A wind power equipment whole life cycle digital clue construction method

Abstract

The present application belongs to the field of industrial software and digital manufacturing technology, and particularly relates to a wind power equipment full life cycle digital clue construction method. The method comprises the following steps: step 1, constructing a unified data model covering the full life cycle of wind power equipment; step 2, constructing a digital clue framework; and step 3, business cooperation and feedback based on the digital clue. The method breaks through the data barriers among the design, manufacturing and operation stages by constructing a unified data model and a digital clue framework taking BOM as the main line, and realizes accurate mapping of full life cycle data and automatic business cooperation by using BOM dynamic mapping technology based on semantic understanding and a rule engine.

Description

Technical Field

[0001] This invention belongs to the field of industrial software and digital manufacturing technology, specifically relating to a method for constructing digital clues for the entire lifecycle of wind power equipment. Background Technology

[0002] Against the backdrop of accelerated global energy transition, the wind power industry continues to expand, with high-end wind power equipment making leaps and bounds towards deep-sea and large-megawatt applications. However, with the increasing complexity of the equipment, its full life-cycle management faces severe challenges.

[0003] Currently, while leading international companies and research institutions have established strong technological advantages in individual stages of wind turbine design (such as OpenFAST and Bladed), manufacturing, and operation and maintenance, the software systems used in each stage (such as CAD, PLM, MES, and SCADA) often exist as independent modules, lacking an integrated collaborative platform that connects the entire process. Domestically, although some companies have initially achieved partial closed loops in "design-simulation-testing" or "manufacturing-testing," the problem of "information silos" has not yet been fully overcome.

[0004] The existing technology has the following main drawbacks: 1. Data fragmentation and lack of a unified framework: Different data models and BOM systems (such as EBOM, PBOM, MBOM, SBOM) are used in the design, manufacturing, and operation and maintenance stages. The data formats and coding rules are not consistent, which makes it impossible for data to be automatically and accurately linked and transferred across stages.

[0005] 2. Delayed collaboration and weak feedback mechanisms: Valuable data such as fault modes and performance bottlenecks discovered during the operation and maintenance phase are difficult to transmit back to the design and process departments in a structured manner. Existing PLM or MES systems mostly use customized interfaces for limited data exchange, rely on manual intervention, and have fixed mapping rules, making it difficult to support real-time closed-loop feedback.

[0006] 3. Rigid Business Collaboration: When a change occurs in a certain link (such as a design change), due to the lack of semantic understanding and dynamic mapping mechanisms, it is impossible to automatically trigger corresponding adjustments in downstream links (such as processes and manufacturing), resulting in high manual coordination costs and a high risk of errors.

[0007] Therefore, there is an urgent need for a method to construct high-fidelity digital clues that span the entire lifecycle of high-end wind power equipment, so as to achieve accurate mapping, real-time connection and efficient business collaboration of multi-source heterogeneous data. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method for constructing a digital thread for the entire lifecycle of wind power equipment. This method breaks down data barriers between design, manufacturing, and operation and maintenance by constructing a unified data model and a BOM-based digital thread framework. It utilizes semantic understanding-based BOM dynamic mapping technology and a rule engine to achieve accurate mapping of lifecycle data and automated business collaboration.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: A method for constructing digital clues for the entire lifecycle of wind power equipment includes the following steps: Step 1: Constructing a unified data model covering the entire lifecycle of wind power equipment; Step 2: Constructing a digital clue framework; Step 3: Business collaboration and feedback based on digital clues.

[0010] Furthermore, step 1 includes: Define a standardized data structure for wind power equipment throughout its entire lifecycle. The data model includes eight core functional modules: basic information module, design data module, process data module, manufacturing data module, operation and maintenance data module, quality data module, cost data module, and resource data module. A hierarchical coding method is adopted to assign a unique identifier code to each data entity. The coding structure includes a data type identifier, a business module code, a data entity code, and a serial number to ensure the uniqueness and identifiability of the data entity. Based on XML Schema, a standardized interface is defined to enable lossless data interaction with heterogeneous systems such as CAD, PLM, MES, and SCADA.

[0011] Furthermore, step 2 specifically establishes a three-layer digital thread framework comprising a data acquisition layer, a data flow layer, and a data application layer, wherein: The data acquisition layer adopts a hybrid acquisition method that combines active pull and passive push, is compatible with multiple industrial protocols, collects full-process data of the entire life cycle of wind power equipment, and performs data cleaning, outlier removal and missing value filling. The data flow layer, as the core of the digital clue framework, deploys the flow engine and BOM mapping center. The flow engine is based on rule-driven (such as Drools) and configures the flow logic of "event-condition-action". When the upstream data changes or specific conditions are triggered in the whole process, it automatically drives its downstream business process. The BOM mapping center is used to realize multi-level dynamic mapping and linkage between design BOM (EBOM), process BOM (PBOM), manufacturing BOM (MBOM) and operation and maintenance BOM (SBOM); it adopts a semantic similarity matching algorithm based on Word2Vec, combined with a special terminology dictionary for the wind power equipment industry, to automatically identify the semantic correspondence between BOM items at different stages, calculate semantic similarity, and replace manual experience mapping. The data application layer provides visualized monitoring, cross-departmental collaborative support, and full lifecycle traceability analysis based on interconnected data.

[0012] Furthermore, step 3 utilizes the constructed digital clue framework to achieve cross-stage business collaboration, including: Positive collaboration: When the design data module changes, the workflow engine automatically triggers the process review in the process data module and the update of the manufacturing data in the manufacturing data module. Reverse feedback: The real-time operation data and fault records collected by the operation and maintenance data module are back-mapped to the design and process models of the design data module and process data module through a unified data model to support design optimization and process improvement.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. Solved the problem of data fragmentation: By using a unified data model and hierarchical coding system, multi-source heterogeneous data that were originally scattered in various systems of design, manufacturing and operation are integrated into a unified framework, fundamentally eliminating "information silos".

[0014] 2. Intelligent dynamic BOM mapping was achieved: A semantic similarity matching algorithm based on Word2Vec was introduced, which solved the problems of traditional BOM mapping relying on manual labor, low efficiency, and easy errors, and ensured the consistency and evolution capability of the bill of materials across stages.

[0015] 3. Enhanced closed-loop feedback throughout the entire process: Digital clues ensure that operation and maintenance data can be fed back to the design and process stages in real time and completely, providing a solid data foundation for the continuous iterative optimization and localization of wind power equipment.

[0016] 4. Improved efficiency of automated collaboration: The rule-driven workflow engine enables business changes to automatically trigger downstream responses, significantly reducing manual coordination costs and achieving millisecond-level data collection and second-level traceability response. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0018] Figure 2This is a schematic diagram of the eight functional modules of the full life cycle data model for high-end wind power equipment in the embodiment.

[0019] Figure 3 This is a schematic diagram of the three-layer architecture of the wind power equipment full life cycle collaborative platform based on digital clues in the embodiment.

[0020] Figure 4 This is a schematic diagram of the entire collaborative scenario triggered by design changes in the embodiment.

[0021] Figure 5 A schematic diagram of the design process when design changes are used as a trigger for business collaboration. Detailed Implementation

[0022] This invention proposes a method for constructing a digital lifecycle narrative for wind power equipment. The embodiment is a method for constructing a digital lifecycle narrative for high-end wind power equipment, encompassing the entire process of design, manufacturing, and operation and maintenance, based on a unified data model and a Bill of Materials (BOM). Brief descriptions of the accompanying drawings are provided below. Figure 1 This is a schematic diagram of the overall process of the wind power equipment full life cycle digital clue construction method of the present invention. It clearly shows the complete technical route from unified data model construction (Step1), digital clue framework construction (Step2) to business collaboration and feedback (Step3). At the same time, it marks the core design points of each step, the external systems to be connected, and the two dimensions of collaborative feedback.

[0023] Figure 2 This is a schematic diagram of the eight functional modules of the unified data model of this invention. The modules are classified according to the entire life cycle stage of wind power equipment, and the core content and data source of each module are marked in detail, intuitively presenting the standardized classification and management system of the entire life cycle data.

[0024] Figure 3 This is a schematic diagram of the three-layer architecture of the digital clue framework of this invention. It shows the hierarchical structure of the data acquisition layer, data flow layer and data application layer from bottom to top. It marks the core implementation methods, technical means, functional effects and supporting technologies of each layer, highlighting the core position of the data flow layer.

[0025] Figure 4 This is a schematic diagram of the entire process collaboration scenario triggered by the design change of this invention. It shows the business flow sequence of design, process, manufacturing and quality links in a node-based form, and marks the unified data model module and data interaction method corresponding to each node, intuitively presenting the automated collaboration and closed-loop feedback mechanism of the entire process.

[0026] The following is in conjunction with the appendix Figure 1-4 The present invention will be further described in detail with reference to specific embodiments.

[0027] The proposed method for constructing a digital lifecycle narrative for wind power equipment is a method for constructing a digital lifecycle narrative for the entire process of design, manufacturing, and operation and maintenance of high-end wind power equipment. It is based on a unified data model and a Bill of Materials (BOM) framework. The overall process is as follows: Figure 1 As shown, the main steps include building a unified data model, building a digital lead framework, and realizing business collaboration and feedback. Among them, the design and implementation of the unified data model and the construction and core function implementation of the digital lead framework are the two core technical parts. Finally, through typical business scenarios, the collaboration and feedback of the entire life cycle are implemented to realize the full-process connection and value mining of data.

[0028] Part 1: Design and Implementation of a Unified Data Model This embodiment first constructs a unified data model covering the entire lifecycle of wind power equipment, such as... Figure 2 As shown, this model is divided into four categories based on the entire life cycle of wind power equipment and management needs: design stage, manufacturing stage, delivery and operation and maintenance stage, and management support. It contains eight independent functional modules, and the functional positioning, core fields, and data sources of each module are standardized, as detailed below: 1. Basic Information Module: Belonging to the design phase, it serves as the core hub for data association across the entire model. Its core fields include the equipment's unique code, model and specifications, manufacturer, and rated power / grid connection time. These fields are the basic identifiers for achieving full lifecycle data traceability and provide a unified benchmark for data association across subsequent modules.

[0029] 2. Design Data Module: Belonging to the design phase, it fully retains the core data of equipment R&D design. The core fields are design BOM (EBOM), CAD drawing number, finite element analysis (FEA) results, material parameters, and design change records. The data source is the R&D design department and serves as the technical basis for subsequent process and manufacturing processes.

[0030] 3. Process Data Module: Belonging to the manufacturing stage, it provides standardized process guidance for the manufacturing process. The core fields are process BOM (PBOM), processing steps, process parameters (welding current, curing temperature, etc.), tooling fixture number, and equipment selection. The data source is the process technology department, which is a key bridge connecting design and manufacturing.

[0031] 4. Manufacturing Data Module: Belonging to the manufacturing stage, it records the entire process data of equipment production and manufacturing in real time. The core fields are Manufacturing Bill of Materials (MBOM), production work order, processing time, equipment operating parameters, and quality inspection results. The data source is the MES system / production quality inspection department, which directly reflects the actual execution of the manufacturing process.

[0032] 5. Operation and Maintenance Data Module: Belonging to the delivery and operation and maintenance phase, it collects operation and maintenance data of equipment during its service phase. The core fields are operation and maintenance BOM (SBOM), real-time power, fault codes, maintenance records, and spare parts replacement information. The data source is SCADA system / on-site operation and maintenance team, and it is key data for equipment performance optimization and fault tracing.

[0033] 6. Quality Data Module: This module belongs to the delivery and operation and maintenance phase, enabling full-process quality control. The core fields are component / finished product quality inspection reports, finished product test reports, and after-sales quality complaint records. The data sources are the quality inspection department and customer service department. It integrates quality data from the design, manufacturing, and operation and maintenance phases to form a complete quality traceability chain.

[0034] 7. Cost Data Module: This module belongs to the management support category and provides data support for cost optimization throughout the entire lifecycle. The core fields are design / material / processing costs, operation and maintenance / recycling costs, and scrapping costs. The data sources are the finance / procurement / operation and maintenance departments, enabling refined accounting and control of costs at each stage.

[0035] 8. Resource Data Module: Belongs to the management support category, it optimizes the resource allocation efficiency of production and operation and maintenance. The core fields are equipment ledger, personnel information, spare parts inventory, warehouse location, and transportation scheduling records. The data source is the equipment management / human resources / logistics department, realizing the unified management and scheduling of human, machine, material, method, and environmental resources.

[0036] like Figure 1 As shown in Step 1, to ensure unique data identification and lossless data exchange, this embodiment, based on the eight functional modules, is equipped with a unified hierarchical coding rule and standardized interface to achieve seamless integration with external systems such as CAD, PLM, MES, and SCADA. The specific design is as follows: 1. Unified Layered Coding Rules: A 13-character unique data identifier is designed using the "layered coding method," with the coding structure XX-XXXX-XXXX-XXX. The definitions and functions of each segment are as follows: First segment (2 digits): Data type identifier, distinguishing core business types such as design, manufacturing, and operation and maintenance, e.g., 01 represents design, 02 represents manufacturing, and 03 represents operation and maintenance; Second segment (4 digits): Business module code, corresponding one-to-one with the eight major functional modules, e.g., 0001 is the basic information module, 0002 is the design data module, and 0003 is the process data module; Third segment (4 digits): Data entity code, distinguishing different data entities under the same module, such as parts, process parameters, and fault codes; Fourth segment (3 digits): Serial number, distinguishing different instances of the same data entity, such as different individual parts of the same type of blade.

[0037] This coding rule enables precise data location. For example, the code "01-0002-0001-001" can be directly located as the component data numbered 001 in the design data module, ensuring the traceability and retrieval of data throughout its entire lifecycle.

[0038] 2. Standardized Interface Design: Based on XML Schema, 32 standardized interfaces are defined, covering all types of interfaces such as data acquisition interfaces, data query interfaces, and data update interfaces. Among them, the data acquisition interface supports RESTful API and JSON format, which can realize lossless data interaction with external systems such as CAD, PLM, MES, SCADA, etc., ensuring that data from external systems can be efficiently and accurately accessed into the unified data model and realizing standardized integration of multi-source data.

[0039] Part Two: Digital Clues Framework Construction and Functional Design Based on the unified data model in Part 1, this embodiment constructs a three-layer digital thread framework, as shown in Figure 3. From bottom to top, it consists of a data acquisition layer, a data flow layer (core layer), and a data application layer. Each layer is progressive and works in synergy to achieve full-lifecycle management of data acquisition, flow, and application. The detailed design, technical means, and functional implementation of each layer are as follows: 2.1 Design of the Data Acquisition Layer: As the foundational layer for building digital clues, its core function is to achieve full-coverage acquisition and standardized preprocessing of multi-source heterogeneous data throughout the entire process, laying a high-quality data foundation for subsequent data flow and application. The specific implementation method is as follows... Figure 3 As shown, it includes: 2.1.1 Determine the data acquisition method: A hybrid data acquisition mode of active pull + passive push is adopted. The frequency of active pull can be customized by the user (such as 1 time per minute for key process parameters and 10 times per minute for normal operating parameters) to meet the needs of routine data acquisition. Passive push supports event-triggered mode. When abnormal situations such as equipment fault alarms or process parameters exceeding thresholds occur, data is pushed in real time to ensure the real-time acquisition of abnormal data.

[0040] 2.1.2 Protocol Compatibility: By developing a dedicated protocol parsing plugin, it is compatible with more than 20 industrial protocols such as OPC UA, Modbus, MQTT, and Profinet. It can be adapted to 12 types of industrial equipment, including CNC machine tools, sensors, PLCs, and wind turbine main control systems, to achieve full coverage collection of multi-source equipment data in all aspects of wind power equipment design, manufacturing, and operation and maintenance.

[0041] 2.1.3 Data Preprocessing Process: Standardized ETL operations are performed, including outlier removal, missing value imputation, data reduction, and format conversion. Outlier removal removes invalid data (such as welding current values ​​that exceed the normal range) by setting reasonable thresholds. Missing value imputation achieves accurate completion based on historical data trend prediction. Data reduction and format conversion convert multi-source heterogeneous data into a unified format, reducing data redundancy and improving data processing efficiency.

[0042] 2.2 Design of the Data Flow Layer: As the core layer of the digital thread framework, its core function is to realize the orderly flow of data between modules and systems and intelligent dynamic BOM mapping. It is key to achieving cross-stage business collaboration. This layer adds Redis distributed caching technology to store hot data and real-time data, reducing database access pressure and improving data flow efficiency. The core includes two major components: the flow engine and the BOM mapping center. The specific design is as follows... Figure 3 As shown: 2.2.1 Design Flow Engine: Based on the Drools rule engine, the core of the design is an "event-condition-action" triggering logic, supporting user-defined data flow paths and triggering conditions. Users can preset various business rules according to the needs of wind power equipment lifecycle management, such as "automatically triggering the process re-review process when the design change rate is greater than 5%" and "automatically pushing to the operation and maintenance data module and triggering spare parts scheduling when the equipment fault code is E001". When upstream data changes or preset conditions are met, the flow engine automatically drives the downstream business process to respond without manual intervention, realizing the automation and intelligence of business linkage.

[0043] 2.2.2 Design of the BOM Mapping Center: This is the core component for resolving cross-stage BOM structural differences and achieving collaboration between design, process, manufacturing, and operation and maintenance. Addressing the issues of traditional BOM mapping relying on manual processes, low efficiency, and susceptibility to errors, a Word2Vec-based semantic similarity matching algorithm is adopted to achieve dynamic mapping of EBOM, PBOM, MBOM, and SBOM. The specific implementation steps are as follows: ① Construct a wind power industry-specific terminology dictionary and corpus, adapting to the professional terminology features of wind power equipment (such as blade shell, blade skin, hub flange, etc.), laying the foundation for semantic matching; ② Convert the material names, descriptions, and other textual information in EBOM, PBOM, MBOM, and SBOM into standardized word vectors; ③ Calculate the cosine similarity of word vectors between different BOMs, and combine this with the hierarchical structure features of the BOM to automatically identify and establish cross-stage BOM mapping relationships.

[0044] For example, it can automatically link the "blade shell" in the design BOM with the "blade skin" in the manufacturing BOM, ensuring consistency across BOM stages and enabling precise flow of material information.

[0045] 2.3 Design Data Application Layer: As the terminal layer for realizing the value of digital clues, its core functions are to provide customized functions such as visual monitoring, cross-departmental collaborative support, and full lifecycle traceability analysis for different users (design, process, manufacturing, operation and maintenance, and management departments), so as to fully explore the value of data. Specific functions include... Figure 3 As shown: 2.3.1 Visual monitoring: ECharts visualization dashboards enable multi-dimensional data display, including equipment status flow, data interface success rate, equipment operation anomaly alarms, process parameter execution status, etc., supporting multi-terminal access, allowing users to intuitively and in real time grasp the operational status of data throughout the entire lifecycle, and achieve refined monitoring.

[0046] 2.3.2 Cross-departmental collaborative support: Breaking down traditional data barriers between departments, enabling cross-departmental data penetration queries, each department can query relevant data at each stage of the entire lifecycle according to its permissions. For example, the operation and maintenance department can directly query material performance parameters such as the fatigue strength of blades in the design stage, the design department can obtain process execution data such as actual welding deviations in the manufacturing process in real time, and the R&D department can retrieve equipment failure data in the operation and maintenance stage, realizing efficient data collaboration among departments and improving work efficiency.

[0047] 2.3.3 Full Lifecycle Traceability Analysis: Relying on unified hierarchical coding rules, the system enables rapid and accurate data traceability. Users input a unique data code into the system, and the system can display the complete flow trajectory of the data from its creation to its current status within 3 seconds, including key information such as the person handling the data, the processing time, the modified content, and the data source at each stage. This achieves full lifecycle data traceability and verifiability, significantly improving the efficiency of problem location and fault tracing.

[0048] Part Three: Implementation of Business Collaboration and Feedback Based on Digital Clues By utilizing the unified data model and three-tiered digital thread framework constructed above, positive business collaboration and reverse data feedback throughout the entire lifecycle of wind power equipment can be achieved, forming a closed-loop optimization system for the entire process. For example... Figure 1 As shown in Step 3, positive collaboration enables the automatic triggering of changes in upstream links to downstream links, while reverse feedback enables the actual data from operation and maintenance, manufacturing, and quality links to flow back to design and process links, providing data support for equipment iteration and optimization.

[0049] This embodiment takes a typical collaborative scenario of "design change - process adjustment - manufacturing response" in the entire life cycle of wind power equipment as an example, combined with Figure 4 A node-based business workflow diagram, detailing the process of achieving business collaboration and closed-loop feedback throughout the entire process. Figure 4 Nodes 1 through 5 proceed sequentially according to the business flow order, with each node connecting to the corresponding module of the unified data model to achieve seamless data flow and closed loop: Node 1: Initiate design change and connect with the design data module. Design changes are initiated by the design department due to reasons such as material performance optimization and customer demand adjustment. In this embodiment, the blade material parameter in the design BOM is changed from "glass fiber" to "carbon fiber". The designer completes the change operation in the design data module of the unified data model, updates the core data such as the design BOM and material parameters. The system assigns a unique identifier to the changed data through hierarchical coding rules, and completes the basic data entry for the design change.

[0050] Node 2: Process scheme adjustment, connecting with the process data module, relying on the BOM mapping center. The digital thread framework's workflow engine captures design BOM change information in the design data module through real-time monitoring. Based on the preset rule that "design BOM changes trigger process data module updates", it automatically sends process data module update notifications to the process department through the data synchronization interface, eliminating the need for manual information transmission.

[0051] The BOM mapping center uses a semantic similarity matching algorithm to quickly identify the core change of "changing the blade material from glass fiber to carbon fiber" and automatically associates it with the corresponding blade processing-related items in the process BOM. This assists the process department in adjusting processing procedures (such as adding a dedicated carbon fiber cutting procedure and deleting a glass fiber polishing procedure) and process parameters (such as adjusting the curing temperature from 120℃ to 150℃ and adjusting the curing time) based on the material properties of carbon fiber. After the process personnel complete the adjustments, they update the process BOM in the process data module to achieve a precise match between the process plan and the design change.

[0052] Node 3: Production work order generation, connecting with the manufacturing data module. After the process BOM in the process data module is updated, the workflow engine triggers the data synchronization rule again, and the adjusted process BOM is automatically synchronized to the manufacturing data module through a standardized interface. The MES system obtains the updated data from the manufacturing data module in real time, and automatically generates new production work orders based on the new process BOM. The work orders contain core information such as the adjusted processing procedures, process parameters, and tooling requirements, and are automatically distributed to each workstation of the wind power equipment production line through the system, enabling the manufacturing process to respond quickly to design changes.

[0053] Node 4: Execution and Data Acquisition, connecting to the Manufacturing Data Module + Quality Data Module Each workstation on the production line carries out production operations according to the new production work order. The manufacturing data module collects the actual processing parameters in the production process in real time, such as the actual curing temperature, actual processing time, and equipment operating parameters, through the "active pull + passive push" mode of the data acquisition layer. The collected actual processing parameters are then automatically pushed to the quality data module through the data synchronization interface to provide data support for subsequent quality inspection and comparison.

[0054] Node 5: Reverse optimization / closed-loop feedback, connecting to the quality data module + design data module After receiving the actual processing parameters pushed by the manufacturing data module, the quality data module automatically compares and analyzes the actual parameters with the design requirements parameters in the design data module. If the actual parameters are within the reasonable range of the design requirements, the entire process of this design change is completed. If there is a deviation in the actual parameters (e.g., the actual curing temperature is only 140℃, which does not reach the design requirement of 150℃), the quality data module will automatically generate a quality analysis report. The report includes deviation data, preliminary analysis of the cause of the deviation, and scope of impact. The quality analysis report is then sent back to the design data module through the workflow engine, and a deviation reminder is sent to the design department.

[0055] Based on the quality analysis report, the design department optimized and adjusted the design parameters (such as appropriately reducing the design value of the curing temperature and optimizing the curing process requirements of carbon fiber materials), and updated the design data module again to complete the closed-loop verification and optimization of the entire life cycle.

[0056] To further illustrate, when business collaboration is triggered by design changes, the specific design process is as follows: Figure 5 As shown, it is: S301: The design department initiates a design change and updates the design data module in the unified data model; S302: When the workflow engine detects a change in the design BOM, it automatically triggers an update notification from the process data module through the data synchronization interface; S303: The BOM mapping center identifies changes and assists the process department in adjusting processing procedures and parameters, and updating the process BOM; S304: The adjusted process BOM is synchronized to the manufacturing data module, and the MES system automatically generates a new production work order and issues it to the production line; S305: The manufacturing data module collects actual processing parameters in real time and pushes them to the quality data module. The parameters are automatically compared with the design requirements. If there is a deviation, a quality analysis report is generated and sent back to the design department to complete the closed-loop verification.

[0057] Mark the steps Figure 4 Above, or add accompanying images: Figure 5A schematic diagram of the design process when business collaboration is triggered by design changes.

[0058] This invention constructs a unified data model covering the entire lifecycle of wind power equipment, a three-layer digital thread framework, and combines the Drools rule engine with Word2Vec-based semantic BOM mapping technology. This achieves precise mapping, real-time integration, and automated business collaboration of data throughout the entire lifecycle of wind power equipment design, manufacturing, and operation and maintenance. It effectively solves core problems in existing technologies such as data fragmentation, delayed collaboration, and rigid business linkage. Practical verification shows that this method can achieve millisecond-level data acquisition and second-level traceability response, improve data query efficiency by over 40%, and significantly reduce the average time for cross-stage BOM updates. This greatly enhances the intelligence and efficiency of wind power equipment lifecycle management, providing solid digital support for the development of high-end wind power equipment towards deep-sea and large-megawatt directions.

Claims

1. A method for constructing digital clues for the entire lifecycle of wind power equipment, characterized in that, Includes the following steps: Step 1: Build a unified data model covering the entire lifecycle of wind power equipment; Step 2: Build a digital clue framework; Step 3: Business collaboration and feedback based on digital clues.

2. The method for constructing digital clues for the entire lifecycle of wind power equipment according to claim 1, characterized in that, Detailed steps: Step 1: Construct a unified data model covering the entire lifecycle of wind power equipment. The unified data model includes eight functional modules: basic information module, design data module, process data module, manufacturing data module, operation and maintenance data module, quality data module, cost data module, and resource data module. Design unified hierarchical coding rules and standardized interfaces for the unified data model to achieve unique data identification and seamless integration with external systems. Step 2: Based on the unified data model, construct a three-layer digital clue framework, which includes a data acquisition layer, a data flow layer, and a data application layer. The data acquisition layer realizes the acquisition and preprocessing of multi-source heterogeneous data throughout the entire process. The data flow layer builds a flow engine based on a rule engine and constructs a BOM mapping center in conjunction with the Word2Vec semantic similarity matching algorithm to realize orderly data flow and dynamic BOM mapping across stages. The data application layer provides visualization monitoring, cross-departmental collaborative support, and full lifecycle traceability analysis functions. Step 3: Based on the aforementioned digital clue framework, realize business collaboration and feedback throughout the entire life cycle of wind power equipment. Trigger automated responses from upstream and downstream businesses through the flow engine, and simultaneously feed back data from operation and maintenance, manufacturing, and quality stages to the design and process stages, forming a closed-loop optimization throughout the entire life cycle.

3. The method for constructing digital clues for the entire lifecycle of wind power equipment according to claim 2, characterized in that, The hierarchical encoding rule adopts a 13-character "XX-XXXX-XXXX-XXX" encoding structure, where the first 2-digit segment is the data type identifier, the second 4-digit segment is the business module code, the third 4-digit segment is the data entity code, and the fourth 3-digit segment is the sequence number, thereby achieving precise data location.

4. The method for constructing digital clues for the entire lifecycle of wind power equipment according to claim 2, characterized in that, The standardized interface is defined based on XML Schema and includes 32 interfaces, including data acquisition interface, data query interface, and data update interface. The data acquisition interface supports RESTful API and JSON format, enabling lossless data interaction with external systems such as CAD, PLM, MES, and SCADA.

5. The method for constructing digital clues for the entire lifecycle of wind power equipment according to claim 2, characterized in that, The data acquisition layer adopts a hybrid acquisition mode of "active pull + passive push". The frequency of active pull is configurable, and passive push supports event triggering. The data acquisition layer has developed a protocol parsing plugin that is compatible with more than 20 industrial protocols such as OPC UA, Modbus, MQTT, and Profinet. The data preprocessing includes outlier removal, missing value imputation, and data reduction operations.

6. The method for constructing digital clues for the entire lifecycle of wind power equipment according to claim 2, characterized in that, The data flow layer's flow engine is designed based on the Drools rule engine, supporting user-defined data flow paths and "event-condition-action" trigger conditions. When upstream data changes or meets specific conditions, it automatically drives downstream business processes to respond.

7. The method for constructing digital clues for the entire lifecycle of wind power equipment according to claim 2, characterized in that, The dynamic mapping method of the BOM mapping center is as follows: first, a terminology dictionary and corpus of the wind power industry are constructed; then, the material descriptions in EBOM, PBOM, MBOM and SBOM are converted into word vectors; finally, the cosine similarity between vectors is calculated and combined with hierarchical structure features to automatically identify the mapping relationship of BOM at different stages.

8. The method for constructing digital clues for the entire lifecycle of wind power equipment according to claim 2, characterized in that, The traceability analysis function of the data application layer supports users to input unique data codes. The system can display the complete flow trajectory of data from creation to the current state within 3 seconds, including the person who processed the data, the processing time, and the content modified.

9. The method for constructing digital clues for the entire lifecycle of wind power equipment according to claim 2, characterized in that, When the business collaboration in step 3 is triggered by a design change, the specific process is as follows: S301: The design department initiates a design change and updates the design data module in the unified data model; S302: When the workflow engine detects a change in the design BOM, it automatically triggers an update notification from the process data module through the data synchronization interface; S303: The BOM mapping center identifies changes and assists the process department in adjusting processing procedures and parameters, and updating the process BOM; S304: The adjusted process BOM is synchronized to the manufacturing data module, and the MES system automatically generates a new production work order and issues it to the production line; S305: The manufacturing data module collects actual processing parameters in real time and pushes them to the quality data module. The parameters are automatically compared with the design requirements. If there is a deviation, a quality analysis report is generated and sent back to the design department to complete the closed-loop verification.

10. The method for constructing digital clues for the entire lifecycle of wind power equipment according to claim 2, characterized in that, The core fields of the eight functional modules are designed as follows: Basic information module: unique device code, model and specifications, manufacturer; Design data module: Design BOM, CAD drawing numbers, finite element analysis results, design change records; Process data module: process BOM, machining operations, process parameters, tooling and fixture numbers; Manufacturing data module: Manufacturing BOM, production work order, processing time, equipment operating parameters; Operation and maintenance data module: Operation and maintenance BOM, real-time power, fault codes, maintenance records; Quality data module: Quality inspection report, finished product test report, after-sales quality complaint record; Cost data module: design cost, material cost, processing cost, operation and maintenance cost, and scrapping and recycling cost; Resource data module: Equipment ledger, personnel information, spare parts inventory, warehouse location, transportation scheduling records.

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