A Digital Thread-Based End-to-End Management System and Method for Power Training

Abstract

This application discloses a power training end-to-end management system and method based on digital threads, belonging to the field of power training information management. The system uses a digital thread engine as its core hub, connecting data and business flows across modules such as training resources, information, examinations, and points. It anchors the entire lifecycle behavior trajectory of trainees through unique identifiers and timestamps, achieving dynamic mapping and real-time traceability of all elements. The digital thread engine incorporates an AI-MLP neural network and a lightweight GNN module, automatically identifying inflection points in skill development and knowledge gaps, accurately attributing them to long-term skill evolution. The examination management module employs an AI anti-cheating mechanism, enabling seamless post-exam learning. The training resource module connects to a digital twin model, supporting AR immersive practical training. This invention constructs a closed loop of "learning-practice-examination-evaluation-application," improving the accuracy and practicality of training, addressing pain points such as low efficiency in new energy operation and maintenance talent training and fragmented training management, and providing solid talent support for the new energy industry.

Description

Technical Field

[0001] This application belongs to the field of power training information management, and specifically relates to a power training full-process management system and method based on digital threads. Background Technology

[0002] Driven by the "dual-carbon" strategy, the new energy industry has entered a period of rapid, large-scale development. By the end of 2025, the installed capacity of wind and photovoltaic power generation nationwide exceeded 800 million kilowatts, and the number of new energy power plants increased by 127% compared to 2020. The contradiction between this explosive industry growth and the insufficient supply of skilled personnel is becoming increasingly prominent: according to the "Power Industry Skilled Personnel Development Report," there is a shortage of over 600,000 new energy operation and maintenance personnel, and only 32% of existing practitioners possess systematic professional skills certification. This "talent shortage" has become a core bottleneck restricting the safe and efficient operation of the industry. However, the traditional power training system has many deep-seated pain points: 1. Low efficiency in talent training: Relying on the experience-based teaching of "mentorship" takes 2-3 years to complete, and the quality of skill transmission is uneven due to the limitations of the mentor's personal ability and energy; the talent training of universities is disconnected from the actual industry, and graduates only master theoretical knowledge and lack on-site operation and maintenance skills, and need to undergo more than 6 months of pre-job training after joining the company.

[0003] 2. Fragmented Training Management: Existing training systems are mostly isolated modules, with training resources, student information, exam data, and point systems being disconnected, forming data silos. For example, student learning records cannot be linked to exam results, incorrect exam questions cannot be accurately pushed to corresponding learning resources, and point incentives are decoupled from skills development, making it difficult to quantify and evaluate training effectiveness and failing to form a closed-loop management system of "learning-practice-testing-evaluation-application".

[0004] 3. Insufficient intelligence: The system lacks dynamic tracking and analysis of students' behavior throughout their entire lifecycle, making it unable to accurately identify inflection points in ability development and knowledge gaps. Exam systems often use fixed question banks, making it difficult to achieve personalized assessments tailored to each student; anti-cheating measures are limited, posing risks such as proxy testing and cheating, resulting in low reliability of exam results.

[0005] 4. Training is disconnected from actual operation and maintenance: The training content is mostly theoretical knowledge and not closely integrated with the actual operation and maintenance scenarios of new energy power plants. Trainees learn only through books or videos, lacking immersive hands-on training, resulting in a common phenomenon of "understanding the theory but not knowing how to operate," and being unable to quickly deal with practical problems such as equipment failure and emergency response after being put on the job.

[0006] Therefore, building a digital thread-based power training full-process management system to achieve dynamic mapping and real-time traceability of all elements such as training resources, student information, exam results, and score changes, and to connect the five-link closed loop of "learning-practice-exam-evaluation-application," has become an urgent need to solve the pain points of talent training in the new energy industry and improve the efficiency and quality of training management. Summary of the Invention

[0007] This application provides a digital thread-based end-to-end power training management system. By constructing a three-tiered collaborative platform architecture of "business-data-technology," it achieves a digital twin of all elements, processes, and dimensions of the training process. Using digital threads as the link, the system automatically connects, dynamically updates, and provides closed-loop feedback on behavioral data from every stage of the training process—from onboarding assessment, course learning, simulation training, practical assessment, competency certification to job placement. This ensures that every access to learning resources, every exam answer, and every points redemption is traceable, analytically correlated, and strategically optimized.

[0008] To achieve the above objectives, this application provides a digital thread-based power training full-process management system, including a training resource management module, an information management module, an examination management module, an points management module, and a digital thread engine; The digital thread engine serves as the core hub, connecting the data and business flows of each module to achieve dynamic mapping and real-time traceability of all elements, including training resources, student information, exam results, and score changes. The digital thread engine anchors each student's entire lifecycle behavior trajectory through unique identifiers and timestamps, supporting closed-loop deduction from resource learning, skills assessment, job certification to competency profiling. Data from each module is injected into the thread graph after unified semantic modeling, ensuring consistency of state, verifiability of timing, and traceability of rights and responsibilities across cross-platform operations.

[0009] In one embodiment, the digital thread engine has a built-in semantic parser that integrates multi-source heterogeneous data, and performs time-series modeling of student behavior logs based on an AI-MLP neural network model to automatically identify inflection points in ability growth and knowledge gap paths. The digital thread engine also integrates a lightweight graph neural network (GNN) module, which is used to dynamically model the generation logic of thousands of papers for thousands of people, the error association graph, and the cross-professional ability transfer path in the exam management module, so as to achieve accurate attribution from the results of a single exam to the long-term ability evolution; at the same time, it supports real-time feedback of exam behavior to the learning resource recommendation and training management dashboard, forming a five-ring closed loop of "learning-practice-exam-evaluation-application".

[0010] In one embodiment, the examination management module adopts an AI-driven online professional job certification mechanism to combat cheating, integrating intelligent identity verification, real-time behavior monitoring, and dynamic question obfuscation technology to ensure that the examination process is traceable and the results are reliable. Each test paper generated is bound to the student's digital thread ID and timestamp, and the wrong question data is injected into the ability graph in real time, triggering intelligent resource recommendations of corresponding difficulty and major in the learning space, so as to achieve a seamless connection of learning immediately after the exam and applying what is learned immediately. The examination management module also supports two-way linkage between job promotion examinations and individual assessment results, semantically aligning personality type profiles with job competency models, and automatically labeling suitable positions and development suggestions; all examination records, assessment reports, and learning behaviors are stored in the digital thread engine in a time sequence, forming a verifiable, auditable, and evolving digital archive of power industry talents.

[0011] In one embodiment, the points management module synchronizes points behavior data in real time based on a digital thread engine, enabling full-chain traceability of the acquisition, redemption, and fulfillment status; it supports multi-dimensional aggregation analysis of points activity and product preferences by venue, profession, and job level, and dynamically optimizes the redemption catalog and incentive strategy; Orders redeemed with points automatically generate unique tracking numbers, and the address information is directly linked to the geofence of the new energy power station after GIS verification. The verification process uses a two-factor authentication method of scanning a code and facial recognition to ensure that the materials are accurately delivered to the designated maintenance personnel. In conjunction with the digital thread engine, the redemption behavior is fed back into the capability map to identify the differences between groups that redeem "high-frequency tool-type materials" and "low-frequency learning-type resources," driving the coordinated iteration of training content and incentive mechanisms.

[0012] In one embodiment, the information management module constructs a comprehensive power training knowledge graph, unifying the management of structured and unstructured resources such as standard courses, practical cases, equipment manuals, and accident reports; it automatically establishes a four-dimensional semantic association of "knowledge point - job position - equipment - procedure" through NLP entity recognition and relation extraction technology; it supports personalized knowledge push based on digital thread ID, dynamically generating micro-course lists and risk warning cards according to the student's current task, historical wrong questions, and years of service; all resource access behaviors are transmitted back to the digital thread engine in real time, forming a traceable knowledge consumption chain.

[0013] In one embodiment, the training resource module supports aggregating students' full-cycle learning behavior by digital thread ID, intelligently identifying skill breakpoints and knowledge decay inflection points; it has a built-in AR practical guidance engine that connects to the digital twin model of the site equipment to achieve "training by scanning a code and learning by seeing"; resource version changes automatically trigger reminders for related students to relearn, and simultaneously update their competency graph weights; all micro-courses, cases, and procedures are embedded with blockchain-based evidence watermarks to ensure content authority and copyright traceability.

[0014] In one embodiment, the training resource module also deeply integrates the actual operation and maintenance scenarios of new energy power stations, links the points behavior with equipment lifecycle management, realizes the points incentive triggered by equipment anomaly warning, automatically converts spare parts replacement records into growth value, and assigns exclusive certification badges to the inspection path optimization results after AI verification.

[0015] In one embodiment, a certificate management module is also included. The certificate management module is deeply coupled with the points management module and the training resource module to realize the dynamic mapping of certificate acquisition, level recognition and points growth: each entry-level certificate is automatically converted into basic points, skill certificates are weighted and scored according to the difficulty coefficient, and qualification certificates are associated with the job competency model to trigger advanced training tasks. Thirty days before the certificate expires, the system intelligently pushes a renewal course package based on the certificate holder's historical learning trajectory and simultaneously generates a pre-assessment mock test paper; all certificate status changes are synchronized to the digital thread ID in real time, driving the automatic recalibration of the capability map.

[0016] A method for managing the entire process of power training based on digital threads includes the following steps: Step 1: Construct a full-link digital thread covering pre-training diagnosis, in-training collaboration, and post-training evaluation; Step 2: Based on the trainee's digital thread ID, automatically aggregate their job resume, equipment operation logs, exam scores, and on-site performance data to generate a dynamic competency profile; Step 3: Based on knowledge graph semantic reasoning and behavioral temporal modeling, accurately identify capability gaps and push customized learning paths; Step 4: Inject multi-dimensional data such as AR practical feedback, online exam results, certificate updates, and points growth into the thread in real time to form a closed-loop evolutionary capability development trajectory; Step 5: Collect full-link behavioral data in real time through the data platform to drive dynamic updates of the 360° talent profile and continuous iteration of the capability model; Step Six: Based on the digital thread ID, output a standardized capability certification report and seamlessly connect with enterprise job rating, promotion review and industry skills assessment systems.

[0017] Compared with the prior art, the beneficial effects of this application are: 1. End-to-End Data Integration and Closed-Loop Management: This invention uses a digital thread engine as its core hub, anchoring the entire lifecycle of trainees' behavior through unique identifiers and timestamps. This enables dynamic mapping and real-time traceability of all elements, including training resources, trainee information, exam results, and score changes. Data from each module is injected into the thread graph after unified semantic modeling, ensuring consistency of status, verifiable timing, and traceability of responsibilities across cross-platform operations. This completely breaks down data silos in traditional training systems, constructing a five-loop closed loop of "learning—practice—exam—evaluation—application," achieving refined management of the entire training process.

[0018] 2. Intelligent Competency Assessment and Personalized Training: The digital thread engine incorporates a semantic parser that integrates multi-source heterogeneous data. Based on an AI-MLP neural network model, it performs time-series modeling of learner behavior logs, automatically identifying inflection points in competency growth and knowledge gaps. It also integrates a lightweight graph neural network (GNN) module to achieve accurate attribution from single exam results to long-term competency evolution. The system can dynamically push personalized learning resources based on learner competency profiles, triggering targeted remedial training paths. This represents a paradigm shift from "passive monitoring" to "active intervention," significantly improving training efficiency and effectiveness.

[0019] 3. A Trustworthy Examination System and Precise Talent Matching: The examination management module employs an AI-driven online professional job authentication mechanism to combat cheating. Through intelligent identity verification, real-time behavior monitoring, and dynamic question obfuscation technology, it ensures the traceability of the examination process and the credibility of the results. It supports two-way linkage between job advancement examinations and individual assessment results, semantically aligning personality type profiles with job competency models, and automatically labeling suitable positions and development suggestions. All examination records, assessment reports, and learning behaviors are compiled into verifiable, auditable, and evolving digital archives for power industry personnel, providing data support for the organization's human resources department to conduct precise talent assessments and talent pipeline development.

[0020] 4. Precise Incentive Mechanism and Human Resource Optimization: The points management module, based on a digital thread engine, enables full-chain traceability of points acquisition, redemption, and fulfillment. It supports multi-dimensional aggregation analysis of points activity and product preferences by site, specialty, and job level, dynamically optimizing the redemption catalog and incentive strategies. Points data is deeply coupled with job competency maps, identifying high-potential groups and skill gaps, driving a shift in human resource allocation from static distribution to dynamic optimization. Simultaneously, it links points behavior with equipment lifecycle management, achieving deep integration of training and actual operation and maintenance scenarios.

[0021] 5. Immersive Hands-on Training and Efficient Knowledge Transfer: The training resource module incorporates an AR hands-on guidance engine, connecting to digital twin models of site equipment to achieve immersive hands-on training through "scan-to-train and learn-as-you-go" interaction. It supports aggregating student learning behavior throughout their entire lifecycle by digital thread ID, intelligently identifying skill breakpoints and knowledge decay inflection points, and automatically triggering reminders for related students to relearn when resource versions change. All micro-courses, case studies, and procedures are embedded with blockchain-based notarization and watermarks to ensure content authority and copyright traceability, effectively solving the problem of the disconnect between traditional training and actual operation and maintenance.

[0022] 6. Intelligent Certificate Management and Continuous Competency Tracking: The certificate management module is deeply integrated with the points management module and training resource module, enabling dynamic mapping between certificate acquisition, level recognition, and points growth. Before certificate expiration, renewal course packages and pre-assessment mock exams are automatically pushed out. All certificate status changes are synchronized to the digital thread ID in real time, driving automatic recalibration of the competency graph. The blockchain-based certificate digital fingerprint is strongly bound to the digital thread ID, ensuring that certificate acquisition behavior, training records, and competency graph evolution are tamper-proof and mutually verifiable throughout the entire process.

[0023] This invention effectively addresses pain points in the new energy industry, such as low efficiency in talent cultivation, fragmented training management, and insufficient intelligence, through the deep integration of digital thread technology with AI, blockchain, and other technologies. It significantly improves the accuracy, efficiency, and practicality of power training, providing solid talent support for the safe and efficient operation of the new energy industry. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A schematic diagram of the overall power training process management system based on digital threads provided in this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application are described clearly and completely below. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are also within the scope of protection of this application.

[0027] See Figure 1 As shown, this application provides a digital thread-based power training full-process management system, including a training resource management module, an information management module, an examination management module, an points management module, and a digital thread engine; The digital thread engine serves as the core hub, connecting the data and business flows of each module to achieve dynamic mapping and real-time traceability of all elements, including training resources, student information, exam results, and score changes. The digital thread engine anchors each student's entire lifecycle behavior trajectory through unique identifiers and timestamps, supporting closed-loop deduction from resource learning, skills assessment, job certification to competency profiling. Data from each module is injected into the thread graph after unified semantic modeling, ensuring consistency of state, verifiability of timing, and traceability of rights and responsibilities across cross-platform operations.

[0028] In this embodiment, the training resource management module relies on a digital teaching resource library to organize content in a structured manner according to device objects, sources of creation, and thematic dimensions, and matches job competency maps according to difficulty levels; the information management module uses digital thread IDs as unique indexes to dynamically aggregate student resumes, certification status, and learning preferences; the examination management module embeds an AI test paper generation engine and anti-cheating situational awareness to achieve millisecond-level linkage between "examination-evaluation-certification"; and the points management module connects creation incentives, learning check-ins, and examination challenges to drive users to continuously participate in ecosystem co-construction.

[0029] The digital thread engine more deeply integrates WebRTC real-time audio and video, gRPC remote calls, and WebSocket multiplexing channels, supporting live training with tens of thousands of concurrent users, millisecond-level flow control response, and seamless continuation of learning behavior across terminals. Relying on the OSS / VOD service chain and SLA guarantee mechanism, it ensures the stability and reliability of the entire link of course transcoding, recording, time shifting, and mixing.

[0030] Optionally, the digital thread engine incorporates a semantic parser that integrates multi-source heterogeneous data and performs time-series modeling of student behavior logs based on an AI-MLP neural network model to automatically identify inflection points in ability development and knowledge gaps. This model continuously learns from students' multi-dimensional interaction data in simulation operations, online exams, and on-the-job training, dynamically optimizing ability assessment weights. When it detects a student's sudden drop in accuracy on relay protection module questions and frequent reference to materials shown in video monitoring, the system automatically triggers a personalized retraining path and pushes 3D animation analysis and expert Q&A short videos tailored to their cognitive style, achieving a paradigm shift from "passive monitoring" to "active intervention."

[0031] The digital thread engine also integrates a lightweight graph neural network (GNN) module for dynamically modeling the generation logic of personalized exams for each student, the error correlation graph, and the cross-professional ability transfer path in the exam management module. This enables precise attribution from single exam results to long-term ability evolution. Simultaneously, it supports real-time feedback of exam behavior to learning resource recommendations and training management dashboards, forming a five-loop closed loop of "learning—practice—exam—evaluation—application." Furthermore, the engine establishes a two-way authentication interface with enterprise HR systems, automatically converting training points into job promotion weights, skill level certification credits, and annual performance bonuses, directly embedding training outcomes into the entire talent development lifecycle.

[0032] In this embodiment, the "360° Talent Comprehensive Assessment" module further leverages the capability evolution map and behavioral attribution model output by the aforementioned digital thread engine to map five dimensions of indicators—professional ability, learning ability, job promotion, execution ability, and collaboration ability—to a dynamic weight matrix in real time. The automatic resume generation logic in the personal center is linked in real time with HR system change events, ensuring synchronized personnel change data and zero-delay resume updates. The learning and examination center, based on behavioral time-series modeling results, automatically identifies learning rhythm breakpoints and knowledge absorption bottlenecks, dynamically adjusting course recommendation priorities and exam difficulty gradients. The points dashboard aggregates behavioral data such as creation, check-in, and challenge completion in real time, generating a personalized growth heatmap and simultaneously pushing advanced tasks and collaborative learning partner matching suggestions tailored to the student's current capability range.

[0033] Optionally, the examination management module adopts an AI-driven online professional job certification mechanism to combat cheating, integrating intelligent identity verification, real-time behavior monitoring, and dynamic question obfuscation technology to ensure that the examination process is traceable and the results are credible. Each test paper generated is bound to the student's digital thread ID and timestamp, and the wrong question data is injected into the ability graph in real time, triggering intelligent resource recommendations of corresponding difficulty and major in the learning space, realizing a seamless connection of learning immediately after the exam and applying what has been learned immediately.

[0034] Intelligent identity verification is based on a multimodal biometric fusion algorithm, employing triple verification through liveness detection, micro-expression analysis, and device fingerprint binding to ensure the uniqueness of examinee identities. Real-time behavior monitoring utilizes edge computing terminals to perform millisecond-level analysis of eye movement trajectories, operation frequency, and screen switching, automatically marking abnormal behaviors and triggering tiered alerts. Dynamic question obfuscation technology generates differentiated question stems and distractors in real time based on the examinee's ability graph, eliminating the risk of question bank leakage and ensuring fairness and scientific rigor for each examinee. The difficulty and knowledge coverage of the questions are dynamically adapted to the job competency model, ensuring that the assessment content closely aligns with core scenarios such as front-line operations, scheduling, and relay protection.

[0035] The test question generation engine is deeply coupled with the digital thread. Each question carries a knowledge point ID, ability dimension tag, and historical answer heatmap weight, supporting traceability to original training materials, practical operation videos, and fault case libraries. Within 30 seconds after the test, an individual ability deviation radar chart is automatically generated, and three types of compensation resources are pushed through the learning platform: micro-lectures, simulation reviews, and a mentorship reservation portal. All data is encrypted and stored in the database in accordance with the "Power Industry Data Security Classification and Protection Specification" (GB / T 41479-2022), ensuring that the entire evaluation data is verifiable, auditable, and controllable throughout the entire chain.

[0036] It should be noted that liveness detection, micro-expression analysis, and device fingerprint binding verify bioactivity by requiring test takers to perform natural movements such as blinking and head shaking. Combined with infrared low-light imaging and inter-frame motion vector analysis, this effectively resists attacks using photos, videos, and 3D masks. The micro-expression recognition module captures seven types of physiological signals, including frontalis muscle contraction and orbicularis oculi muscle blinking, within 0.8 seconds to determine the authenticity of the test-taking state. Device fingerprint binding collects GPU rendering features, keyboard keystroke timing, and network MAC address hash values ​​in real time to construct an uncopyable terminal identity credential. The triple verification results are aggregated using a federated learning framework to generate a dynamic credibility score; those below the threshold are automatically placed in a manual review queue.

[0037] During the examination, the system simultaneously activates a behavior monitoring subsystem, relying on a lightweight edge AI chip to perform real-time analysis of dual-channel video streams and screen-shared data: the main camera focuses on facial micro-expressions and changes in head posture angles, while the side camera captures hand movements and abnormal objects on the desktop; the screen stream undergoes OCR recognition and process tree comparison, intercepting high-risk operations such as screen switching, screenshotting, and external device calls within milliseconds; the network traffic probe continuously samples DNS requests and TLS handshake characteristics, automatically blocking encrypted channels suspected of cloud-based question searching or remote control. All behavior logs are injected into the digital thread according to the timeline and dynamically linked with the candidate's competency graph, forming a traceable, verifiable, and accountable holographic examination file. After the examination, the data is synchronously uploaded to the blockchain through a notarized storage node, generating an immutable audit certificate with a timestamp and hash value. The competency deviation radar chart is automatically linked to the job competency baseline, triggering a "red, yellow, and blue" three-level warning for dimensions below the threshold, and pushing typical handling paths from the adaptive simulation fault library. All compensation resource call behaviors, retraining completion rates, and secondary assessment results feed back into the digital thread in real time, driving the dynamic iteration of the question confusion strategy and competency graph.

[0038] The examination management module also supports two-way linkage between job promotion examinations and individual assessment results, semantically aligning personality type profiles with job competency models, and automatically labeling suitable positions and development suggestions. All examination records, assessment reports, and learning behaviors are stored in the digital thread engine in a time sequence, forming a verifiable, auditable, and evolving digital archive for power industry talents. This archive spans the entire lifecycle of onboarding, on-the-job training, and promotion, supporting human resources departments in conducting precise talent inventory and talent pipeline construction, and realizing a fundamental leap in talent development from experience-driven to data-driven.

[0039] Optionally, the points management module uses a digital thread engine to synchronize points behavior data in real time, enabling traceability of the entire process from acquisition and redemption to fulfillment. It supports multi-dimensional aggregation analysis of points activity and product preferences by site, specialty, and job level, dynamically optimizing the redemption catalog and incentive strategies. The system has a built-in intelligent recommendation engine that pushes highly matched redemption products and training resources in real time based on user history, skill graphs, and site material shortage data. The points exchange rate automatically fluctuates monthly according to the difficulty coefficient of new energy operation and maintenance, strengthening positive guidance for remote sites and high-risk operations. All points operations are traced through digital threads and synchronized with blockchain evidence nodes to ensure clear ownership, compliant transfer, and reliable auditing. Points data is deeply coupled with job competency graphs. When an employee's points growth trend continuously deviates from their job level baseline curve, the system automatically triggers a development suitability diagnosis, generating a personalized growth path map. For those who frequently redeem points for safety courses or emergency drill resources, their risk handling weight coefficient in site shift scheduling is dynamically increased. All points-based behaviors are mapped in real time to the organization's talent heatmap via digital threads, supporting management in identifying high-potential groups and underdeveloped areas. This enables a paradigm shift in talent resource allocation from static distribution to dynamic optimization, truly making data the driving force for organizational evolution.

[0040] Orders redeemed with points automatically generate unique tracking numbers, and the address information is directly linked to the geofence of the new energy power station after GIS verification. The verification process uses a two-factor authentication method combining QR code scanning and facial recognition to ensure accurate delivery of supplies to designated maintenance personnel. In conjunction with the digital thread engine, redemption behavior is simultaneously fed back into the capability map, identifying the differences between groups that "high-frequency redeem tool-type supplies" and "low-frequency redeem learning resources," driving the collaborative iteration of training content and incentive mechanisms. Tracking numbers and GIS trajectory data are transmitted back to the digital thread in real time, automatically linking to the corresponding station equipment ledger and maintenance work orders. Facial recognition verification results simultaneously trigger updates to job capability tags, strengthening the weight of practical dimensions such as "tool proficiency" and "on-site response timeliness." All delivery closed-loop data feeds back into the resource recommendation engine, continuously optimizing the configuration strategy for emergency supply packages at remote stations and the skill matching model for frontline personnel.

[0041] After being cleaned, logistics trajectories and verification data are injected into the data platform in real time, driving the dynamic updating of 360° talent profiles. Practical indicators such as the timeliness of station-level material delivery, frequency of tool use, and accuracy of emergency response are automatically incorporated into the calibration factors of the job competency model. The digital thread continuously accumulates a closed-loop evidence chain of "learning-application-certification," making each redemption not only an incentive realization but also a credible scale for competency advancement.

[0042] Optionally, the information management module constructs a comprehensive power training knowledge graph, unifying the management of structured and unstructured resources such as standard courses, practical cases, equipment manuals, and accident reports; it automatically establishes a four-dimensional semantic association of "knowledge point - job position - equipment - procedure" through NLP entity recognition and relation extraction technology; it supports personalized knowledge push based on digital thread ID, dynamically generating micro-course lists and risk warning cards according to the student's current task, historical wrong questions, and years of service; all resource access behaviors are transmitted back to the digital thread engine in real time, forming a traceable knowledge consumption chain.

[0043] The knowledge graph and digital threads are deeply intertwined. Every learning trajectory, every case review, and every procedure query is given a unique time-series label and capability anchor. Knowledge consumption behavior is analyzed in real time by the graph reasoning engine, automatically marking three types of warning labels: "cognitive blind spots," "skill breakpoints," and "weak procedure associations." This is then linked to the points mall to push targeted reinforcement training packages. The popularity data of graph nodes optimizes the course update strategy. High-frequency searches of equipment manual chapters trigger expert live Q&A sessions. A surge in searches for knowledge points related to accident reports initiates the automatic generation process of VR debriefing courseware. This truly realizes the intelligent leap of knowledge supply from "people searching for information" to "information matching people."

[0044] The dynamic evolution mechanism of the knowledge graph is coupled with the digital thread in real time, enabling a two-way flow of knowledge supply and ability growth. For example, when a student clicks on the micro-course "Troubleshooting of SVG Reactive Power Compensation Devices," the system not only pushes standard operation videos but also instantly links them to the work order trajectory of handling similar alarms at a photovoltaic power station last week, the historical defect records in the corresponding equipment ledger, and the top 3 typical fault cases of the same model of SVG in the entire network of power stations. At the same time, based on the student's redemption record for the "Power Electronic Device Thermal Management" course in the points mall, the system intelligently overlays a 3D disassembly diagram of heat dissipation design principles and a comparison video of infrared temperature measurement practice. All interactive data is written back to the digital thread in real time, generating a closed-loop heat map of the student's ability evolution in the SVG field, providing verifiable decision-making basis for the next round of certification assessment and on-site teaching assignment.

[0045] Optionally, the training resource module supports aggregating students' full-cycle learning behavior by digital thread ID, intelligently identifying skill breakpoints and knowledge decay inflection points; it has a built-in AR practical guidance engine that connects to the digital twin model of the site equipment to achieve "training by scanning a code and learning by seeing"; resource version changes automatically trigger reminders for related students to relearn, and simultaneously update their competency graph weights; all micro-courses, cases, and procedures are embedded with blockchain-based evidence watermarks to ensure content authority and copyright traceability.

[0046] The certificate management module is deeply integrated with the knowledge graph. Every time a student completes a micro-course or practical assessment, the system automatically verifies the corresponding job qualification requirements and updates the certificate level certification status in real time. The digital fingerprint of the certificate stored on the blockchain is strongly bound to the digital thread ID, ensuring that the certificate acquisition behavior, training records, and competency graph evolution are tamper-proof and mutually verifiable throughout the entire process. 30 days before the expiration date, the system intelligently triggers the recertification task flow, automatically matches the reinforcement training package and the mock assessment question bank, and simultaneously pushes them to the points mall redemption channel.

[0047] Optionally, the training resource module also deeply integrates the actual operation and maintenance scenarios of new energy power stations, links the points behavior with equipment lifecycle management, realizes the points incentive triggered by equipment abnormality warning, automatically converts spare parts replacement records into growth value, and assigns exclusive certification badges to the inspection path optimization results after AI verification.

[0048] For example, when a wind farm's SCADA system issues an alarm for "pitch motor temperature exceeding limit", the system automatically sends an instant incentive of 50 points to the three most recent maintenance personnel certified for that model, and simultaneously generates an emergency response micro-course containing on-site video feedback guidance. After the work order is completed and closed, the points are automatically redeemed and recorded in the equipment health record, forming a positive cycle of "early warning - response - feedback - growth".

[0049] Optionally, a certificate management module is also included. This module is deeply coupled with the points management module and training resource module to achieve a dynamic mapping between certificate acquisition, level recognition, and points growth: each entry-level certificate is automatically converted into basic points, skill certificates are weighted and scored according to difficulty coefficients, and qualification certificates are linked to the job competency model to trigger advanced training tasks. Points growth data feeds back into the dynamic evaluation of certificate levels in real time. When an employee's accumulated points exceed the threshold for senior operations and maintenance personnel and there are no knowledge decay warnings for three consecutive months, the system automatically initiates a qualification upgrade process, simultaneously freezing the old certificate, issuing a new electronic certificate with a digital signature and timestamp, and pushing it to the employee's personal workbench homepage. The new certificate is also updated to the human resources multi-dimensional evaluation system, automatically strengthening the weight coefficients of the employee in the dimensions of "professional ability," "execution ability," and "collaboration ability."

[0050] Thirty days before the certificate expires, the system intelligently pushes a recertification course package based on the certificate holder's historical learning trajectory and simultaneously generates a pre-assessment mock test paper. The difficulty of the test paper is dynamically adapted to the accuracy curve of the holder's three most recent assessments, and incorrect answers are automatically associated with corresponding micro-lessons and practical video anchor points. After the recertification is passed, the digital fingerprint of the new certificate is immediately written into the blockchain and triggers the talent comprehensive evaluation system to recalibrate the weights of the "learning ability" and "job promotion" dimensions.

[0051] All certificate status changes are synchronized to the digital thread ID in real time, driving the automatic recalibration of the capability map. The digital thread ID serves as a unique identity anchor point, spanning the entire career cycle of employees, supporting precise decision-making in enterprise talent inventory, succession planning, and strategic position succession planning, making capability evolution visible, measurable, and predictable.

[0052] A method for managing the entire process of power training based on digital threads includes the following steps: Step 1: Construct a full-link digital thread covering pre-training diagnosis, in-training collaboration, and post-training evaluation; Step 2: Based on the trainee's digital thread ID, automatically aggregate their job resume, equipment operation logs, exam scores, and on-site performance data to generate a dynamic competency profile; Step 3: Based on knowledge graph semantic reasoning and behavioral temporal modeling, accurately identify capability gaps and push customized learning paths; Step 4: Inject multi-dimensional data such as AR practical feedback, online exam results, certificate updates, and points growth into the thread in real time to form a closed-loop evolutionary capability development trajectory; Step 5: Collect full-link behavioral data in real time through the data platform to drive dynamic updates of the 360° talent profile and continuous iteration of the capability model; Step Six: Based on the digital thread ID, output a standardized capability certification report and seamlessly connect with enterprise job rating, promotion review and industry skills assessment systems.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A power training end-to-end management system based on digital threads, characterized in that: It includes training resource management module, information management module, examination management module, points management module, and digital thread engine; The digital thread engine serves as the core hub, connecting the data and business flows of each module to achieve dynamic mapping and real-time traceability of all elements, including training resources, student information, exam results, and score changes. The digital thread engine anchors each student's entire lifecycle behavior trajectory through unique identifiers and timestamps, supporting closed-loop deduction from resource learning, skills assessment, job certification to competency profiling. Data from each module is injected into the thread graph after unified semantic modeling, ensuring consistency of state, verifiability of timing, and traceability of rights and responsibilities across cross-platform operations.

2. The power training full-process management system based on digital threads according to claim 1, characterized in that: The digital thread engine has a built-in semantic parser that integrates multi-source heterogeneous data, and performs time-series modeling of student behavior logs based on the AI-MLP neural network model to automatically identify inflection points in ability growth and knowledge gap paths. The digital thread engine also integrates a lightweight graph neural network (GNN) module, which is used to dynamically model the generation logic of thousands of papers for thousands of people, the error association graph, and the cross-professional ability transfer path in the examination management module, so as to achieve accurate attribution from the results of a single examination to the long-term ability evolution; at the same time, it supports real-time feedback of examination behavior to the learning resource recommendation and training management dashboard, forming a five-ring closed loop of "learning-practice-examination-evaluation-application".

3. The power training full-process management system based on digital threads according to claim 1, characterized in that: The examination management module adopts an AI-driven online professional job certification mechanism to combat cheating, integrating intelligent identity verification, real-time behavior monitoring, and dynamic question obfuscation technology to ensure that the examination process is traceable and the results are reliable. Each test paper generated is bound to the student's digital thread ID and timestamp. Incorrect question data is injected into the ability graph in real time, triggering intelligent resource recommendations of corresponding difficulty and major in the learning world, realizing a seamless connection of learning immediately after the exam and applying what is learned immediately. The examination management module also supports two-way linkage between job promotion examinations and individual assessment results, semantically aligning personality type profiles with job competency models, and automatically labeling suitable positions and development suggestions; all examination records, assessment reports, and learning behaviors are stored in the digital thread engine in a time sequence, forming a verifiable, auditable, and evolving digital archive of power industry talents.

4. The power training full-process management system based on digital threads according to claim 1, characterized in that: The points management module uses a digital thread engine to synchronize points behavior data in real time, enabling full-link traceability of the acquisition, redemption, and fulfillment process. Supports multi-dimensional aggregation and analysis of points activity and product preferences by venue, profession, and job level, and dynamically optimizes the redemption catalog and incentive strategies; Orders redeemed with points automatically generate a unique tracking number, and the address information is directly linked to the geofence of the new energy power station after being verified by GIS. The verification process uses a two-factor authentication method combining QR code scanning and facial recognition to ensure that materials are accurately delivered to designated maintenance personnel. In conjunction with the digital thread engine, the redemption behavior is fed back into the capability graph to identify the differences between groups that "high-frequency redeem tool-type materials" and "low-frequency redeem learning-type resources," driving the coordinated iteration of training content and incentive mechanisms.

5. The power training full-process management system based on digital threads according to claim 1, characterized in that: The information management module constructs a comprehensive power training knowledge graph, unifying the management of structured and unstructured resources such as standard courses, practical cases, equipment manuals, and accident reports; it automatically establishes a four-dimensional semantic association of "knowledge point - job position - equipment - procedure" through NLP entity recognition and relation extraction technology; it supports personalized knowledge push based on digital thread ID, and dynamically generates micro-course lists and risk warning cards according to the trainee's current task, historical wrong questions, and years of service. All resource access behavior is transmitted back to the digital thread engine in real time, forming a traceable knowledge consumption chain.

6. The power training full-process management system based on digital threads according to claim 1, characterized in that: The training resource module supports aggregating students' full-cycle learning behavior by digital thread ID, intelligently identifying skill breakpoints and knowledge decay inflection points; it has a built-in AR practical guidance engine that connects to the digital twin model of the site equipment to achieve "training by scanning a code and learning by seeing"; resource version changes automatically trigger reminders for related students to relearn, and simultaneously update their competency graph weights; all micro-courses, cases, and procedures are embedded with blockchain-based evidence watermarks to ensure the authority of the content and the traceability of copyright.

7. The power training full-process management system based on digital threads according to claim 6, characterized in that: The training resource module also deeply integrates the actual operation and maintenance scenarios of new energy power stations, links the points behavior with equipment life cycle management, realizes the points incentive triggered by equipment abnormality warning, automatically converts spare parts replacement records into growth value, and assigns exclusive certification badges to the inspection path optimization results after AI verification.

8. The power training full-process management system based on digital threads according to claim 1, characterized in that: It also includes a certificate management module, which is deeply coupled with the points management module and the training resource module to realize the dynamic mapping of certificate acquisition, level recognition and points growth: each entry-level certificate is automatically converted into basic points, skill certificates are weighted and scored according to the difficulty coefficient, and qualification certificates are linked to the job competency model to trigger advanced training tasks. Thirty days before the certificate expires, the system intelligently pushes a renewal course package based on the certificate holder's historical learning trajectory and simultaneously generates a pre-assessment mock test paper; all certificate status changes are synchronized to the digital thread ID in real time, driving the automatic recalibration of the capability map.

9. A method for managing the entire process of power training based on digital threads, characterized in that: Includes the following steps: Step 1: Construct a full-link digital thread covering pre-training diagnosis, in-training collaboration, and post-training evaluation; Step 2: Based on the trainee's digital thread ID, automatically aggregate their job resume, equipment operation logs, exam scores, and on-site performance data to generate a dynamic competency profile; Step 3: Based on knowledge graph semantic reasoning and behavioral temporal modeling, accurately identify capability gaps and push customized learning paths; Step 4: Inject multi-dimensional data such as AR practical feedback, online exam results, certificate updates, and points growth into the thread in real time to form a closed-loop evolutionary capability development trajectory; Step 5: Collect full-link behavioral data in real time through the data platform to drive dynamic updates of the 360° talent profile and continuous iteration of the capability model; Step Six: Based on the digital thread ID, output a standardized capability certification report and seamlessly connect with enterprise job rating, promotion review and industry skills assessment systems.

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