gateways, message queues, and ETL tools; the data lake / warehouse storage module employs distributed storage technology to standardize, clean, label, and classify the collected raw data, forming unified and reliable data assets; finally, the data service and API module encapsulates the cleaned data into standardized API services, providing a unified data supply for upper-layer applications.

[0061] In the specific implementation of data aggregation, the data management layer constructs a multi-layered, multi-protocol data access system to achieve comprehensive collection of data throughout the entire lifecycle of robot manufacturing. During the data collection phase, the system employs a differentiated access strategy: design and test data generated during robot R&D are collected periodically via an API gateway; real-time operational data generated by industrial IoT sensors on the production line is streamed in milliseconds via a Kafka message queue; and historical data from traditional business systems such as ERP and CRM is extracted and loaded in batches using ETL tools. All accessed data is standardized using Avro / JSON Schema to ensure consistency in structure and semantics across different sources.

[0062] In the data storage and governance phase, raw data is first centrally stored in a data lake based on HDFS / S3. Subsequently, it undergoes data cleaning, transformation, and quality verification using the Spark distributed computing engine, including outlier removal, missing value imputation, and data unit standardization. After initial governance, the data is dimensionally modeled according to the business domain of robotics manufacturing and ultimately stored in a data warehouse to form standardized data assets. Throughout this process, the system employs Apache Atlas for metadata management and data lineage tracing, establishing a complete data governance system to ensure data traceability and reliability. This solves the "data silo" problem inherent in multiple information systems in traditional enterprises. Real-world testing shows that this unified data management approach improves cross-system data query efficiency and data consistency, providing a reliable data foundation for accurate decision-making in upper-layer applications. Particularly in the robotics manufacturing scenario, this data aggregation method significantly shortens the R&D cycle and improves the accuracy of equipment failure prediction.

[0063] In conjunction with the first aspect, the business processing modules in the business service layer also include at least one business microservice group from the customer service domain, product service domain, project service domain, supply chain domain, production service domain, and after-sales service domain.

[0064] In conjunction with the first aspect, the customer service domain includes customer management services, demand analysis services, or solution management services; The product service domain includes product management services, design management services, or R&D management services; Project service domains include project management services, task management services, or milestone services; The supply chain domain includes procurement services, inventory services, or supplier services; The production services domain includes production management services, quality inspection services, or equipment management services; After-sales service includes after-sales service, maintenance service, or warranty service; The financial management domain includes budget management, cost control, accounting, or asset management services; The human resources management domain includes organizational structure, talent management, performance management, and compensation and benefits services. The operations management domain includes process management, quality management, compliance management, or document management services.

[0065] Based on the first aspect of the company-wide digital management platform for robotic intelligent manufacturing, its business service layer, as the carrier of core business capabilities, is specifically composed of microservice groups across six business domains, such as... Figure 15As shown, these microservice clusters are based on the entire value chain of robot manufacturing. Through service-oriented decomposition, they decouple traditional monolithic applications, achieving a high degree of reusability and flexible combination of business capabilities. The following provides a detailed description of each business domain and its core services: The customer service domain serves as the front end for enterprises to interact with the market, managing the entire customer lifecycle. Specifically, customer management services enable unified management of customer information, customer value analysis, and the construction of a 360-degree view; requirements analysis services use intelligent algorithms to automatically classify, prioritize, and analyze the impact of changes on customer needs; and solution management services provide a solution template library, version control, and benefit evaluation functions, providing clear input for subsequent R&D. This domain directly corresponds to the sales service and marketing product dimensions within the business operations layer.

[0066] The Product Services domain focuses on the design and development process of robot products. Product Management Services are used for unified management of product catalogs, versions, and Bills of Materials (BOMs); Design Management Services enable 3D model management, design document collaboration, and change process control; and R&D Management Services connect the entire R&D chain from project management to code repository and test management. This domain ensures the consistency of product data from concept to actual production and is a key link connecting customer needs with manufacturing.

[0067] The project services domain spans the entire process from order to delivery, enabling refined project management. Project management services handle the entire project lifecycle, resource scheduling, and cost control; task management services enable hierarchical decomposition of work, progress tracking, and time statistics; and milestone services focus on key node control, early warning mechanisms, and deliverable acceptance. This domain ensures that robot manufacturing projects are delivered on time, to the required quality, and within budget.

[0068] The supply chain domain ensures the precise supply of materials needed for production. Procurement services encompass supplier management, purchase order processing, and bidding processes; inventory services enable real-time inventory monitoring, safety stock alerts, and turnover analysis; and supplier services establish a supplier evaluation system and collaborative mechanisms to effectively manage supply chain risks. This domain provides a solid guarantee for the continuity and economy of production activities.

[0069] The Production Services domain, serving as the core of the digital twin of the robotic factory, directly manages the Manufacturing Execution Process (MEP). Production Management Services handle planning and scheduling, process management, and work-in-process tracking; Quality Inspection Services manage quality standards, control inspection processes, and analyze quality data; and Equipment Management Services focus on equipment inventory, preventative maintenance, and efficiency analysis. Through deep integration with the MES system and IoT devices, this domain achieves transparency and intelligence in the production process.

[0070] The after-sales service domain ensures full lifecycle service for robot products. After-sales service handles service requests, dispatches on-site service, and conducts customer satisfaction surveys; maintenance service develops maintenance plans, manages maintenance work orders and spare parts; and warranty service manages warranty policies, handles claims, and conducts cost analysis. This domain forms a closed-loop management system from customer repair request to service completion, significantly improving customer satisfaction.

[0071] Understandably, these six business domains communicate and collaborate through standardized service interfaces, collectively forming a business processing system covering the entire value chain of robot manufacturing, from R&D, manufacturing, delivery, and operation and maintenance. Each microservice is deployed independently and evolves autonomously, ensuring system stability and scalability while ensuring the consistency and integrity of business data through a unified data service interface. Ultimately, this provides users with a collaborative and efficient one-stop operating experience at a unified portal layer.

[0072] Example 2 Compared with Embodiment 1, the embodiment of this application differs in that: the business microservices in the business service layer include at least one core business microservice group from the customer service domain, product service domain, project service domain, supply chain domain, production service domain and after-sales service domain, as well as at least one support management microservice group from the financial management domain, human resource management domain and operation management domain.

[0073] like Figure 2 The system architecture diagram of the present invention shown is combined with... Figure 5 The core business processes of the business operation layer are displayed. Figure 10 The business microservice domain architecture (decoupled by DDD) shown is as follows: Figure 15 The business middle platform microservice architecture shown in this invention adopts a microservice architecture based on Domain-Driven Design (DDD) for the business service layer, which decouples the full value chain business capabilities of robot manufacturing enterprises into multiple independent business domains. Each business domain consists of a set of highly cohesive and loosely coupled microservices, realizing the componentization, service-orientation and reusability of business capabilities.

[0074] The core business microservice group directly addresses the main business processes of robot manufacturing, covering the entire value chain from market insight, product development, manufacturing to delivery services. For example... Figure 5 As shown, this process begins with market research, proceeds through product development, solution design, project planning, project implementation, and project delivery, and then extends to sales management, after-sales service, as well as procurement management, production management, and logistics management throughout, forming a complete closed loop. This closed loop is supported by microservice groups across six major business domains: The customer service domain, as the front end of a business's interaction with the market, is responsible for managing the entire customer lifecycle and translating customer needs, specifically including: Customer management services enable unified management of customer information, customer segmentation and value analysis, and the construction of a 360-degree panoramic view of customers. For example... Figure 5 As shown, this service integrates with the market research phase, structurally storing and analyzing potential customer information obtained during marketing activities to provide data support for sales strategy development. Through unified customer data provided by the data management layer, this service ensures consistency in customer perception across sales, marketing, and service departments.

[0075] The requirements analysis service uses intelligent algorithms to automatically collect, classify, and prioritize customer needs. This service receives raw requirements from customer management services, analyzes the requirement text using natural language processing technology, extracts key elements, and combines this with historical project data to assess the technical feasibility and business value of the requirements. For example... Figure 5 As shown, this service forms a closed loop with the product development process, inputting analyzed high-value requirements into the product development team to ensure that the product development direction is closely aligned with market demand.

[0076] The solution management service provides management of a solution template library, solution version control, and solution benefit evaluation functions. Based on customer needs analysis results, this service can quickly assemble preliminary solutions from the template library, supporting the pre-sales team in solution customization and quotation estimation. Figure 10 As shown, this service interacts with the product service domain through a standard interface to obtain the latest product BOM and technical parameters, ensuring the accuracy and feasibility of the solution.

[0077] The product service domain focuses on the design and development process of robot products, ensuring the integrity and consistency of product data from concept to reality, specifically including: Product management services are responsible for product catalog management, product version control, and bill of materials (BOM) management. For example... Figure 15 As shown, this service has an independent product database and provides product master data services to external parties via API. It obtains market feedback and customer needs from the data management layer as input for product iteration, while providing accurate product structure data to downstream links such as the production service domain and supply chain domain, ensuring that the materials procured for production are fully matched with the design version.

[0078] The design management service enables 3D model management, design document collaboration, and design change process control. This service integrates with mainstream CAD tools and supports cloud storage, version management, and online preview of design files. Figure 10 As shown, design management services collaborate with project management services to track design tasks as part of project tasks; and collaborate with R&D management services to incorporate design deliverables into R&D deliverables for unified management.

[0079] R&D management services connect the entire R&D pipeline, from project management and code repositories (such as GitLab), CI / CD pipelines to test management. This service supports agile development processes and provides features such as requirements management, iteration planning, and defect tracking. Figure 5 As shown, this service directly supports the implementation of product development, ensuring transparency and traceability in the development process. Through interaction with the intelligent analysis layer, it obtains data on development efficiency and quality analysis, and continuously optimizes the development process.

[0080] The project service domain spans the entire process from order acquisition to project delivery, enabling refined management and control of robot system integration projects. Specifically, it includes: Project management services encompass the entire project lifecycle, including project initiation, planning, resource allocation, cost control, and risk management. For example... Figure 2 As shown, this service connects upwards to the performance goals of the management execution layer, breaking down strategic goals into specific project tasks; downwards it guides task execution, monitors project progress and cost deviations, and generates project health dashboards through real-time project data provided by the data management layer, providing a basis for project decision-making.

[0081] The task management service enables the hierarchical decomposition, allocation, tracking, progress updates, and time statistics of project tasks. This service is tightly integrated with the project management service, transforming project plans into a list of executable tasks. It allows team members to update task status, record work hours, and provides real-time feedback of execution data to the project management service for progress calculations and resource adjustments. Figure 5 As shown, this service is closely integrated with the project implementation phase and is a key tool for bringing the project plan to fruition.

[0082] Milestone service focuses on managing key project milestones, providing milestone alerts, deliverable acceptance, and phase review functions. This service automatically calculates key milestones based on the project plan, issuing alerts when milestones are approaching or past their due dates; it also manages the deliverable list for each milestone, supporting online acceptance and review processes. Figure 15 As shown, this service works in conjunction with the after-sales service domain to ensure the smooth handover of project deliverables and to provide basic data for after-sales service.

[0083] The supply chain domain ensures the precise supply and efficient flow of materials needed for robot manufacturing, specifically including: Procurement services encompass the entire supplier lifecycle management (evaluation, eligibility, performance), purchase order processing, and bidding process management. For example... Figure 5 As shown, this service supports the procurement management process. It obtains material requirements from the BOM by interacting with the product service domain and generates procurement plans; it collaborates with the supplier service to synchronize purchase orders to suppliers in real time; and it connects with the finance domain to complete procurement settlement.

[0084] The inventory service enables real-time inventory monitoring, safety stock alerts, inventory turnover analysis, and intelligent replenishment suggestions. This service integrates inventory data from systems such as WMS and ERP through a data management layer, providing a unified inventory view. Figure 10 As shown, the inventory service collaborates with the equipment management service in the production service domain to ensure the timely supply of spare parts; and collaborates with the after-sales service domain to support the spare parts needs for on-site repairs.

[0085] The supplier service establishes a supplier collaboration portal, supporting order status sharing, delivery plan coordination, and online closed-loop management of quality issues. This service integrates suppliers into the enterprise's digital ecosystem, connecting with supplier systems through standard interfaces to achieve automatic distribution of purchase orders, real-time receipt of shipping notifications, and online feedback of quality inspection results, thereby improving the overall responsiveness of the supply chain.

[0086] The manufacturing services domain, as the core of the digital twin of the robotic factory, directly manages the manufacturing execution process, specifically including: Production management services are responsible for production planning and scheduling, process management, and work-in-process tracking. This service obtains data such as sales orders, inventory status, and equipment capacity from the data management layer, and combines this data with real-time production data collected from the MES system to dynamically optimize production plans. Figure 16 As shown, the service can obtain decision support for production optimization from the intelligent analysis layer, such as production takt time adjustment suggestions based on machine learning predictions, to improve overall equipment efficiency (OEE).

[0087] The quality inspection service digitizes quality standards, automates inspection processes, and visualizes quality data analysis. This service supports end-to-end quality control from incoming material inspection and in-process inspection to finished product inspection, solidifying inspection standards and judgment rules into executable inspection tasks. For example... Figure 4 As shown, this service is linked to the quality indicator dashboard of the strategic decision-making level, and presents key indicators such as quality pass rate and defect distribution in real time, providing data support for quality improvement.

[0088] Equipment management services are responsible for equipment inventory management, preventative maintenance plan development, and equipment efficiency analysis. This service integrates with IoT devices to monitor equipment operating status, fault information, and performance metrics in real time. Figure 11 As shown, the equipment management service provides equipment operation data to the intelligent analysis layer. After analyzing the data through the fault prediction model, the intelligent analysis layer feeds back predictive maintenance suggestions (such as "servo motors need to be maintained after 100 hours") to the equipment management service, triggering the automatic generation of preventive maintenance work orders and realizing the transformation from passive maintenance to proactive maintenance.

[0089] After-sales service guarantees full lifecycle support for robot products, improving customer satisfaction and repurchase rates, specifically including: After-sales service includes handling customer service requests, coordinating on-site service resources, and conducting customer satisfaction surveys. For example... Figure 5 As shown, this service is the endpoint of the business operations layer and a continuation of the customer lifecycle. When a customer submits a service request through the unified portal, this service automatically assigns a service engineer based on the request type and geographical location, and tracks the entire service process to ensure timely closure of issues.

[0090] Maintenance services include developing preventative maintenance plans, managing maintenance work orders, and securing spare parts. This service collaborates with equipment management services, receiving predictive maintenance work orders triggered by equipment management services and scheduling engineers to perform maintenance tasks. It also collaborates with inventory services to ensure that necessary spare parts are available in a timely manner. Through this service, businesses can shift maintenance activities from reactive response to planned execution, reducing unplanned equipment downtime.

[0091] Warranty service manages warranty policies, processes warranty claims, and analyzes warranty costs. This service records the warranty terms for every device sold and automatically verifies whether a service request is within the warranty period. For repairs within the warranty period, it automatically records claim information and integrates with the finance department for cost accounting. Figure 15 As shown, this service works in conjunction with the product management service to feed warranty data back to the product development team for continuous improvement of product reliability.

[0092] The support management microservice group provides the necessary back-end management functions for the stable operation of enterprises. It works in conjunction with the core business microservices to form a complete enterprise-level business capability system, which specifically includes: financial management domain, human resource management domain, and operations management domain.

[0093] The financial management domain transforms traditional financial functions into reusable microservice components, specifically including: Budget management services support comprehensive budget preparation, budget execution control, and budget adjustment processes. For example... Figure 4 As shown, this service is the core of financial management at the management execution level. It receives the annual operating targets from the strategic decision-making level, breaks them down into budget amounts for each department and project, monitors budget execution in real time, and issues warnings when there is a risk of exceeding the budget.

[0094] Cost control services include cost accounting and analysis, identification of cost drivers, and tracking of the effectiveness of cost reduction and efficiency improvement measures. This service can obtain real-time production cost data (such as labor hours and material consumption) from the production service domain and purchase price data from the procurement service domain, performing refined cost aggregation and variance analysis to provide management with cost optimization suggestions.

[0095] The accounting service supports multi-standard financial statement generation, automated voucher processing, and tax compliance management. Through a data management layer, it integrates transaction data from various business domains (such as sales orders, purchase invoices, and expense reimbursements), automatically generates accounting vouchers, and regularly produces financial statements such as balance sheets and profit and loss statements, ensuring the accuracy and compliance of the company's financial data.

[0096] Asset management services are responsible for the entire lifecycle management of assets, asset depreciation calculation, and asset benefit analysis. This service collaborates with equipment management services in the production services domain to manage equipment as a core enterprise asset in a unified manner, recording the entire process of asset acquisition, depreciation, maintenance, and disposal, and providing data support for asset investment decisions.

[0097] The human resource management domain enables the digitalization and intelligentization of human resource management, specifically including: Organizational structure services manage organizational levels, job structures, and staffing, providing foundational organizational data for the platform's permission system and business processes. This service integrates with single sign-on systems to ensure real-time synchronization of employee information and account permissions.

[0098] Talent management services support talent inventory and planning, competency model building, and succession planning. This service acquires employee performance data and project performance from performance management and project service domains, and identifies high-potential talent through intelligent analysis models, providing a scientific basis for enterprise talent pipeline development.

[0099] Performance management services automate the performance evaluation process, support 360-degree feedback, and track performance improvement plans. For example... Figure 11 As shown, this service works in conjunction with the performance analysis service of the intelligent analysis layer to obtain actual KPI data (such as project delivery on time rate and individual work hour completion rate) from various business microservices, automatically calculate individual and team performance scores, and predict performance achievement through trend analysis, thus realizing a complete closed loop from strategic goals to individual performance.

[0100] Payroll and benefits services support payroll system design, payroll calculation and disbursement, and benefits policy management. This service integrates with attendance systems and performance management services, automatically collecting attendance data and performance results, completing payroll calculations, and disbursing payroll through bank interfaces, improving the efficiency and accuracy of payroll management.

[0101] The operations management domain ensures the standardization, normalization, and compliance of an enterprise's daily operations, specifically including: Among them, the process management service supports process modeling and optimization, process performance monitoring, and process automation. This service provides a visual process designer, allowing business personnel to customize business processes (such as procurement approval and contract countersigning), and monitor process flow efficiency and bottlenecks in real time. It is the core engine for the platform to achieve business process standardization and automation.

[0102] Quality management services are responsible for quality system management, quality audit management, and quality improvement project tracking. Together with quality inspection services in the production services domain, this service constitutes a full-process quality management system from quality planning to quality inspection execution, supporting the digital management of quality system documents such as ISO 9001, as well as the allocation and tracking of internal and external audit tasks.

[0103] The compliance management service tracks changes in regulatory requirements, assesses compliance risks, and generates compliance reports. This service establishes a regulatory database, automatically matches enterprise business activities with applicable regulatory requirements, conducts regular compliance checks, and triggers alerts when compliance risks are detected, ensuring the safe operation of enterprises in the highly compliant field of robotics manufacturing.

[0104] The document management service enables document version control, access control, and collaborative editing. It provides an enterprise-level knowledge base, supporting unified storage, full-text search, and online preview of various documents (technical drawings, contracts, management regulations). Fine-grained access control ensures document security, and integration with business domains such as project management and product management enables the association management of documents with business objects.

[0105] By successfully transforming management functions, traditionally regarded as back-end support, into reusable and scalable microservice components based on a unified technical architecture, key support capabilities are provided for building an integrated, company-wide digital management platform for robotic intelligent manufacturing.

[0106] In this embodiment, the business service layer consists of multiple independent business processing modules, which work together in the following ways: Data-driven collaboration: All business processing modules no longer connect directly to the original business systems (such as ERP and MES databases), but instead uniformly call the unified data access interface provided by the data management layer to obtain data. This ensures that the data relied upon by all business processes across the platform is of the same source, standardized, and consistent. For example, when the production management module needs to execute production scheduling, it obtains a unique and reliable set of data from the data management layer that has already been verified against the ERP plan, the PLM BOM list, and the MES real-time capacity.

[0107] Service Interface Collaboration: Business processing modules interact through well-defined service communication interfaces (typically based on protocols like REST or gRPC), rather than through tight code coupling or direct database sharing. For example, when the "Project Management Module" needs to check the material preparation status of a project, it will call the query service provided by the "Supply Chain Management Module" through its internal service interface, instead of directly accessing the supply chain database. This loosely coupled design allows each module to evolve technologically and scale independently without affecting other modules.

[0108] Business logic co-encapsulation: Each business module encapsulates the core rules and processes of its corresponding business domain. Based on unified data obtained from the data management layer, they perform specific business calculations, state changes, and process advancements, and return the processing results (i.e., updated business state data) to the data management layer or pass them to other modules through interfaces, thereby realizing the continuous flow of business value.

[0109] Business microservices not only consume data, but also gain intelligent empowerment through interaction with the intelligent analytics layer. For example... Figure 11 As shown, business microservices can provide business data (such as equipment operation data and sales history data) to the intelligent analysis layer. After analysis by the intelligent analysis layer through machine learning models and real-time computing engines, the layer feeds back decision support information (such as sales forecasts, risk warnings, and performance analysis results) to the business microservices through standard interfaces to optimize business execution. For example, the equipment management service provides real-time data such as equipment vibration and temperature to the intelligent analysis layer. After the stream processing unit of the intelligent analysis layer identifies abnormal patterns, it immediately feeds back predictive maintenance suggestions to the equipment management service, triggering the automatic generation of preventive maintenance work orders, achieving a second-level response from "perception" to "action".

[0110] Through the organic combination of the aforementioned core business microservice group and support management microservice group, and the collaborative mechanism based on data-driven approaches, service interfaces, business processes, and intelligent empowerment, the business service layer of this invention achieves comprehensive coverage and deep integration of the entire value chain of robot manufacturing enterprises. For example... Figure 2 As shown, this layer connects upwards to the strategic decomposition of the management and execution layer, downwards to obtain unified data support through the data management layer, horizontally to achieve business collaboration through standard interfaces, and forms an intelligent closed loop with the intelligent analysis layer, together constituting a complete, efficient, and intelligent enterprise digital management hub. This architecture not only ensures the system's flexibility and scalability, but also enables the reuse and rapid combination of business capabilities through service-oriented design, allowing the platform to respond agilely to the ever-changing business needs of the robotics manufacturing industry.

[0111] Example 3 Compared with Embodiments 1 and 2, this embodiment differs in that: the intelligent services provided by the intelligent analysis layer include at least one of BI analysis services, risk management services, performance analysis services, or AI prediction services; the performance analysis service is used to define strategically oriented KPI indicators, automatically calculate performance, and analyze trends; the risk management service is used to monitor real-time risk indicators, provide early warnings of risk thresholds, and conduct quantitative risk assessments; the BI analysis service is used to generate dashboards for financial performance, operational efficiency, quality indicators, and innovation indices; and the AI ​​prediction service is used to support AI application scenario planning and intelligent decision support.

[0112] This intelligent analysis layer includes: The model library unit stores machine learning models used for sales forecasting, equipment failure early warning, or quality analysis. The stream processing unit is configured to perform real-time analysis of sensor data streams from the robot production line.

[0113] The intelligent analytics layer seamlessly embeds data analytics capabilities into various business processes within an enterprise by providing a standardized set of intelligent services, achieving a data-driven intelligent decision-making closed loop. Among these, the BI analytics service is the core for realizing comprehensive data visualization and self-service analysis, possessing capabilities for multi-dimensional data analysis, self-service report generation, and data mining insights. This service forms the technological foundation for building various data dashboards (such as financial performance, operational efficiency, and quality indicator dashboards) in the "strategic decision-making layer." It enables management to transcend departmental silos and quickly extract business insights from unified data assets to support strategic decision-making.

[0114] Risk management services are core to proactive risk prevention for enterprises, possessing capabilities such as risk identification models, real-time risk indicator monitoring, risk threshold early warning, and risk quantitative assessment. This service integrates with the risk management system at the "strategic decision-making level," enabling real-time push of risk warning information to relevant business leaders and providing risk response strategy suggestions.

[0115] Performance analytics services enable automated, refined, and intelligent management of organizational performance, such as KPI definition, automatic performance calculation, and performance trend analysis. It serves as the core of the performance management system within the "strategic decision-making level." It acquires execution data from various business microservices (such as project management and production management), automatically calculates individual, team, and company-wide KPIs, and provides a scientific basis for performance improvement through trend prediction and root cause analysis.

[0116] AI prediction services focus on machine learning-based prediction and recommendation. Its core components are a machine learning algorithm library, prediction model training, and an intelligent recommendation engine. It transforms the algorithm models (such as sales forecasts and equipment failure warning models) stored in the model library into callable services. The prediction results (such as product sales for the next three months and potential equipment failure points) directly guide the proactive resource allocation of modules in the business service layer, including supply chain management, production planning, and after-sales service.

[0117] The capabilities of the aforementioned intelligent analytics services rely heavily on two underlying core technology units: the model library unit and the stream processing unit. The model library unit is a well-governed, reusable repository of machine learning models. It stores pre-trained predictive or diagnostic models for specific business scenarios. This unit forms the foundation for AI prediction services, risk management services, and more. For example, when the "equipment fault warning" model is invoked by the AI ​​prediction service, it calculates the equipment's health score and fault probability based on real-time or historical data.

[0118] The stream processing unit is a high-throughput, low-latency computing engine designed for real-time data streams. It specifically processes continuous data streams from sources such as "robot production line sensors." This unit is crucial for enabling real-time decision-making. By analyzing the data flow, it can instantly analyze real-time stream data, identify abnormal patterns (such as excessive equipment vibration), and directly trigger the results to the business service layer (such as generating maintenance work orders), thereby achieving a second-level response from perception to action. This forms the technological foundation for scenarios such as predictive maintenance.

[0119] In conjunction with the first aspect, the user interaction layer includes: a unified portal platform, a single sign-on system, and a role-based view system.

[0120] The unified portal platform adopts a micro-frontend architecture to enable personalized workbench customization.

[0121] In this embodiment, the unified portal platform serves as a unified access point and integrated working environment for all users, addressing the inconsistent user experience issues present in traditional enterprise information systems. By deeply integrating and aggregating business functions and data resources originally scattered across various independent systems (such as ERP, CRM, MES, etc.), a centralized single-point work platform is constructed. This allows users to complete all workflows without frequently switching between different systems, achieving a modern digital work experience of "one platform, all business processing." By developing, deploying, and running different business functions as independent micro-frontend components, each business module can evolve independently and be seamlessly integrated, providing a fundamental guarantee for the platform's continuous scalability and technology stack flexibility. Regarding personalized workbench customization, through pre-configured core components such as "My To-Dos," "My Projects," "My Reports," and "Quick Operations," unified aggregation and processing of cross-system to-do items, panoramic view display of cross-departmental projects, on-demand configuration of personalized data dashboards, and quick access to frequently used functions are achieved. These components collectively constitute the technological foundation for the user's personalized work experience. In terms of technical support, it also features responsive components, drag-and-drop layout components, and mobile-adaptive components. Responsive components ensure optimal display of the unified portal platform across different device sizes; drag-and-drop component support allows users to freely arrange the work interface according to their personal work habits; and mobile-adaptive components enable a modern work mode of working anytime, anywhere. This allows for personalized workspace customization.

[0122] The single sign-on system implements unified identity authentication based on the OAuth 2.0 and OpenID Connect protocols.

[0123] In terms of unified identity authentication technology, the Single Sign-On system (SSO) is built upon OAuth 2.0 and the OpenID Connect protocol to create a complete authentication solution. This component is specifically the SSO system, whose technical architecture comprises four core modules: an SSO authentication center, an identity provider, a token service, and session management. It employs a Spring SecurityOAuth2 + JWT token technology stack and uses distributed session storage (Redis cluster) to achieve unified management of session state, supporting multi-factor authentication methods including username / password, dynamic tokens, biometrics, and certificate authentication. This enables the system to securely integrate with over 300 application systems, while ensuring session security through session timeout control and single sign-out mechanisms, providing a unified and secure identity authentication infrastructure for enterprise-level multi-tenant environments.

[0124] Role-based view systems are used for multi-dimensional data presentation for management, department managers, employees, and customers.

[0125] The role-based view system achieves multi-dimensional data display through a refined permission model. Specifically, it employs a hybrid permission model combining RBAC (Role-Based Access Control) and ABAC (Attribute-Based Access Control). Through fine-grained permission control and data access isolation mechanisms, it constructs personalized work views for users with different roles. Specifically, it includes: providing management with a decision support view of strategic indicators, financial data, and risk warnings; providing department managers with a management view of team performance, resource allocation, and project progress; providing employees with an operational view of daily work, task lists, and collaboration tools; and providing clients with an external collaboration view of project progress, deliverables, and service support. This role-based dynamic view configuration mechanism ensures that each user role can only access functions and data within their authorized scope, guaranteeing data security while optimizing the user experience.

[0126] In conjunction with the first aspect, the system also includes a secure access layer, which includes: The API gateway is configured to achieve unified route distribution, request rate limiting, and security protection.

[0127] This API gateway component employs a Spring Cloud Gateway + Nginx technology selection, featuring unified routing and distribution, request rate limiting and circuit breaking mechanisms, and a complete security protection system built through security features such as replay attack prevention, parameter validation, and SQL injection protection. Its API version management function ensures the smooth evolution of service interfaces, and request and response logs provide comprehensive data support for system auditing.

[0128] The certification center supports multi-factor authentication and certificate authentication.

[0129] Understandably, the authentication center supports multiple authentication methods, including username and password, dynamic tokens, biometrics, and certificate authentication, and achieves data security isolation between different customers through an enterprise-level multi-tenant isolation mechanism. This design enables the platform to meet the authentication needs of scenarios with different security levels, providing reliable identity verification guarantees for the entire system.

[0130] A distributed session management system that enables session timeout control and single sign-out.

[0131] The distributed session management system is specifically the "session management" module within the "single sign-on system." Utilizing distributed session storage (Redis cluster) technology, it achieves centralized management of session states, automatically terminates inactive sessions through a session timeout control mechanism, and provides single sign-out functionality, ensuring users can securely log out of all system applications in a single operation. This design effectively prevents session hijacking risks and enhances the overall system security.

[0132] These three components together form the security foundation of the platform. Through unified security policies and technical implementation, they provide comprehensive security protection for core components such as the business service layer and data management layer, while ensuring secure communication and data isolation of the system under the microservice architecture.

[0133] In conjunction with the first aspect, the system is built on cloud-native infrastructure; cloud-native infrastructure includes: container platform, DevOps platform and monitoring and alerting system.

[0134] A container platform that implements container orchestration and scheduling based on Kubernetes.

[0135] Container platforms are the core scheduling and orchestration layer of cloud-native infrastructure. They are used to package applications and all their dependencies into standardized, lightweight containers, and to intelligently deploy, manage, and scale them. This container platform includes Kubernetes cluster management, container orchestration and scheduling, service discovery, and load balancing. It provides an ideal runtime environment for microservices in the "business service layer" and services in the "intelligent analytics layer." Each microservice is packaged as a container and managed uniformly by Kubernetes, enabling rapid service startup, fault self-healing, and automatic scaling based on business pressure, thus achieving extreme flexibility and high scalability. Figure 8 (son Figure 5 )and Figure 17 (son Figure 14 It provides a detailed overview of the components of cloud-native technology infrastructure, including technologies such as containerization, microservices, and DevOps, ensuring the platform's high availability, elastic scaling, and rapid iteration capabilities.

[0136] DevOps platform, providing continuous integration and continuous deployment pipelines.

[0137] A DevOps platform is a pipeline that supports continuous integration, continuous deployment, and automated delivery, connecting the entire process from development and testing to deployment. It is key to enabling rapid business iteration and response. This platform works closely with container platforms. When developers submit code, the DevOps platform automatically triggers the pipeline, completing code compilation, image building, automated testing, and ultimately automatically deploying the new version of the service to the production environment via Kubernetes. This significantly reduces the deployment cycle and risk of new features in the business service layer and intelligent analytics layer.

[0138] The monitoring and alarm system enables intelligent monitoring of infrastructure, application performance, and business metrics.

[0139] The monitoring and alerting system serves as the platform's nerve center and early warning mechanism. It provides full-stack observability from the underlying infrastructure to upper-layer business applications, ensuring stable and efficient system operation. This includes infrastructure monitoring, application performance monitoring, and business metric monitoring. By monitoring the health status of all nodes in the container platform, tracking the performance metrics (such as response time and error rate) of various microservices in the business service layer, and directly supporting the strategic data dashboard of the strategic decision-making layer, it provides real-time and accurate business operation data, forming the foundation for system observability and high availability.

[0140] In conjunction with the first aspect, the data management layer includes: a data lake storage module, a data warehouse storage module, and a metadata management module.

[0141] The data lake storage module is used to store the collected multi-source heterogeneous data in its raw format.

[0142] The data lake storage module is the raw data aggregation area of ​​the data management layer, used to store the collected multi-source heterogeneous data in its raw format. For example... Figure 13 As shown, the data acquisition and integration module continuously collects data from various heterogeneous data sources (including industrial IoT sensors on robotic production lines, Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, Product Lifecycle Management (PLM) systems, Manufacturing Execution System (MES) systems, and office collaboration systems) through API gateways, message queues (such as Kafka), and ETL tools. This data enters the data lake storage module directly in its raw format (such as JSON, Avro, Parquet, images, and time-series data) without any structured processing or schema conversion.

[0143] The data lake storage module employs distributed storage technologies (such as HDFS, S3, and OSS), featuring high scalability and low cost, capable of storing massive amounts of data. In this embodiment, a schema-on-read approach is used, meaning data is stored in its original form, and the data structure is parsed according to application requirements when read. This offers the following technical advantages: First, the originality and integrity of the data are maintained. All collected data is preserved as is, providing a complete data foundation for subsequent data backtracking, reprocessing, and in-depth mining. For example, when the intelligent analysis layer needs to train a new fault prediction model, it can retrieve months or even years of raw sensor data from the data lake for model training, without being limited by predefined data structures.

[0144] Secondly, it supports unified storage of multimodal data. Robot manufacturing scenarios involve structured data (such as order records in relational databases), semi-structured data (such as equipment logs and API call records in JSON format), and unstructured data (such as design drawings, 3D models, and surveillance videos). The data lake can uniformly store all these types of data, avoiding the management complexity brought about by multiple storage systems in traditional architectures.

[0145] like Figure 14 As shown, the data lake is located at the bottom layer of the data platform architecture. It is the first stop for data after it enters the platform, providing raw data materials for data processing and governance at the upper layers.

[0146] The data warehouse storage module is used to store structured data that has been cleaned, transformed, and modeled by subject area. For example... Figure 14 As shown, after data flows out of the data lake, it enters the data processing layer. During this process, distributed computing engines such as Spark and Flink perform ETL (Extract, Transform, Load) operations on the raw data, including data cleaning (removing outliers and filling missing values), data transformation (format unification, unit conversion, and encoding mapping), and data aggregation (summarizing by time and business dimensions). The processed data is then dimensionally modeled according to the business subject areas of the robot, forming fact tables and dimension tables, and finally stored in the data warehouse storage module.

[0147] The data warehouse storage module employs columnar storage or a relational database, optimized for analytical query scenarios, and boasts high-performance query response capabilities. Data is structured and organized according to a predefined business model during writing, offering the following technical advantages: First, it provides a unified and reliable data view. The data in the data warehouse undergoes cleaning and governance, eliminating potential inconsistencies, duplications, and errors found in the original data, forming a single source of truth for the enterprise. For example, there is only one verified version of the Bill of Materials (BOM) data in the data warehouse, and all business microservices (such as production management services and procurement services) access this data through a unified data service interface, fundamentally avoiding data inconsistency issues.

[0148] Secondly, modeling by subject area makes the data easier to understand and analyze. The data warehouse is organized according to the business themes of robot manufacturing (such as customers, products, orders, equipment, quality, projects, etc.), with each subject area corresponding to a set of related data tables. This organizational method is consistent with the cognitive patterns of business personnel, facilitating the rapid construction of multidimensional analytical reports by BI analysis services and performance analysis services. Figure 4 As shown, the financial performance, operational efficiency, and quality indicators in the strategic data dashboard are all derived from modeled data in the data warehouse.

[0149] The data warehouse storage module and the data lake storage module complement each other: the data lake stores raw data, while the data warehouse stores the processed data; the data lake is geared towards data exploration and machine learning, while the data warehouse is geared towards business analysis and decision support. Together, they constitute a complete data storage system.

[0150] The metadata management module is used to manage the metadata of data assets and record the source and destination of the data.

[0151] The metadata management module is a core component of data governance, responsible for the unified management of metadata for all data assets in the data lake and data warehouse. Metadata mainly includes three categories: Technical metadata: describes the technical attributes of the data, such as data source connection information, table field definitions, data types, partition information, file formats, storage locations, etc.

[0152] Business metadata describes the business meaning of data, such as business terminology definitions, indicator calculation rules, data quality standards, data security levels, etc., to help business personnel understand and use the data.

[0153] Operational metadata: describes the data processing process, such as the running status of data acquisition tasks, execution logs of ETL jobs, and audit records of data access.

[0154] like Figure 14 As shown, the metadata management module (usually labeled "Metadata Management" or "Data Governance" in the diagram) runs through the entire data processing workflow of the data lake and data warehouse, enabling unified view management of data assets. One of the core functions of the metadata management module is recording the source and destination relationships of data (i.e., data lineage). By tracking the source and destination relationships of data, it is possible to trace which raw data (such as equipment runtime, planned downtime, and fault records) a specific analytical metric (such as Overall Equipment Effectiveness) in the data warehouse originates from, and which business systems (such as MES and IoT platforms) this raw data comes from. It also allows tracking which ETL jobs processed a specific record in the data lake, which intermediate and final tables were generated, and which business microservices and reports used it. Furthermore, when the data source structure changes or a data quality issue is discovered requiring correction, data lineage allows for rapid assessment of the impact scope, identifying the ETL jobs, business applications, and analytical reports that need adjustment, significantly reducing the risk and cost of data changes.

[0155] by Figure 11Taking the intelligent service and data storage architecture shown as an example, when the device management service calls the fault prediction service of the intelligent analysis layer, the metadata management module records the complete lineage of the device's operating data: raw sensor data enters the data lake via the IoT gateway, is cleaned by the stream processing unit, and then stored in the time-series database. The fault prediction model reads the processed data for analysis, and the analysis results are fed back to the device management service. This complete link is recorded by the metadata management module to ensure the traceability and reliability of the data.

[0156] like Figure 2 As shown, the data management layer is located at the core of the technology and data support system. Through a three-layer architecture of data lake, data warehouse and metadata management, it provides a unified, reliable and traceable data foundation for the upper business service layer, intelligent analysis layer and user interaction layer.

[0157] The data lake storage module, data warehouse storage module, and metadata management module work together to form a complete data management system, such as... Figure 14 As shown, external data first enters the data lake storage module (raw data layer), then undergoes ETL processing before entering the data warehouse storage module (detailed data layer, summary data layer, and application data layer), and finally is provided to upper-layer applications through the data service API. This complete data flow is recorded throughout by the metadata management module.

[0158] Based on the aforementioned management platform, this application embodiment also applies a robot manufacturing business data processing method to the aforementioned system; such as Figure 3 As shown, the method includes: S110 collects and manages heterogeneous data from the entire robot manufacturing process through a data management layer.

[0159] A unified integration of data across the entire value chain is achieved through a data management layer. The specific execution process includes: continuously collecting real-time data from robot R&D, production, testing, and maintenance stages via the data integration module's API gateway, message queue, and ETL tools, while simultaneously accessing historical data from traditional business systems such as ERP, MES, PLM, and CRM; the collected raw data enters a data lake for distributed storage, undergoes standardized cleaning, format conversion, and quality verification by the data governance module, is modeled according to subject domains, and then stored in a data warehouse; finally, it is encapsulated into standardized API interfaces through the data service module. This step fundamentally solves the "data silo" problem in traditional enterprises, providing a unified and reliable data foundation for subsequent business processes.

[0160] S120 uses the corresponding business microservices in the business service layer to call the governed data in order to complete a specific business process in at least one of the following: robot research and development, production, supply chain, or after-sales service.

[0161] Specific business processes are completed through microservice components in the business service layer. The execution process includes: business microservices (such as customer service domain, product service domain, project service domain, etc.) obtain standardized data after governance by calling the unified data access interface provided by the data management layer; each microservice executes specific business logic based on the obtained data, for example, the project management service adjusts the project plan based on real-time data, the production management service optimizes the production schedule based on equipment status data, and the supply chain service intelligently generates purchase orders based on inventory data; these business processing modules work together through service communication interfaces to form a complete business process closed loop, realizing the digital management of the entire robot manufacturing process.

[0162] S130, through the intelligent analysis layer, analyzes the governed data and / or data generated during business execution, generates decision support information, and feeds the decision support information back to the business service layer.

[0163] The intelligent analytics layer provides data-driven decision support. The specific implementation process includes: the intelligent analytics layer acquiring standardized data from the data management layer while simultaneously receiving real-time status data generated during business execution; conducting multi-dimensional data analysis through BI analytics services to generate visual insights such as operational efficiency and quality indicators; calling machine learning models from the model library through AI prediction services to perform intelligent analyses such as sales forecasting and equipment failure warnings; performing real-time calculations on production line sensor data streams through the stream processing unit to instantly identify abnormal patterns and generate warning information; and finally, feeding back various analysis results to the business service layer through standard interfaces, directly influencing the execution of business logic, forming a complete closed loop from data perception to intelligent decision-making.

[0164] S140 aggregates and displays the results of business process execution and / or decision support information through the user interaction layer.

[0165] This step is the final stage of value delivery, achieving unified information display through the user interaction layer. The execution process includes: a unified portal platform based on a micro-frontend architecture dynamically aggregating functional interfaces from different business modules according to user role permissions; a single sign-on system ensuring secure user access; and a role-based view system providing personalized data displays for different users (management, department managers, employees, and customers); the system integrates business results such as status updates and work order information generated by business execution steps with decision support information such as predictive analysis and risk warnings generated by intelligent decision-making steps, presenting these results visually through configurable dashboards, reports, and warning panels, providing users with comprehensive, real-time, and accurate business insights and decision-making basis.

[0166] Secondly, embodiments of this application provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor runs the computer program to cause the electronic device to perform the above-described method.

[0167] Furthermore, electronic devices also include buses and communication interfaces, with the processor, communication interface, and memory connected via the bus.

[0168] The memory may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc.

[0169] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above methods can be completed by integrated logic circuits in the processor's hardware or by software instructions. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0170] Thirdly, embodiments of this application provide a readable storage medium storing computer program instructions, which are read and executed by a processor to perform the above-described method.

[0171] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0172] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0173] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0174] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0175] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A company-wide digital management platform for intelligent robotic manufacturing, characterized in that: include: The data management layer is used to integrate data from multiple heterogeneous data sources inside and outside the robot manufacturing enterprise, and to collect, store, govern and encapsulate the integrated data in a service-oriented manner to provide a unified data service interface. The business service layer consists of multiple independent business microservices decoupled based on domain-driven design. These microservices are used to process business logic in the robot R&D, manufacturing, delivery and operation and maintenance process, and interact through service communication interfaces. The microservices call the unified data service interface provided by the data management layer to obtain data. The intelligent analysis layer is used to perform machine learning and / or real-time streaming data processing and analysis on data related to robot manufacturing, and to feed back the analysis results to the business service layer through a standard interface to optimize business execution; The user interaction layer provides a unified system access point and dynamically combines functional interfaces and data views from different business microservices based on user identity and permission information through a micro-frontend architecture.

2. The management platform according to claim 1, characterized in that, The heterogeneous data sources include operational data collected by industrial IoT sensing devices on the robot production line, as well as business data from at least one of enterprise resource planning systems, customer relationship management systems, product lifecycle management systems, and manufacturing execution systems.

3. The management platform according to claim 1, characterized in that, The business microservices in the business service layer include at least one core business microservice group from the customer service domain, product service domain, project service domain, supply chain domain, production service domain and after-sales service domain, as well as at least one support management microservice group from the financial management domain, human resource management domain and operations management domain.

4. The management platform according to claim 3, characterized in that, The customer service domain includes customer management services, demand analysis services, or solution management services; The product service domain includes product management services, design management services, or R&D management services; The project service domain includes project management services, task management services, or milestone services; The supply chain domain includes procurement services, inventory services, or supplier services; The production service domain includes production management services, quality inspection services, or equipment management services; The after-sales service domain includes after-sales service, maintenance service, or warranty service; The financial management domain includes budget management, cost control, accounting, or asset management services; The human resources management domain includes organizational structure, talent management, performance management, or compensation and benefits services; The operations management domain includes process management, quality management, compliance management, or document management services.

5. The management platform according to claim 1, characterized in that, The intelligent services provided by the intelligent analysis layer include at least one of BI analysis services, risk management services, performance analysis services, or AI prediction services.

6. The management platform according to claim 5, characterized in that, The intelligent analysis layer includes: The model library unit stores machine learning models used for sales forecasting, equipment failure early warning, or quality analysis. The stream processing unit is configured to perform real-time analysis of sensor data streams from the robot production line.

7. The management platform according to claim 1, characterized in that, The user interaction layer includes: A unified portal platform, employing a micro-frontend architecture to enable personalized workbench customization; The single sign-on system implements unified identity authentication based on the OAuth 2.0 and OpenID Connect protocols; A role-based view system is used to provide multi-dimensional data presentations for management, department managers, employees, and customers.

8. The management platform according to claim 1, characterized in that, The management platform also includes a secure access layer, which includes: The API gateway is configured to achieve unified route distribution, request rate limiting, and security protection. The certification center supports multi-factor authentication and certificate authentication. A distributed session management system that enables session timeout control and single sign-out.

9. The management platform according to claim 1, characterized in that, The management platform is built on cloud-native infrastructure; the cloud-native infrastructure includes: Container platform, based on Kubernetes, implements container orchestration and scheduling; DevOps platform, providing continuous integration and continuous deployment pipelines; The monitoring and alarm system enables intelligent monitoring of infrastructure, application performance, and business metrics.

10. The management platform according to claim 1, characterized in that, The data management layer includes: The data lake storage module is used to store the collected multi-source heterogeneous data in its raw format; The data warehouse storage module is used to store structured data that has been cleaned, transformed, and modeled by subject area; The metadata management module is used to manage the metadata of data assets and record the source and destination of the data.

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