593results about How to "Improve decision-making efficiency" patented technology

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention relates to the technical field of data processing, in particular to a decision-making method and system for multi-source information fusion. The method comprises the following steps: obtaining hyperspectral image data, visible light image data and infrared image data collected by an unmanned aerial vehicle, and collecting ground environment monitoring data collected by a ground sensor; the hyperspectral image data, the visible light image data and the infrared image data are subjected to image preprocessing, so that a standardized multispectral data set is obtained, and image preprocessing comprises geometric correction, radiation correction and atmospheric correction; and color and texture feature extraction is carried out on the visible light image in the multispectral data set, and appearance feature and growth density identification of crops is carried out so as to obtain morphological feature data. Through integrated analysis of multi-source data, abnormal growth of crops and potential pest and disease damage risks can be identified more accurately, so that engineers are assisted to make irrigation and fertilization decisions.

The invention relates to the field of geological disaster monitoring and early warning, in particular to a slope catastrophe early warning method and system. The method comprises the following steps: establishing a space-time database, storing and managing preprocessed slope data from multiple monitoring sources, and sorting to obtain a slope catastrophe data set; performing feature extraction on the slope catastrophe data set to obtain a key information set reflecting slope catastrophe features; analyzing the key information set to obtain a slope catastrophe value; calculating an early warning threshold value and a real-time displacement prediction value of the side slope based on the side slope data in the space-time database, and fusing the real-time displacement prediction value and the side slope catastrophe value to obtain a fusion value; and evaluating the real-time stable state of each monitored section of the slope according to the obtained fusion value and an early warning threshold value, and judging whether to send out an early warning signal and an alarm signal or not according to an evaluation result. According to the invention, accurate identification of the slope catastrophe value and the potential risk area is realized, and the accuracy and reliability of the early warning system are improved.

The invention discloses a self-adaptive power grid state court intelligent fusion terminal and a control method thereof, relates to the technical field of intelligent power grids, and solves the defects that an existing fusion terminal is insufficient in self-adaptive capability and limited in data processing and analysis capability. Learning power grid state change in real time through a deep power grid dynamic control algorithm and a prediction analysis engine combining LSTM and random forest, and autonomously adjusting a control strategy; the edge calculation and distributed processing module is utilized, so that the data processing flow is optimized, and the data processing speed and efficiency are greatly improved; the application of time sequence analysis and a machine learning model further excavates and understands a complex data mode; the resource configuration efficiency is improved through an intelligent scheduling algorithm, and a cross-layer coordination mechanism is established through a distributed coordination algorithm, so that rapid issuing and feedback collection of a control instruction are ensured; according to the invention, the intelligent degree of the existing fusion terminal is greatly improved.

The invention provides a method and system for realizing electronic channel risk control processing based on a code insertion technology, and relates to the technical field of electronic channel risk control, and the method comprises the steps: encrypting user information and channel identification information through a dual Hash algorithm to generate a dynamic session identification code, and forming a target coding sequence through employing a self-adaptive segmentation algorithm and a business feature flag bit. And constructing a dynamic risk control rule model by using a graph neural network, extracting user multi-dimensional features, carrying out weighted fusion to generate a user risk score, comparing the user risk score with an adaptive risk threshold, analyzing abnormal behaviors based on a probability graph model, and generating an intelligent risk early warning report. And calling a reinforcement learning risk control strategy engine to determine an optimal disposal strategy, and issuing a multi-factor interaction verification instruction. And after verification, selectively activating a blocking mechanism according to a verification result, synchronizing interaction data and a result to a distributed risk control data center, and continuously optimizing the risk control model based on federal learning.

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention relates to the technical field of medical data management, in particular to a medical data document classification and marking system which comprises a document structure analysis module, a semantic information identification module, a classification and marking module and a document intelligent label module. According to the method, document visualization and retrieval efficiency is improved by analyzing document content and extracting titles, texts and appendixes, analytic performance and operability of data are enhanced by identifying key information and semantic relations and accurately extracting and labeling medical terms, medicine research data and medical record records, storage is optimized according to priorities in combination with classification and marking, and the method is high in practicability and easy to popularize and use. Clinical values and application scenes of documents are clarified, document management processes are refined, management compatible with clinical data formats is formed through structured storage of marked data and classification label generation, storage normalization and retrieval efficiency are improved, the requirement for accurate information calling in clinical tests is met, document management capacity is improved, and the document management efficiency is improved. And support is provided for improvement of clinical decision-making and research efficiency.

The invention belongs to the technical field of network security, and provides a network security threat tracing method and system based on association analysis, and the technical scheme is as follows: constructing a network security situation map based on multi-source network security data, and performing association analysis on attack elements by adopting a graph mining algorithm based on the network security situation map to obtain an association analysis result, based on the correlation analysis result, the multi-path traceability strategy is used for tracing the attack and determining the attack source and the propagation path, potential threats can be automatically identified, risks can be evaluated, the optimal attack path can be found out, the accuracy and traceability efficiency of threat identification are improved, and a basis is provided for subsequent defense and countermeasures.

The invention relates to a power log intelligent analysis system based on a knowledge graph and a large language model. The power log intelligent analysis system comprises a data collection and preprocessing module, a knowledge graph construction and management module, a context understanding and correlation analysis module, a personalized report generation module and a multi-modal data integration module. According to the method, information such as equipment, events and operations in the power logs is structured through the knowledge graph and is associated with domain knowledge, so that deep understanding and association analysis of log contents are realized, and the problem of insufficient context understanding in a traditional method is solved. By utilizing the strong generation capability of a large language model and based on context information provided by a knowledge graph, a personalized report meeting roles and requirements of a user can be automatically generated, and the user experience and practicability of log analysis are improved; log data in various formats are integrated into the knowledge graph, a comprehensive summary report containing information such as charts and timelines is generated through a large language model, and the problem that heterogeneous data are difficult to integrate is solved.

The invention relates to the field of data visualization, in particular to a visualization method and system based on a vehicle service supply chain. The method comprises the following steps: acquiring whole-process information of a vehicle service supply chain; carrying out supply chain main body identification on the whole flow information of the vehicle service supply chain, carrying out spatial topology distribution analysis, and constructing a supply chain main body topology distribution map; obtaining supply chain main body interaction logic data based on the plurality of supply chain main bodies; performing topology interaction logic connection on the supply chain main body topology distribution graph by utilizing supply chain main body interaction logic data, and constructing a supply chain topology interaction network; performing intra-period path transportation frequency calculation on the whole-process information of the vehicle service supply chain to generate logistics demand trend characteristics; and performing operation behavior analysis of each link on the whole-process information of the vehicle service supply chain to obtain the capacity characteristics of each link. According to the invention, through visualizing the vehicle service supply chain, the resource scheduling efficiency and flexibility of the supply chain are improved.

The invention discloses a reservoir group scheduling decision-making method considering multi-source uncertainty propagation and evolution tracking, relates to the technical field of reservoir scheduling, and is used for solving the problem of reservoir scheduling multi-target risk decision-making in a random environment. According to the method, a space-time two-dimensional risk deduction model is constructed, refined risk diagnosis and dynamic deduction are performed on a reservoir group scheduling process, an uncertainty propagation and evolution tracking mechanism is obtained, and the robustness and reliability of a reservoir scheduling decision in an uncertainty environment are effectively improved; and a preference demodulator FAHP-OWA which comprehensively considers subjective weight and preference conflicts is adopted to obtain a high-dimensional weight vector in the aspect of obtaining group preference information. Meanwhile, multiple rounds of interactive optimization and a weight distribution mechanism are combined, preference information of decision makers can be introduced step by step, the scheduling process is more flexible, decision groups are more harmonious, the decision efficiency and accuracy of the decision groups in an uncertain environment are improved, and accurate judgment of the advantages and disadvantages of the scheme is achieved.

The invention provides a 5G network intelligent switching management method based on artificial intelligence, and the method comprises the following steps: collecting and processing network related data, and obtaining a network data set; constructing a switching demand prediction model used for predicting network switching time and a target and a switching effect prediction model used for predicting network performance after switching; optimizing the initial switching strategy in combination with reinforcement learning to obtain an optimal switching strategy; deploying a network switching engine model on the edge computing node, and adjusting an optimal switching strategy in real time according to the network environment change; a virtual simulation digital environment is constructed for network simulation, an optimal switching strategy is evaluated according to the simulated virtual network environment, and then a network switching engine model is optimized in real time according to an evaluation result. According to the invention, the intelligent switching decision is realized, the precision and efficiency of the switching decision can be improved, the network adaptability and diversified requirements are enhanced, and the user experience is improved.

The invention provides an automatic task allocation method and system for an elevator emergency rescue platform, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining elevator operation state data and environment information data, constructing an emergency task model, and obtaining classified emergency tasks; inputting the classified emergency tasks into a deep reinforcement learning model to generate a task allocation scheme; the deep reinforcement learning model is formed by combining a deep Q network structure with a priority scanning algorithm, and a priority experience playback mechanism is introduced; and constructing a multi-objective optimization module comprising a time constraint, a space constraint and a resource constraint, and optimizing the task allocation scheme by the multi-objective optimization module based on an improved particle swarm algorithm to obtain an optimal task allocation scheme.

The invention discloses a fresh commodity replenishment method and system, electronic equipment and a storage medium, and relates to the field of commodity management. According to the method, fresh food hot topics are obtained from social media, demand hotspots are determined according to the fresh food hot topics, weather influence factors are determined according to predicted weather data, and holiday influence factors and seasonal influence factors are determined according to the current date; determining sales influence factors according to the second sales data of the competitor and the first sales data of the target store; through a preset prediction model, according to the sales influence factors, the demand hotspots, the weather influence factors, the holiday influence factors and the seasonal influence factors, determining predicted demand quantities of various fresh commodities in a second preset time period after the current moment; and determining the purchase quantity according to the predicted demand quantity and the stock quantity of the target store, and determining a purchase scheme according to the purchase quantity and the supplier information. By implementing the technical scheme provided by the invention, the accuracy and efficiency of replenishment of fresh commodities are improved.

The invention relates to the technical field of agricultural machinery automation, in particular to an intelligent seeding machinery automation control system which comprises a soil variability analysis module, a dynamic seeding mode adjustment module, a soil structure adjustment module, a seeding control and optimization module, an intelligent fault diagnosis module and a visualization module. Soil data are collected in real time through a soil variability analysis module, spatial variability and seeding suitability of soil are analyzed by utilizing a machine learning algorithm, and a dynamic seeding mode adjustment module automatically adjusts the seeding depth, the seeding speed, the seed spacing and the feeding amount according to an analysis result. The soil structure adjusting module can intelligently adjust the structure according to the compaction or loosening condition of soil, the intelligent fault diagnosis module monitors the operation state of the seeder in real time and diagnoses and repairs faults, and the visualization module provides a remote monitoring interface. According to the invention, the seeding process can be accurately controlled according to different soil conditions, the seeding uniformity and the crop germination rate are improved, and the efficiency and precision of agricultural production are remarkably improved.

The invention discloses a power decision-making system and method based on cloud side-end coordination, and the system comprises a terminal equipment node which is used for obtaining equipment monitoring data corresponding to power equipment, carrying out the preprocessing of the equipment monitoring data, obtaining target monitoring data, and transmitting the target monitoring data to an edge calculation node; and the edge computing node is used for determining a data analysis result corresponding to the power equipment according to the target monitoring data, and generating a short-time scheduling decision corresponding to the power equipment according to the short-time decision model, the data analysis result and the target monitoring data. Based on the above technical scheme, the equipment monitoring data corresponding to the power equipment is collected, and the edge computing node determines the short-time scheduling decision according to the processed target monitoring data, so that the scheduling decision corresponding to the power equipment is determined at the cloud side end, the decision making efficiency of the power system is improved, and the power system experience is improved. And the data processing pressure of the cloud is reduced.

The invention discloses a visual network freight platform transportation transaction management system. The system comprises an order management module, a vehicle scheduling module, a transportation monitoring module, a transaction settlement module and a visual display module. Each module has innovative functions, such as order intelligent classification, vehicle optimization scheduling algorithm to minimize comprehensive cost, real-time monitoring and accurate settlement. The corresponding management method adopts advanced technologies to ensure efficient operation of each link, including block chain recording, intelligent contract settlement and the like. The whole system realizes efficient management, real-time monitoring and convenient visualization of transportation transaction of a freight platform, and improves the efficiency and quality of logistics transportation; according to the visual network freight platform management system, efficient and accurate order management is realized, vehicle scheduling is optimized, the cost is reduced, comprehensive and real-time transportation monitoring is guaranteed to be safe, safe and accurate transaction settlement is realized, convenient and visual display is convenient to make decisions, and the logistics transportation efficiency, quality and user experience are improved.

The invention relates to the technical field of oil field geological prediction, in particular to an oil field geological parameter intelligent prediction system based on machine learning, which acquires static geological data and environmental data in real time through a data acquisition module, and performs noise reduction, normalization and cleaning on the acquired data through a data preprocessing module, so as to obtain an oil field geological parameter prediction result. The feature extraction module adopts an XGBoost model and a long-short-term memory network model to process static features and time sequence data respectively. And the model optimization and self-adaption module dynamically adjusts model parameters according to real-time feedback data by using a reinforcement learning algorithm so as to ensure the accuracy and adaptability of a prediction result. The visualization module provides visual display of a prediction trend, a geological data prediction value and a risk indicator, and helps an operator to make a decision based on data driving. According to the method, the prediction precision of the oil field geological data can be effectively improved, the adaptability to environmental changes is enhanced, and a scientific basis is provided for oil field management and production optimization.

The invention discloses an intelligent online monitoring device for a transformer, and relates to the technical field of transformers. The intelligent on-line monitoring device for the transformer comprises a front-end acquisition layer, a data transmission layer, a data processing and analysis layer, a cloud monitoring and management layer and an emergency response layer, through the deployment of the high-precision sensor network and the edge computing nodes, the system can accurately collect and process the operation data of the transformer in real time, and the monitoring accuracy and efficiency are improved. And secondly, the data transmission layer adopts a self-adaptive communication protocol and a data encryption technology, so that real-time and reliable transmission and security protection of the data are ensured, and meanwhile, traceability and tampering prevention of the data are realized by utilizing a block chain technology. In the data processing and analysis layer, the system realizes intelligent early warning and continuous optimization of transformer data through an integrated learning and deep learning model, and improves the accuracy and reliability of early warning. And the emergency response layer is arranged, so that the system can quickly respond to faults.

The invention relates to the technical field of remote sensing, and discloses a remote sensing ecological index assessment method based on deep learning, which comprises the following steps: acquiring Landsat remote sensing image data and land utilization data of a research area, and extracting remote sensing ecological index factors of the land utilization data and the preprocessed Landsat remote sensing image data; normalizing the remote sensing ecological index factors and performing dimension reduction analysis to obtain dimension-reduced remote sensing ecological index factors, inputting the dimension-reduced remote sensing ecological index factors into a time sequence convolutional network model, and generating a comprehensive feature vector through causal convolution, extended convolution and global average pooling operations; performing linear combination on the comprehensive feature vector and the principal component analysis feature value, and constructing a time sequence convolutional neural network remote sensing ecological index model for evaluating the ecological environment quality condition of the research area; according to the method, the accuracy, the reliability and the practicability of remote sensing ecological index evaluation are improved by adding the remote sensing ecological landmark factors reflecting human disturbance and water environment change and constructing the time sequence convolutional neural network remote sensing ecological index model.

The invention discloses a polyurethane permeation grouting diffusion simulation method, device and equipment and a medium, and the method comprises the steps: building a geometric model of permeation grouting diffusion of polyurethane in a to-be-injected porous medium according to the physical attribute data of the to-be-injected porous medium; establishing a physical field of the geometric model by adopting a Richardz equation and a porous medium dilute material transfer equation, and adjusting the geometric model according to material attribute parameters of the geometric model obtained by the physical field; setting a boundary condition and an initial value of the adjusted geometric model, and performing grid division on the adjusted geometric model based on a local refinement method to obtain a permeation grouting diffusion simulation model; and solving the permeation grouting diffusion simulation model according to the transient solver, and completing simulation of permeation grouting diffusion of polyurethane in the porous medium. According to the method provided by the invention, the modeling accuracy of the slurry diffusion process is improved.

The invention discloses a big data read-write adaptive method based on a decision algorithm and a big data read-write system, and belongs to the technical field of intelligent decision of a data center, high-speed read-write operation of data is processed in real time in a service system, and a data read-write strategy and resource allocation are dynamically adjusted by analyzing a data access mode and a system load condition; data are preloaded through an intelligent prefetching strategy, and the storage and access paths of hotspot data are optimized, so that the waiting time of a user and the consumption of system resources are reduced. According to the method, data management can be simplified, data access efficiency can be improved, data consistency and security can be enhanced, the problem of multi-database management and query complexity faced by enterprises can be effectively solved, and more efficient and more consistent data access and management experience can be provided for the enterprises.

The invention belongs to the technical field of automatic driving, and discloses an autonomous driving decision near-end strategy optimization method based on distributed exploration enhancement. According to the invention, a distributed architecture of the reinforcement learning decision module of the autonomous vehicle is designed, through parallelization and distributed processing, multiple agents can execute strategy training in different environments at the same time, and strategy updating and empirical data are shared in real time. In addition, the distributed architecture allows the method to operate in a larger-scale data set and a complex environment, and the adaptability and generalization ability of autonomous driving decision making are improved. In addition, a random network exploration (RND) module is also combined, the diversity and effectiveness of exploration are further enhanced, and a near-end strategy optimization algorithm used by the reinforcement learning decision module is perfected. By combining the distributed architecture and the RND module, the training process is remarkably accelerated, the exploration efficiency is improved, the decision-making quality of the automatic driving system is comprehensively improved, and the reliability and efficiency of automatic driving decision-making are improved.

The invention relates to the technical field of communication interaction, in particular to a data interaction method based on an intelligent control platform. The method comprises the following steps: acquiring multi-dimensional data of production equipment, performing anomaly marking and format unification processing, generating a production line data stream, performing long and short term dependency analysis on the data stream, and constructing a time-space association network, thereby realizing prediction of an anomaly situation, generating an anomaly intelligent analysis model by using a multi-modal fusion modeling technology, and performing data analysis on the anomaly intelligent analysis model. And through the model, abnormity traceability is carried out, and abnormity reasons are clarified. According to the method, a corresponding correction strategy is constructed based on an abnormal reason, tree-shaped interaction sharing is performed through an intelligent control platform, a sharing correction instruction is formed, abnormal correction processing is performed on a production line according to the sharing instruction, feedback is collected through the intelligent control platform, an effective data interaction mechanism is formed, and the reliability and efficiency of the production line are improved. The data interaction method based on the intelligent control platform is more accurate and more comprehensive.

The invention relates to a ship hybrid formation control method, device and equipment and a medium, and belongs to the technical field of ships, and the ship hybrid formation control method comprises the steps: constructing a hybrid formation of unmanned ships guided by a man-driven ship through a LoRa technology, obtaining the dynamic data of the hybrid formation, fusing the dynamic data, and obtaining the hybrid formation based on the fused dynamic data. An EMD-LSTM network is adopted to predict the navigation trajectory of the mixed formation to obtain a predicted trajectory, a navigation risk early warning system is constructed based on the obtained actual trajectory of the mixed formation and the predicted trajectory, the navigation risk of the mixed formation is evaluated based on the navigation risk early warning system to obtain an evaluation result, and the mixed formation is controlled based on the evaluation result. And the formation decision-making efficiency and accuracy are improved.

The invention provides an artificial intelligence agency method and device based on a large model, and the method comprises the steps: carrying out the intention recognition of input information in user information in response to the user information sent by a user, and obtaining an intention recognition result; determining a target service scene type based on the intention recognition result, wherein the target service scene type at least comprises a first type of service scene or a second type of service scene; if the type of the target service scene is a first type of service scene, performing matching on the basis of the input information and a trigger keyword corresponding to a sub-service scene to determine an interface corresponding to the target service scene; if the target service scene type is a second type of service scene, performing intention recognition based on the input information to obtain theme information; determining an interface corresponding to the target business scene based on the theme information; and calling an interface of the target service scene to perform information processing, and returning response information. In the mode, the decision-making efficiency and the intelligent level of the artificial intelligence agent system in a complex scene are improved.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a decision generation method based on big data, and the method comprises the steps: collecting multi-dimensional data related to a target business, extracting key features, such as a behavior mode, a potential problem and resource stability, of a customer, constructing a customer analysis model, and carrying out the decision generation. Generating a time sequence data set in combination with the behavior trend data; a behavior data set is generated through scene analysis and quantitative analysis is carried out to obtain a behavior mode and a resource utilization trend of a client, and finally a strategy adapted to each business scene is generated. According to the method, the service strategy is finally generated by collecting the multi-dimensional data, extracting the customer characteristics, constructing the analysis model, analyzing the customer behavior trend, generating the time series data set and combining different service scenes to perform scene analysis and quantitative processing, so that the real-time analysis, scene decision and dynamic adjustment of the multi-dimensional data are realized; the flexibility and accuracy of the system in a complex service scene are improved, and the service decision-making efficiency and accuracy are improved.

The invention discloses a data processing method and device based on a visual database and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring a data processing request of a user; analyzing the data processing request to obtain a corresponding key field and a logic conjunction; according to the key field, performing semantic matching in a preset data model library to obtain a corresponding target data table and a target field; synthesizing the logic conjunction, the target data table and the target field to generate a corresponding database processing statement; executing the database processing statement, and obtaining a result data set generated after the database processing statement is executed; and transmitting the result data set to a visual database terminal, and displaying the result data set on the visual database terminal according to a preset visual template. By implementing the technical scheme provided by the invention, the data query efficiency is improved.

The invention, which relates to the technical field of the warehousing system, discloses an intelligent warehousing system comprising a warehouse management information system and an equipment monitoring and scheduling system. The warehouse management information system comprises a goods management module, a warehouse management module, an order management module, an inventory management module, a warehouse-in and warehouse-out management module, an operation management and implementation module and an abnormity early warning module. According to the intelligent warehousing system, real-time tracking and data collection of articles are achieved through RFID, sensors, bar codes and the like, the data accuracy is improved, the warehouse management system integrates the functions of inventory control, order processing, cargo tracking and the like, automatic management of warehousing operation is achieved, inventory layout is optimized through data analysis and a machine learning algorithm, and the demand trend is predicted; and the decision-making efficiency is effectively improved.

The invention relates to the technical field of crack detection, in particular to a bridge concrete crack intelligent detection method based on a deep neural network. The method comprises the following steps: firstly, acquiring images of a bridge crack high-incidence area through an unmanned aerial vehicle, constructing an inspection data set, and performing preliminary classification on the images by using a mutual information clustering algorithm to distinguish crack images from non-crack images; acquiring position information of a crack area based on the crack image, and acquiring ultrasonic data of the crack area by using an ultrasonic probe; establishing a multi-modal crack data set by fusing ultrasonic data and crack image data, detecting the multi-modal crack data set by using a multi-modal fusion network, and distinguishing true cracks from false cracks; the method comprises the following steps: carrying out 3D modeling on true cracks to generate a visual crack model, grading the cracks based on a damage evaluation method, and formulating corresponding repair measures for mild, moderate and serious damages to ensure the safety and stability of a bridge structure.