67 results about "Path diagram" patented technology

The invention discloses an energy storage system fault database indexing method, and particularly relates to the technical field of fault prediction. The method comprises the following steps: firstly, constructing an energy disturbance vector sequence matrix in a unified time window, extracting features based on a continuous variation rate and energy residual distribution, and generating an energy disturbance feature spectrogram; establishing an energy propagation path atlas in combination with a system module topological relation, and introducing a time sequence consistency identifier; a fault evolution fingerprint is generated through graph embedding coding, similarity index matching is carried out in combination with a standard fault trajectory, and a fault type and a positioning weight are output; real-time energy indexes are further fused, a micro-fault position prediction map is generated, and dynamic early warning is achieved; according to the method, accurate identification and visual positioning of the micro-fault of the energy storage system in a complex scene can be realized, and the operation safety and the intelligent operation and maintenance capability of the system are improved.

The invention discloses a BIM and block chain-based construction whole process quality tracing method and system, and relates to the technical field of building construction quality management. In a material entering stage, material information is collected through the Internet of Things and is associated with a BIM component identifier, and an initial block is generated by using an intelligent contract; in the process execution stage, BIM 4D / 5D simulation data is associated with a construction log, and a Hash chain algorithm is adopted to generate a process block containing IPFS Hash; in the acceptance inspection stage, the consistency of detection results is verified through zero-knowledge proof, and a new block is generated; checking the block data through a consensus mechanism to obtain a complete data set; constructing a graph database index based on the BIM component tree, and generating a chain path graph; responding to a query request, traversing path diagram nodes, comparing hash values and outputting a tracing result; trusted communication, tamper-proof evidence storage and intelligent tracing of data in the whole construction process are achieved, and the accuracy and reliability of project quality management are remarkably improved.

The invention relates to the technical field of verification and evaluation, in particular to a reusable rocket attitude control system redundancy verification and fault tolerance evaluation method, which comprises the following steps: performing multi-dimensional combination on disturbance units according to disturbance types, action time periods and intensity grades to form a disturbance combination set, constructing a group of induction task profiles covering different attitude control abnormal situations; recording a switching state of an internal control path of the attitude control system, a redundant component intervention time sequence and a function takeover sequence in real time, and constructing a dynamic response path map of a redundant link; and generating a response integrity index and a fault tolerance conflict degree criterion of redundancy configuration as a fault tolerance capability evaluation result of the attitude control system. Compared with an existing method which only verifies individual static disturbance scenes, the method has the advantages that systematic design and composite excitation of disturbance conditions can be realized, scene complexity and coverage of redundancy verification are enhanced, and authenticity and representativeness of fault-tolerant testing are improved.

The invention discloses an industrial internet risk analysis system and method based on a knowledge graph, and the method comprises the steps: collecting data, and defining a multi-source original data flow and the knowledge graph; performing data preprocessing, unifying sample data, and performing mapping splicing on each mode in the multi-source original data stream; constructing, storing and updating a knowledge graph; carrying out risk modeling based on attention, and introducing an attention mechanism and a causal relationship model; carrying out risk scoring and inference; and performing risk early warning and visualization, outputting an early warning list, a propagation path diagram and key hub nodes, and performing butt joint with an existing industrial safety platform for display through an analysis report interface. The invention provides an industrial internet risk analysis scheme which not only can fuse the structural modeling capability of the knowledge graph, but also can utilize a self-attention mechanism to improve the key risk feature recognition precision.

The invention discloses a transformer area high-frequency acquisition data quality improvement method, system and device and a medium, and the method comprises the steps: constructing a multi-level rule field, converting a rule into a continuous potential energy function with directivity and range control, and forming a regular force field in a semantic space; defining a field state function, and generating a plurality of field completion candidate values through rule-driven nonlinear evolution reasoning; a candidate value path diagram is constructed, nodes of the path diagram represent candidate values, and edge weights are determined according to semantic and rule association features among the candidate values; calculating the global path participation degree of each node based on the path diagram, and selecting an optimal complement value according to the global path participation degree; and executing multi-dimensional consistency verification and high-confidence judgment, and marking and supplementing the credibility level according to a verification result, thereby realizing closed-loop improvement of data quality. According to the method, the accuracy, the logic consistency and the high confidence of missing field supplementation of the high-frequency acquisition data of the transformer area in a complex interference environment are remarkably improved.

The invention is suitable for the technical field of bearing fault prediction and artificial intelligence, and provides a bearing residual life prediction method and system, and the method comprises the following steps: obtaining a bearing full-life vibration signal; preprocessing the full-life vibration signal of the bearing, identifying a degradation stage of the performance of the bearing, and constructing a high-dimensional feature space; based on variational mode decomposition and double-graph structure construction, the high-dimensional feature space is decomposed to obtain a trend component and a signal component, a path graph is constructed by the trend component, a radius graph is constructed by the signal component, and spatial-temporal features are mined; performing feature extraction and fusion on the path diagram and the radius diagram to obtain space-time fusion features; and inputting the fused space-time fusion features into a prediction layer, and outputting a bearing residual life prediction result. According to the invention, through a double-graph structure and a spatial-temporal feature fusion mechanism, the over-smoothing problem of the graph convolutional network is effectively relieved, and the feature expression capability and the life prediction precision are improved.

The invention provides a multi-parameter water quality data fusion analysis method and system. The method comprises the steps that water quality parameters are collected to form a three-dimensional data cube; constructing a space-time tensor model by using Tucker decomposition and a graph convolution network, and generating a core tensor matrix; constructing a dynamic constraint library and embedding the generative adversarial network; training a generative adversarial network by using Transform and physical constraint loss, generating synthetic data and verifying the synthetic data; performing space-time fusion by using meta learning weight distribution and Bayesian deep learning to generate a weight matrix and a confidence interval; missing data are restored through physical constraint interpolation and Gaussian process regression, and SHAP and LIME interpretation and path diagrams are generated; and performing real-time analysis by using an edge-cloud collaborative architecture to generate an intelligent report. Through physical constraint modeling, dynamic weight distribution and edge-cloud collaborative architecture, the problems that synthetic data violates physical laws, weight staticization, response lag and insufficient interpretability are solved.

The invention discloses a deviation correction method and system for load prediction during severe weather. The method comprises the following steps: acquiring historical load data and weather prediction information; generating a baseline load prediction value through a baseline prediction model based on historical data; judging whether a major weather event is triggered based on the weather information; if so, dynamically extracting deviation correlation characteristics and inputting the deviation correlation characteristics into a compensation signal generator to obtain a load deviation compensation value; and superposing the baseline prediction value and the compensation value and outputting a final load prediction correction result. By designing a two-channel architecture in which baseline prediction and dynamic compensation are parallel and performing whole-process tracking and simulation on major weather events in combination with a deviation path diagram, accurate and forward-looking compensation on load deviation is realized, the overall precision and timeliness of load prediction during major weather are improved, and the load prediction efficiency is improved. And the scheduling reliability and the operation safety level of the power grid in the extreme weather are obviously improved.

An enterprise ESG rating task-oriented double-path graph representation learning method is provided in the embodiment of the application, and belongs to the technical field of data processing, and specifically comprises the following steps: step 1, constructing an ESG heterogeneous graph atlas with a target enterprise as a center node, and extracting a target subgraph of multiple types of nodes and their corresponding relationships from the k-hop neighborhood thereof; step 2, dynamically selecting a structure enhancement mode for the target subgraph based on a learnable enhancement strategy, and generating multiple enhanced views with different structures but consistent semantics; step 3, performing semantic smoothing and structure denoising processing on each enhanced view to improve semantic consistency and structure purity; step 4, aggregating multiple denoised enhanced views to form a unified enterprise graph representation vector; and step 5, inputting the enterprise graph representation vector into a classification model to predict the enterprise ESG rating or risk category. Through the scheme of the application, the rating accuracy and adaptability are improved.

The invention discloses a digital twinning-based steel smelting process management and control method, equipment and a medium, and relates to the technical field of industrial intelligent management and control, and the method comprises the steps: compiling a process card into an executable production path diagram, analyzing an equipment nameplate, a permission window and the process card to obtain a continuous casting grouped execution package, performing constraint embedding and decision variable setting in the executable production path diagram by using the constraint set and the optimization target parameter set, and establishing a mapping relation between the constraint set and decision variables to obtain a binding relation table; performing feasible region calculation on the executable production path diagram and the binding relation table based on the continuous casting group execution package to generate a feasible region space; constructing the optimization target parameter set and the feasible region space into a convex quadratic programming model and solving the convex quadratic programming model to obtain production configuration; according to the method, the optimal balance among the production efficiency, the energy efficiency and the carbon emission is realized by establishing a quick adjustment mechanism of feasible region calculation and production configuration.

The application discloses a kind of natural ester transformer main insulation's insulation performance evaluation method, device, terminal equipment and storage medium, it is related to transformer technical field, method includes: obtaining the structure parameter of transformer, constructs two-dimensional axis symmetry model;Obtain electric field distribution by finite element simulation;Path graph model is constructed, and path graph node is determined;Ant colony parameter is initialized;Repeat executing ant colony search process until reaching maximum iteration number, the discharge path of minimum insulation margin is regarded as potential discharge path;In ant colony search process, each ant constructs discharge path according to current pheromone concentration and path graph node;According to electric field distribution, calculate insulation margin;Pheromone concentration is updated;According to the insulation margin of potential discharge path, the insulation performance of transformer is evaluated. By implementing the present application, the problem that the existing evaluation method based on local maximum field strength cannot reflect the true tolerance state of the transformer can be solved, and the accuracy of the insulation performance evaluation is improved.

The invention provides an intelligent production line fault prediction method and system based on random forest enhancement. The method comprises the following steps: generating a production line equipment association map including equipment nodes, connection edges and operation state files; continuously receiving a real-time state signal flow packaged by a self-adaptive time window from the sensing array, and dynamically adjusting the collection granularity according to the equipment fault sensitivity; inputting the state flow into a random forest enhanced classifier into which a fault sample distribution weight is introduced, and performing step-by-step decision traversal to output a membership probability vector set of each equipment node belonging to a preset fault mode; traversing equipment nodes in the atlas, performing breadth-first search by taking a fault mode exceeding a confidence threshold as an activated node, and recording a fault propagation direction sequence and time delay distribution; and finally, coding the propagation sequence and the delay distribution into a fault evolution path diagram, calculating a fault arrival probability and an expected influence range of a propagation path, and generating a prediction and early warning instruction. According to the invention, accurate prediction and propagation traceability of production line faults are realized.

The application relates to the technical field of circumferential road paving topology reconstruction, and particularly discloses a circumferential road paving topology reconstruction method for a grounding grid drawing missing working condition, which injects a pulse sequence signal carrying a unique pseudo-random code feature, accurately extracts a target response from a strong interference background by using cross-correlation operation, and improves detection anti-interference capability; meanwhile, a conductor intersection point or a branch point is pre-judged by using external stray current information, and the requirement for detection equipment resolution is reduced; a preliminary conductor path diagram is generated by connecting path tracking coordinate points, and a topology node to be verified is identified based on an interference field intensity diagram; finally, a branch path is confirmed by multi-directional detection, and a complete grounding grid topology structure diagram is generated; the method aims to solve the problems of grounding grid drawing missing or inconsistency with the actual due to historical reasons or poor data management, and has high precision, high efficiency and high safety, and provides a solid data basis for grounding grid operation and maintenance.

The application provides a kind of intelligent production line fault prediction method and system based on random forest enhancement, by generating production line equipment association graph containing device node, connection edge and running state archive;Continuously receive real-time state signal stream encapsulated with adaptive time window from sensor array, and dynamically adjust the collection granularity according to the device fault sensitivity;The state stream input is introduced into the random forest enhanced classifier of fault sample distribution weight, and the membership degree probability vector set of each device node belonging to the preset fault mode is output by traversing output step by step decision;Traverse the device node in the graph, and take the fault mode exceeding the confidence threshold as the activated node to perform breadth-first search, record the fault propagation direction sequence and time delay distribution;Finally, the propagation sequence and delay distribution are encoded into fault evolution path graph, the fault arrival probability and expected impact range of propagation path are calculated, and the prediction warning instruction is generated.The application realizes the accurate prediction and propagation tracing of production line fault.

This application provides a method for multi-source intelligence semantic analysis and compliance risk early warning, applied to intelligent processing devices. The method includes: transforming multi-source heterogeneous intelligence data into a unified semantic representation vector through a cross-modal semantic mapping model; performing entity linking and relationship reasoning on the unified semantic representation vector based on an enterprise multi-source intelligence knowledge graph library to generate an intelligence semantic analysis graph; matching the intelligence semantic analysis graph with a multi-level compliance rule library through an enterprise compliance rule reasoning engine to mine and assess the severity and associated transmission paths of potential compliance risk points to generate a risk transmission path diagram; and generating tiered compliance risk early warning information based on the risk transmission path diagram. This application improves the effectiveness of multi-source intelligence semantic analysis and the rationality of compliance risk early warning compared to existing solutions, and better meets the actual needs of enterprises for refined management of compliance risks.

The application provides a risk transmission prediction method and system based on a time sequence diagram and a large model joint deduction, relates to the technical field of artificial intelligence and knowledge engineering, and comprises the following steps: extracting forward-looking time sequence facts based on hierarchical prompt words and a structured constraint mechanism; extracting conflict events and generating time sequence relationships; updating a historical time sequence knowledge graph based on a time sequence relationship and a confidence weighted update strategy; determining a central entity, initiating a structured query to the updated historical time sequence knowledge graph from the central entity, and generating a context knowledge subgraph; converting the context knowledge subgraph into a natural language description with logical relationships, injecting a risk hypothetical event, constructing a prompt word as a new input of a large model, and outputting a structured JSON object containing a complete reasoning chain; and based on a risk path refining and visualizing algorithm with influence weight attenuation, converting the structured JSON object to obtain risk warning information and a visualized transmission path diagram.

This invention discloses a method, apparatus, and medium for spatial coordinate unfolding and reconstruction, comprising: extracting spatial expression points labeled as target tissue types from spatial group data to generate a standardized binary image; performing a closing operation and smoothing edges, extracting multiple contour regions and merging them into a main contour, and performing an image skeletonization algorithm on the main contour to generate a skeleton path map; selecting the longest path from the shortest paths as the central main path; backprojecting the central main path in the image coordinates to the original spatial coordinate system to determine the central main path and its starting and ending points; constructing a tangent-normal coordinate system and calculating the distance from the spatial expression points to a first reference point; generating edge reference lines; projecting all spatial expression points to the unfolded coordinate system to determine the projection target; and projecting according to the projection target to obtain the unfolded coordinates. This invention automatically generates complete skeleton paths and highly smooth edge curves, effectively restoring the true spatial positions of the host and / or microorganisms and spatial expression points.

The invention discloses a cable damp fault diagnosis method and system based on machine learning, and relates to the technical field of intelligent monitoring of power equipment. According to the method, key dimension information such as partial discharge, temperature distribution and leakage current is integrated through multi-source data feature-level deep fusion, the complementary value of each data source is accurately captured in combination with an attention mechanism, and then through the collaborative architecture of the multi-scale convolutional neural network and the bidirectional LSTM, the multi-scale convolutional neural network and the bidirectional LSTM are integrated. Meanwhile, local detail features and long-time-history space-time correlation features of the signals are mined, fine recognition of the damp state is achieved, data deep correlation is effectively mined, the early damp recognition accuracy can reach 95% or above, the SHAP interpretability analysis technology is innovatively integrated, the contribution degree of each feature to the diagnosis result is quantified, and the diagnosis accuracy is improved. And a visual feature importance thermodynamic diagram and a decision path diagram are generated, physical significance interpretation of a diagnosis basis is provided in combination with the insulation characteristics of the power equipment, and targeted operation and maintenance suggestions are directly output.

The invention relates to the technical field of biological information, in particular to a method for establishing a visual model of degradation paths and products of carbohydrates, which comprises the following steps: acquiring chemical structure data of target carbohydrates, standardizing the chemical structure data, and outputting a standardized molecular map; recursively splitting the standardized molecular map, and outputting a candidate intermediate set and a reaction sequence; according to the reaction sequence of the reaction sequence, assembling the candidate intermediate set, and outputting a path diagram; distributing candidate enzymes and species for each step of the path diagram, and outputting a step-enzyme-substance triple; step rating and polymerization splitting are carried out on the step-enzyme-substance triple, and a confidence interval is output; and outputting the degradation path of the target saccharide and a visual model of the product according to the confidence interval. The problems that in the prior art, the product false positive is high, the path cannot be explained, and the consistency with literature / experiment is insufficient are solved.

The invention discloses an integrated fault injection automatic configuration system and method for automatic driving, and belongs to the technical field of automatic driving function safety testing. The system comprises a graphical user interface module for receiving multi-dimensional fault parameters and supporting AUTOSAR signal tree navigation; the template generation module is used for generating a structured template file embedded with safe upper and lower text fields according to the fault parameters; the XML analysis module is used for performing grammar and semantic double-layer analysis, calling a whole vehicle communication matrix to identify a signal dependency relationship, and generating a fault propagation path diagram and an injection matrix data structure; the multi-platform adaptation engine module is used for automatically outputting scripts of platforms such as ETAS INCA, dSPACE Control Desk, Vector CANoe, NI VeriStand and the like; and the script previewing and debugging module renders and executes grammar check in real time, and dynamically adjusts the fault weight and the sorting priority through online learning. According to the system, automatic configuration and multi-platform adaptation of the fault injection test are realized, and the efficiency and accuracy of the safety test of the automatic driving function are remarkably improved.

The application relates to the technical field of computer vision, and provides a high-stability virtual object position display method and device. In a mapping stage, the object structure of a virtual scene is acquired, and an exception handling boundary is established; in the exception handling boundary, a virtual object in the virtual scene is regarded as a general engine object, coordinate data corresponding to the general engine object is acquired based on the object structure, and a path graph is drawn according to the coordinate data; the running state of a data processing process in the exception handling boundary is monitored, if running exception is detected in any processing period, the exception data corresponding to the running exception is discarded; in a virtual scene running stage, the position information of the virtual object is acquired in the exception handling boundary, the position information is matched with the path graph, the relative position of the virtual object in the path graph is determined, and the relative position of the virtual object in the path graph is displayed. Runtime exceptions in memory reading or drawing processes are effectively intercepted and isolated.

The application discloses a kind of urban space layout influencing factor space-time analysis method and system. Including: obtaining the structured data set of each time section of the city space to be analyzed.Acquire the change value data set of the city space to be analyzed according to the structured data set of different time section.Each structured data set and change value data set is regressed and analyzed using geographic weighted regression model, identifies and quantifies the different effects of various influencing factors on each grid, solves the problem of spatial heterogeneity.According to the regression analysis result, construct static multivariate density value data set and dynamic multivariate density value data set.Using structural equation modeling to analyze the mediating effect of static multivariate density value data set and dynamic multivariate density value data set, generate static influence path network and dynamic influence path network.All static influence path network and dynamic influence path network are drawn as influence path diagram, show the spatial correlation and influence path of each influencing factor.

The invention provides a path planning method based on regret minimization, and belongs to the technical field of path planning. The method comprises the following steps: constructing a path diagram with multi-dimensional attributes according to real road network information and a set of various attribute points on a road network; generating a path set from a starting point to all interest points based on the path diagram, and determining a Skyline path set based on the path set according to a comprehensive cost function; and calculating a utility function of each path of the Skyline path set, and selecting a minimum regret rate based on the utility functions to determine a recommendation set. According to the method, the Skyline path set is selected through the cost set, the calculation efficiency is improved, meanwhile, the recommendation set is selected through the regret value, and the preference of the user and the selection of the optimal path are met.

This invention relates to the field of enterprise digital marketing and data intelligence analysis technology, specifically to an intelligent analysis method and system for enterprise-wide marketing data. The method includes: transforming multi-channel marketing behaviors into standardized data by constructing a unified semantic structure, establishing a semantic sequence of marketing behaviors; identifying marketing reach cycles and segmenting the behavior sequence into paths, constructing a marketing path graph network, and evaluating the importance and stability of each path; establishing a behavior influence propagation matrix, calculating the comprehensive propagation intensity of each behavior and its contribution to the final conversion node, and forming a comprehensive contribution score; identifying high-value marketing behaviors and their strategic characteristics based on the contribution score, constructing a dynamic resource allocation model to optimize marketing resource allocation, and using a path structure change prediction model to assess and correct the stability of strategy adjustments, continuously collecting new behavior data to form a closed loop of strategy iterative optimization. This invention achieves intelligent and adaptive optimization of enterprise marketing decisions.

The invention provides an intelligent labeling relation reasoning method and system based on a knowledge graph, and belongs to the technical field of data processing. Code submission, version and vulnerability information are obtained from an open source software code warehouse and a vulnerability database to construct an initial knowledge graph containing codes, versions and vulnerability entities. By parsing the differential content and time series of the code submission records, modification, movement, and multiplexing events of the code blocks are tracked to generate a code evolution path graph. And mapping the semantic features of the code entities and the evolution features of the code blocks to the same vector space to generate vector representation fusing semantics and evolution modes. And based on the vector, calculating semantic similarity and evolution association degree between code entities, and performing multi-hop association reasoning to determine a vulnerability propagation link. According to the evolution path diagram and the propagation link, the vulnerability propagation path and the risk level are marked for the affected target code and the version entity, and the vulnerability propagation path analysis accuracy and the risk assessment comprehensiveness can be improved.

The invention discloses an image version evolution path identification method and device based on atlas modeling. The method comprises the following steps: preprocessing a received original image; extracting image fingerprint data from the preprocessed image, performing fingerprint matching, if matching succeeds, determining that an original image and a historical image of a credit material uploaded by a user are candidate similar graphs, taking the candidate similar graphs as nodes, establishing directed edges for the candidate similar graphs according to a similarity relationship, and calculating weights of the directed edges; according to the method, a directed acyclic graph is constructed, a graph structure of the directed acyclic graph is constructed, an evolution path with the maximum possibility is calculated among nodes based on the constructed directed acyclic graph, a chain-level risk score of the evolution path with the maximum possibility is calculated, and a path graph with a current candidate similar graph as a starting point is output. According to the method, the path relation of the image in tampering, evolution and cross-user propagation can be intuitively described, and the crossing from single-point detection to full-link association tracking is realized.

The application provides a traffic flow analysis method and a server based on a space-time graph neural network. The method comprises the following steps: first, acquiring traffic state parameter sequences of each traffic node in continuous collection periods to form a sequence set; constructing a traffic node state change correlation graph of each collection period according to the traffic state parameter change amount between adjacent periods to represent the synchronization relationship of the change amount between nodes; tracking the dynamic evolution of the correlation graph in time sequence to generate a time sequence causal coupling pair set representing the state-induced relationship between nodes; performing a nonlinear causal edge pruning operation on the time sequence causal coupling pair set to eliminate the coupling pairs satisfying the nonlinear causal inhibition condition to obtain a pruned coupling pair set; and finally generating a causal propagation diffusion path graph structure according to the state-induced direction in the pruned coupling pair. The causal influence relationship between nodes is autonomously discovered from time sequence data and a propagation path graph is constructed, so that dynamic and high-fidelity analysis of the traffic flow propagation path is realized.

The invention discloses an atlas tracking chain determination method and device, electronic equipment and a storage medium. The method comprises the following steps: inputting second data into a second target model, and performing activation probability screening on the second data in each layer of the second target model to obtain a first path set; constructing an interlayer node matching graph based on the first path set, and obtaining a second path set based on the interlayer node matching graph; respectively injecting a structure disturbance signal into each path in the candidate path diagram to form a disturbance sample set; inputting the disturbance sample set into a second target model, determining an output change offset amplitude, and determining a third path set based on the output change offset amplitude; for each path in the third path set, determining a path contribution score based on the output change offset amplitude; and generating an atlas tracking chain corresponding to the first target model based on the first path set, the second path set, the third path set and each path contribution score.

The invention discloses a desktop weighing and sorting error correction method based on deep learning, and the method comprises the steps: collecting multi-source real-time data, and carrying out the standardization, denoising and sliding window processing to form a standardized time series data frame; constructing a dynamic feature sequence by the key data, inputting the dynamic feature sequence into an improved Mamba-2 model, sequentially completing embedded mapping, gating state updating and multi-head self-attention modeling, generating an error dynamic change path diagram and an error feature diagram, and calculating a correction error; based on real-time feedback data, a generalized PBT method is adopted to perform periodic fine tuning to remove hyper-parameters such as a drift factor, a steady-state gain, a learning rate and a sliding window length, an optimized improved Mamba-2 model is generated and deployed on weighing and sorting equipment, and a corrected weighing value, a sorting result and an error correction report are output in real time. And self-optimization and self-adaptive adjustment in equipment operation are achieved, and the weighing and sorting precision and stability in long-term operation are effectively improved.

The invention relates to the technical field of data management, in particular to a human resource data management method and system based on big data, and the method comprises the steps: based on a multi-dimensional field basic information set, mapping semantic propagation paths for various fields, and extracting semantic corresponding relations among the fields, business cooperation time association information and post function association attributes; obtaining a semantic-business collaborative fusion path diagram; and based on the semantic-business collaborative fusion path diagram, identifying records of collaborative business behaviors between the employees, extracting semantic features and time features corresponding to the collaborative behaviors, determining efficiency association tags and efficiency association scores of the employees, and obtaining an efficiency association data set. According to the invention, by using the efficiency association data set, the efficiency association label and the efficiency association score of the employee can be extracted based on the record of the collaborative business behavior, a new way for quantifying the performance of the employee is provided for an enterprise, and a management layer is helped to make a more scientific decision.