1258 results about "Structure diagram" patented technology

The invention relates to an unmanned aerial vehicle intelligent dynamic path guidance method and system based on deep reinforcement learning. The method comprises the steps of obtaining three-dimensional terrain point cloud and obstacle information of a flight area, constructing an environment topological structure diagram, extracting features through a three-layer diagram convolutional network, and generating high-dimensional environment feature representation. And a deep reinforcement learning state space is constructed, and a multi-target reward function is designed. And initializing the deep reinforcement learning network by using [-0.1, 0.1] uniformly distributed random parameters to generate an initial path scheme. Optimizing through a dynamic planning algorithm, adjusting a node sequence according to a path length and a task dynamic threshold value, and calculating a target function value. And judging whether the current path scheme reaches the optimal balance point, if not, adjusting the weight of the reward function by using a gradient descent method, and regenerating the path scheme until the path scheme is optimal. And finally, converting the scheme into a waypoint coordinate and speed instruction, performing real-time monitoring during flight, and triggering re-planning when an obstacle exceeds a safety threshold, so as to achieve the purposes of safe flight of the unmanned aerial vehicle in a complex environment and the like.

The invention relates to the technical field of image processing, and discloses an AR technology-based precise positioning method and system for minimally invasive surgery of hepatobiliary surgery. The method comprises the steps that blood vessel network data are extracted through a medical image sequence, a three-dimensional structure diagram is generated, operation target points are marked, and a coordinate system is established; collecting dynamic data for mapping matching, and establishing a real-time tracking model; constructing an augmented reality scene and implanting a navigation path; acquiring an instrument position calculation track, and comparing with a navigation map to generate an instruction; constructing a projection interface to display the key area; recording the whole process and evaluating the operation quality. By establishing the real-time tissue deformation tracking model, a multi-source data fusion accurate positioning mechanism and an intelligent operation quality evaluation system, the accuracy and reliability of operation navigation are effectively improved.

The invention relates to the technical field of industrial component detection, in particular to a transformer iron core detection method based on computer vision, which comprises the following steps of: acquiring an iron core image, extracting key pixel characteristics, screening a directional scattering abnormal region to generate an interference map, extracting a consistent gradient region correction image to generate a reconstruction map, and positioning a symmetric disturbance generation structure map by integral gray difference. And analyzing an overlapping relation by a superposition structure graph to generate an abnormal component graph, and evaluating a risk level by matching a reference index to generate an early warning graph layer. Interference reflection and structural features can be distinguished through linkage analysis of the pixel direction vector and the brightness change frequency, correction of a distorted area in an image is realized based on a gray statistical stable value, and the distortion of the image is corrected by constructing a symmetric point map and analyzing the change trend of a gradient difference value sequence. And the structural overlapping relation is quantitatively judged by combining a component mapping profile diagram, so that the relevance between an abnormal region and a key component is clearly expressed, and the grading evaluation capability of various fault risks in the iron core is improved.

The invention discloses an epilepsy prediction method based on a self-adaptive space-time attention and dynamic fusion network, and relates to a brain signal preprocessing module, a context attention coding layer, a long-distance content dependence network, a self-adaptive time sequence state space modeling module and an epilepsy prediction module. The brain signal preprocessing module obtains corresponding time domain information, frequency domain information and an adjacent matrix; an adjacent matrix is regarded as a graph structure, the weight of each edge of the graph structure is input into a context attention coding layer, spatial features between channels are extracted, the weight is dynamically adjusted through aggregation of neighbor node information and attention, and a node feature matrix is output; inputting the node feature matrix into a long-distance content-dependent network to output global spatial features and corresponding time sequences; obtaining space-time dynamic fusion features through a self-adaptive time sequence state space modeling module; and inputting the space-time dynamic fusion features into an epilepsy prediction module and then outputting an epilepsy prediction result According to the invention, accurate prediction can be realized.

The invention discloses an intrusion detection method and device based on a graph neural network and a medium, and relates to the field of computer networks, and the method comprises the steps: constructing a graph structure according to network flow data in a current time window; the graph structure comprises a node set, an edge set, a node feature matrix, an edge feature matrix and an adjacent matrix; the adjacent matrix is used for representing a communication state between the nodes; inputting the graph structure corresponding to the current time window into a network node traffic type identification model to obtain the traffic type of each node; the network node traffic type identification model is obtained by training a preset graph neural network by adopting a training sample set; in a preset graph neural network, for an input graph structure, a Bi-LSTM layer is adopted to extract time feature information, and a graph convolution layer and a graph attention mechanism are combined to extract space feature information in a time dimension, so that a traffic type of each node is obtained. According to the invention, more accurate network intrusion detection is realized.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention discloses a new energy project intelligent planning method and system based on a large language model, and relates to the technical field of new energy planning, and the method comprises the steps: constructing a new energy project planning knowledge base; the new energy project planning knowledge base comprises basic data required by project planning and is used for providing data support for the optimization model; based on the training data in the new energy project planning knowledge base, performing field adaptability training on the large language model to obtain a special large language model in the new energy project planning field; and based on a project demand input by a user, analyzing project parameters through the special large language model, inputting an analysis result into the optimization model, and generating a planning scheme of a new energy project. According to the method, automatic conversion from a natural language demand to a professional planning scheme is realized, the accuracy of energy balance constraint is ensured through the topological structure diagram of the energy system, a flexible switching optimization target of economic benefits and environmental benefits is supported, and finally intelligent generation and evaluation of the planning scheme are realized.

The invention discloses a large model reasoning method and system based on tree diagram and knowledge graph retrieval enhancement, and the method comprises the steps: segmenting a document into text blocks, recognizing the entities, relationships and factual descriptions of the text blocks, and forming a knowledge graph of the text blocks; constructing a factual summary of the documents based on the knowledge graph of the text blocks, and performing clustering recursion on all the documents by using a Gaussian mixture model to generate a similarity tree structure graph; the method comprises the following steps: obtaining a user problem, selecting a reasoning path from a similarity tree structure diagram through a large language model debate iteration method, obtaining a corresponding document cluster, pruning the cluster based on the user problem and a confidence score of a document, selecting a plurality of text blocks from the pruned document, and finally obtaining a knowledge graph based on the selected text blocks. And generating a reasoning result. The invention relates to the technical field of natural language processing, can realize retrieval enhancement based on a tree diagram and a knowledge graph, and accurately queries related information when a large model answers a complex multi-hop question, so that the reasoning accuracy is effectively ensured.

The invention discloses a concrete crack three-dimensional reconstruction method and system based on multi-modal fusion and a medium, and relates to the technical field of structural engineering detection, and the method comprises the following steps: obtaining a structural image frame sequence and a structural laser radar point cloud frame sequence of a target structure; obtaining a crack mask image frame after crack segmentation; an integral structure de-noised point cloud picture is obtained; obtaining a visible point cloud coloring point cloud map and a visible point cloud semantic segmentation point cloud map; obtaining an overall three-dimensional coloring point cloud map and an overall three-dimensional semantic segmentation point cloud map; obtaining an overall three-dimensional point cloud marking map; and performing three-dimensional attribute measurement on the crack based on the three-dimensional point cloud marking map to obtain three-dimensional geometric information of the crack. According to the method provided by the invention, a multi-frame and multi-modal fused crack structure reconstruction framework is designed, the method can adapt to crack detection of various three-dimensional structures, and simultaneous detection of crack width information, crack position and crack trend information can be realized based on an overall three-dimensional point cloud marking map.

A human body posture estimation method based on a dynamic graph convolutional network belongs to the field of human body posture estimation, and comprises the following steps: step 1, simplifying a human body structure in a video, and constructing a human body skeleton key point structure chart; 2, defining a graph structure to represent a spatial relationship between human joints; thirdly, a graph structure is defined to represent the motion relevance between the joints of the human body; 4, constructing a dynamic graph convolutional network by using dynamic space-time graph convolution; 5, 3D human body posture behavior data enhancement is carried out; and 6, constructing human body behavior data in the virtual scene by using the 3D human body posture data. According to the method, the modeling capability of the complex relation between the joints is improved, the robustness of the model is improved, and complex behaviors are recognized more accurately.

The invention discloses a multi-modal-based software architecture intelligent design and optimization system, which relates to the field of intelligent design and optimization, and comprises the steps of performing modal perception and preprocessing on architecture design demand information input by a user, converting the architecture design demand information into structured semantic data, and optimizing and enhancing semantic expression by introducing a reinforcement learning strategy, forming a structured user intention vector set; through a semantic mapping and reasoning processing unit, user intention vectors in the user intention vector set are constructed into a semantic-component alignment graph, and graph structure modeling and confrontation generation algorithm combination are adopted to generate a candidate structure graph set; a structure diagram generation step in the structure generation module effectively improves the rationality, diversity and adaptation capability of an automatically generated structure, and provides core support for realizing automatic construction of a target-demand-oriented architecture structure.

The invention relates to the technical field of industrial image recognition, in particular to an automatic visual inspection system applied to charging pile production. The method comprises the following steps: the image enhancement module is used for acquiring an omnibearing production monitoring image; carrying out image detail convolution enhancement on the omnibearing production monitoring image, and carrying out topological structure fitting to generate a component topological structure diagram; the twinborn model module is used for performing production process action recognition on the assembly topological structure diagram based on the omnibearing production monitoring image, performing three-dimensional simulation modeling and constructing a dynamic production twinborn model; and the deviation identification module is used for identifying the position deviation of the dynamic production twin model assembly according to a preset production planning log, then carrying out reference positioning deviation calculation, and generating assembly position deviation parameters. According to the invention, efficient and accurate charging pile production diagnosis detection is realized, so that the production process of the charging pile is optimized.

The invention discloses a power distribution network digital twin dynamic sensing method and system, and the method comprises the steps: obtaining the real-time data of the operation of a power distribution network, and monitoring the node change and line state change of the power distribution network based on the real-time data; a power distribution network topological structure diagram in the established power distribution network digital twin model is updated in real time based on node changes and line state changes; based on the real-time data and the historical data, analyzing the operation parameters of the power distribution network through a digital twin model of the power distribution network to obtain an analysis result; and detecting and positioning operation faults of the power distribution network through a power distribution network digital twinborn model based on an analysis result. The invention provides a power distribution network digital twinborn dynamic sensing method and system based on multi-element network topology and parameter change, which can capture and reflect variable network structure and parameter change in a power distribution network in real time and improve the dynamic sensing capability of the digital twinborn so as to support fault early warning and rapid recovery of the power distribution network.

The invention discloses a semiconductor packaging test optimization method and system, and relates to the field of packaging testing, and the method comprises the steps: collecting and preprocessing a multi-dimensional abnormal signal, generating a standard data set, carrying out the feature extraction of data in the standard data set, obtaining a standard feature vector, and inputting the standard feature vector into a constructed abnormality detection model, the method comprises the steps of obtaining an abnormal signal report, distributing a test item for a chip through the abnormal signal report and a predefined mapping rule, generating a structure map and an electrical map, inputting the structure map and the electrical map into a multi-mode Transform model, outputting a fusion map, calculating a test weight according to the fusion map, and generating a test weight map and a region priority list. According to the invention, the testing efficiency, the micro defect detection precision and the boundary failure prediction of advanced packaging are improved.

The invention relates to the technical field of script execution, and provides a multi-script execution method for a virtual controller, and the method comprises the steps: setting a script structure, a graph model structure and a graph node structure; the method comprises the following steps: performing grammar and semantic check on script data to be executed, converting the script data into a script object according to a script structure, converting the script data into a directed acyclic graph according to a graph model structure, and traversing the directed acyclic graph to obtain an execution sequence of graphs in each level of the directed acyclic graph; and scheduling the script object according to an FIFO algorithm, traversing the directed acyclic graph during scheduling to obtain graph nodes in each execution sequence, and performing interpretive execution on the script object according to an ignition rule and the graph nodes in each execution sequence. The method aims at analyzing and executing various scripts, the reliability of the virtual production line is further improved, and the use cost of the platform is reduced.

The invention relates to the technical field of fault prediction, in particular to an intelligent fault prediction method and system for a power adapter. The method comprises the following steps: acquiring a circuit board structure diagram in the power adapter; marking circuit component nodes based on the circuit board structure chart; performing spatial topological structure analysis on the circuit component nodes to obtain a component spatial topological structure diagram; carrying out three-dimensional topology reconstruction on the circuit component nodes, and carrying out local detail iterative optimization so as to construct a local optimization three-dimensional topology structure model; acquiring multi-dimensional operation state data of different parts in the power adapter based on multiple sensors; and performing state feature analysis on the multi-dimensional operation state data of the different parts, and performing state trend evolution so as to obtain state trend evolution data of the different parts. According to the invention, efficient and accurate power adapter fault prediction is realized.

The invention discloses a real-model experiment distribution line fault positioning method and system. The method comprises the following steps: acquiring a topological structure diagram of a target power distribution network in a real-model experiment; based on the topological structure diagram of the target power distribution network, combining a preset diagram input feature selection logic, and obtaining target power distribution network node and edge features as input of a power distribution network fault model to be established; according to the input of the to-be-established power distribution network fault model and the output of a preset power distribution network fault model, training the power distribution network fault model based on the improved graph neural network, the output of the preset power distribution network fault model being the probability of the line fault in the power distribution network; and according to the trained power distribution network fault model based on the improved graph neural network, real-type experiment power distribution line fault positioning is completed. The system and the method have good expansibility and adaptability, can be suitable for power distribution networks of different scales and structures, and have wide application prospects.

The invention provides an equipment fault prediction method and system based on deep learning, and relates to the technical field of computers, and the method comprises the steps: obtaining first information, second information and third information; extracting historical dynamic operation characteristics of the equipment according to the third information to obtain an operation state characteristic matrix; performing graph construction processing according to the second information, and respectively constructing to obtain a structure graph, a wiring characteristic graph and a control logic graph; according to the operation state characteristic matrix, the structure diagram, the wiring characteristic diagram and the control logic diagram, performing fusion processing to obtain a comprehensive diagram structure; according to the comprehensive graph structure, using a deep learning algorithm to construct and obtain a fault prediction model; and inputting the first information into the fault prediction model to obtain a prediction result. According to the method, the time domain, frequency domain and time-frequency domain characteristics are extracted from the time sequence of the historical operation data in a segmented manner, a multi-dimensional comprehensive graph structure is constructed in combination with the equipment structure, the signal wiring characteristics and the control logic, and the internal characteristics of the equipment and the incidence relation thereof are fully excavated.

The invention discloses a safety monitoring management method and system for a chemical product production workshop and a medium, and relates to the technical field of workshop safety management, and the method comprises the steps: dividing the chemical product production workshop according to regional functions, and building a regional conduction topological structure diagram; arranging multiple types of monitors, and collecting multi-source detection data; training a classifier, performing security identification classification, and positioning a security identification result; according to the regional conduction topological structure diagram, regional fixed-point identification and regional conduction risk prediction are carried out, an early warning level is generated, and graded early warning and product production process emergency management control are carried out according to the early warning level. The technical problems that in the prior art, safety monitoring of a chemical product production workshop is not comprehensive, recognition is not accurate, risks are difficult to effectively pre-judge, the accident occurrence probability is high, and production safety is difficult to guarantee are solved, graded early warning and emergency management control in chemical product production process management are achieved, and the safety of the chemical product production workshop is improved. And the workshop safety monitoring management level is improved.

The invention discloses a line planning method and system, and relates to the technical field of electric power line inspection. According to the line planning method, a plurality of feasible inspection paths are generated by obtaining a list of tasks to be inspected and three-dimensional coordinates of an electric tower and an inspection part of the electric tower, combining regional environment data, constructing a semantic knowledge graph and a spatial topological structure chart, determining an optimal path through evaluation, screening and analysis, and transmitting the optimal path to an unmanned aerial vehicle to execute an inspection task. According to the method, the flight state is monitored in real time, the abnormal condition is found in time, and the residual path is dynamically replanned in the inspection process, so that the smooth execution of the inspection task is ensured. The flight state and the environment interference of the unmanned aerial vehicle are monitored in real time, and the dynamic path is replanned according to the actual condition; according to the method, the flexibility and emergency response capacity of the inspection task are remarkably improved, the efficiency and safety of the inspection task are effectively improved by dynamically adjusting the path, and the adaptive capacity of the unmanned aerial vehicle in a complex environment is enhanced.

The invention discloses an intelligent material property prediction system based on a graph neural network, and the system comprises the following modules: a structure graph construction module which is used for analyzing material structure data and constructing a three-order tensor graph; the tensor graph coding module is used for inputting the third-order tensor graph into a multi-scale tensor graph neural network model to generate high-dimensional tensor node embedding representation; the asymmetric propagation module is used for constructing a directional tensor connection structure based on the high-dimensional node embedding representation and generating an updated feature representation; the model optimization module is used for inputting the updated feature representation into an improved eagle swarm search algorithm to generate optimal configuration; the multi-target prediction module is used for loading optimal configuration and performing multi-target material property prediction; and the continuous learning module is used for identifying a deviation sample based on the prediction error, feeding back and updating the multi-scale tensor graph neural network model, and writing the model into a learning buffer area. According to the invention, multi-channel graph modeling and an improved eagle swarm search algorithm are fused, and an intelligent material property prediction system is constructed.

The present invention relates to the technical field of tray line layout optimization, and provides a tray line layout optimization method and system based on Internet of Things. The method comprises: receiving from a user terminal a circuit topological structure diagram of a preset area to perform cable relationship assignment; constructing a cable layout tree diagram; extracting an i-th level cable set for multi-level clustering analysis; generating a cable set clustering result; setting wiring constraint directions and wiring constraint areas; setting tray distance constraint parameters; generating a three-dimensional model of the preset area; on the basis of the clustering result and under the described constraints, performing cable layout optimization in the three-dimensional model; generating a tray line layout optimization scheme; and sending the tray line layout optimization scheme to the user terminal. The present invention solves the technical problems in conventional tray line layout methods of an unsatisfactory layout result caused by relatively vague layout constraints and layout requirements, and low layout efficiency caused by excessive manual intervention and lack of automation and intelligence in the layout process.

The present invention discloses a fire monitoring method and system based on three-dimensional laser scanning technology, which relates to the field of fire monitoring technology, including collecting a fire scene structure diagram for path planning, deploying sensors on a robot for real-time path navigation and obstacle avoidance; performing three-dimensional laser scanning and collecting data after reaching the target point, processing the collected data and performing three-dimensional modeling. The present invention combines three-dimensional laser scanning technology with a robot intelligent navigation system, utilizes high-precision three-dimensional point cloud data and sensor fusion technology, realizes comprehensive monitoring of the fire scene, and through the robot's autonomous path planning and obstacle avoidance, can dynamically collect data at the fire scene and perform three-dimensional modeling, analyze heat source distribution and smoke diffusion trajectory, and then predict the fire spread path, and generate a real-time updated three-dimensional fire scene map, which can provide efficient and accurate fire information in a complex environment, and provide an important basis for decision support and emergency rescue at the fire scene.

The present application provides a CAD model analysis method and apparatus, a device and a storage medium, which relates to the technical field of industrial manufacturing. The CAD model based on a BRep representation is transformed into a graph structure. The graph structure is input into an analytical model to obtain a modeling sequence of the CAD model. By transforming the CAD model with a BRep representation into the graph structure, the analytical model learns the modeling feature in the graph structure, and the modeling feature type and the feature parameter are obtained, so as to enable the prediction of the model building process of the CAD model and facilitate to subsequently make parametric re-editing of the CAD model

The invention discloses a multi-modal image matching method and system based on saliency graph structure enhancement, and belongs to the field of image processing. The method comprises the following steps: firstly, innovatively constructing a pixel-level saliency confidence graph for measuring the matching potential of each region, and guiding an attention mechanism to be dynamically focused on a key region in a graph structure through the graph; secondly, multi-scale structure features and semantic segmentation information are fused, and the semantic perception ability of feature expression is enhanced; and finally, constructing two heterogeneous graph structures of an in-image structure graph and an inter-image semantic guidance graph, and realizing global-local information enhancement and cross-modal semantic alignment on the graph structures by introducing a self-attention and cross-attention mechanism of saliency modulation, so that the matching precision and stability are remarkably improved, and the matching accuracy is improved. And semi-dense matching of multi-modal images is realized.

The invention provides a risk control strategy dynamic optimization method and system based on a large model, and the method comprises the steps: determining a current risk control strategy system and corresponding strategy execution data, calling the large model to carry out the conjoint analysis of the current risk control strategy system and the corresponding strategy execution data, and generating a strategy structure map and a deviation diagnosis report; based on the strategy structure map and the deviation diagnosis report, generating a strategy adjustment scheme containing a rule logic reconstruction suggestion and a rule priority optimization sequence, and according to the strategy adjustment scheme, updating the current risk control strategy system to obtain an optimized risk control strategy system; and finally, effect verification is carried out on the optimized risk control strategy system through a dynamic verification mechanism, and a verification result is used as newly added data of strategy execution data, so that dynamic optimization of the risk control strategy is realized, and the accuracy and adaptability of the risk control strategy are improved.

The invention provides an unmanned aerial vehicle ad hoc network transmission and calculation integrated resource scheduling method based on task driving, and the method comprises the steps: building a multi-dimensional resource pool model which comprises the communication bandwidth, calculation resources and storage resources of an unmanned aerial vehicle, and collecting the resource state vector of each unmanned aerial vehicle node in real time; a dynamic topology sensing network is constructed, link duration is predicted through relative motion speed between unmanned aerial vehicle nodes, and a network structure chart with weights is generated; constructing a decision model based on a fusion architecture of a preset message passing neural network and a deep reinforcement learning network, and inputting the network topology features of the network structure chart and the resource state vector into the decision model; and outputting an optimal scheduling strategy including target node selection and multi-hop path planning through the decision model, and maximizing system benefits while meeting constraints of tasks on communication and computing resource quality. The problems that existing unmanned aerial vehicle networking communication is high in time delay, low in reliability and difficult to calculate and maximize utilization of resources are solved.

The invention relates to the technical field of property right transaction recommendation, in particular to a property right transaction recommendation system and method based on a graph neural network. The method comprises the steps that a data collection unit collects property transaction data; the graph construction unit constructs a graph structure according to the property transaction data; the graph neural network unit performs feature learning on the constructed graph structure by using a graph neural network GCN, extracts a high-order relationship and generates feature embedding; and the recommendation generation unit performs matching calculation through cosine similarity based on feature embedding, and generates a property right transaction recommendation result. According to the method, multiple relations (such as a transaction relation, a click behavior relation and an asset category information relation) between the user and the asset are integrated, and the influence of the different relations on the node characteristics is optimized, so that the interest and preference of the user can be more accurately captured, and in addition, the user experience is improved. Factors such as geographic positions and user behavior sequences are also considered, so that recommendation results are closer to actual demands of users.

The invention discloses a seabed sonar image recognition system based on a graph neural network, and the system comprises the following modules: an image collection module which is used for collecting an original sonar image and constructing an original sonar image data set; the preprocessing module is used for generating a de-noised sonar image data set; the three-dimensional landform structure modeling module is used for constructing a three-dimensional landform structure diagram; the graph structure coding module is used for obtaining multi-scale landform semantic feature representation; the decoding and evaluation module is used for reconstructing a preliminary seabed landform semantic segmentation map through jump connection and calculating a fitness vector; the optimization scheduling module is used for obtaining an optimized submarine landform semantic segmentation map; and the post-processing and output module is used for generating a final segmentation result and obtaining a landform category identification result. According to the method, the problems of fuzziness and fracture of a traditional convolutional neural network on a sea ditch-sea mountain-hill complex nested landform boundary are effectively solved.

The invention relates to the technical field of medical management, and discloses a digital twin-driven hospital operation simulation and quality control optimization system, which comprises the following modules: a multi-source medical data fusion module for collecting heterogeneous medical data of a hospital and fusing to construct a dynamic medical diagram structure; the graph structure behavior modeling module is used for constructing a trajectory graph of the patient and generating potential trajectory representation of the patient in the medical process; the stable path evolution constraint module is used for introducing a path evolution model of a compression mapping function and generating a patient trajectory prediction result; the digital twin simulation module is used for generating multi-dimensional operation index data; and the strategy evaluation and closed-loop optimization module is used for constructing a multi-target evaluation model and carrying out optimization adjustment on the operation strategy according to the multi-target evaluation model. According to the application, the digital twinborn simulation module is introduced to construct the dynamic virtual environment capable of mapping the real hospital operation state, so that the patient path and the resource use condition can be completely reproduced on the basis of not interfering the existing process.