379 results about "Graph computation" patented technology

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention discloses an electronic material life cycle quality tracing method based on digital twinning, and relates to the technical field of industrial Internet of Things, the digital twinning of an electronic material is constructed, a material constitutive equation, a process parameter threshold library and historical quality data are integrated, and a multi-dimensional virtual model is formed; a production line real-time data stream including an equipment state, environmental parameters and material attributes is collected. According to the method, the virtual model containing the material constitutive equation and the process parameter threshold library is constructed, the real-time data flow dynamic evolution is combined, and the graph calculation and the causal reasoning algorithm are applied, so that the interaction effect of the equipment state, the environmental parameters and the material attributes can be associated, the core influence factor chain of the quality abnormality can be positioned, the single-point alarm limitation is broken through, and the quality abnormality can be accurately detected. The quality problem is deeply analyzed from the angle of multi-factor coupling, a comprehensive and systematic analysis framework is provided for accurate attribution, the source of the quality problem can be quickly and accurately found, and the efficiency and accuracy of quality tracing are improved.

The invention provides a full-text retrieval method and system fusing various types of documents, and relates to the technical field of information retrieval, and the method comprises the following steps: obtaining document representation through document content extraction and structure recognition, generating a cross-modal semantic vector by using word embedding and nonlinear transformation, constructing a hierarchical index and a cross-document association graph, and obtaining a full-text retrieval result; the basic correlation score is calculated after the query request is received, and the comprehensive score of the candidate content segments is calculated based on the association graph to determine the optimal retrieval result, so that unified representation and retrieval of heterogeneous documents are realized, the cross-document retrieval precision and relevance are improved, and the processing capability of a retrieval system on complex queries is enhanced.

The invention provides an intelligent data consanguinity tracking and visualization method based on graph calculation, and the method comprises the steps: carrying out the data structure analysis and metadata extraction of original data assets, so as to generate a standardized data asset package; performing graph node attribute definition on the data entities in the standardized data asset package, and performing graph edge attribute definition on the association relationship between the data entities to generate a first data blood relationship model; performing blood relationship path mining on graph nodes and graph edges in the first data blood relationship graph model to obtain a basic blood relationship path set, and performing quantitative calculation and feature labeling on path association strength in the basic blood relationship path set to generate a second data blood relationship graph model; and performing visual rule mapping on graph node attributes and graph edge attributes in the second data blood relationship graph model to construct a standardized visual data set and generate a data blood relationship visual interaction interface according to the standardized visual data set.

The invention discloses an FTU-based power distribution network fault positioning method and system, and belongs to the technical field of power distribution automation, and the method comprises the steps: constructing a space-time correlation feature matrix according to a transient current sequence and a voltage drop sequence during a fault period, and extracting the convolution features of a graph to obtain a fault feature graph containing the fault correlation degree between nodes; according to a static topological structure in the power distribution information model, virtual impedance is calculated based on the fault feature graph, and network equivalent topology is dynamically identified to obtain a dynamic virtual topological structure graph; and based on the dynamic virtual topological structure diagram structure and the fault feature diagram, performing fault section confidence competing decision through the intelligent agent unit corresponding to each FTU based on an incomplete information game, and outputting a fault section positioning result. The power distribution network fault positioning method solves the problems that a traditional power distribution network fault positioning method is insufficient in positioning accuracy and poor in fault tolerance and excessively depends on centralized processing and global information synchronization when information is incomplete, fault features are complex and network topology dynamically changes.

The invention discloses a highway defect visual identification system and method based on artificial intelligence, and relates to the technical field of highway defect detection.The method comprises the steps that a pavement image and metadata are input, and an initial defect candidate set is output through a reference artificial intelligence model; calculating a context attenuation factor based on the metadata to obtain a preliminary calibration confidence coefficient; constructing a spatial association graph according to the initial defect candidate set, and calculating a dynamic risk initial evaluation value; identifying a depth verification defect subset based on the dynamic risk initial evaluation value, the initial calibration confidence and the frame risk baseline value; a normal road surface generation model is utilized to analyze physical characteristics of the difference chart, a physical rationality score is obtained, and optimization confidence is generated; extracting feature fingerprints of which the optimization confidence degree meets the confidence degree condition defect, and performing similar case matching in a historical defect library; if a target historical case of which the similarity exceeds a similarity threshold value is matched, generating a risk final value, and generating a historical attribution conclusion; and outputting the structured diagnosis report.

A graph computing-based multi-stage topology reconstruction method and system for a power distribution network. The method comprises: providing a power distribution network reconstruction method based on an optimal flow method and a branch exchange method (S110); constructing a power distribution network multi-stage reconstruction model with optimal power loss (S120); providing a graph computing-based multi-stage reconstruction computing method for the power distribution network (S130); and solving the graph computing-based optimal power loss model to obtain a power distribution network reconstruction technology (S140).

The invention discloses a service abnormity monitoring method and system based on dynamic graph calculation, and relates to the technical field of service monitoring data processing, and the method comprises the following steps: generating a service graph structure based on real-time service interaction data and service association attributes of an ERP report, performing node rationality dynamic evaluation based on the node basic stability parameter and the associated service parameter, and executing node dynamic adjustment based on the node update resource occupation condition and the report granularity mode according to a node rationality dynamic evaluation result; performing node weight dynamic processing based on the service interaction parameter of each associated service of the node and the node rationality dynamic evaluation result, and performing service influence dynamic evaluation based on the node weight dynamic processing result and the node level; and performing node risk dynamic assessment based on the service influence dynamic assessment result and the node dynamic stability parameter, and performing monitoring dynamic regulation and control processing based on the node risk dynamic assessment result.

The invention relates to the technical field of operation and maintenance monitoring, and discloses a multi-service system automatic centralized monitoring management method and system. According to the method, monitoring data streams of a multi-service system are captured in real time through a distributed data acquisition agent, and noise filtering and format standardization processing are carried out to generate a standardized monitoring data sequence; automatically identifying monitoring elements in the service system by adopting a pattern recognition algorithm based on the normalized sequence, and packaging each element into a monitoring entity; dynamically generating a monitoring strategy rule according to the characteristics of the monitoring entity and the association relationship, and storing the monitoring strategy rule to a monitoring strategy knowledge graph; analyzing a logic relationship of strategy rules in the knowledge graph, and constructing a monitoring strategy dependency graph by using a graph calculation engine; performing structural integrity verification and performance optimization on the dependency graph to ensure the performability of the dependency graph; deploying a monitoring executor to a target business system according to the optimized dependency graph to start an automatic monitoring process; and collecting data returned by the monitoring actuator in real time to generate a monitoring analysis report.

The invention discloses a wind power plant data synchronous correction method based on view topology and a mask graph neural network, and belongs to the field of wind power plant data processing and artificial intelligence space-time sequence prediction.The method comprises the steps that firstly, historical space-time observation data of unit nodes are obtained, and a space-time feature tensor is constructed; generating a mask matrix; extracting space and time sequence waveform correlation characteristics of a plurality of unit nodes, and constructing a global multi-view adjacency matrix; inputting the spatio-temporal feature tensor and the global multi-view adjacency matrix into a spatio-temporal diagram neural network, performing blocking and shielding operation in a message passing stage of the network, extracting full-field features, and outputting an initial deduction sequence; and obtaining a boundary physical residual error, reversely compensating the boundary physical residual error to the initial deduction sequence, and generating and outputting final correction data. According to the method, test data high-fidelity error correction under high concurrency missing rate and strong noise interference is realized, and the space-time diagram calculation memory overhead of a bottom-layer deep learning framework is remarkably reduced by designing a static topology cache mechanism.

The invention provides a dam safety monitoring data analysis method and system based on a multi-modal knowledge graph and a large language model, and belongs to the technical field of calculation models. The method comprises the following steps: collecting multi-modal data of dam monitoring, and preprocessing the multi-modal data; constructing a dam safety analysis knowledge graph by adopting a water conservancy object multi-modal knowledge graph construction mode; an intelligent positioning function is used for finding an associated object for the monitored abnormal signals, information of the associated object is obtained, and a dam analysis model is used for association analysis; generating an abnormal associated sub-graph; and forming a multi-dimensional analysis report conclusion by using a pre-trained water conservancy professional knowledge large language model in combination with cue words and user questions. Multi-aspect information generated in the dam safety management process is integrated by utilizing the knowledge graph to form a comprehensive information network, efficient query and reasoning are realized by virtue of a graph computing technology, and a monitoring analysis result for explaining the dam is clearly, intuitively, accurately and comprehensively presented.

The invention belongs to the technical field of big data processing and artificial intelligence, and particularly relates to a distributed efficient processing and intelligent analysis service system and method for mass big data. Comprising a distributed storage management layer, an intelligent metadata index layer, a task semantic analysis layer, an affinity perception scheduler, a multi-mode execution engine and a credible feedback control unit. High-affinity matching of tasks and data is realized by fusing data distribution perception, task semantic understanding and resource collaborative scheduling. According to the scheme, remote data pulling can be remarkably reduced, the scheduling efficiency and the resource utilization rate are improved, and collaborative efficient execution of multi-modal tasks such as streaming, graph calculation and AI reasoning is supported.

The invention relates to the technical field of image processing, and discloses a warping process monitoring method and system based on image recognition, and the method comprises the steps: obtaining a yarn image in the operation process of a warping machine, and inputting the image into a monitoring model; generating a direction gradient prior graph, and calculating a structure tensor representing yarn texture complexity; discrete wavelet transform is carried out on the preorder layer feature map of each dense layer, and a high-frequency detail component and a low-frequency contour component are obtained through separation; performing weighted fusion on the high-frequency detail component and the low-frequency contour component to generate a fusion feature map; performing channel splicing on the directional gradient prior image and the fusion feature image, and inputting a splicing result into a sub-pixel convolution layer for up-sampling to obtain a high-resolution monitoring image; calculating a local anisotropy index of each pixel; and the local anisotropy index and the pixel gray value are combined to identify the yarn broken end or hairiness defect, and a monitoring result is output. According to the method, the reliability of detecting tiny defects such as broken ends and hairiness under a complex background is improved.

The invention provides a three-dimensional part retrieval method based on graph similarity search, relates to the field of computer graphics, and solves the technical problems that in the prior art, geometric and design semantic information of CAD cannot be fully utilized, and a topological relation is difficult to capture and display, so that the retrieval efficiency is low. The method comprises the following steps: acquiring part three-dimensional data of a computer-aided design (CAD) model, and constructing a training data set; constructing an edge-surface connection diagram based on the three-dimensional data of the part; calculating a graph editing distance (GED) matrix of all edge surface connection graphs in the training data set as a supervision signal; based on the GED matrix, training a sorting model, mapping an edge-surface connection graph to a hidden space, and constructing a part vector database according to an output graph-level embedding vector; the sorting model is constructed based on a graph attention network; and inputting a to-be-queried CAD part into the trained sorting model to obtain a feature vector, carrying out nearest neighbor search in the part vector database, and returning a similar part result.

Techniques for implementing hybrid retrieval augmented generation for rich document queries using a large language model (“LLM”) are disclosed. In an example method, a computing system receives documents which are added to databases. The computing system receives a query, from which tokens are generated. The computing system determines a first ranking of the documents using a probabilistic ranking method and a second ranking of the documents in response to a vector database query using an embedded representation. The computing system determines a third ranking of the documents based on the first ranking and the second ranking. The computing system selects top documents from the third ranking. The computing system determines a knowledge graph based on the top documents. The computing system outputs the query, the top documents, and at least a portion of the knowledge graph to an LLM. The computing system receives and outputs a response.

The invention provides a lead mark arrangement method and device, electronic equipment and a storage medium, relates to the technical field of computers, in particular to the fields of engineering drawing, computer aided design and the like, and can be used for application scenes such as drawing lead mark arrangement and the like. According to the specific implementation scheme, the method comprises the steps of obtaining a plurality of to-be-arranged lead marks and a plurality of mark objects; aiming at each to-be-arranged lead mark, determining a lead-out point coordinate of a lead based on a corresponding mark object; in response to a text arrangement mode selected by a user, determining text coordinates according to the extraction point coordinates; and generating a lead mark arrangement result according to the lead-out point coordinate and the text coordinate corresponding to each to-be-arranged lead mark. According to the scheme, the lead text annotation arrangement can be quickly carried out, various arrangement effects can be obtained by setting a simple arrangement mode, and the operation efficiency of arrangement and annotation when a user makes a picture is greatly improved.

The invention discloses an integrated three-dimensional space modeling method based on Gaussian splashing and a three-dimensional point cloud. The method comprises the following steps: step 1, converting a scene multi-view image and depth data into the three-dimensional point cloud; 2, constructing a point sequence input set; step 3, inputting the point sequence input set into a PointMamba model; 4, performing Gaussian parameter initialization, performing scale expansion on a local sparse region, and performing direction constraint on a global key region; 5, obtaining a prediction depth map through a binocular parallax matching algorithm, calculating a rendering depth map of the initial Gaussian point set, and adjusting the optimization intensity; and step 6, executing an adaptive encryption operation, and outputting a three-dimensional space model. According to the method, high-precision three-dimensional reconstruction of a complex scene is realized, and the method is suitable for scenes needing high-density point cloud modeling and semantic understanding, such as building scanning, industrial detection and virtual reality.

The invention relates to a multi-view graph time sequence cooperative detection method and device for encrypted malicious traffic, and belongs to the technical field of network security. A'multi-granularity feature-multi-view graph time sequence 'cooperative detection framework is constructed, multi-granularity features such as IP segmented coding and load entropy are fused, MLMC sampling and bidirectional time sequence convolution are combined, and space-time cooperative modeling of large-scale flow network nodes is achieved in a unified framework. The bottleneck that a traditional method is single in feature view angle and low in total graph calculation efficiency is effectively solved, and the precision and robustness of malicious traffic detection are improved.

The invention provides a graph data communication intensity mapping method and system based on optical transport network topology. According to the method, an adjacent matrix is established by extracting graph data structure information, vertex centrality and a clustering coefficient are calculated, and a graph data communication feature matrix is generated; oTN network equipment information and optical link topology are collected, link parameters and wavelength resource states are measured, and an OTN physical network feature matrix is obtained; establishing a double-layer mapping relation matrix through a resource mapping algorithm; integrating the communication frequency, the time delay sensitivity, the bandwidth requirement, the wavelength resource and the topological matching degree, and calculating a communication strength weight matrix; generating a candidate layout scheme, performing multi-dimensional evaluation, and determining an optimized data layout scheme; and dynamically adjusting the mapping relation by adopting an incremental updating algorithm. According to the invention, the efficient collaboration of the graph computing task and the optical transport network resource is realized, and the performance and resource utilization efficiency of the distributed graph computing system are obviously improved.

The invention discloses a file semantic similarity calculation method based on an improved DeBERTa model, and the method comprises the steps: dividing a file according to a structure, marking role labels, completing the denoising and coding, and generating a hierarchical input sequence; encoding an input sequence through an improved DeBERTa model, and outputting a multi-level semantic vector and a structure semantic representation; constructing a semantic energy resonance diagram, calculating energy conduction and coupling strength between hierarchies, and generating resonance spectrum characteristics; performing statistics on multi-level energy distribution and resonance intensity to form an energy spectrum feature vector for describing structural stability; constructing a semantic field curvature function, mapping energy and semantic representation to a unified space, and obtaining alignment representation; and fusing the energy spectrum characteristics with the semantic alignment representation, calculating comprehensive semantic similarity and outputting a final result. According to the method, a DeBERTa model is improved, and a semantic energy resonance and curvature self-alignment mechanism is introduced, so that high-precision calculation of the semantic similarity of the long text is realized.

The embodiment of the invention discloses a risk perception method and device for a global supply chain enterprise and a medium, and relates to the technical field of data analysis, and the method comprises the steps: receiving a risk perception request of a target enterprise for an enterprise perception object, the enterprise perception object comprises a target product and a target shipping port, the risk perception request comprises real-time perception and simulation interruption perception; based on the risk perception request, performing entity relationship extraction from a pre-constructed dynamic knowledge graph to form an evaluation sub-graph, and calculating a multi-dimensional quantitative toughness index through the evaluation sub-graph; and fusing the multi-dimensional quantitative toughness indexes to generate a comprehensive toughness index corresponding to the enterprise perception object, and carrying out risk perception on the enterprise perception object based on the comprehensive toughness index.

The invention discloses a standardized work management system for a standard system revision project, and the system comprises a heterogeneous data fusion module which captures the multi-modal process data of each stage in real time, and converts the multi-modal process data into a process state vector sequence; a time sequence constraint graph rule base is built in the compliance graph calculation module, a process state graph with time sequence attributes is constructed, and the multi-dimensional compliance deviation degree is calculated through graph mode matching; the strategy generation and conflict resolution module is used for generating a candidate set with a weight adjustment instruction through a learnable strategy priority ranking model based on the deviation degree and resolving logic conflicts; and the trusted execution and feedback closed loop module atomizes an execution instruction and carries out uplink evidence storage, and collects project response data to update rule base parameters and model weights online. According to the method, automatic and accurate compliance evaluation is realized through graph calculation, self-evolution of the system is realized through intelligent strategy generation and credible execution feedback, and the management efficiency, compliance and intelligent level are comprehensively improved.

The invention discloses a community discovery and dynamic window strategy-based decommissioned battery hierarchical recombination method, system and device, and a storage medium, and belongs to the technical field of battery echelon utilization. The method comprises the following steps: firstly, constructing a battery dynamic fingerprint based on relaxation time distribution, constructing an electrochemical similarity network by utilizing Zensen-Shannon divergence, and realizing accurate decoupling of an aging mode through a community discovery algorithm; then, the residual capacity is predicted through a multi-layer perceptron, a dynamic greedy sliding window algorithm is adopted for module reconstruction, and according to the algorithm, through descending priority matching and a window self-adaptive reduction mechanism, the grouping rate is remarkably improved while the module consistency is guaranteed. According to the invention, the dual problems of mechanism mismatch and low grouping rate caused by the heterogeneity of the retired batteries are effectively solved. The system comprises a test module, a graph calculation module, a capacity prediction module and a grouping decision module. The invention further relates to electronic equipment and a storage medium for implementing the method.

The invention discloses a terminal strip drawing terminal identification and cable connecting line matching method and system, and the method comprises the steps: obtaining the layer, geometric shape, text and other information of a terminal strip CAD graph, and extracting the needed line segment and text; training a target detection model, identifying a terminal strip area in the terminal strip drawing, and cutting out a sub-graph; adopting a sliding window method to cut the terminal row into a plurality of sub-graphs with a certain overlapping area; labeling table cells in the sub-graphs, making a data set, and training an RT-DETR model; inputting the sub-graph into the trained RT-DETR model for prediction, outputting cell coordinate information in the sub-graph, and restoring the cell coordinate information to the original graph; all cable connecting lines connected with the terminal strip area are calculated, and the cells representing the terminals are matched with the cable connecting lines according to the geometrical relationship. According to the invention, the matching relation between the terminal and the cable connecting line can be effectively detected, the structured data can be automatically generated, and rapid extraction and analysis of drawing information can be realized.

The invention relates to an educational resource intelligent allocation method and device, equipment and a medium. The method comprises the following steps: receiving an educational resource assignment request, and decomposing the educational resource assignment request into a plurality of atomic subtasks; based on each atomic subtask and the global educational resource list, performing modeling processing to generate a task directed acyclic graph and a resource dependency graph; then, based on the task directed acyclic graph and the resource dependency graph, calculating dynamic priority scores of the ready tasks, and arranging the ready tasks in a descending order according to the dynamic priority scores to generate a dynamic priority queue; and removing queues with resource conflicts from the dynamic priority queues, generating execution queues without resource allocation conflicts, and performing atomic subtask execution processing according to the execution queues to obtain an intelligent educational resource allocation result. By adopting the method, multi-task deep dependence can be cooperatively processed, resource state changes can be sensed in real time, and allocation conflicts can be actively avoided.

The invention discloses a neighborhood center layout-based power distribution looped network diagram automatic generation method, system and device, and a storage medium, and relates to the technical field of electrics, and the method comprises the steps: carrying out the branch model cutting and preprocessing of a power distribution looped network diagram device topology model relation; and generating a trunk path equipment set comprising a transformer substation power supply node, a connection switch node and a trunk path switch node, and complementing feeder attribute information through a width search algorithm when feeder attributes are missing and wrong. And constructing a graph calculation model according to the main path equipment set and the line topological relation, and generating a power supply node set and a node and edge data structure. And calculating the connection degree between the power supply nodes based on the power supply node set, performing ascending sorting, executing initial layout of the power supply nodes according to a sorting result, performing neighborhood center layout and local orthogonal processing on non-power supply nodes by using an initial layout result, and generating node positions of the looped network graph with balanced layout. According to the invention, the intuition readability of the distribution ring network graph is improved.

The invention discloses a tourism economy operation data investigation and monitoring system. The system comprises a data acquisition layer which is used for constructing a tourist behavior acquisition matrix; the graph space-time fusion layer integrates a TGN framework, defines a node type and a hyperedge type, adopts a Transform structure to capture a seasonal mode by a time encoder, integrates a meta-path knowledge injection mechanism and a CTBN space-time probability graph model, performs tourist state discretization by using an H3 hexagonal grid, and performs space-time dependency relationship capture through an Flink CEP extended dynamic geo-fencing operator; the data processing layer is used for carrying out track semantic clustering by adopting a TRACLUS + + algorithm, and automatically generating space-time cross characteristics in combination with Featuretools; the data storage layer is used for configuring InfluxDB to store space-time state chain data; and the data analysis layer is used for calling a PyG model to carry out implicit relationship mining by applying a near-graph calculation acceleration technology, and constructing a tourist volume prediction model by fusing a graph attention network and a space-time convolutional network to capture spatial dependence and time trend.

The invention belongs to the field of three-dimensional scene reconstruction, and particularly relates to a three-dimensional Gaussian reconstruction method based on feature matching and multi-view consistency, and the method comprises the steps: obtaining a feature matching graph and a reliable matching mask graph between image pairs; constructing a surface fitting constraint loss function, and minimizing a component along the z-axis direction in the three-dimensional Gaussian ellipsoid zoom vector to enable the three-dimensional Gaussian ellipsoid zoom vector to become a two-dimensional Gaussian plane; calculating an unbiased depth value of each pixel based on a two-dimensional Gaussian plane normal and a camera line-of-sight direction; calculating a depth re-projection error of the current view angle and the adjacent view angle according to the filtering feature matching graph, and constructing a depth consistency loss function; comparing normal directions of the current view angle and the adjacent view angle according to the filtering feature matching graph, and constructing a normal consistency loss function; and weighting the surface fitting constraint loss function, the depth consistency loss function and the normal consistency loss function to obtain overall optimization loss, and performing back propagation to finally obtain a three-dimensional reconstruction result. The geometric accuracy of a reconstruction result is remarkably improved.

The invention discloses an interpretable teaching cognition interaction method and system based on a knowledge graph, and a medium, and relates to the field of education management information systems, and the method specifically comprises the steps: collecting answer data of a learner, calculating a knowledge mastering state vector, mapping the knowledge mastering state vector to a knowledge graph node, and carrying out the feature decomposition; obtaining a cognitive feature matrix and a knowledge node cognitive difficulty coefficient; constructing a cognitive path graph based on the cognitive difficulty coefficient, and calculating the migration probability of adjacent nodes to generate a cognitive migration path; and constructing a multi-layer knowledge network on the path, calculating node weights and knowledge circulation coefficients, generating a teaching knowledge point sequence, and encoding the sequence to generate stepped teaching content. According to the method, personalized content recommendation and scientific allocation of teaching resources can be provided for an education platform, and intelligent optimization and management transparency of the teaching process are realized.

The invention relates to the technical field of modular design, in particular to a parameter matching and debugging method and system for modular transformation of a large-scale wire barrow, and the method comprises the following steps: collecting operation data of the wire barrow, generating a real-time data set, calculating the offset of a part, analyzing a residual error, generating a comparison table, and calculating response time and inertia characteristics; the method comprises the following steps of: acquiring and synchronously storing data in real time, analyzing correction quantity and sequencing through an optimization algorithm, correcting parts and generating a response curve, analyzing errors and generating a feedback graph, calculating offset, and re-correcting if the error exceeds a threshold value. In the method, through real-time data acquisition and synchronous storage, monitoring the running state of the equipment, capturing the dynamic change of the parts, calculating the offset and the deviation, and evaluating the stability of the equipment; based on an offset value comparison table and a particle swarm optimization algorithm, components are corrected, the priority is reasonably sorted and adjusted, the correction time and cost are reduced, optimization is continued until an expected state is reached, the debugging precision and efficiency are improved, and long-term stable operation of equipment is ensured.