322 results about "Affiliation network" patented technology

The invention relates to a method convenient for data consanguinity collection and analysis, which comprises the following steps of: obtaining original consanguinity data comprising a task execution log, application metadata and a cross-system dependency relationship, and carrying out standardization processing on the original consanguinity data to generate structured consanguinity information comprising an asset unique identifier, an upstream and downstream association relationship and a data operation type; structured consanguinity information is synchronously written into a graph database and a distributed data warehouse, the graph database stores real-time association topology, the distributed data warehouse stores full-amount historical versions, and a transaction consistency algorithm is adopted to ensure the atomicity of double-write operation, so that the data storage efficiency is improved. Single-asset-level consanguinity tracking is performed based on real-time topology of a graph database, global consanguinity analysis is performed based on batch computing power of a distributed data warehouse, a direct dependence path, a deep association network and a closed-loop link detection result are generated, and a closed-loop management mechanism from data acquisition, analysis to optimization is formed. And the problem that an analysis result is disjointed from an acquisition end in a traditional scheme is solved.

The invention provides an AI agent process automation method and system for an education scene, and relates to the technical field of AI education, and the method comprises the steps: constructing a hierarchical knowledge graph through semantic segmentation, and constructing an agent encoder model through a graph attention network and comparative learning. Based on agent operation data and a feedback mechanism, operation parameters are dynamically adjusted, task decomposition is performed, sub-tasks are processed by using a meta-learner, and a knowledge cache module is established. And semantic analysis and association network construction are performed by using the knowledge cache module, a decision model is trained, and an execution process is optimized. The workflow evolution trend is predicted according to the agent state data, sub-task configuration is optimized, closed-loop optimization is formed, and therefore the automatic processing efficiency and adaptability of the AI agent in an education scene are improved.

The invention relates to the field of internet attack detection, in particular to a data security event real-time monitoring method and system, and the method comprises data collection, multi-modal fusion, threat detection, causal reasoning, dynamic response and feedback optimization. Compared with the traditional security analysis which depends on isolated data dimension or simple rule association, is difficult to capture a cross-data-source complex attack mode, and is faced with the limitations of low calculation efficiency, slow link traceability, storage access bottleneck and the like in mass data association analysis, the scheme integrates multi-source heterogeneous data into a dynamic association network through graph structure modeling, so that the security analysis efficiency is improved. Hidden association and behavior patterns among users, equipment and IPs are deeply mined by utilizing a GNN framework, the suspicious degree among entities can be accurately quantified, and hidden attack chains can be identified; and meanwhile, a hybrid storage architecture and a query optimization technology are adopted, so that a security analyst can backtrack a complex attack path in a second level while breaking through the bottleneck of large-scale graph data access performance, and the threat hunting efficiency and the high-level attack traceability are remarkably improved.

The invention relates to the technical field of intelligent bid evaluation, and provides an intelligent multi-dimensional bid evaluation analysis and decision-making method based on big data, which comprises the following steps: acquiring original bid evaluation data from a multi-source heterogeneous data interface, and fusing through semantic role labeling and a timestamp alignment algorithm to generate a time-space association data set. And performing multi-level cleaning to generate a high-confidence bid evaluation data set. And extracting a multi-dimensional index based on the domain knowledge graph, generating a dynamic feature tensor, and dynamically allocating a weight by adopting a coupling attenuation weight model. And constructing a bidder association network, calculating a node influence score, detecting a potential bidding behavior and generating a risk correction coefficient. And injecting the real-time data stream into the dynamic feature tensor, updating the index weight, and generating a three-dimensional scoring vector through a multi-target aggregation decision algorithm. And performing Pareto optimization by using the asymmetric game equilibrium model, and outputting an optimal bid-winning party sequence and a risk early warning report. The bid evaluation efficiency and fairness can be improved, and the bid invitation risk is reduced.

The invention provides an intelligent interactive question and answer method and system based on multi-modal large model intention recognition, and relates to the technical field of intelligent question and answer, and the method comprises the steps of feature extraction, cross-modal semantic mapping, intention matching, key information extraction and response generation, achieves the unified processing of multi-modal information, can accurately recognize the intention of a user, and improves the user experience. And the association network is dynamically constructed to extract key information, so that the accuracy and semantic coherence of intelligent questions and answers are improved, and the man-machine interaction experience is enhanced.

The invention discloses a supply chain production scheduling optimization method and system based on multi-source data. According to the method, supplier purchase data is extracted through a data crawler, raw material sharing is analyzed in combination with a knowledge graph, and a raw material sharing list is generated; on the basis of delivery records and product attributes, a quality stable value is predicted through linear regression, process similarity is calculated through a text similarity algorithm, and a process similarity matrix is constructed; in combination with historical sales data, a market demand prediction model is constructed by applying multiple linear regression and time sequence analysis. A complex relation among raw material sharing, process similarity and market demands is described through a graph neural network, a product association network graph is generated, an optimal balance point is found between delivery time and a resource utilization rate through Pareto optimization, and a production scheduling scheme is formed; and finally, integrating the performance score, the quality stable value, the raw material sharing list and the production scheduling scheme through an integer programming algorithm, and optimizing a supplier combination. According to the invention, the production scheduling efficiency and precision are improved, and complex business requirements are met.

The invention relates to an AI Agent-driven credit risk early warning strategy automatic evaluation method and device, control equipment and a computer readable storage medium, and the method comprises the steps: obtaining structured financial data, unstructured text data and dynamic time sequence data of an enterprise through multi-source data, carrying out the standardization processing, and outputting a standardized risk data set; based on the standardized risk data set, static financial features, dynamic derivative features and associated network features are extracted, and a high-discrimination feature set is generated through feature screening and dimensionality reduction optimization; a multi-model integration strategy is adopted, the high-discrimination feature set is input into a neural network, a Stacking integration model and a graph neural network, and enterprise risk scores and grades are output; and automatically matching an early warning rule based on the enterprise risk score and the grade, and carrying out real-time monitoring and early warning on enterprise credit according to the early warning rule.

The invention belongs to the field of electric digital data processing, and particularly relates to a multi-source cross-modal threat intelligence processing method, device and equipment and a medium. Innovation of a threat intelligence processing normal form is realized through a technical route of'double-layer progressive fusion ': a cross-modal fusion layer focuses on heterogeneous feature deep coupling of natural language CTI and picture CTI, and mapping of natural language semantics and visual information is realized by adopting a three-stage processing mechanism of'intra-modal self-attention, cross-modal cross attention and LSTM time sequence fusion'; and the multi-source alignment layer is used for carrying out joint modeling on CTI feature vectors subjected to cross-modal fusion, malicious codes and log data, constructing a three-dimensional association network of a CTI knowledge graph, a malicious code graph and a log time sequence graph, and realizing cross-source semantic space unification through a heterogeneous graph attention network. The problems that when a traditional threat intelligence processing method faces multi-source cross-modal data, collection and integration are difficult, preprocessing is insufficient, cross-modal feature fusion is difficult, and knowledge graph construction is imperfect are solved.

The invention discloses a multi-modal fusion lithium iron phosphate battery thermal runaway early warning method and system, and the method comprises the steps: collecting multi-source heterogeneous data, such as temperature, voltage, gas concentration and shell strain pressure, in real time through deploying a heterogeneous sensor network, and carrying out the noise reduction and time sequence feature extraction through employing a sub-linear time low-rank approximation algorithm of a Hankel matrix; constructing a cross-modal feature association network by applying a secondary time algorithm of a maximum weight sparse subgraph problem, inputting a fused feature vector into a Bayesian network health degree evaluation model for probabilistic reasoning calculation to obtain a battery health degree score and a thermal runaway risk level, generating a graded early warning signal through multi-level early warning threshold comparison, and performing early warning on the battery health degree score and the thermal runaway risk level. And corresponding prevention and control suggestions are matched. The method solves the technical problems that single physical quantity monitoring is difficult to comprehensively reflect the complex change in the battery and the response delay of a centralized processing architecture causes the early warning lag, and achieves the timely capture and accurate early warning of the early weak characteristics of thermal runaway.

The invention provides a learning task generation method based on historical answering and real-time evaluation data, and the method comprises the steps: firstly constructing an initial state vector of a student, extracting a knowledge point range, building an association network, and then evaluating a current mastering state of the student through a probability graph model; for weak knowledge points, a greedy strategy optimization algorithm is adopted to generate a task sequence, and grading is performed according to difficulty and content depth; dynamically adjusting task difficulty and a content presentation sequence in combination with learning preferences of students, and generating a customized task sequence; through a real-time feedback mechanism, student state vectors are continuously updated, the mastery degree is re-evaluated, and a task sequence is optimized; finally, a personalized task set is generated by considering time constraints. According to the invention, accurate evaluation of the learning state of the student and dynamic optimization of the task sequence are realized, and the effect of personalized learning is effectively improved.

The invention discloses a photovoltaic power station operation data monitoring method and system, and relates to the technical field of photovoltaic power station equipment monitoring, and the method comprises the steps: collecting equipment operation parameters to form an original data set; constructing an equipment association network by taking the equipment as a node; based on the data set and the association network, time sequence collaborative change and correlation analysis are carried out on the equipment parameters, and a collaborative change model is established; judging that equipment is abnormal through a model, and calling same-type parameters of directly connected nodes to form a to-be-verified set; in combination with the network connection edge weight, calculating the influence degree of the associated parameters on the abnormal equipment by applying a dynamic weight evaluation algorithm, and generating a priority list; and performing iterative optimization on the collaborative change model according to the priority list, and outputting a monitoring report. The system comprises a data acquisition module, a network construction module, an analysis modeling module, an anomaly judgment module, a weight evaluation module, a model optimization module, a data storage module and the like to realize abnormal associated factor identification and monitoring logic optimization.

The invention provides a network malicious behavior message backtracking generation method and system based on information guidance, and the method comprises the steps: firstly collecting an original flow data set of a target network node, which comprises a plurality of groups of communication protocol identifiers and corresponding load content sequences, extracting transmission features of the original flow data set, and obtaining protocol identifier distribution features and interaction time sequence track features; and calling an abnormal traffic detection model based on the protocol identifier distribution characteristics and the interaction time sequence track characteristics to judge malicious behaviors, and obtaining abnormal traffic data and an attack stage tag sequence. Performing behavior feature vector mapping processing on the abnormal flow data according to an attack stage label sequence to generate a behavior feature vector set, and finally constructing a multi-level association network based on the behavior feature vector set, generating a message backtracking path and outputting a malicious behavior message backtracking chain. Therefore, the malicious network behavior message can be backtracked accurately and efficiently, and the network security protection capability is improved.

The invention discloses a flexible automatic production management and control system for a new energy high-power high-frequency transformer, and relates to the field of production management and control, the flexible automatic production management and control system comprises a production demand receiving module, a production resource management module, a production planning module, a production execution module and a quality control module, an order is received through an API or EDI, and a work order is generated; collecting equipment and material information in real time, and constructing a production resource association network; carrying out production scheduling and process matching by adopting an improved deep learning model; scheduling materials, procedures and equipment based on a multi-level analysis control model; the quality is evaluated by using a two-dimensional cloud model, and data recording is performed through a distributed database, so that the traceability of the quality problem is ensured. According to the system, the production efficiency and the quality management level are effectively improved, the problem that existing rigid automatic equipment lacks flexibility is solved, and efficient and accurate production of the new energy high-power high-frequency transformer is ensured through cooperative work of a plurality of modules so as to meet different production requirements.

The invention discloses an import and export risk real-time early warning method and system based on multi-modal data fusion, and relates to the technical field of data analysis, and the method comprises the steps: obtaining multi-modal data in real time, and carrying out the preprocessing; designing a dynamic weight distribution algorithm, and endowing different types of data with differentiated weights to realize dynamic fusion according to quantitative indexes of timeliness, authority and target transaction association degree; fusion data features are extracted through a streaming computation engine, real-time risk calculation is carried out based on an Apache Flink cluster, and an association network is constructed in combination with a six-dimensional knowledge graph; on the basis of the extracted features and the association network, a CEP rule engine quickly identifies and calculates potential risk events and accurately locates risk points by relying on a predefined multi-class composite risk mode; and setting a grading early warning mechanism, enabling the identified risk mode to correspond to a corresponding early warning grade, generating a grading early warning signal, and synchronously prompting a dynamic disposal suggestion. According to the method, the risk identification accuracy can be improved, and the early warning response delay is shortened.

The invention discloses a knowledge base knowledge association fusion method based on a knowledge graph, and the method comprises the steps: integrating structured, semi-structured and non-structured data through a cross-modal alignment technology, and constructing a multi-source heterogeneous data association network of a unified semantic space; newly added external data are fused to a multi-source heterogeneous data association network in real time by using a dynamic attention mechanism, and entity conflicts are eliminated by combining rule reasoning and a machine learning model; hidden association among entities in the multi-source heterogeneous data association network is mined based on the graph neural network, and a knowledge graph logic chain is complemented; based on the knowledge graph, intelligent question and answer and risk assessment decision scenes are supported through a hybrid retrieval architecture and an inference engine; and automatically updating and associating the knowledge base of the network extension knowledge graph by adopting an incremental learning technology. Natural language questions and answers are supported, accurate answers are generated through knowledge reasoning, and user experience is remarkably enhanced.

The invention provides a knowledge graph-based public security multi-source risk factor association identification analysis method, which relates to the technical field of risk identification, and comprises the steps of obtaining multi-source risk factor data, extracting information from unstructured data, constructing an initial association network, performing feature analysis and calculating a similarity matrix; and the close association subgroups are identified through community discovery, a multi-level association network is constructed, a conduction path is analyzed, a weight is calculated, and finally risk early warning information is generated. According to the invention, the complex association between public security risk factors can be effectively identified, and the risk prediction accuracy is improved.

The invention relates to the field of data mining and machine learning, and relates to a bridge group deformation mode evaluation method and system based on PS-InSAR and complex network theories. The method comprises the following steps of: 1, performing refined preprocessing on high-dimensional time series data of engineering structure monitoring points obtained by multiple sources; 2, constructing a behavior pattern distribution vector of each monomer structure by adopting a UMAP-KDPI algorithm; and step 3, constructing a group association network by calculating behavior similarity between structures, and performing community discovery, key node identification and multi-level association characteristic deep analysis by using a network science theory to realize bridge group deformation mode evaluation. The problem that in an existing monitoring data analysis method for an engineering structure group, potential and common behavior patterns in complex high-dimensional time series data are difficult to effectively recognize, and an effective means for accurately quantifying and comparing behavior similarity among structure individuals is lacked is solved.

The invention relates to the technical field of data management, and discloses a digital twin water plant cluster comprehensive visual management system. The system comprises a processing module, a modeling module, a process flow topology self-adaptive visual management interface and a flow control module, wherein the processing module carries out standardization processing on multi-source parameter data of a water plant to generate real-time data flow; the modeling module constructs a three-dimensional digital twinborn model by utilizing an oblique photography technology; the extraction module extracts spatio-temporal characteristics through multi-channel data fusion to obtain early warning information, the calculation module diagnoses abnormity based on a ternary association network and a heterogeneous graph attention algorithm, and the coding module generates a digital watermark health state code to form a predictive maintenance scheme. The panoramic display of the running state of the water plant, the accurate identification of the abnormal mode and the optimal configuration of maintenance resources are realized. According to the invention, panoramic display of the running state of the water plant, accurate identification of the abnormal mode and optimal configuration of the maintenance resources are realized, so that the overall running efficiency and management level of the water plant cluster are improved.

The invention discloses an AI-based computer basic course personalized learning path recommendation method and system, and belongs to the technical field of AI-based data processing. According to the system, behavior data, cognitive data and course interaction data of a learner are acquired through a multi-dimensional data acquisition module, a computer basic course knowledge point association network is established in combination with a dynamic knowledge graph construction module, and a personalized learning path is generated by using an improved deep reinforcement learning algorithm. And the path is dynamically adjusted through the real-time feedback module. The core of the method is that a learner portrait is fused with space-time correlation features of a knowledge graph, a cognitive evaluation model is updated in real time through a Bayesian network, the problems that in a traditional recommendation method, paths are solidified, and the dynamic learning state of an individual is ignored are solved, more accurate personalized learning guidance is achieved, and the learning efficiency and effect of a computer basic course are improved.

The invention relates to the technical field of intelligent questioning and answering, in particular to an intelligent questioning and answering system implementation method and system.The intelligent questioning and answering system implementation method comprises the following steps of document collection and preprocessing, document dicing, document layout analysis and table layout analysis; the method has the beneficial effects that a semantic association network and a multi-modal index system are formed through offline document collection, preprocessing, slicing, layout analysis, data extraction and knowledge graph construction; in the online part, the capabilities of Embedding vectorization, multi-index joint retrieval, tensor reordering, AI database integration and large language model generation are combined, accurate semantic understanding and rapid knowledge matching of user questions are realized, high-quality answers are generated, and interactive feedback optimization is supported.

The invention relates to the technical field of background investigation, and particularly discloses a talent background investigation method based on multi-source data evaluation, which comprises the steps of constructing a dynamic talent knowledge graph, performing semantic alignment processing on multi-source cross-modal data, evaluating data quality based on a multi-dimensional confidence model, and constructing a confidence closed-loop correction mechanism. Performing intelligent correction on the low-confidence data, and reversely updating the credibility weight of the data source according to a correction result; an event-driven incremental update architecture is adopted, and dynamic events of multi-source data are captured in real time through a distributed event awareness engine. According to the method, the dynamic knowledge graph is used as a unified carrier, and the multi-source data is upgraded from fragmented storage to a semantic association network, so that the talent background survey data is converted from static piling to a dynamically evolved knowledge network, and the accuracy and timeliness of evaluation are remarkably improved.

The invention discloses a carbon emission prediction, regulation and control system and method based on multi-scale dynamic optimization. The system comprises a data acquisition layer, a five-dimensional coupling model layer and an optimization regulation and control layer. The data acquisition layer acquires multi-source heterogeneous data in real time by deploying an Internet of Things sensor network; a five-dimensional dynamic coupling model is arranged in the five-dimensional coupling model layer and comprises a multi-scale carbon emission kinetic equation, an industrial association network model, a space-time coupling prediction model and a dynamic optimization control model; an improved particle swarm optimization algorithm and a non-dominated sorting genetic algorithm are integrated in the optimization regulation and control layer, and parameter dynamic identification and multi-target optimization solution in the five-dimensional dynamic coupling model are realized based on multi-source heterogeneous data acquired by the data acquisition layer. The method can be applied to a regional carbon emission management platform, can be embedded into a high-energy-consumption enterprise production system, optimizes energy distribution and technical investment in real time, can also be used for carbon trading market auxiliary decision making, and provides data support for enterprise carbon quota distribution and emission reduction strategy making.

The invention relates to the technical field of course analysis management, in particular to a course analysis management system based on deep learning. The method has the advantages that multi-modal data deep analysis realizes full-dimensional analysis of unstructured data by integrating a 3D-CNN model, a Transform architecture and a BERT model and synchronously extracting an attention hot area, a voice emotional state and a text knowledge point association network of a classroom video; nonlinear behavior modeling adopts an LSTM network and time convolutional network fusion model, a knowledge internalization path and forgetting curve prediction are dynamically generated, parameters are optimized in combination with incremental learning, and a transition rule across knowledge points is captured; a teaching scene-evaluation threshold mapping table is constructed based on a reinforcement learning algorithm through dynamic decision and resource collaboration, collaborative optimization under multi-campus data privacy protection is achieved in combination with a federated learning framework, GPU computing nodes are dynamically allocated through a heterogeneous resource scheduling engine, and the analysis efficiency is improved.

The invention discloses a cloud mobile phone market demand analysis method and related equipment, and relates to the technical field of cloud mobile phones, the method comprises the following steps: obtaining multi-source heterogeneous data, the multi-source heterogeneous data comprising structured sales data and unstructured user behavior data; based on a preset semantic alignment rule, performing cross-modal fusion on the multi-source heterogeneous data to generate space-time correlation features; based on a dynamic federated learning framework, training a market demand prediction model by using the space-time correlation features, and determining a prediction result of the cloud mobile phone market; and based on the association network atlas, performing association analysis on the prediction result, and determining market decision information. According to the application, the comprehensiveness, prediction precision and decision support efficiency of market analysis are improved, and an accurate and prospective strategic decision basis is provided for each link of the cloud mobile phone industry chain.

The invention discloses a learning content determination method and device based on an intelligent education platform and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: extracting a basic knowledge point cluster and a key knowledge point cluster from a student teaching material, and generating a target question bank containing different difficulties and question types; generating a knowledge point association network according to the association relationship and strength of the knowledge points corresponding to the questions in the target question bank; and in combination with the learning track of the student in the learning map, generating the target association strength between the target knowledge points, updating the knowledge point association network according to the target association strength, obtaining a student exclusive target association network, and updating the target question bank to determine the target learning content of the user. According to the scheme, the knowledge points are extracted through the textbook, the target question bank of the multivariate question types is generated, the problem of single question type in the prior art is solved, the question bank is dynamically updated in combination with the learning track and the association network, students can focus on core knowledge points, and the learning efficiency is improved.

The invention discloses a user behavior prediction method based on a causal tendency score. The method comprises the following steps: collecting historical behavior information of a to-be-detected user from a historical database; carrying out behavior feature extraction, and carrying out normalization processing on behavior features; establishing a behavior prediction model; model parameters are adjusted through a back propagation algorithm and an optimizer; analyzing a causal relationship between the behavior characteristics; analyzing a reason for generating a prediction error result by the behavior prediction model; and performing user behavior prediction by using the optimized behavior prediction model, and making an optimization strategy according to a causal relationship revealed by the tendency score. According to the method, deep mining and quantitative evaluation of the causal relationship behind the user behavior are realized by constructing the feature association network, screening the causal candidate pairs, quantifying the causal effect and calculating the causal tendency score, so that a root cause of a behavior prediction error is disclosed, and the interpretability and decision support capability of the model are also remarkably improved.

The invention provides a digital intelligent base service achievement management method and system, and relates to the technical field of digital intelligent base service management.The method comprises the steps that firstly, a basic feature set containing attributes, sources and process features of service achievements in a digital intelligent base is collected, then associated features of the basic feature set are mined, and a service achievement associated network is formed; then, a multi-level management index containing a transverse attribute dimension and a longitudinal flow node dimension is constructed based on the service achievement association network, and a management strategy covering storage, updating and sharing features is generated in combination with index features and service achievement real-time state features; and finally, performing dynamic management and control on the business achievements in the digital intelligent base according to the management strategy, thereby deeply mining the relationship between the business achievements, realizing multi-dimensional fine management, dynamically adapting to business changes, and improving the management efficiency.

The invention belongs to the technical field of bidding and tendering risk identification, and specifically discloses a dynamic association network-based bidding and tendering risk prediction method and system, and a terminal device. The method comprises the following steps: constructing a dynamic graph structure model; obtaining bidding document text data, industry and commerce information data of a bidding enterprise and historical bidding behavior data; respectively extracting bidding document text semantic features, enterprise attribute features and behavior time sequence features to form a feature vector of each node; calculating the weight of each edge in the graph structure model; updating the graph structure model; and taking the updated graph structure model as input, obtaining a node embedding vector by using a graph neural network model, and calculating an association degree matrix between the enterprises based on the node embedding vector so as to predict whether a surrounding and bidding behavior exists between the enterprises. According to the method, multi-source data features such as text semantics, enterprise attributes and behavior time sequences are comprehensively considered, the implicit incidence relation between enterprises is fully captured, and the recognition accuracy of the bidding behavior is improved.

The invention discloses a pump station unit state comprehensive evaluation method, and belongs to the technical field of pump station unit detection. The method comprises the following steps: acquiring multi-source monitoring data and generating a standardized monitoring data set; time-varying mutual information between indexes is calculated, a dynamic threshold value is determined in combination with current working condition parameters and a water level difference correction term, and a dynamic association network is constructed; performing physical mechanism characteristic decoupling on the data, including stripping a vibration signal working condition drift component based on a reference curve to obtain a vibration residual error, and calculating an equivalent standard working condition temperature based on a heat balance principle; respectively calculating a first weight based on data statistics and a second weight based on network topology, and adaptively generating a comprehensive coupling weight according to a consistency coefficient of the first weight and the second weight; and finally, judging a health state level by using a cloud model. Through deep fusion of a physical mechanism and data driving, the problems that fault features are difficult to extract and the model robustness is poor under variable working conditions are solved, and accurate evaluation of the unit state is achieved.

The invention provides a traffic protection facility maintenance plan optimization method and device, a medium and a product, and relates to the technical field of traffic protection facility detection.The method comprises the steps that when a facility maintenance instruction is received, an initial maintenance plan of a traffic protection facility is determined according to a preset basic database; collecting damage degree data, use frequency data and environmental influence factor data of the traffic protection facility according to a preset period; establishing a traffic protection facility digital twinborn model according to a preset basic database and the three data; environment association fields are added in the digital twinborn model to construct a multi-dimensional facility portrait; constructing a facility association network map of the traffic protection facility according to the digital twin model added with the field; and performing optimization processing on the initial maintenance plan according to the multi-dimensional facility portrait and the facility association network atlas. The technical problem of low maintenance accuracy of the traffic protection facility in related technologies is solved, and the technical effect of improving the maintenance accuracy of the traffic protection facility is achieved.