71 results about "Multidimensional index" patented technology

The invention relates to the technical field of business data insight, and discloses a method for disassembling insight business data through indexes. The method comprises the following steps: firstly, constructing a multi-dimensional index system, determining key business indexes and associated dimensions, and outputting an index disassembling framework; the method comprises the following steps: collecting multi-source data in a business process, performing cleaning and standardization processing, extracting index characteristics and dimension attributes, and constructing a business data association graph; introducing an adaptive weight distribution mechanism and a hierarchical iterative algorithm to optimize a preset attribution model, and generating a preliminary index contribution degree sequence based on an index disassembly framework; setting a monitoring threshold value of each dimension index, and tracking service data in real time; dynamically adjusting an index weight and a model parameter according to a tracking result, and recalculating and updating an index contribution degree sequence; and evaluating the service health degree, identifying abnormal index nodes, and starting a root cause analysis process for the abnormal index nodes. According to the method, multi-dimensional and dynamic insight of the business data can be realized.

The invention provides an MES-based solar cell production and manufacturing execution method and system, and the method comprises the steps: collecting equipment states and technological parameters in real time, combining a bar code system to achieve the single-chip-level metadata association, constructing a standardized data architecture, building a multi-dimensional index, converting the expert experience into reasonable rules, and carrying out the calculation of the reasonable rules. The method comprises the following steps: simulating the influence of parameter combination on product performance, triggering an edge computing node to call a knowledge graph to perform parameter optimization and production simulation based on a dynamic threshold early warning mechanism, identifying process improvement opportunities through a batch tracing report and clustering analysis, forming knowledge base iteration update, predicting inventory risks in combination with a production plan and equipment load, and performing production management. According to the method, intelligent purchasing is automatically triggered, raw material distribution is optimized, technological parameter self-optimization, abnormity self-healing, quality traceability and supply chain collaboration are achieved through data driving, the production efficiency, the product consistency and the resource utilization rate are improved, and a whole-process digital solution is provided for photovoltaic manufacturing.

The invention relates to the technical field of knowledge graph construction, and discloses a knowledge graph-based agent behavior description method, which comprises the following steps of: firstly, analyzing an unstructured agent operation log data stream through a processor, and mapping the unstructured agent operation log data stream into a discretized module access sequence by utilizing a topological structure of a code knowledge graph so as to remove text redundancy; performing multi-dimensional time sequence analysis on the sequence, and calculating resident distribution data representing operation continuity, frequency domain fluctuation data representing switching rhythm and multi-scale coverage extension data representing a traversal range; and then aggregating the data into a fixed-length multi-dimensional index vector, constructing an index node associated with a session entity in a graph database, and writing the vector as a binary structured attribute into a storage field. According to the method, massive unstructured logs are converted into compact graph structured indexes, and quick positioning and direct retrieval of a complex agent operation mode are supported while the storage space is remarkably saved.

The invention discloses a data interaction method and system, and relates to the technical field of data processing, and the method comprises the steps: decoupling a data model, interface display and business logic in a scientific research information service process, and building a semantic tag model and a multi-dimensional index structure of scientific research information; extracting a user operation in the client application, packaging the user operation into an intention, transmitting the intention to the semantic tag model, and performing data interaction in combination with the multi-dimensional index structure to obtain a multi-source data set; and based on a responsive view updating mechanism, mapping the multi-source data set to a front-end interface component, and performing one-stop query display of scientific research information. The technical problems that in the prior art, a traditional retrieval method mainly depends on keyword matching, the data integration capacity is insufficient, and the retrieval precision and efficiency are low are solved, and the technical effects that through multi-module linkage and response type updating, the intention of a user is accurately understood, retrieval is conducted in real time, and the information obtaining efficiency and quality are improved are achieved.

The application relates to the field of information technology, in particular to an intelligent processing method and system for new media data. The method comprises the following steps: reading historical new media material data; analyzing the historical new media material data, calculating an availability estimation value of the historical new media material, and sorting and screening the historical new media resource according to the availability estimation value; storing the analyzed historical new media material data in a classified manner, and establishing a multidimensional index of the historical new media material data; receiving a new media content generation demand, converting the new media content generation demand into a new media material matching condition; screening and sorting new media materials based on the new media material matching condition, and selecting the first N new media material data as basic new media material data; and fusing a theme content based on the basic new media material, generating multi-form new media content, and realizing intelligent processing of high-efficiency, high-quality and self-optimizable new media content.

The invention discloses a user dynamic access control method and system based on multi-dimensional data, and belongs to the technical field of software. The invention aims to solve the problems of user access control policy jitter, security vulnerability and difficulty in explaining compliance caused by static threshold, module independent decision and non-explicit modeling user experience cost and compliance risk cost in the prior art. Therefore, the invention provides a dynamic access control method, which comprises the following steps of: constructing a session-level multi-dimensional access context; dividing risk signal detection according to a visual angle; carrying out risk evidence combination and mode recognition; performing risk trajectory and strategy hysteresis control based on a state machine; unifying action arrangement and conflict resolution of the strategy center; strategy execution and session consistency control are carried out; and effect evaluation and strategy evolution based on multi-dimensional indexes are carried out. Compared with the prior art, multi-view evidence combination, session-level trajectory analysis, state machine and constraint solution and other means are introduced, multi-layer integration from a data layer, a logic layer, a time layer to a control layer is achieved, strategy jitter is effectively relieved, user experience is improved, service continuity is guaranteed, and compliance interpretability is enhanced.

The invention discloses a water ecosystem safety assessment method for cascade reservoir development, and relates to the technical field of water ecology and environment assessment. Comprising the following steps: constructing a water ecosystem safety assessment multi-dimensional index system under cascade reservoir development; acquiring data of the cascade reservoirs and performing standardization processing; determining the initial weight of each index, and dynamically adjusting the weight of each index; calculating a static safety index; calculating a dynamic toughness index; calculating a comprehensive safety index according to the static safety index and the dynamic toughness index; and according to the size of the comprehensive index, determining the safety level of the water ecosystem in cascade reservoir development, establishing an early warning mechanism, and identifying the key influence index. According to the method, ecosystem toughness quantification is incorporated into an evaluation system, the cumulative effect and dynamic change of the cascade reservoir can be reflected, comprehensive and dynamic evaluation of the health condition of the watershed water ecosystem under the influence of the cascade reservoir is realized, and a scientific basis is provided for watershed ecological protection and reservoir ecological scheduling.

The invention discloses an electric power system planning evaluation method and system based on multi-dimensional index decision support, and the method comprises the steps: constructing a scene set containing faults, fluctuation and load growth through obtaining a topological structure, line parameters and historical operation data of an electric power system; generating a topological risk index in the scene set by utilizing power flow calibration and topological risk identification, and dynamically adjusting a multi-dimensional index weight in combination with a phase factor in an impact and recovery stage; further fusing Monte Carlo distribution and Markov distribution through a control variable method to obtain a robust reliability index; mapping impact into stress, mapping function loss into strain, and constructing an elastic curve of the power system; and finally, extracting elastic indexes such as a yield point, ductility, recovery time and a function loss area, and outputting a comprehensive evaluation result of power system planning in combination with a robust reliability index so as to realize scientific comparison and optimization of different planning schemes.

The invention relates to the technical field of reinforcement learning, in particular to a reinforcement learning algorithm performance evaluation method, device and equipment based on a multi-dimensional index, and the method comprises the steps: collecting multi-dimensional index data in a reinforcement learning training process, and carrying out the preprocessing of the multi-dimensional index data, and obtaining the preprocessed multi-dimensional index data; calculating an efficiency score, a stability score and a safety score based on the preprocessed multi-dimensional index data, and analyzing the correlation among the multi-dimensional indexes according to the efficiency score, the stability score and the safety score based on a preset Pearson's correlation coefficient calculation formula to obtain a correlation evaluation result; and generating an optimization suggestion strategy based on the correlation evaluation result, and executing an optimization action according to the optimization suggestion strategy. Therefore, the problems that in an existing reinforcement learning evaluation method, the index is single, and the overall performance of the algorithm is difficult to accurately reflect are solved, and more accurate and comprehensive evaluation and optimization are achieved through comprehensive analysis of multi-dimensional indexes.

The invention discloses a ship path planning method and system, a storage medium and a processor, and belongs to the field of marine ship sailing, and the method comprises the following steps: S1, constructing a dynamic environment model; s2, optimizing a node combination by adopting a DE algorithm, and outputting a global optimization path; s3, based on the real-time sensing data and the global optimization path, evaluating a collision risk through a multi-dimensional index, and outputting a local collision avoidance path; s4, in combination with the dynamic flow field model and the ship energy consumption model, calculating an energy consumption gradient, backtracking along the gradient through a Level Set algorithm, and optimizing and generating an energy optimal path on the basis of the collision avoidance path; and S5, performing safety compliance verification. By adopting the ship path planning method and system, the storage medium and the processor, ship path planning with COLREGs compliance, multi-objective optimization, high energy efficiency and real-time collision avoidance is realized, and the ship path planning method and system are suitable for a complex ocean dynamic environment.

The invention discloses a comprehensive assessment method for weak links of a power system involving multi-dimensional indexes in extreme weather. The method comprises the following steps: firstly, establishing a multi-dimensional index system containing safety and optimized operation, wherein the system covers the line outage probability, the load loss probability, the electric energy insufficiency expectation, the line heavy load rate and the network loss rate; secondly, constructing a line fault model and a cascading fault model under extreme storm weather, describing fault probability change in combination with a Gaussian function and Weibull distribution, and simulating line segment failure by adopting Poisson distribution; a system state after a fault is calculated through a direct-current optimal power flow model, and a large amount of simulation data is obtained in combination with a non-sequential Monte Carlo method. Furthermore, a fuzzy analytic hierarchy process and a CRITIC method are adopted to calculate subjective and objective weights respectively, subjective and objective comprehensive evaluation is realized through weight fusion, and weak links of the system are identified. The method gives consideration to evaluation scientificity and calculation efficiency, and is suitable for real-time risk evaluation and weak link identification of a large-scale power system in extreme weather.

The application discloses a kind of audio and video code stream real-time monitoring and diagnosis method and system, it is related to data monitoring technical field.The method comprises the following steps: constructing causal diagram model, real-time acquisition multidimensional index data;Real-time calculation is carried out to multidimensional index data, to obtain the quantization characteristic value of each index at current time, whether there is preliminary abnormal node according to quantization characteristic value;When there is preliminary abnormal node, the state combination between the two nodes directly connected in causal diagram model is detected, to identify joint abnormal mode, and the abnormal state set is formed based on preliminary abnormal node and joint abnormal mode;Abnormal state set is input into causal diagram model, the probability that all upstream nodes become the root cause of abnormal state set is calculated, and the node or group with the highest probability is determined as the root cause of the present abnormality.

The application provides an intelligent fault self-healing method based on multidimensional indexes, relates to the technical field of fault detection and repair, comprises over-collecting multi-source data and performing feature engineering processing, using an XGBoost classifier to perform fault classification, selecting and combining a repair strategy according to a fault type, a controller type and a service level feature, and performing a repair operation through a service dependency graph atlas optimization. The application can improve fault diagnosis accuracy, shorten fault repair time, reduce the demand for manual intervention, and improve the reliability of cloud platform services.

The invention discloses a differential aggregation SAR (Synthetic Aperture Radar) image quality comprehensive evaluation method, which is used for improving the rationality and accuracy of an existing comprehensive evaluation method. According to the method, image quality comprehensive evaluation is optimized into two core links of index differentiation aggregation and sub-graph differentiation aggregation based on the idea of double differentiation aggregation of multiple sub-graphs and multi-dimensional indexes, and an index weight calculation method combining analytic hierarchy process and information entropy and a sub-graph weight calculation method based on the information entropy are adopted, so that the image quality is comprehensively evaluated. The method comprises the following steps: respectively converting the difference between sub-graphs among indexes into respective weight coefficients, rasterizing and partitioning an image into a plurality of sub-graphs, selecting multi-dimensional evaluation indexes to establish a sub-image quality evaluation matrix, obtaining sub-graph evaluation results, calculating the weight of each sub-graph based on sub-graph information entropy, and finally aggregating the sub-graph evaluation results to obtain the final evaluation of the whole graph. Compared with a traditional method, the rationality and accuracy of image comprehensive evaluation are greatly improved.

This invention relates to the field of renewable energy output prediction technology, and discloses a method, equipment, and medium for renewable energy output prediction based on multidimensional index correlation analysis. The method acquires historical renewable energy output data and preprocesses the data; establishes a multidimensional index system and performs correlation analysis and screening of the multidimensional indicators to determine key influencing indicators; the correlation analysis includes analyzing the linear and nonlinear relationships between the indicators and renewable energy output; constructs a hybrid prediction model based on convolutional neural networks and long short-term memory networks, using the preprocessed data corresponding to the key influencing indicators as input to train and predict future renewable energy output data; wherein, convolutional neural networks are used to extract the interaction features between multidimensional indicators, and long short-term memory networks are used to capture time-series features. This invention improves prediction accuracy and provides reliable technical support for grid connection of renewable energy with high penetration rates.

The invention discloses a large model test set evaluation method based on a project reaction theory, which belongs to the technical field of large language models, and comprises the following steps: (1) data preparation; (2) embedding and obtaining questions; (3) reference instance selection; (4) obtaining a model test result; (5) constructing a network structure; (6) model training; (7) Fisher information calculation is carried out; and (8) performing quality evaluation and parameter analysis on the test set. According to the method, the Fisher information, the difficulty parameter b, the distinction degree parameter a, the guess parameter c, the feasibility parameter d and other multi-dimensional indexes of each question are obtained, and the quality of each benchmark test is analyzed and evaluated based on the indexes, so that the quality of the benchmark test is analyzed more accurately and more comprehensively, and then the capacity of a large model and the quality of a test set are evaluated more accurately.

The present application relates to a kind of physical knowledge guided model explainability analysis method and diagnostic system, belong to oil well fault diagnosis technical field, method includes: collecting sensor data in dynamometer, data is normalized to obtain preprocessed gray image, as oil well fault diagnosis offline data set;Build the physical knowledge guided oil well working condition intelligent diagnosis model based on improved EffcientNet, well fault diagnosis offline data set is as model input;Using the multi-task collaborative training mechanism based on uncertainty weighting, the parameters of oil well working condition intelligent diagnosis model are optimized, and the optimized oil well working condition intelligent diagnosis model is obtained;Using multidimensional index, the performance of the optimized oil well working condition intelligent diagnosis model is quantitatively evaluated;Real-time monitoring and diagnosis of oil well working condition is carried out, and the output result is responded to abnormality.The present application realizes the accurate and explainable intelligent diagnosis of oil well real-time working condition.

The invention discloses a character sentiment classification task-oriented confrontation test method and evaluation device, and belongs to the technical field of natural language processing. The method is realized through the following steps: importing test task information, and configuring a tested model, a text data set and related parameters; applying disturbance to the text data set based on a selected confrontation algorithm to generate a confrontation sample; performing a multi-dimensional index test on the generated confrontation sample, wherein the multi-dimensional index test comprises a model performance index and a text quality index; and iteratively adjusting the disturbance parameters through a stepping test method, generating an adversarial sample meeting a preset disturbance distance requirement, and recording a complete test result. Through stepping testing and multi-index fusion, high-precision, controllable and reproducible model evaluation is realized, and the problems in the prior art are solved.

The application discloses a bridge safety evaluation method based on multi-intelligent algorithm fusion, and belongs to the technical field of bridge engineering and intelligent algorithm. In view of problems of insufficient multi-source data processing and missing physical constraints of traditional evaluation technology, the method integrates multidimensional indexes through eight steps of multi-source data fusion preprocessing, physical information neural network construction and self-adaptive deep forest feature learning, and generates a structure vulnerability index (SVI) to guide maintenance decision-making. The method has the following innovative points: a five-algorithm deep coupling architecture, a physically embedded digital twin, and a dynamic weight proof theory. The method has the following beneficial effects: high-precision identification of bridge damage, interpretable prediction and visualized decision-making, significantly improved evaluation accuracy and operation and maintenance efficiency, and is suitable for full life cycle management of various bridges.

The invention relates to a lightweight network flow storage and management method and system based on flow clustering, and belongs to the field of computer network security, and the method comprises the steps: deploying a Zeek monitoring component and P4 equipment at a network boundary and a core node, and carrying out the structural analysis of a multi-source weblog; based on data source and flow feature clustering and grading processing, dividing into a key flow, a common flow and a secondary flow; hierarchical forwarding and priority scheduling of the logs are realized through Filebeat and Kafka; a hierarchical storage architecture of a core flow layer, a main service flow layer and an archiving flow layer is constructed based on ClickHouse, and efficient retrieval and traceability analysis are realized through a materialized view and a multi-dimensional index. According to the invention, flexible scheduling and grading processing of traffic can be realized in a high-concurrency network environment, the overall throughput efficiency and security situation awareness capability of the system are improved, real-time performance and storage cost control are both considered, and the method is suitable for threat monitoring and event tracing scenes in a large-scale network environment.

The invention relates to the technical field of cold-chain logistics, and provides a cold storage site selection method, device and equipment and a storage medium. The method comprises the following steps: constructing a cold storage site selection evaluation model based on a geographic information system, a multi-dimensional evaluation index of cold storage site selection and a spatial constraint condition; performing spatial clustering on target site selection areas obtained based on the cold storage site selection evaluation model to obtain a plurality of target cold storage alternative areas; and determining a target address of the refrigeration house from the target refrigeration house alternative area based on the refrigeration house demand information, a preset refrigeration house decision strategy and a constructed total cost function. According to the cold storage site selection method, device and equipment and the storage medium, modeling can be carried out through a geographic information system, multi-dimensional indexes and spatial constraints, the alternative areas are screened through spatial clustering, the target address of the cold storage is determined from the target cold storage alternative areas in combination with cold storage requirements and the total cost function, and the rationality of cold storage layout is effectively improved.

The application discloses a kind of solid crystal machine temperature sensitive point selection method, device, equipment and storage medium, belong to equipment manufacturing technical field.The method comprises: obtaining the temperature data of first preset quantity temperature measuring points of each position point of solid crystal machine;The position point of solid crystal machine includes second preset quantity position points;Using improved AP clustering algorithm, in combination with multiple preset cluster numbers, the temperature data of each position point obtained is clustered, and the clustering result corresponding to multiple preset cluster numbers of each position point is obtained;According to multidimensional index, the clustering result corresponding to multiple preset cluster numbers of each position point is analyzed, and target cluster number is determined;Based on the clustering result corresponding to multiple preset cluster numbers of each position point, in combination with target cluster number, determine candidate point set;Temperature sensitive point set is determined from candidate point set using greedy selection algorithm.The effectiveness and accuracy of temperature sensitive point selection can be improved using the technical scheme provided by the application.

The invention discloses a standard system intelligent evaluation system and method based on a database, and relates to the technical field of intelligent evaluation. The system comprises a data acquisition and sub-network construction module, a multi-dimensional index calculation module, a cross-domain reference verification module and an evaluation result generation and output module. According to the method, standard structured data is obtained, a sub-reference network is divided, the intra-domain reference proportion and the core standard reference concentration ratio are calculated, the cross-domain reference technology logic relevance is verified, and a comprehensive matching degree evaluation result and a problem list are generated. According to the method, the defects that the deep structure of the standard reference network is difficult to mine and hidden problems cannot be identified in the existing method are overcome, intelligent and accurate evaluation of the standard system is realized, the technical collaboration and support efficiency is improved, and the high-quality development requirement of the industry is met.

The invention discloses a mass open source code storage method and system based on atomization and bidirectional indexing, and the method comprises the steps: splitting accessed software code data into unique and immutable code atoms, storing the unique and immutable code atoms into a source code compression storage structure based on content addressing, and constructing an index; the source code compression storage structure comprises three areas: a metadata area, an atomic code pool and a multi-dimensional index area; the metadata area is used for a format version number, storage statistical information, a physical offset address and length of an atomic code pool, and a physical offset address and length of the multi-dimensional index area; the atomic code pool is used for storing the code atoms; the multi-dimensional index area is used for storing a version reconstruction forward index and a code snippet reverse index; the statistical information comprises the number of software code items, the number of software code versions and the total number of code atoms. The method has remarkable advantages and beneficial effects in the aspects of storage efficiency, access performance, analysis capability, system architecture and the like.

The application discloses a super-converged server multi-resource integration system and a scheduling method, belongs to the technical field of computer resource management, and aims to overcome the problems of traditional super-converged server resource scheduling, such as dispersion, low storage efficiency, and difficulty in identifying abnormalities. Multi-source data is acquired by relying on distributed acquisition nodes, stored by using a distributed time sequence database architecture, a standard data set containing multi-dimensional indexes is constructed, and efficient data storage and rapid retrieval are realized. A graph model is constructed by positioning target data, a layered and partitioned distributed graph database is built, and dynamic updating is realized by capturing change events in real time. The causal relationship of resources is mined by using a graph neural network, and effective causal chains are screened. At the same time, the weight of the monitoring node and the connection edge is monitored according to the graph model, and differential monitoring is implemented to accurately identify potential abnormal points, and a resource scheduling strategy is generated in combination with the causal relationship. In execution, real-time feedback adjustment is realized, the database is updated, multi-resource deep integration and intelligent scheduling are achieved, and the system resource utilization rate and stability are significantly improved.

The present application relates to a kind of coal gasification plant performance evaluation method and system, the coal gasification plant performance evaluation method is by collecting coal gasification plant operating data, and pre-processing is carried out, establishes standard database, and based on the data in this standard database, constantly process obtains the performance evaluation basic index, performance evaluation comprehensive index and value quantization index of the coal gasification plant, then again using these indexes to do multidimensional performance evaluation to the coal gasification plant, not only automatically realizes to the coal gasification plant real-time collection, and greatly reduces the time-consuming problem of the collection statistics for various operating data, adapts the digital construction requirement of coal chemical industry enterprise, to more low-cost way through multidimensional index realizes the performance real-time evaluation and management to the coal gasification plant, improves the device management efficiency.

The invention discloses a power consumption management priority dynamic evaluation method, which comprises the following steps of: acquiring power grid operation, user behavior and environment data in real time through an Internet of Things terminal, and finishing data cleaning and space-time alignment at an edge computing node; dynamically generating a power grid side power supply margin / node vulnerability, a user side elastic coefficient / urgency index and an environmental economy side carbon cost multi-dimensional index; adopting a deep reinforcement learning model (DQN) to optimize index weights in real time by taking the power grid frequency, the user complaint rate and the operation cost as reward functions; solving a multi-target Pareto optimal solution set through an improved grey wolf algorithm, outputting a load priority sequence, and carrying out blockchain smart contract evidence storage; and finally, dividing a rigid group, an elastic group and a tradable group according to load characteristics to execute differentiated regulation and control. The problems that a traditional evaluation method is poor in dynamic adaptability and low in credibility are solved, quick response, user elastic quantification and regulation and control instruction tampering prevention are achieved, and the emergency response efficiency of a power grid is improved.

The invention provides a multi-dimensional resource dynamic management and efficient access method, terminal equipment and a storage medium. The method comprises the following steps: defining a node data structure of a hierarchical tree; constructing a static hierarchical skeleton structure of the hierarchical tree; dynamically adding resource index nodes; establishing a multi-dimensional index mapping table; establishing a node ID index mapping table; realizing a hierarchical caching strategy; batch cache writing is realized; efficient subsequent node traversal is realized; batch traversal operation is realized; and the cache consistency is ensured. According to the method, dynamic construction of hierarchical data, efficient indexing of multi-dimensional resources, differentiated cache management and rapid traversal of batch data can be realized.

A kind of reinforcement learning multidimensional test system based on adaptive framework belongs to intelligent software testing technical field, including: environment and model adaptive configuration module is generated after environment instantiation, model loading and interface compatibility verification according to the configuration file provided by user, sends to basic performance test module and multidimensional index depth test module;Basic performance test module generates basic performance index dataset after carrying out multiple rounds of test execution and data acquisition processing and sends to result visualization module;Multidimensional index depth test module carries out multidimensional depth test to model, generates multidimensional depth index dataset and sends to result visualization module;Result visualization module, based on basic performance index dataset and multidimensional depth index dataset, generates comprehensive test report containing chart and analysis abstract and is exported.This application improves the efficiency and comprehensiveness of test.

The invention relates to the technical field of carbon emission optimization, in particular to a digital asset-driven carbon neutralization path recommendation method, which comprises the following steps of: acquiring carbon neutralization path records in a digital asset full life cycle, screening frequent combinations, evaluating carbon factors and response efficiency, eliminating invalid paths, extracting intersection segments, optimizing and reserving, and generating a recommendation result. According to the method, high-frequency mode recognition of carbon neutralization path execution behaviors can be achieved through combination frequency statistics and sorting of path execution records in the digital asset life cycle, an evaluation system is constructed based on multi-dimensional indexes such as carbon factor fluctuation, task response time and path execution density, the actual effect of each path combination can be quantified, and the evaluation accuracy is improved. According to the method, low-efficiency or invalid path pairs are screened out through an evaluation result, the accuracy and effectiveness of path recommendation are guaranteed, path intersection segments are extracted in combination with continuous and consistent path number sequences in active digital assets, and path combinations identified as low-quality paths can be eliminated.