1019 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 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 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 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.

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 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 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 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 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.

The invention discloses an aberration correction and image quality enhancement method for a laminated structure image, and the method comprises the steps: carrying out the deconvolution preprocessing of a to-be-detected marked image according to an aberration priori set, and obtaining an aberration-free image; meanwhile, combining label data to obtain a data set; feature extraction is carried out based on shallow convolution according to the data set, and global feature information is generated through activation function operation; enhancing the feature data by adopting a frequency domain feature and spatial domain feature fusion strategy; the enhanced feature map realizes initial aberration restoration through an aberration correction module; the corrected feature map is processed by a double-channel attention mechanism, and the global context modeling capability of the self-attention mechanism and the spatial perception characteristic of the position attention unit are fused in parallel; and the image resolution is improved through a super-resolution reconstruction module comprising a sub-pixel convolution layer. By adopting the technical scheme of the invention, the accuracy of overlay error detection is improved.

The invention relates to the technical field of state monitoring, in particular to an energy state monitoring deduction method and system based on digital twinning, and the method comprises the following steps: obtaining a power value and a direction to construct a chain structure map, marking a mutation point to extract a linkage path, screening out an abnormal path, screening an available index, and combining state behaviors to form an evolution trajectory. And comparing the measured data to generate a mapping result. According to the method, through cooperative calculation of the multi-period node power change direction and the response time difference, the energy transfer path relation is defined by utilizing the construction of an inter-node chain transmission structure, and the node coincidence frequency is superposed in the multi-path sudden change behavior time sequence intersection statistics to realize the judgment and elimination of the abnormal path; path availability screening is realized based on response direction consistency of terminal nodes in residual paths, and accuracy improvement of energy flow anomaly identification, continuity guarantee of path deduction results and verification of equipment response states are realized.

The invention discloses a visual setting calculation system and method, and relates to the technical field of power grid setting calculation, and the method comprises the following steps: obtaining operation data of a target system, extracting feature data, and constructing a knowledge graph of entities and relationships, the feature data including control data; constructing a system structure diagram, and generating a semantic enhancement diagram in combination with semantic information in the knowledge graph; inputting the semantic enhancement graph into a pre-trained graph neural network model, performing node feature aggregation and edge weight learning in combination with a prior rule of a knowledge graph, and outputting sensitivity scores of all control data nodes on performance indexes; according to the sensitivity score, constructing a boundary constraint condition, and generating a control data setting candidate solution based on a CSP solver; performing parameter response simulation verification on the control data setting candidate solution, and screening out an optimal control data setting candidate solution; according to the application, accurate sensitivity scoring can be realized by fusing the knowledge graph, the semantic enhancement graph and the graph neural network.

The invention discloses a data processing method and system based on intelligent correction and electronic equipment, and relates to the technical field of educational informationization, and the method comprises the steps: analyzing a standard answer through natural language processing, extracting a standard knowledge point node and a connection edge, distributing a logic priority, calculating a node weight, and constructing a standard cognitive path map; analyzing student answers, mapping student knowledge point nodes through semantic matching, and constructing a student cognitive path map; comparing the two maps, and generating a score mark through a node matching state and sequence offset; detecting and complementing missing nodes and fracture paths, and constructing an atlas residual error scoring structure atlas; and calculating a structured score, and generating a report containing explanatory feedback. The system comprises five corresponding modules, and the equipment comprises a memory and a processor. The correction accuracy and interpretability are improved, the output report adapts to student feedback and teacher rechecking, and the method is suitable for education informatization automatic correction.

The invention discloses a special reasoning model-oriented data set construction method, and particularly relates to the technical field of data set construction. According to the method, domain task text information, rule constraint information and causal dependency information are analyzed, and a dependency structure chart of a target reasoning task is constructed; on the basis of the dependency structure diagram, topology path expansion and anti-fact topology path backtracking analysis are carried out according to the semantic relation direction and the constraint reverse relation, forward path topology feature data and reverse topology verification data are formed, and therefore a forward and reverse dependency consistency matrix is established for topology conflict recognition, and a consistency correction result is output; and finally, according to a consistency correction result, carrying out topology label labeling, topology equivalence judgment and multi-path admission screening to obtain a topology fidelity sample set, and carrying out dependent signature labeling and topology label coding to generate a special reasoning model training data set of the target reasoning task. And the reasoning path reliability and the data set structuring level of the special reasoning model are improved.

The invention discloses a network attack near-source blocking method of a hierarchical and domain security control protocol, which comprises the following steps of: acquiring network topology information and flow data of a cross-domain network environment in real time, and generating a network topology structure diagram; node and edge dynamic features are extracted by using a graph neural network, and a network attack topology path and abnormal traffic features are determined; identifying a network attack near source position; activating a defense agent to generate an initial defense blocking strategy; a multi-agent cross-domain collaborative optimization blocking strategy is adopted; and executing network attack traffic blocking in real time. According to the method, the attack blocking accuracy and response efficiency are improved, and the method is suitable for cross-domain network attack defense.

The invention discloses a pipe gallery risk identification and positioning method and system under multi-protocol driving, and relates to the technical field of pipe gallery risk management, and the method comprises the steps: obtaining a pipe gallery structure diagram, carrying out the deployment of a multi-source sensor, and constructing a sensor topology network; the pipe gallery is continuously monitored; characterization risk identification is carried out, characterization risk nodes and characterization risk feature vectors are determined, association link retrieval is carried out, and a target association link set is determined; extracting backtracking data, and determining a backtracking data sequence set; and carrying out serialized risk trend identification, determining an implicit risk node set, and taking the characterization risk node and the implicit risk node set as a risk identification positioning result. The technical problem of low risk monitoring efficiency caused by single sensor deployment and difficulty in accurate positioning of hidden risk nodes in pipe gallery risk monitoring in the prior art is solved, and the technical effects of realizing efficient identification and positioning of pipe gallery risks and effectively improving the pipe gallery risk monitoring and management and control capability are achieved.

The invention discloses a block chain risk address identification method for a dual-structure time perception graph neural network, and the method comprises the steps: carrying out the analysis and cleaning of original data, generating a transaction pair and an account pair based on an effective transaction record, and finally carrying out the standardization processing of a timestamp. Carrying out model training on the marked training data and dividing a data set; and then a double-structure graph model of an account graph and a transaction graph is constructed, heterogeneous characteristics of accounts and transactions in the block chain are distinguished for the first time, an interaction relationship and behavior evolution are modeled respectively, and the comprehensiveness and accuracy of risk identification are improved. Relative and absolute time coding is introduced, short-term behavior modes and long-term trends are captured in a differentiated mode, and the perception ability of dynamic risks is enhanced. Compared with a traditional single graph model, the scheme has the advantages that multi-dimensional information is effectively fused, the model generalization performance is remarkably improved, overfitting is reduced, and the method is suitable for cross-scene and cross-cycle risk detection.

The invention discloses an SAR image few-sample target identification method driven by electromagnetic scattering characteristics, and the method comprises the steps: selecting a scattering center parameter of an SAR aircraft image, and extracting an attribute scattering center parameter through employing an AML algorithm; utilizing attribute scattering center parameter clustering to generate an aircraft target subcomponent structure chart to make an aircraft classification data set; graph construction and graph aggregation are carried out, setting of edge weights between nodes is constrained by a structure connection relation of an aircraft target, and target local scattering topological structure features are extracted by using a graph neural network; and carrying out feature fusion on the local scattering topological structure features extracted by the graph neural network and the global depth features extracted by using the residual network to obtain a target classification result. According to the method, global visual features and local scattering structure features are combined, prior is provided for a data driving method by using known physical knowledge, the interpretability of the model is improved, and the overall classification precision is improved.

The invention discloses a transformer fault diagnosis and root cause positioning method based on a space-time diagram neural network, and relates to the field of transformer fault diagnosis, and the method comprises the steps: S1, carrying out the preprocessing of the structure information and DGA time series data of a transformer, and obtaining a topological network structure diagram and a DGA time series; s2, obtaining a spatial vector based on a message passing mechanism of a graph convolutional network; s3, a time sequence vector is obtained in combination with a Transform encoder and multi-head self-attention; s4, obtaining space-time fusion features; s5, constructing a multi-task prediction head based on the space-time fusion feature, the fault type historical data and the fault root cause; and S6, carrying out fault detection and outputting a corresponding fault type and root cause positioning result. According to the application, the accuracy of fault type identification can be remarkably improved, and accurate positioning of the fault root cause is realized.

The invention relates to the technical field of image precision optimization, in particular to a surveying and mapping image mathematical precision evaluation method based on dense point cloud matching, which comprises the following steps: acquiring and preprocessing image data, synthesizing an image gradient modulus length and a maximum principal curvature, and introducing a surface normal vector partial derivative to obtain a surface normal vector partial derivative; calculating a curvature-driven depth discretization step length, and generating an initial point cloud data set; constructing a point cloud complex network structure diagram and calculating the topology durability of a point cloud topology structure to obtain an optimized point cloud data set; based on the optimized point cloud data set, in combination with the target image information, the adaptive curvature gradient weight and the error diffusion control value, constructing an adaptive matching cost function, and calculating a point cloud matching cost matrix; and calculating an optimal point cloud transformation matrix based on the point cloud matching cost matrix. According to the surveying and mapping image mathematical precision evaluation method based on dense point cloud matching, low-texture region point cloud matching error suppression and structure optimization of high-curvature region point cloud on a complex curved surface are realized.

The invention discloses a multi-mode communication protocol optimization method based on deep learning, and the method comprises the following steps: S1, collecting operation data of a communication terminal, and carrying out the preprocessing of the operation data; s2, constructing a multi-mode communication protocol structure diagram, and generating an adjacent matrix; s3, constructing a structured LSTM model, and modeling time sequence characteristics of a communication environment and structural dependence information between protocols; s4, performing performance prediction by using the structured LSTM model; s5, constructing a protocol cause subgraph between the time sequence feature vector and the performance prediction vector; and S6, performing multi-target comprehensive evaluation on the communication protocol, and determining an optimal communication protocol configuration scheme. According to the method, structured deep learning and causal modeling are fused, a multimode communication protocol selection process is optimized, and the method has the advantages of high prediction precision, high decision interpretability and good environmental adaptability.

The invention discloses a server configuration method and system, a server, equipment and a medium, and relates to the technical field of computers.The method comprises the steps that a configuration file sent by a front-end tool is received and analyzed through a unified configuration service interface, and a to-be-configured item and to-be-configured information corresponding to a current configuration event are obtained; obtaining an execution topological structure chart matched with the current configuration event; according to the execution topological structure diagram, sending a modification instruction carrying to-be-configured information to a to-be-configured module in a server; and according to a preset event atomization configuration rule, if any to-be-configured module runs abnormally, rolling back each to-be-configured module corresponding to the current configuration event to the previous configuration version. Centralized management of configuration is realized through a unified configuration service interface; and through the event atomization configuration rule, the consistency of the overall configuration state is ensured. The problem that the current configuration automation degree is low can be solved, and the effect of improving the configuration automation degree and the configuration efficiency is achieved.

The invention discloses an industrial drawing analysis method and system combined with a multi-modal large model and OCR, and relates to the technical field of drawing analysis, the method comprises the following steps: carrying out layout area segmentation, text recognition and geometric element extraction on an industrial drawing to obtain a structured information set; constructing a multi-relation structure chart set of the industrial drawings; performing structure embedding and feature bias enhancement to obtain a structure feature set; performing semantic understanding on the industrial drawing to obtain semantic features, and performing multi-channel coding to obtain a multi-modal feature set; obtaining a preliminary analysis result set of the industrial drawing; and performing rule verification, and outputting an industrial drawing analysis result. The technical problems of low industrial drawing analysis efficiency, inaccurate automatic scheme analysis and limited processing capacity in the prior art are solved, and the technical effects of realizing full-process automatic analysis of the industrial drawing by combining the multi-modal large model and the OCR, improving the precision and efficiency of industrial drawing analysis and standardizing output information are achieved.