338 results about "Path recognition" patented technology

The invention relates to the technical field of image recognition, in particular to an image processing method and system for rehabilitation training action analysis. According to the method, a multi-view image sequence is collected based on a binocular camera device, skeleton key point data of a user in a training process is extracted by utilizing a three-dimensional attitude reconstruction technology, and an action three-dimensional time sequence data set is constructed. The method comprises the following steps: firstly, constructing an individual standard power generation characteristic model of a user, modeling a skeleton driving path of a main muscle group, and forming a personalized power generation reference structure; and then a standard rehabilitation action path is matched through a dynamic time warping algorithm, and the attitude deviation under the key frame is identified. And the system dynamically compares the identified non-standard motion mode with the individual model, judges whether abnormal force generation exists or not and outputs the type and the part of the muscle compensation behavior. And finally, multi-modal feedback information with highlighted graphs, voice prompts and character suggestions is generated in combination with an identification result, so that the identification precision and personalized guidance capability of rehabilitation training are remarkably improved.

The invention relates to the technical field of visual navigation, in particular to a robot navigation method based on vision, which comprises the following steps: acquiring a grayscale image to extract dynamic point locations, screening boundary features to reconstruct a feature set, calculating gradient change to generate candidate guide points, converting coordinates to construct a path track set, and outputting a navigation planning path. According to the method, rapid elimination of static backgrounds is realized through difference threshold selection, dynamic region extraction accuracy is effectively improved, combined screening of boundary line feature point frequency and average feature density is combined, key feature stability is enhanced, and the identification capability of image local structure mutation is enhanced by using a variance change rate of a gradient amplitude sequence. Robustness of path guide point extraction is improved through judgment of an abnormal section, dynamic construction of a path track is realized by means of continuous time integration of image coordinates, path identification precision and spatial positioning continuity are optimized, and continuity, precision and real-time performance of navigation path planning are improved.

The invention discloses a method and a system for processing route planning data for electric power line inspection. The method comprises the following steps: acquiring line distribution and topographic feature data; gridding the power network region, determining the patrol priority, and generating a region priority sequence; the overall patrol task is decomposed, a plurality of task subsets are generated, one task is allocated to each unmanned aerial vehicle, and each task subset comprises an initial position and a target line section; generating an initial route path based on the topographic features; comprehensively analyzing all initial paths, identifying potential airspace intersections and performing conflict prediction, and generating a conflict-free route path set by performing height hierarchical adjustment on related paths; and calculating predicted flight time and energy consumption of each unmanned aerial vehicle, redistributing boundary tasks according to a load balancing principle, and finally generating a balanced multi-unmanned aerial vehicle cooperative route scheme. According to the method, the safe and efficient multi-machine collaborative inspection route can be automatically generated through systematic task decomposition, conflict prediction and load balancing optimization.

The invention relates to the technical field of structure health monitoring, in particular to an immovable cultural relic structure health detection system based on multi-source sensor fusion, which comprises a multi-source texture response acquisition module, a curved surface drift path identification module, a crack region extraction module, a stress response trend offset module and a health level region output module. According to the method, the continuous coding of the structure texture is combined with the micro-strain response characteristics, so that the accurate judgment of the synchronous drift of the surface texture of the cultural relic structure is realized, the identification sensitivity of potential micro deformation is improved, a multi-period track set is constructed according to the center deviation trend in a time sequence, and the evolution tracking capability of space behaviors is enhanced; the crack linkage area is subjected to consistency screening through image gray abrupt change and strain direction, the accuracy of crack boundary identification is improved, the safety of an immovable cultural relic structure is guaranteed, and meanwhile collaborative improvement of real-time monitoring, the response speed and the automation level is considered.

The invention, which relates to the technical field of engineering automation, discloses a BIM-based cable bridge routing autonomous optimization visualization system comprising a heterogeneous professional semantic modeling module, a path identification module, a path generation module, a path conflict evolution module, a path reconstruction module and an output module. The system identifies a cable thermosensitive area by constructing a cross-professional semantic map, constructs a multi-target path evaluation function and generates a high-quality candidate path by adopting a variable weight genetic algorithm; introducing a construction stage dynamic compression prediction mechanism to improve the path robustness; visual expression of the whole process of path design is realized through versioned path reconstruction and a three-dimensional animation evolution function; and finally outputting structured path data and an obstacle avoidance strategy which can be used for construction guidance. According to the invention, the intelligent level of cable bridge path planning, the engineering implementability and the system integration efficiency are significantly improved.

The invention relates to the field of e-commerce recommendation, and discloses a user behavior data analysis system and method based on a time series, and the method comprises the steps: continuously capturing user operation events, extracting a short-term interest fluctuation factor, a category offset index and an interaction strength parameter, and forming dynamic weight mapping for constructing a transient preference map; implicit association detection of a cross-category event chain is executed on the transient preference map, and a multi-dimensional influence gradient vector reflecting the potential interest evolution trend is generated; tracking an offset path of a key interest node through a hierarchical interest-driven algorithm by utilizing a multi-dimensional influence gradient vector, and identifying candidate behavior fragments causing recommendation delay; performing priority label reconstruction on the candidate behavior fragments to form a local behavior network with a weight gradient; based on a local behavior network, key nodes with unbalanced interest distribution in a specific time window are detected, and an evolution path for real-time adjustment of a recommendation engine is generated. The method has the advantage of improving the relevance and instantaneity of the recommended content.

The invention relates to an industrial internet multilayer causal motif abnormal propagation path identification method and system, and the method comprises the steps: firstly carrying out the construction and extraction of a multilayer high-order motif, extracting a motif unit which expresses the local high-order structure features through the construction of a semantic hierarchical graph structure in combination with a frequent sub-graph mining and cross-layer motif alignment mechanism, and carrying out the recognition of the abnormal propagation path of the multilayer causal motif. Stable and uniform multi-layer motif representation is formed; then, on the basis of the structural equation model, motif variables are regarded as endogenous variables of a causal model, a causal path between motifs is mined by introducing conditional mutual information and a Bayesian structure learning algorithm, an average causal effect is calculated to construct a causal consistency matrix, and causal community division is realized in combination with a weighted modularity optimization method; and finally, quantifying the dynamic change of a community causal structure by constructing a causal deviation graph between an expected causal graph and an observed causal graph, and assisting in identifying a causal-driven abnormal propagation path. According to the method and the system, accurate detection and causal traceability of equipment-level and subsystem-level abnormal modes in an industrial system can be realized.

The invention relates to the technical field of transaction verification, in particular to a block chain financial transaction tracing method and system, and the method comprises the following steps: calculating a node interaction proportion, recognizing illegal chain segments and marking nodes, analyzing a field combination sequence matching path structure, judging numbering jump and behavior path pairing, and extracting a field corresponding relation and a matching difference. And identifying a node state offset and a structure jump trend, and generating a state chain segment backtracking section record. According to the method, the interaction frequency and the behavior type proportion are introduced to serve as the node state recognition basis, dynamic credible judgment of boundary nodes is achieved, the recognition precision of chain segment structure interruption is enhanced through the path field combination sequence and the serial number continuity, the serial number jumping behavior and node action offset are combined, the chain jumping path recognition capacity is optimized, and the reliability of the system is improved. The field consistency verification effect is improved by adopting field logic pairing and value domain offset comparison, and the backtracking positioning capability of the broken link node is enhanced by using a path state offset and backtracking pointer association strategy.

The invention relates to the technical field of artificial intelligence, in particular to a big data information analysis method and system based on artificial intelligence, and the method comprises the following steps: obtaining a jump segment direction vector, classifying label nodes with consistent directions, splitting a path lacking a mapping relation to form an interruption segment, recognizing a node with continuous directions as a bridge segment entrance, and obtaining a bridge segment direction vector; according to the method, by means of classification according to the node sequence and the channel number in the segment hopping direction, direction aggregation boundary determination and semantic field combination path position interruption recognition, the node sequence structure positioning precision and the paragraph division reasonability are improved, and the focus analysis set is generated by combining the path segments. Bridge segment connection is screened based on direction consistency, path continuation logic, behavior tracks and aggregation numbers are kept in sequence and hierarchy regularization, a behavior aggregation sequence of a semantic chain is constructed, and the continuity of path recognition in a channel aggregation area and the stability of node attribution are enhanced.

The invention provides an intelligent mine management and control method and system based on the Internet of Things technology, and relates to the technical field of mine management and control, and the method comprises the steps: obtaining the roadway feature data and mining progress data of a mine in a mine; the influence degree on sensor signal transmission is calculated according to geological stress change data and equipment electromagnetic radiation intensity, and the distribution positions of the sensors are planned by combining roadway distribution data. And generating a sensor migration path plan, adjusting a point distribution position to obtain an optimized point distribution position, simulating a signal propagation path by adopting a Monte Carlo algorithm, and identifying a key area of which signal attenuation exceeds a preset threshold value. And adjusting the optimized point distribution position of the key area to obtain key area distribution points, and formulating a sensor distribution and control scheme in combination with the optimized point distribution position. According to the invention, the distribution positions of the sensors are optimized, the monitoring efficiency and the stability of signal transmission are improved, and the real-time monitoring and dynamic adjustment of the mine environment are realized.

The invention relates to the technical field of image recognition, in particular to a blind sidewalk recognition method based on image processing. The method comprises the following steps: collecting a ground scene image, and carrying out noise suppression and illumination normalization processing to obtain a ground scene image to be processed; inputting the to-be-processed ground scene image into a pre-constructed convolutional neural network model to identify feature textures of the blind sidewalk bricks; dividing a blind sidewalk area in the ground scene image to be processed by using the blind sidewalk brick feature texture, and determining a direction gradient feature of the blind sidewalk area; executing blind sidewalk direction consistency constraint based on the direction gradient features to collect blind sidewalk structured path segments; recognizing a blind sidewalk fracture area based on directional gradient features; and performing cross-frame target tracking according to the structured path segment of the blind sidewalk, and outputting a continuous blind sidewalk trajectory. The automatic recognition rate of the blind sidewalk area is improved based on the image recognition technology, and the continuous recognition capacity of the blind sidewalk path in the complex illumination and shielding environment is enhanced.

The invention discloses a clock domain crossing circuit timing sequence closed-loop optimization method and system, and relates to the technical field of integrated circuit design, and the method comprises the steps: reading a circuit netlist and timing sequence constraints, recognizing a converged path, and carrying out the clustering analysis to form a plurality of path groups; establishing a time sequence analysis model, and dynamically calculating the maximum delay constraint of the data signal in the path group; matching and executing at least one time sequence optimization strategy for each path group; and carrying out closed-loop time sequence verification, retaining a legacy path set which does not meet the constraint, and repeatedly executing the time sequence optimization strategy until a convergence condition is reached or the maximum preset number of iterations is reached. According to the method disclosed by the invention, the cross-domain path needing to be optimized in the DMUX structure can be accurately positioned without manual intervention, so that the path identification efficiency is improved, constraint omission or grouping deviation caused by manual operation is avoided, design defects are reduced from the source, and the design reliability of a chip is improved.

According to the passive underwater cooperative positioning system and method provided by the invention, an intelligent signal processing and analysis module in the system receives a target underwater sound backscattering signal and a target environment parameter output by an environment adaptive feedback module, and then a path identification result and a positioning result are obtained by combining an identification model; obtaining a sound velocity correction value and a propagation time correction value based on the target environment parameters and a sound velocity correction model so as to obtain the moving speed of the target object; the cooperative positioning calculation module predicts the positioning information of the next moment based on the historical positioning information and the positioning information of the current moment; the environment self-adaptive feedback module receives the underwater sound backscattering signals generated by the multi-mode signal acquisition module and the acquired environment parameters, and transmits the underwater sound backscattering signals and the acquired environment parameters to the intelligent signal processing and analysis module as target underwater sound backscattering signals and target environment parameters; and the dynamic adjustment module is also used for dynamically adjusting the state parameters of the underwater acoustic backscattering signals and the environmental parameters so as to obtain new target underwater acoustic backscattering signals and target environmental parameters.

The invention provides a power grid fault propagation path identification method and system based on a graph neural network, and relates to the technical field of power grid fault identification, and the method comprises the steps: obtaining a power grid topological structure and fault observation data, constructing a direction perception graph neural network, achieving the one-way message passing based on potential energy difference, and obtaining a node potential energy value and embedded representation; edges are screened to generate a directed graph, and candidate paths are generated in the potential energy gradient direction; and verifying the satisfaction degree of the graph neural network evaluation path to the physical constraint through an invariant, and finally selecting the path with the minimum topological entropy. According to the invention, the accuracy and reliability of fault propagation path identification are improved.

The invention belongs to the technical field of power transmission and transformation project construction, and provides an ecological environment protection method and system for a power transmission and transformation project, which can construct a frequency portrait of a bird flapping behavior in real time by fusing multiple technical means such as high-frame-rate visual perception, target tracking, individual flapping frequency extraction and frequency domain behavior modeling. The operation state of the rotating device is adaptively adjusted according to a frequency interference similarity principle, so that high-frequency phase dislocation disturbance with the most interference effect is triggered at the moment when birds enter a key critical area, the flight rhythm and the path recognition capability of the birds are effectively damaged, and the birds are promoted to deviate from a flight path of a power transmission equipment area; birds can be prevented from approaching a lead area in time, and the risks of short-circuit tripping, biological casualties and other events caused by the fact that the birds strike the lead are remarkably reduced.

The invention relates to the technical field of intelligent traffic, in particular to a large-scale road network inter-region hot spot path identification method, which comprises the following steps: dividing a target road network into a plurality of regions, obtaining license plate identification data of a period on the target road network, extracting a single vehicle OD travel according to the license plate identification data, and screening out a cross-region single vehicle OD travel as to-be-analyzed data; grouping the to-be-analyzed data, wherein each group of data comprises a cross-regional single-vehicle OD travel in the same starting and ending region and in the same preset time interval; performing simplification and similarity calculation on each group of data to obtain a representative path of the data to be analyzed; aggregating the representative paths of the to-be-analyzed data to obtain a representative path list; and according to the representative path list, obtaining an occurrence frequency of each representative path, and according to the occurrence frequency, determining an inter-region hotspot path in the target road network. According to the method and the device, the hot spot paths among the regions can be efficiently, correctly and intelligently mined.

The invention relates to the technical field of acoustic analysis, in particular to a self-adaptive acoustic analysis method and system, and the method comprises the following steps: obtaining a sound pressure amplitude screening channel, extracting a main sound point time index, judging a fluctuation direction to generate a path identifier, and completing channel alignment and path binding to generate an analysis result. According to the invention, by acquiring the maximum sound pressure amplitude of each channel and dynamically screening the effective channel, the capability of capturing significant sound in a complex sound source environment is improved, and by combining directional extraction and fluctuation trend judgment of main sound point time index differences in a continuous period, dynamic perception and track identification of sound source feature changes are realized. The stability of path identification is enhanced by using direction consistency and time sequence alignment operation, accurate binding and real-time adaptive sound source tracking of multiple channels are completed, the identification accuracy and path continuity in a sound source dynamic change scene are effectively improved, meanwhile, redundant channel interference is avoided, and the accuracy of path identification is improved. And the processing efficiency and the adaptive capacity of the acoustic model are enhanced.

The invention relates to the technical field of network security, in particular to a network security operation and maintenance automatic analysis and response system based on artificial intelligence, which comprises an operation sensing module for acquiring a server state, extracting a security log, monitoring a network protocol and a transmission direction, identifying a performance bottleneck, sorting event data and generating a sensing summary sheet, the path offset identification module tracks a cross-domain path, identifies an abnormal path and generates an offset list; the behavior chain construction module sorts an event sequence and generates a behavior chain graph; the response processing module arranges processing steps, records a response process, generates an operation and maintenance execution sequence, synthesizes an output module to complete a process and generates an analysis result. According to the invention, by comparing the server resources with the historical reference, the abnormity is identified, and the event behavior basis is provided. And tracking cross-domain path lag and abnormity, and positioning threats. Arranging event data, and constructing an attack chain. The automatic response improves the speed and accuracy, shortens the response time, and enhances the attack handling capability.

The present application relates to the technical field of supply chain management, and in particular to a supply chain resilience path identification method based on fuzzy set qualitative comparative analysis. The method comprises: acquiring historical operation data of nodes in a supply chain and response data under a sudden incident; on the basis of the similarity of the historical operation data between the nodes, obtaining the degree of operational correlation between the nodes; grouping all the nodes to obtain a plurality of node group clusters; on the basis of the response consistency of the nodes under the sudden incident, screening out a key node group cluster; on the basis of the distribution characteristics of the response data of different nodes, obtaining the response priority of each node; on the basis of the change difference of the response data between each node in the key node group cluster and other nodes in the same batch, obtaining the degree of external interference of each node; and on the basis of the degree of external interference, correcting the response priority to obtain a corrected response priority. The present invention can improve the evaluation efficiency and decision support capability of supply chain resilience.

The invention provides a database fault root cause positioning method and device based on causal discovery, and belongs to the technical field of database operation and maintenance and fault diagnosis. Comprising the following steps: generating a two-dimensional data table and a statistical information set by using a multi-source operation log of a database system; constructing a causal discovery algorithm set A, and training the gradient boosting tree model by using the training set and the path combination to obtain an agent model Fb; establishing a Monte Carlo tree, selecting child nodes of root nodes according to performance expectation and exploration rewards, and expanding the child nodes to leaf nodes layer by layer; running the Monte Carlo tree, and obtaining a causal graph G * and a performance score by using a voting mode according to all causal relationships in the causal graph obtained by each node; calculating an exploration reward and a performance expectation of each node in a complete algorithm path, and returning the exploration reward and the performance expectation upwards to a root node from a leaf node along the path; updating Fb based on the searched path; and processing the new task by using a Monte Carlo tree, and identifying an affected processing variable according to a path pointing to an abnormal result variable in the causal graph.

The invention relates to the technical field of education informatization, in particular to a multidisciplinary-oriented intelligent homework reviewing management platform and method, and the platform comprises the following steps: an image structure subdivision module, a behavior path recognition module, a rule execution monitoring module, an error cause structure labeling module, and a data label filing module. According to the method, stroke path analysis is combined with corner frequency and closed region detection, the handwritten character recognition precision is improved, the misjudgment rate of interdisciplinary symbol hybrid scenes is reduced, the structural expression ability of the problem solving process is enhanced by means of sequential construction of writing paths and logic nodes, and the problem solving efficiency is improved by combining parameter linkage of scoring frequency and scoring fluctuation. According to the method, the dynamic adaptive capacity of the scoring rule is optimized, the sequence comparison of a causal chain and knowledge nodes is adopted, the logic integrity of error cause positioning is improved, label clustering and distribution statistics are used, homework data archiving has the multi-dimensional screening and teaching backtracking capacity, and the collaborative efficiency of a homework reviewing system is promoted.

The invention relates to an equipment assembly debugging teaching method and system based on virtual reality, and relates to the technical field of assembly debugging teaching. The equipment assembly debugging teaching method based on virtual reality comprises the following steps: obtaining production line equipment assembly data of a plurality of students, and analyzing to obtain a production line topology harmonic index; acquiring machine path planning data, and performing predictive analysis by combining a pre-trained path recognition model to obtain a path collaborative optimization index; then obtaining equipment debugging data, performing analysis to obtain a debugging efficiency index of each student, performing comprehensive analysis in combination with a production line topology harmonic index and a path collaborative optimization index to obtain an assembly debugging mastering index, and finally taking intelligent teaching measures based on the assembly debugging mastering index of each student. Therefore, the teacher can comprehensively evaluate the actual operation capability of the student, so that the teacher can make a personalized teaching scheme more accurately, targeted tutoring is provided, and the comprehensive capability and the actual operation level of the student are improved.

The invention relates to the technical field of software security, and discloses a variational inference-based micropatch repair path adaptive optimization method, system, device and medium, and the method comprises the steps: constructing a joint control flow diagram and a data flow diagram of a to-be-analyzed program, and forming a program structure diagram; based on the program structure diagram, modeling and sampling potential variables of the repair paths through variational inference, and generating a plurality of candidate repair paths; for each candidate repair path, generating a micropatch statement according to context semantics of each node in the path, and inserting the micropatch statement into a corresponding instrumentation position in the source code; and performing multi-dimensional scoring and sorting on the candidate repair paths, and selecting an optimal repair path for deployment according to a scoring result. According to the method, the structural breakthrough of the whole process is realized in the aspects of path identification, patch generation, strategy optimization, engineering deployment and the like, and a technical support is provided for constructing a software vulnerability repair system with autonomous generation, semantic understanding and controllable deployment capabilities.

The invention discloses a wind generating set fault processing method and device based on multi-modal information, equipment and a medium, and relates to the technical field of wind power generation, and the method comprises the steps: preprocessing the multi-modal fault related information of a wind generating set, and constructing a multi-modal fault database; constructing a multi-modal joint semantic embedding space based on the multi-modal fault database, and determining an optimized current multi-modal large model in combination with a cross-modal hierarchical attention mechanism; dynamically constructing a three-dimensional knowledge graph based on a multi-modal joint semantic embedding space, and determining a path recognition result for a newly occurring target fan fault event in a reinforcement learning process of performing fault propagation path recognition by using the knowledge graph; and determining a structured decision report based on the path identification result and the current multi-modal large model to trigger equipment maintenance operation, and determining a correction feedback report based on the corresponding maintenance record and the current multi-modal large model. Defects in an existing solution can be solved, and the accuracy of fault processing decision is improved.

The invention relates to the technical field of optical detection, in particular to an LED chip defect detection method and system, and the method comprises the following steps: obtaining an LED chip wiring area pattern layer, positioning interval change pixel point pairs, dividing image blocks, constructing a crack trend through a path consistent with the extension direction, supplementing a wiring boundary extension pattern layer, and obtaining an LED chip wiring area pattern layer. And extracting an electrode tail end brightness sequence, screening out disturbance lines, and concluding the disturbance lines as defect image areas according to a position range. According to the method, the crack trend is screened through the path extension direction, direction line segments are formed through communication, the coherence of structure extension information is enhanced, the wiring boundary is covered by a layer supplementing synchronous extension area, the discrimination precision of the spatial corresponding relation is enhanced, and the boundary brightness detection is combined with mirror image contrast to remove interference. A symmetric disturbance area is reserved as a judgment basis, a path extension trend and a wiring area direction are spatially matched, a crack propagation path identification chain is constructed, and defect area tracking integrity and stability are improved.

The invention relates to a low-voltage distribution line comprehensive intelligent protection method based on multi-parameter linkage, and the method comprises the following steps: collecting power distribution abnormal data, calculating a trigger interval, screening segments in a period, generating a sequential segment group, analyzing a serial number difference value to form a continuous path, recognizing rhythm abnormality, and eliminating interference flow. And a time fault is checked to construct a closed chain table, a rhythm stable closed loop structure is screened, and an intelligent protection result is output. According to the method, continuous fragments are constructed based on trigger rhythms and event numbers in abnormal data processing, path continuity is judged through difference values, effective flow directions are recognized, disturbance fragments are eliminated in combination with rhythm comparison, stable closed paths are constructed, and sequences with consistent rhythms and continuous numbers are screened to serve as a judgment basis. Accurate description of an abnormal propagation path and effective tracking of a response rhythm are realized, and identification sensitivity and protection strategy adaptability of a distribution line under multi-source interference are enhanced.

The invention relates to the technical field of supply chain traceability, in particular to a mother and baby product production traceability management method and system based on a block chain, and the method comprises the following steps: obtaining raw material and packaging chain segment data, extracting batch information and detection numbers, recognizing nodes and recording cross chains, counting the cross frequency of finished product segments, and constructing a path index map. And screening an optimal main path, generating a main path identifier, judging a filling data density grade, marking a chain segment needing to be adjusted, and generating a scheduling strategy parameter table. According to the method, field splicing is performed on the batch number, the raw material batch number and the detection report number to compare the segment identifier, so that node connection identification and cross structure establishment are realized, chain segment relevance is enhanced, cross node frequency is counted, high-frequency node pairs are extracted to construct the path, and the path identification accuracy is improved; and screening a path with the fewest skipping times and complete node coverage as a main path, enhancing the traceability stability, sensing the frequency based on the time interval and the chain block number, and realizing the dynamic scheduling of the chain segment state.

The invention provides a discrete mixed job production line scheduling method based on an improved genetic algorithm, and relates to the technical field of industrial automation and production scheduling, and the method comprises the steps: S1, carrying out the modeling and data input of a production line scheduling problem, constructing a flexible job shop scheduling model, defining a decision variable, a constraint condition and a target function, and inputting basic data; s2, generating an initial population by adopting a hybrid initialization strategy, wherein the initial population comprises randomly generated individuals and individuals generated based on a heuristic rule; s3, chromosome coding is carried out on the scheduling scheme in a two-segment coding mode, wherein a process sorting segment and a machine distribution segment are included; s4, the fitness is calculated, and individual selection is carried out by adopting a tournament selection method; s5, executing improved genetic operations including adaptive crossover and mutation operations; s6, carrying out local search on the elite individuals, wherein the local search comprises key path identification and neighborhood disturbance; and S7, judging a termination condition, if the termination condition is met, outputting an optimal scheduling scheme, otherwise, returning to the step S4.

The invention discloses an intelligent fatigue prediction method and system based on recreation facilities, and belongs to the technical field of intelligent operation and maintaining.The method comprises the steps that stress-strain data, environmental parameters and acoustic emission signals of a target recreation facility in multiple operation modes are collected, and an original multi-source data set is constructed; identifying microcrack initiation time nodes and fatigue early-stage features thereof based on change point detection and spectral analysis; a fatigue response sensitivity matrix is generated by constructing a microscopic response model fused with environmental factors; predicting a microcrack propagation path by using a time recursion model, identifying a fatigue risk point, and automatically generating a maintenance suggestion; if a frequency domain abnormal signal is found, carrying out residual life reevaluation and dynamically optimizing model parameters; according to the method, early fatigue recognition, trend prediction and adaptive optimization can be realized, and the safety and maintenance efficiency of recreation facilities are improved.

The invention relates to the technical field of purchase matching, in particular to an online purchase processing method and system oriented to multiple subjects and configurable rules, and the method comprises the following steps: obtaining parameter contents, verifying semantic relationships, field formats and numerical boundaries, embedding state paths, synchronously updating parameter positions, constructing a rule configuration set, and establishing a rule matching set; dividing a role permission supplement operation path, mapping a multi-subject behavior structure, screening a same-value field pushing round sequence, marking time, and matching a response sequence to judge a path inheritance relationship. According to the method, through a parameter checking and semantic analysis mechanism, the consistency of content embedding paths is kept, a multi-role action sequence is constructed based on permission mapping, an operation path is divided and cooperatively controlled, a field aggregation and timestamp extension rule is identified, a quotation round sequence is constructed, and an action path is inherited and promoted by depending on a response matching judgment mechanism. The state link dynamic state is updated, the interface is synchronized, and the overall linkage of parameter analysis, path control, behavior recognition and process scheduling is enhanced.