207 results about "Linked data" patented technology

The invention discloses a process parameter tracing and quality collaborative management system for ceramic production, and relates to the technical field of ceramic production, the management system comprises a step of obtaining a plurality of process data and quality data in a ceramic production process, and each group of process data comprises a raw material ratio, a forming pressure, a firing temperature and a firing time. According to the process parameter tracing and quality collaborative management system for ceramic production, a specific process parameter deviation link can be quickly positioned by reversely tracing to a raw material source from a finished product quality problem and combining batch association identifiers and data records of all links; the quality problem solving efficiency is improved, and batch loss caused by traceability lag is avoided; besides, a process quality association relationship constructed by the module is established, a quality detection result is closely associated with process parameters, and multi-dimensional analysis is performed, so that a quality problem can be fed back to process parameter adjustment in time, and targeted measures can be taken according to an analysis result.

The invention relates to the technical field of informatization management systems, and particularly discloses an AI-aid decision-making product full-life-cycle informatization management system which comprises a full-link data sensing module, a digital main line construction module, an AI decision-making center module, a service execution interface module and a man-machine collaborative interaction module. The method comprises the following steps: collecting multi-source heterogeneous data of each stage of a full life cycle of a product through a full-link data sensing module to form a full-amount data sequence with a space-time stamp; and in combination with a data fusion engine, an association graph construction unit and a twinborn model driving unit of the digital main line construction module, data cleaning, alignment, semantic fusion and cross-stage association are realized, a dynamically updated product digital twinborn model is constructed, real-time mapping of a physical entity and a virtual model is achieved, and the real-time mapping of the physical entity and the virtual model is realized. Unified and complete data support is provided for full-link decision making, and the problems of data fragmentation and prominent collaborative barriers of a traditional system are thoroughly solved.

The invention relates to the technical field of catch safety tracing, and discloses a block chain verification method for catch quality safety tracing. The method comprises the following steps: acquiring environmental parameter data of catches in the links of fishing, transportation and storage through a distributed sensor network, and encrypting according to a timestamp sequence to generate a catches environmental data chain; key environment indexes are extracted based on the data chain, an abnormal deviation degree is calculated by using a dynamic weight distribution algorithm, and a catch quality risk score is generated; dividing risk levels according to the risk scores, mapping data of different risk levels to a block chain differentiation storage layer, and constructing a fragment storage structure; performing redundancy verification on the high-risk data through an intelligent contract triggering mechanism, and calling a consensus node to perform cross verification to generate a verification result; and performing association analysis on the verification result and the risk score, updating the risk tag and synchronizing the risk tag to a block chain distributed account book. According to the method, full-link data tracing and verification of the catches are realized, and the data credibility and the quality control accuracy are improved.

The invention relates to the technical field of supply chain management, in particular to a multi-dimensional data visualization supply chain process optimization method and system based on big data, and provides a supply chain dynamic optimization thought based on multi-source heterogeneous data fusion and intelligent decision closed loop. By constructing an end-to-end multi-source heterogeneous data real-time cooperative processing and intelligent decision closed-loop architecture, dynamic integration, intelligent analysis and automatic response and update optimization of full-link data of the supply chain are realized, so that the real-time analysis capability and dynamic adjustment capability of the supply chain are remarkably improved.

The invention discloses a toothpaste production raw material tracing method and system based on a block chain, and relates to the field of block chains, and the method comprises the steps: distributing a unique identifier for a raw material, binding basic information to form an initial data packet, taking a supplier as an alliance chain node, uploading the initial data packet, and verifying the qualification through an intelligent contract; the transportation data is encrypted in real time through the Internet of Things and edge calculation and is uploaded, and a detection report deviation value is verified during warehousing; raw materials and finished product batches are associated in a production link, and process parameter compliance is automatically checked and then written into a block; in the warehouse logistics link, environment data and transportation information are integrated, and a tracing sub-block is generated after a goods receiver is verified through code scanning; an improved PBFT consensus mechanism and a cryptographic algorithm are adopted to ensure data security, and transparent tracing of consumers, supervisors and enterprises is realized through hierarchical authority management. The method has the advantages that the raw materials and the finished product full-link data are bound through the unique traceability identifier, non-tampering traceability of the toothpaste raw materials from supply to the terminal is achieved, and multi-party hierarchical query requirements and product safety are met.

The invention discloses an industrial building business service management method and device and a medium, and belongs to the technical field of building service management, and the method specifically comprises the steps: constructing a multi-dimensional data fusion model; defining cost, progress, quality and security dimension data ranges and acquisition nodes, and storing the data ranges and the acquisition nodes in a model database after standardization; based on the standardized data of the multi-dimensional data fusion model, analyzing a dimension internal relationship by using an association rule mining algorithm, defining a dimension mapping relationship, and integrating to form an association rule base; establishing a multi-dimensional linkage analysis module; the module is established by taking a model database as a data source and a multi-dimensional data association rule base as a logic support, and a threshold triggering mechanism and a cross-link data calling function are set; constructing a business collaborative decision support module; business requirements of multiple departments are integrated; a multi-dimensional data visualization monitoring platform is built; and designing a linkage trend chart based on a result of the multi-dimensional linkage analysis module.

The invention relates to the technical field of data management, and discloses a dynamic partitioning and priority scheduling method based on time sequence data, which comprises the following steps: collecting time sequence characteristics of an industrial data source through an edge gateway and carrying out structured storage in Redis, the time sequence characteristics comprising collection frequency, historical peak time period and service priority; load indexes are calculated based on time sequence characteristics, and a distributed message system is driven to execute dynamic partition adjustment of pre-judgment type capacity expansion, hot spot precise splitting and low-peak intelligent capacity reduction; and full-link data priority scheduling is realized through production end route isolation, Broker end resource inclination and consumption end thread pool isolation. According to the invention, the dynamic adaptation of the distributed message system resource and the industrial time series data load is realized, the real-time performance of high-quality data is ensured, the integrity of data transmission and the system adaptability are improved, and the problems of poor load fluctuation adaptation, high-quality data delay and easy data loss due to faults in the prior art are effectively solved.

A system including a media data optimization engine (MDOE) and a method for automatically creating structured data objects for media content rendered in one or more languages in an electronic document of a business entity are provided. The MDOE identifies non-textual objects including media content rendered in one or more languages in the electronic document and generates textual objects in the corresponding language(s) therefrom. The MDOE transforms the textual objects into structured data objects based on configurable criteria and generates a dynamic index-oriented object for the structured data objects specific to the business entity. The MDOE connects the structured data objects to the dynamic index-oriented object by creating linked data nodes therefrom with the dynamic index-oriented object as a core. The MDOE connects the dynamic index-oriented object with the linked data nodes to the electronic document, thereby facilitating dynamic changes to the electronic document and dynamically optimizing the electronic document.

One or more data objects from at least one of HR data system may each contain data fields storing HR information. A graphical user interface (GUI) may present primitive coding blocks representing standard graphic programming functions and custom business blocks representing high-level operations customly directed to the one or more data objects. A visual workflow may be constructed using at least one block selected, by a user of the GUI, wherein the at least one block represents operations linking the data fields of the one or more data objects to corresponding input fields of the at least one block. The at least one block of the visual workflow may be translated into a custom data connector comprising executable instructions for obtaining values for the corresponding input fields from the linked data fields and generating, based on the values, at least one corresponding data object storing the HR information.

The invention relates to the technical field of intelligent supply chain management, in particular to an intelligent procurement source searching method and system based on multi-target dynamic optimization and a real-time feedback mechanism, and the method comprises the steps: collecting full-link data of a supply chain in real time, and enabling the data to comprise enterprise internal ERP system data, supplier database data, market public opinion platform data and Internet of Things equipment data; the integrity and credibility of the data are ensured through an API interface and a block chain technology; carrying out denoising and normalization processing on the collected multi-source data, eliminating data redundancy and contradiction, and constructing a unified data model; the method has the beneficial effects that operational research, machine learning, a dynamic game theory and a real-time data analysis technology are fused, and an intelligent source searching decision system is constructed through a multi-target dynamic optimization model, a real-time feedback closed-loop system and a multi-source heterogeneous data fusion framework. The core technology covers a multi-target weight dynamic allocation model based on reinforcement learning, and collaborative optimization of dimensions such as cost, quality, delivery cycle, risk and the like is realized.

The invention relates to the technical field of carbon emission, in particular to an on-chain enterprise carbon emission accounting method and system. The method comprises the following steps: acquiring energy consumption, process flow and supply chain collaborative data of an enterprise in links of raw material acquisition, production processing, logistics transportation, equipment operation and recovery processing through Internet of Things equipment, and generating a carbon emission data set; based on a life cycle evaluation theory, boundaries of direct emission, indirect energy emission and supply chain associated emission are divided, and enterprise-level, product-level and supply chain-level carbon emission accounting models are constructed. A machine learning algorithm is utilized to analyze production fluctuation data in real time, carbon emission factor weights and accounting boundary parameters are dynamically adjusted, and an accounting result is optimized. And outputting a result to a green purchase decision-making system, a carbon asset management system and a supply chain collaboration platform. The system integrates industrial Internet of Things equipment, block chain nodes and enterprise resource planning system interfaces, collects and verifies full-link data in real time, solves the problem of data collection, and improves the accounting reliability.

The invention provides a full-link data tracing processing method and system for a cross-border home supply chain, and relates to the technical field of supply chain management, and the method comprises the steps: 3, dividing a plurality of data unit sets which are correlated with each other based on the process data of the supply chain, determining a reference data unit set, and obtaining two groups of different data correlation rules; analyzing an interaction relationship between the two groups of data association rules, establishing a data state identification range, and selecting two data verification reference points in the data state identification range; and according to the evolution relationship of the data verification reference point on the time sequence, the evolution span is quantified by calculating the comprehensive distance between two points on the time and data feature dimensions, a data state evolution path is constructed to generate a data calibration coefficient, and a data link track comprising a timestamp and link information is formed. According to the invention, full-link data tracing of the cross-border home supply chain is realized, and the collaboration efficiency of each link of the supply chain is improved.

The invention discloses a cloud computing bare metal hardware market distribution and management optimization system and method. The system comprises a resource access module, a distribution channel management module, an intelligent matching module, a transaction settlement module, a data analysis and optimization module, a safety and operation and maintenance management module and the like. Standardized aggregation of heterogeneous resources is realized through a resource access system, and the problem of fragmented management is solved; the distribution channel management system dynamically adjusts distribution rules and improves the channel cooperation efficiency; the intelligent matching system is combined with user behavior analysis to realize accurate supply and demand connection; the transaction settlement system integrates payment channels and marketing tools and simplifies fund circulation; the data analysis system precipitates full-link data to support decision optimization; the safety and operation and maintenance system guarantees safe and stable operation. According to the method, through the steps of resource access standardization, channel rule dynamic and the like, efficient, intelligent and standardized management of the whole distribution process is realized, and the market distribution efficiency, accuracy and safety are improved.

The invention relates to the technical field of electric data processing, in particular to a data blood relationship automatic tracking method and system based on a table in AI data, and the method comprises the steps: obtaining metadata in the table in the AI data, and analyzing a task log of a calculation engine to construct an initial directed acyclic graph; the initial directed acyclic graph comprises data nodes and explicit blood edges connected with the data nodes; and calculating a flow entropy coupling index between any two data nodes. According to the method, a flow entropy coupling index based on update time correlation and logic evolution divergence based on key entity distribution similarity are constructed, a graph comparison learning model is combined, under the condition that an explicit SQL grammatical structure is lacked, the implicit dependency of upstream and downstream nodes is automatically identified, and the flow entropy coupling index and the logic evolution divergence are combined. And end-to-end full-link data consanguinity complete tracking in a complex AI feature engineering scene is successfully realized.

The invention discloses a traditional Chinese medicine whole industry chain big data construction method and system, and relates to the field of data links, and the method comprises the steps: generating a standardized metadata template for seven links of a traditional Chinese medicine industry chain; block chain nodes are deployed in each link of an industrial chain, timestamp fingerprints are generated for key process data, and the key process data are automatically chained for evidence storage; constructing a data fusion model based on a graph neural network, and generating cross-link associated data; constructing a traditional Chinese medicine ontology knowledge graph, and mapping the association relationship between the cross-link associated data in the graph; full-chain digital tracing from implantation to clinic is realized through a path reasoning algorithm; based on the hybrid collaborative storage architecture, access management is carried out through dynamic fragmentation strategy data points; and constructing a process prediction model, detecting process parameter deviation, and carrying out risk early warning. The method has the advantages that through block chain evidence storage and combination of the graph neural network and the knowledge graph, full-chain digital tracing and risk early warning from implantation to clinic are realized.

The invention relates to the technical field of data processing, in particular to a financial contract online editing system based on multi-person collaboration, and the method comprises the steps: capturing an operation feature vector with a space-time mark in real time through an acquisition unit; the analysis unit generates a regional operation intention set in combination with the contract semantic tag library; the conflict probability calculation unit fuses the space overlapping degree, the logic association chain and the time sequence overlapping window to quantify the conflict risk; the dynamic partition response unit triggers area locking, operation confirmation or background monitoring according to probability value grading; the legal rule verification unit implements compliance interception and outputs a forced interrupt signal; the business logic serialization unit reconstructs an instruction sequence; the multi-strategy synchronous execution unit carries out atomic broadcasting, transaction binding and batch asynchronous updating by differentiating a core term channel, a dependency group channel and a common term channel; and the closed-loop audit tracking unit aggregates full-link data to generate a verifiable influence chain. The risk of data inconsistency caused by concurrent operation of financial contracts is systematically solved.

The invention relates to the technical field of automatic driving, and particularly discloses an off-line evaluation and iterative optimization method and system for an automatic driving system, and the method comprises the steps: recording a task-level data packet, and binding a reproducible fingerprint for the task-level data packet; in the off-line stage, a consistent input data stream is reconstructed, and the consistent input data stream is input into a current online running old model in parallel to be compared with a to-be-evaluated candidate new model for evaluation; according to a comparison evaluation result, extracting a scene fragment data packet based on a preset multi-dimensional evaluation index and a triggering rule; the method comprises the steps that scene fragment data packets are obtained, failure type labels are automatically generated for the scene fragment data packets, bucket dividing and weighted sampling are carried out according to scene features, and scene fragment data are returned to a model training data pool, full-link data packaging is carried out with task-level data packets as the granularity, reproducible fingerprints are bound for all the data packets, and therefore the scene fragment data can be obtained. Therefore, the offline playback, the A / B evaluation result and the backflow training sample can be subjected to consistent indexing and tracing through the fingerprint.

The invention discloses an induction cooker production line productivity prediction system using big data analysis, and relates to the technical field of industrial Internet of Things, the induction cooker production line productivity prediction system comprises a production line management center, the production line management center is in communication connection with the following modules: a multi-source data acquisition module used for acquiring full-link data from each link of an induction cooker production line, carrying out preprocessing to form a full-link data sequence table; and the digital twinning virtual mapping engine is used for constructing a virtual model of the induction cooker production line in combination with a digital twinning technology. The real state of the production line can be reflected in real time by collecting the equipment operation state, the production progress and the material inventory data of the induction cooker production line in real time and combining with the virtual model constructed by the digital twin technology, so that the system can capture abnormities in the production process in time, accurate productivity prediction is carried out, and the production efficiency is improved. The problem that productivity adjustment is not timely due to information lag is effectively avoided, and the accuracy and flexibility of a production plan are remarkably improved.

The invention discloses an AI-driven polymer composite material process optimization method, and aims to solve the problems of data islands, process optimization lag and insufficient model timeliness in a polymer material production process. The method comprises the following steps: collecting full-link data according to a six-level customer product coding specification; the state parameters of the high-frequency equipment are safely stored in the sub-table 1 through encryption and identity authentication; constructing a structured database based on the production batch number association main table, the raw material sub-table set, the sub-table 2 and the sub-table 3; training a multi-model artificial intelligence system fusing gradient boosting regression, Bayesian optimization, a neural network and a random forest, and realizing a bidirectional linkage closed loop of formula recommendation, process optimization and performance prediction; and incremental learning is carried out by adopting a sliding window mechanism in combination with an online gradient descent and elastic weight consolidation strategy. According to the technical scheme, intelligent, efficient and safe optimization of the high polymer material process can be achieved, and the product quality and the production efficiency are remarkably improved.

Identity nodes within bidirectional dual node hashchain (BDNH) bundles generate hash keys by processing partial metadata and linking them through identity graphs. Translation nodes perform identity resolution, data anonymization, and re-keying by transforming original identifiers into anonymous IDs and re-key IDs. Laplace noise enabler capsules introduce Laplace noise into translation layer logs for differential privacy. BDNHs share metadata between adjacent bundles for secure and efficient data transmission. A cognitive analytics layer shared among all bundles verifies hashed encrypted data packets using a gossip protocol for real-time validation. An analytics workspace layer activates re-key IDs and creates digital tags for datasets using BERT transformers, linking data through knowledge graphs. The system manages parallel processing and includes machine learning modules, homomorphic encryption, federated learning, zero-knowledge proofs, attribute-based encryption, and anomaly detection. Bundles' AI engines work with the Laplace noise enablers to analyze data, optimize noise, and enhance the data clean room functionality.

Embodiments of the present disclosure provide for improved identification of ownership and / or ownership lineage for a data repository, such as a shared file drive. Ownership data and / or ownership lineage data is identified based on data associated with the data repository itself and / or file data objects stored thereon in conjunction with data from a user linkage database. In this regard, accurate identification of a particular owner or owner(s) of a data repository may be determined from the data repository, and the user linkage database may serve additional details of such owner(s), linkages to other user(s) and / or group(s) of users embodying owner(s), and / or the like. Identified ownership data and / or ownership lineage data may be used for a myriad of additional processes, such as to notify users, determine what data repositories to perform detailed, efficient scans of based on group membership of the owner of said data repositories, or the like.

The invention provides a metadata-driven integrated data warehouse and governance platform construction method and system, and the method comprises the steps: collecting full-quantity metadata in a data source through a metadata automatic collection adapter, extracting a full-link blood relationship map and an enterprise data map, and constructing a target data set; executing a data table one-key synchronization operation by adopting a control table and parameterized pipeline mode, and generating an ODS target table and an ETL synchronization task; constructing a DDL table establishment statement according to the ODS target table and the ETL synchronization task; on the basis of DDL table establishment statements, dividing models according to business subject domains, and combining data magnitude prediction to automatically recommend a bucket dividing strategy to generate a data warehouse model; a table structure of a data warehouse model, a full-link blood relationship graph and a partitioning strategy are adopted to construct a one-stop data governance platform. A data warehouse table model can be quickly constructed, data can be automatically synchronized, and full-link data development, treatment and operation capabilities are realized.

The invention relates to a financial regularization conversion system based on a rule chain engine. The system comprises a data acquisition module used for acquiring multi-source business data from a data lake; the standard unification module is used for presetting a unified data standard rule and an articulation rule, obtaining a data circulation map based on the multi-source business data, and storing an articulation library of the articulation rule and the standardized multi-source business data; the data flow link building module is used for building a data flow link for the standardized multi-source business data, distributing a unique traceability ID for each piece of business data and generating a full-link data traceability log; and the rule chain engine processing module is used for identifying a business scene based on the standardized multi-source business data, carrying out data circulation in the business scene according to a data circulation link, calculating various financial elements according to a set rule chain, and finally forming a financial voucher. Compared with the prior art, the method has the advantages of full-link tracing, reliable result and the like.

The invention provides a data security risk assessment method and system. According to the method, a data access request is acquired, a target tracking identifier corresponding to the data access request is generated or received for the data access request based on a target entry service, and inter-service calling related to the data access request is tracked according to the target tracking identifier to acquire calling link data corresponding to the data access request. On the basis of pre-established sensitive field metadata, labeling a field access behavior of target business data involved in calling link data as a corresponding sensitive data access event, constructing a data stream sample graph according to an association relationship between the sensitive data access event and a target tracking identifier, performing risk analysis on the data stream sample graph, and obtaining a sensitive data stream; therefore, the data security risk assessment result corresponding to the single data access request can be output at the request level, and the full-link assessment of the data security risk under the micro-service architecture is realized.

The invention belongs to the technical field of self-media operation and business decision, and particularly relates to a self-media account business intelligent quotation method and system, and the method comprises the following specific steps: S1, collecting self-media account basic data, user portrait data, commercial value data and market dynamic data; s2, encrypting and storing the sensitive data through AES-256; s3, adopting a random forest algorithm to construct a basic quotation model, and combining account operation fluctuation and market nodes to realize dynamic adjustment; s4, displaying quotation composition details to the account party and supporting parameter adjustment preview; and S5, generating a multi-format personalized quotation list, and negotiating and recording full-link data based on the quotation and the cooperation effect. According to the method and the system, through integrating the self-media account basic data, the user portrait data, the commercial value data and the market dynamic data, the key information influencing the quotation is covered in multiple dimensions, the quotation association factors are comprehensively considered, the deviation caused by single data limitation is reduced, and the quotation accuracy is improved.

The invention discloses a public opinion data structuring method based on a Re-Act framework, and relates to the technical field of public opinion data processing and intelligent structuralization, and the method comprises the steps: firstly constructing a Re-Act public opinion structuring system comprising a thinking module, an action module and an observation module, then collecting data through multiple rounds of closed-loop iteration until a full link is covered, and finally obtaining the data. And finally, processing based on the full-link data through a structured tool, outputting standardized data and a relation graph, and terminating. According to the scheme, public opinion cause, response, action, interaction and ending full-link data are dynamically complemented through multiple rounds of closed loops of thinking, action and observation under the Re-Act framework, structured quality is optimized by combining multi-module processing, standardized data and a relation graph are output, public opinion dynamic characteristics are adapted, a complete and accurate support is provided for deep analysis and decision making, and the public opinion analysis and decision making efficiency is improved. And the limitation that the link is easy to break and the adaptability is poor in the traditional method is overcome.

The invention discloses a planning process generation method based on a block chain. Comprising the following steps of full-link marketing data collection and block chain evidence storage, planning process template creation, planning demand and multi-source data chaining, intelligent planning scheme generation, task decomposition and intelligent contract driving, planning process full-link tracing, on-chain data intelligent analysis and feedback and automatic settlement and optimization iteration. The invention discloses a planning process generation system based on a block chain. The system comprises a data acquisition and evidence storage module, a template management module, a demand and data processing module, an intelligent planning engine module, a task execution and cooperation module, a full-link data acquisition module, a data analysis and feedback module and a tracing and redisk module. The invention belongs to the technical field of planning process management, and particularly provides a block chain-based planning process generation method and system of an intelligent marketing ecosystem, which integrate intelligent planning, block chain credible cooperation and full-link data credible analysis.

Data recorder (7; D) for storing data traffic in intercommunicative data transmission links of hardware components (10', 11', 12', 13', 14', 15', 16', 17') that are integrated or can be integrated into at least one data communication network (1), wherein at least some of the hardware components use different data communication standards; wherein the data recorder (7; D) has a data conversion device (70) or is connectable to a data conversion device (70); wherein the data recorder (7; D) has at least one mass storage device (74; M) and wherein the data recorder (7; D) is configured to convert incoming data information conforming to an external data communication standard into a recorder-proprietary data communication standard and to store the data information in the recorder-proprietary data communication standard characterized by that the data recorder (7; D) is designed such that, in the event that it is not possible to convert the incoming data information into the recorder's proprietary data communication standard, it stamps the data information with a date and time stamp and stores it in the external data communication standard.

This invention discloses a three-dimensional state assessment and closed-loop feedback system based on multimodal physiological signals, comprising a healthy state nurturing subsystem, a core monitoring subsystem, a state control unit, and a unified user profile management unit. The core monitoring subsystem constructs a three-dimensional gradient assessment architecture encompassing physiological, emotional, and cognitive aspects, outputting state indices hierarchically. The healthy state nurturing subsystem, through a three-source driven and seven-stage progressive module, achieves phased task nurturing and automatic unlocking. The system features dual operating modes: local offline and authorized cloud encrypted upload, balancing data privacy with in-depth analysis. A unified user identifier links data from both systems, generating a state time-series graph. This invention solves the technical problems of traditional solutions, such as single assessment dimensions, lack of hierarchical progressive analysis, weak privacy control, and fragmented data. It achieves multimodal fusion assessment, three-dimensional hierarchical recognition, hierarchical privacy control, and full-cycle closed-loop adjustment, and is adaptable to smart wearables, smart cushions, and other terminal devices.

This invention discloses a method and system for integrated end-to-end data management of complex equipment. First, the server acquires structured and unstructured data from the design, manufacturing, and operation / maintenance stages of the system via a data acquisition module. Second, edge nodes perform data preprocessing, unifying data formats and time bases. Subsequently, the system constructs entity attribute and time-series models through a unified data modeling module, and achieves multimodal information alignment and representation through a data fusion module. The system also incorporates a quality risk early warning model, combining historical data and real-time status to achieve risk scoring and fault prediction. Finally, the application service module supports business functions such as intelligent scheduling, quality traceability, predictive maintenance, and performance analysis. This invention integrates the entire data flow across design, manufacturing, and operation / maintenance, solving problems such as data silos, low system collaboration efficiency, and difficulty in quality traceability, thereby improving the intelligence level and decision-making capabilities of complex equipment manufacturing.