505 results about "Hierarchical analysis" patented technology

The invention provides an enterprise process intelligent analysis system based on a large language model. The enterprise process intelligent analysis system comprises a master control scheduling module, a data preprocessing module, a hierarchical analysis module, an insight extraction module, a report generation module and a knowledge retrieval module. The master control scheduling module generates a scheduling plan based on chain thinking reasoning, and dynamically calls each module; the data preprocessing module carries out cleaning and structured conversion on the enterprise event logs and outputs standardized JSON (JavaScript Object Notation) data; the knowledge retrieval module is combined with an RAG technology and a vector database to provide context support for a large language model; the hierarchical analysis module drives a model to execute process discovery and bottleneck identification through a structured cue word template; the insight extraction module converts an analysis result into a commercial insight text containing reasons, influences and suggestions, and has a self-repairing mechanism to guarantee consistency; and the report generation module automatically generates an image-text report. The system can improve the efficiency and accuracy of process analysis.

The invention relates to the technical field of power systems, in particular to a multi-dimensional network performance index evaluation system and method based on measured data of a new energy station. Comprising a data acquisition preprocessing unit; a multi-dimensional index construction unit; the weight adjustment unit dynamically adjusts the index weight according to the operation condition based on a fusion algorithm of an improved analytic hierarchy process and an entropy weight method; the performance evaluation model unit adopts a comprehensive evaluation model fused with a random forest algorithm to generate multi-dimensional performance scores and risk early warning; and the visual interaction terminal unit is used for displaying the evaluation result in real time and generating a trend analysis report. According to the invention, the three-dimensional evaluation system covering the static electrical parameters, the dynamic stability indexes and the harmonic pollution indexes is constructed, so that the limitation of single-dimensional evaluation in the prior art is broken through, and the extension of the network-related performance of the new energy station from local detection to full-dimensional coverage is realized; and the multi-scene evaluation requirements of the power grid on the steady-state operation, the transient response and the electric energy quality of the station are accurately matched.

The invention discloses an integrated scheduling system for realizing a PCS, an EMS and a BMS, and relates to the technical field of power control, and the system comprises a multi-dimensional performance evaluation module which constructs a battery aging dynamic model, carries out the training, carries out the health state pre-judgment through the battery aging dynamic model based on a standardized state vector, and generates a multi-dimensional performance evaluation index; the multi-objective optimization module is used for generating a collaborative scheduling strategy set by combining a fuzzy analytic hierarchy process with a multi-objective optimization solver of an improved genetic algorithm based on the multi-dimensional performance evaluation indexes; the dynamic derating module is used for generating an executable instruction queue with security constraints by combining an industrial internet of things protocol stack with a dynamic derating coefficient algorithm based on the collaborative scheduling strategy set; according to the invention, through the physical driving characteristic layer and the dynamic parameter calibration layer, the nonlinear coupling modeling of the cyclic attenuation and calendar aging mechanism in the battery aging dynamic model is realized.

The invention discloses a method for constructing a digital twinborn model of a full life cycle of an automobile part, which relates to the technical field of digital twinborn, and comprises the following steps: inputting a life cycle data set into an initial life digital twinborn model, carrying out time domain local feature fusion by a feature extraction layer, capturing a degradation trend by a time sequence analysis layer, and constructing a digital twinborn model. Extracting an actual detection value in the life cycle data set; calculating a deviation value between the life prediction value and the actual detection value of the automobile part; carrying out sequential relation mapping on the deviation value to generate a dynamic causal atlas; carrying out graph traversal on the dynamic causal atlas to obtain key degradation path nodes; the dynamic causal reasoning is used for carrying out correlation analysis on the key degradation path nodes, degradation characteristic parameters are generated, the depth-first search and analytic hierarchy process are adopted, dynamic causal correlation precise modeling of the life digital twin model is achieved, and the robustness of the life digital twin model under the actual complex working condition is remarkably enhanced.

The embodiment of the invention provides a charging service real-time monitoring method and device based on a service probe, and the method and device achieve the precise construction of a service event chain through constructing a multi-layer data analysis mechanism and integrating the data of an application layer, a session layer and a network layer. And designing an exception recognition strategy based on multi-dimensional feature fusion, and establishing a neural network classifier to perform exception event analysis in combination with hardware state features and scene features. A real-time monitoring engine is introduced, and dynamic early warning is carried out on business abnormity through hierarchical analysis and correlation analysis. According to the method, the defects of the traditional technology in the aspects of data analysis, feature analysis, real-time monitoring and the like are effectively overcome, and the intelligent level and the early warning effect of charging service monitoring are remarkably improved.

The invention provides a resource urgency degree evaluation and dynamic scheduling method and system based on behavior characteristics, and the method comprises the steps: collecting equipment protocol characteristic data, equipment resource state data and user historical behavior data, and carrying out the preprocessing, and obtaining a standardized data set; by fusing a convolutional neural network, a bidirectional gating circulation unit and a network model of an attention mechanism, extracting spatial-temporal characteristics and a long and short term dependency relationship of the standardized data set, and generating depth behavior characteristics; dynamically calculating a task urgency degree weight from three dimensions of business priority, an equipment performance baseline and environment health degree by adopting a fuzzy analytic hierarchy process based on the depth behavior characteristics, and generating a comprehensive urgency degree score; according to the comprehensive urgency score, generating a dynamic scheduling strategy and executing resource allocation; and monitoring the resource utilization rate and the service level agreement compliance rate, and feeding back for optimizing a subsequent scheduling strategy.

The invention relates to the technical field of intelligent management of oilfield water injection systems, and particularly discloses a water injection pipe network operation situation analysis and energy consumption evaluation method and system. According to the method, real-time data and pipe network parameters of an oilfield water injection system are collected, and after anomaly detection and data cleaning treatment, a node pressure matrix and pipe section flow distribution are generated; performing residual analysis and time sequence modeling by combining real-time data and a simulation result to realize accurate positioning of operation abnormity; a fuzzy analytic hierarchy process for dynamic weight adjustment is adopted to construct a four-stage energy efficiency evaluation system of water injection station-pipe network-water injection well-overall system, and an energy consumption bottleneck thermodynamic diagram is generated; and finally outputting a strategy rehearsal report and a fusion cockpit. According to the method, the change of the water injection pipe network from'black box 'to'transparent' management is realized, the technical problems of single energy efficiency evaluation, scheduling dependence on experience and the like in a traditional method are solved, the system operation efficiency can be remarkably improved, and the energy consumption is reduced.

The invention discloses a cable insulation aging multi-modal data fusion evaluation system, particularly relates to the field of power equipment state monitoring and evaluation, and comprises a multi-dimensional data acquisition module, a progressive data processing module, a hierarchical analysis module, a multi-stage output module, a data storage module and a self-diagnosis module. The multi-dimensional data acquisition module synchronously acquires electrical, thermal and chemical parameters; the progressive data processing module carries out three-level processing on data, namely preprocessing, feature extraction and fusion, the hierarchical analysis module evaluates the aging level through single-mode identification, multi-mode association verification and a fuzzy comprehensive evaluation model, and the multi-level output module outputs basic snapshots, early warning information and trend reports. The data storage module stores full-process data, and the self-diagnosis module regularly detects and pushes a fault report. The system solves the problems of one-sided evaluation, data asynchronization and the like in the prior art, improves the evaluation accuracy and the operation and maintenance convenience, and guarantees the safe operation of the cable.

The invention discloses a student user portrait construction method based on multi-modal data fusion and a terminal. The method comprises the following steps: acquiring learning behavior data, social behavior data and hobby and interest data of a user, and respectively extracting corresponding feature information; calculating the fusion weight of the feature information of each mode and generating a comprehensive feature vector; and on the basis of the comprehensive feature vector, performing student user portrait construction by using a clustering algorithm, an analytic hierarchy process and knowledge graph analysis. According to the method, data features of learning behaviors, social behaviors and hobbies and interests are extracted, modal information is synthesized by using a fusion weight mechanism, and finally a multi-dimensional and fine-grained student portrait is formed. Compared with a traditional single data source modeling method, the method has the advantages that the comprehensiveness, the accuracy and the personalized adaptive capacity of portraits are remarkably improved, and various application scenes such as personalized teaching recommendation, academic early warning and interest expansion activity matching can be effectively supported.

The invention discloses a dynamic identification system and method for fault nodes of a heat pump measurement and control network, and aims to solve the problems of one-sided weight distribution, lack of directional modeling and insufficient dynamic adaptability in the prior art. The system comprises an energy transfer topology network model building module which is used for respectively quantifying the physical connection strength and the directional energy transfer path of the heat pump system by building an undirected graph and directed graph bimodal model; the multi-dimensional weight distribution module adopts a subjective and objective fusion strategy and combines a complex network theory, an entropy weight method and an analytic hierarchy process to distribute comprehensive weights for nodes and explicit modeling directivity dependence; the dynamic robustness analysis module is used for simulating and positioning structural hub nodes through static attacks, simulating and tracking cascade failure and topology reconstruction processes through dynamic attacks, and comprehensively evaluating the robustness of the system; and the fault simulation verification module verifies weight rationality and method effectiveness from four dimensions of objectivity, center matching degree, interpretability and robustness based on a fuzzy comprehensive evaluation framework. According to the method, through fusion topology modeling, dynamic weight distribution and dynamic and static combination robustness analysis, key fault nodes are accurately recognized, the system vulnerability is revealed, a data driving basis is provided for redundancy design, intelligent operation and maintenance and reliability improvement of the heat pump system, and the operation and maintenance cost is remarkably reduced.

The invention provides an emergency resource intelligent scheduling optimization method and system based on large model analysis, and relates to the technical field of emergency resource scheduling. The method comprises the steps of collecting emergency event data and environment data; extracting emergency event features and environment features, constructing a prediction model based on a long short-term memory network, and obtaining a required emergency resource list; constructing an emergency resource evaluation model based on an analytic hierarchy process, and obtaining priority scores of the required emergency resources; constructing a multi-objective optimization model, and solving the multi-objective optimization model by using a particle swarm algorithm to generate a scheduling scheme; and constructing a decision function, and solving by using a genetic algorithm to obtain a scheduling decision. According to the method, through accurate prediction, scientific evaluation, multi-objective optimization and decision in combination with priority, the accuracy and efficiency of emergency resource scheduling and the resource utilization efficiency can be remarkably improved, powerful support is provided for emergency management work, and the method has important practical significance and application value.

The invention discloses a long document intelligent retrieval method and system based on hierarchical analysis and multi-modal fusion, and the method comprises the following steps: carrying out hierarchical analysis on an input long document, and obtaining a physical layout structure, a logic structure and a division and association relationship of multi-modal elements of the long document; respectively extracting text features of the text elements and visual features of the multi-modal elements; performing deep fusion on the text features and the visual features through a cross-modal fusion model to generate a unified multi-modal semantic representation vector; constructing a hybrid index; receiving user query, and performing preliminary retrieval by using the hybrid index to obtain a candidate result set; reordering the candidate result set; and outputting the retrieval result after reordering. Through visual-logic collaborative analysis, gating enhanced bilinear attention fusion and graph attention-based context reordering, a collaborative gain effect is generated in a long document intelligent retrieval task, and the retrieval precision and the user experience are remarkably improved.

The invention provides a multi-direct-current feed-in power grid transformation method and system based on risk assessment. The method comprises the steps that risk assessment comprehensive indexes of flexible transformation schemes of all receiving ends are obtained through an electromechanical electromagnetic hybrid transient simulation model; obtaining a risk evaluation result of each receiving end flexibility transformation scheme according to the risk evaluation comprehensive index, and screening a preset number of receiving end flexibility transformation candidate scheme sets; performing multi-target evaluation by using an evaluation model based on an analytic hierarchy process, and determining a final receiving end flexible transformation candidate scheme; and transforming the multi-direct-current feed-in power grid according to the determined final receiving end flexible transformation candidate scheme. According to the method, comprehensive, accurate and efficient evaluation of the receiving end flexible transformation candidate scheme is realized aiming at the direct current planning requirement of a load concentration large area, and the technical effect of improving the overall stability and the fault handling capacity of a power grid is further achieved.

The invention discloses a ship collaborative collision avoidance decision-making method based on a multilayer coding genetic algorithm. The method comprises the following steps: obtaining and processing related information of each ship in an encounter situation based on a sensor; based on a collision risk degree model, constructing a dynamic early warning mechanism in a multi-ship encounter scene: constructing a multi-stage ship collision avoidance strategy based on a queuing theory theoretical framework, establishing a multi-ship collision avoidance decision queuing system with specific space-time coupling characteristics, introducing a dynamic time window distribution mechanism and a space-time constraint coupling network modeling method, and establishing a multi-ship collision avoidance decision queuing system; multi-objective optimization is carried out on the ship decision scheme of the first priority gradient, and a ship intelligent collision avoidance strategy with dynamic adaptability is provided; a multi-ship collaborative collision avoidance decision optimization model is constructed on the basis of a genetic evolution mechanism, a multi-target adaptation function is integrated, a decision scheme when multiple groups of ships meet in a first priority is determined through the synergistic effect of dynamic priority management and multi-layer coding optimization, and hierarchical analysis of decisions in a multi-ship meeting situation is achieved.

The invention relates to an intelligent city sweeper operation monitoring management method and system. The method comprises the following steps: carrying out multi-source data acquisition processing on vehicle operation data of a cleaning vehicle to obtain a state fusion data set for representing operation behavior characteristics of the cleaning vehicle; according to a matching relationship between a preset standard operation template and the state fusion data set, performing hierarchical analysis processing on the operation behavior characteristics of the sweeping vehicle to obtain an operation behavior evaluation result; according to the operation behavior evaluation result, a matched monitoring feedback strategy is executed on the sweeping vehicle, an operation monitoring feedback information set is generated, and the operation monitoring feedback information set comprises operation abnormity alarm information, track deviation labeling information, resource allocation suggestion information and state diagnosis result information; the method is used for providing operation scheduling management basis for the sweeping vehicle for the preset monitoring platform. By adopting the method, fine identification and efficient management of the operation state of the sweeping vehicle can be realized.

The embodiment of the invention provides a big data analysis visualization method and device based on a big model, a medium and a product. The method comprises the following steps: receiving a natural language demand input by a user; performing semantic layer, logic layer and task layer decomposition on the natural language demand by adopting a preset hierarchical analysis model to generate an executable data analysis instruction; obtaining target data required by the data analysis instruction; according to the data analysis instruction, the target data, a preset chart recommendation algorithm and an aesthetic evaluation optimization algorithm, generating a basic visual chart; and learning the user preference characteristics through the visual generative adversarial network, dynamically adjusting the basic visual chart according to the user preference characteristics, and generating a personalized visual result corresponding to the natural language demand. According to the scheme, the defects of an existing dragging type visualization system and an existing programming type visualization scheme in the visualization process are overcome.

The invention discloses a privacy protection effect evaluation method and system, and the method comprises the steps: carrying out the hierarchical analysis of a privacy protection effect evaluation index system, defining the definition and calculation boundaries of four first-level dimensions, i.e., data features, compliance, availability and safety, and subordinate second-level indexes, carrying out the adaptation of a calculation algorithm for different types of indexes, and carrying out the weighted integration of the second-level indexes through employing an analytic hierarchy process, thereby achieving the evaluation of the privacy protection effect. Multi-dimensional quantitative evaluation is realized; an index association rule base and a matrix are constructed through static analysis and dynamic mining, and linkage analysis is achieved; determining an index fluctuation reference according to scene characteristics and historical data, converting user demands into a variation amplitude of a fluctuation reference unit, and realizing index dynamic linkage updating based on an incidence matrix; and finally, the optimization rule is verified through simulation testing and historical data backtracking, and a dynamically adaptive and self-iterative optimization relation rule system is formed, so that the limitation of traditional evaluation is broken through, a scientific basis is provided for privacy protection strategy optimization, and the privacy governance efficiency is improved.

The invention relates to the technical field of medical data analysis and rehabilitation, in particular to a cerebral apoplexy patient rehabilitation exercise analysis system based on medical big data, which comprises a data acquisition module used for acquiring electromyographic signals, exercise data and behavior data of a patient to form a data set; the data processing and fusion module is used for preprocessing the electromyographic signals in the data set, normalizing motion and behavior data and fusing the motion and behavior data into a multi-modal feature vector; the hierarchical analysis module is used for performing motion intention recognition according to the preprocessed electromyographic signals and outputting an intention classification result, recognizing the motion intention and predicting a rehabilitation effect through a double-flow Transform encoder, and evaluating a behavior risk level in combination with a risk factor formula; the optimization module is used for dynamically adjusting the mirror image treatment intensity according to the risk level and optimizing the prediction model by adopting incremental learning; and the interaction module is used for generating a thermodynamic diagram superposition rehabilitation curve in the X-Y coordinate system, and marking doctor intervention suggestions to complete visual interaction.

The invention relates to the technical field of optical communication, in particular to an algorithm service system and method for an optical transport network, and the system comprises an optical network sensing module which is used for collecting optical layer performance parameters, service flow data and node equipment operation states of the optical transport network; the deep algorithm decision center predicts service traffic based on a space-time diagram convolutional network, and optimizes routing wavelength allocation through reinforcement learning; the strategy adaptation module is used for dynamically generating an operation and maintenance strategy and an energy-saving scheduling instruction according to the prediction result and the optimization scheme; the risk assessment unit is used for assessing a link fault risk, a service interruption risk and a network congestion risk in real time, quantifying a risk weight through an analytic hierarchy process, and optimizing an operation and maintenance strategy in combination with historical fault data; and the intelligent resource reconstruction unit is used for performing optical channel reconstruction according to the optimized operation and maintenance strategy, dynamically allocating optical layer resources or switching redundant transmission paths. Therefore, the problems of poor strategy adaptability, low resource scheduling efficiency, insufficient fault-tolerant capability and the like in the prior art are solved.

The invention discloses a complex system data visualization modeling method based on GIS and topological graph fusion, and relates to the technical field of data visualization modeling, and the method comprises data fusion modeling, space-time anomaly reasoning, state propagation modeling and multi-dimensional visualization enhancement. The method comprises the following steps: performing unified access, cleaning and standardization processing on operation data, topological relation data, geographic space data, function configuration data and environment data of a complex system, and constructing a unified data model fusing geographic attributes and topological attributes; realizing abnormal root cause reasoning and spatial constraint propagation modeling of a system state by using a convolutional time sequence modeling method combined with a graph neural network; a dynamic evolution rule of a node state is described through a time gating mechanism, and interactive display is realized; according to the method, global visualization of the operation state of a complex system, dynamic backtracking of an abnormal propagation path and hierarchical analysis of key nodes can be realized, and the intelligent monitoring, situation awareness and decision support capabilities of the system are improved.

The invention discloses a graph topology-based power distribution network protection parameter automatic checking method and system, and belongs to the technical field of power transmission and distribution. The method comprises the steps of obtaining a transformer substation outgoing line protection constant value, a terminal protection constant value, line zero-sequence current data, a topological relation below the transformer substation, line parameters and operation state data of lines, switches and distribution transformers; sVG analysis and power distribution network topological graph construction are carried out; subgraph extraction and hierarchy analysis; line parameter and protection constant value verification, including line parameter verification and protection constant value verification; the line parameter verification comprises branch line segment verification and switch position verification; the protection constant value verification comprises overcurrent I section verification, overcurrent II section verification and zero sequence protection verification. According to the method, automatic checking of the power distribution network protection parameters is realized, the checking efficiency and accuracy are greatly improved, and the method is suitable for a protection constant value checking scene of a complex power distribution network.

The embodiment of the invention discloses a traffic accident data processing and analyzing method and device and electronic equipment. The method comprises the steps of performing data fusion on traffic accident data in various data formats based on a large-model heterogeneous field mapping method to obtain an accident data set, performing data cleaning on the accident data set according to a dynamic rule engine and a correction mechanism to obtain a target data set, and storing the target data set in a database. And finally, performing hierarchical analysis on the target data set based on the multi-dimensional accident analysis model to obtain a data analysis result, and performing visual display on the data analysis result. Through multi-source data intelligent fusion, data quality control and a multi-dimensional accident analysis model, the data quality of the multi-source data is improved, the purpose of efficiently and automatically processing different data formats is achieved, a large amount of human resources are saved, and meanwhile the accuracy and reliability of a data processing analysis result are improved.

The invention provides an artificial intelligence semantic processing system and method oriented to digital media creation, and relates to the technical field of artificial intelligence semantic process.The artificial intelligence semantic processing method comprises the steps that predicate argument relation pairs of language texts are extracted, object space relation pairs of sketch images are extracted at the same time, and a basic semantic unit set is constructed; the integrity and accuracy of cross-modal semantic understanding are ensured, further, semantic units are clustered by using a dynamic routing algorithm, a semantic concept cluster with a clear importance weight is generated, deep mining and structured representation of creation intentions are realized, and the creation intentions are quickly and accurately understood. An initial semantic relation graph is constructed, a graph attention network is used for dynamic reweighting, finally, an enhanced dynamic semantic graph is generated, complex association and a hierarchical structure between semantic concepts are effectively captured, finally, hierarchical analysis is carried out on the semantic graph, and a structured semantic blueprint is output, so that the dynamic semantic graph is obtained. And a reliable semantic processing technology is provided for creation of high-quality digital media contents.

The invention discloses a low-altitude take-off and landing infrastructure grading site selection method, device and equipment, and relates to the technical fields of low-altitude economy, urban planning and the like, and the method comprises the steps: obtaining a low-altitude scene demand, and carrying out the quantification processing, and obtaining demand data; a facility grading system is established based on the demand data, configuration data corresponding to the facility grading system is obtained, and the facility grading system comprises at least three grades; performing overlay analysis on the limiting factor data, and determining a candidate region; and performing hierarchical analysis on the candidate region to obtain suitability distribution data under the low-altitude scene requirement, and performing solution based on the suitability distribution data and the configuration data to obtain site selection data corresponding to the facility grading system.

The invention relates to the technical field of education evaluation, and discloses a course teaching effect evaluation method, which comprises the steps of constructing a two-dimensional evaluation index system containing student ability and teacher teaching as a basic framework of evaluation; based on the two-dimensional evaluation index system, determining an initial weight by adopting an interval intuitionistic fuzzy analytic hierarchy process, and dynamically correcting the initial weight in combination with an entropy weight method to form a combined weight; collecting multi-source data corresponding to the two-dimensional evaluation index system through a triangular verification method, and preprocessing the multi-source data; combining the combined weight with the preprocessed multi-source data, and performing layered fuzzy comprehensive evaluation, thereby realizing quantitative evaluation and weak link diagnosis of the course teaching effect; according to the method, expert knowledge and objective data are fused, so that the evaluation weight is dynamically changed along with actual teaching data, and the problem that evaluation is separated from reality due to weight solidification is solved.

The invention relates to the technical field of crop image quality inspection, and discloses a crop sample live-action image automatic warehousing and intelligent quality inspection system and method. An image acquisition module of the system acquires an initial real-scene image and preprocesses the initial real-scene image to generate a target real-scene image, and shooting interference is eliminated to guarantee the quality of basic data; the hierarchical analysis module performs multi-level feature analysis on the target live-action image, generates initial quality calibration, and divides the image into a plurality of sub-quality inspection areas when to-be-verified calibration is identified, so that accurate analysis from the whole to the local is realized; and the dynamic processing module extracts spectral feature values of the sub-regions, compares the spectral feature values with a standard threshold value, divides a feature set, performs correlation verification through adjacent feature values and performs statistics on an effective correlation number, so that the scientificity and objectivity of quality inspection are improved. According to the system, automatic warehousing and intelligent quality inspection of crop sample images are realized, manual intervention is reduced, the quality inspection efficiency and reliability are improved, and high-quality data support is provided for related work of national land change investigation.

The invention relates to the technical field of language barrier evaluation, and discloses a deep learning-based language barrier executive intervention effect evaluation method. The method comprises the following steps: acquiring real-time voice data and facial expression data of a language barrier patient in intervention training through a multi-modal data acquisition device to form an original behavior feature set; performing acoustic feature hierarchical analysis on the voice data by adopting a time sequence feature extraction network to generate a voice time sequence feature vector; performing micro-expression dynamic capture on the expression data through a three-dimensional convolutional neural network to generate an expression state feature vector; inputting the two types of vectors into a multi-modal feature fusion layer to carry out cross-modal correlation analysis, and generating a comprehensive behavior evaluation matrix; on the basis of the matrix, an intervention effect analysis model driven by an attention mechanism is adopted, a behavior improvement degree index of the current intervention stage is calculated, accurate evaluation of the intervention effect is achieved, and support is provided for dynamic adjustment of language barrier rehabilitation intervention.

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent building system and method based on BIM, and the system comprises a key node sorting module, a continuity index calculation module, a node coupling judgment module, a resource period regulation and control module and a node path rearrangement module. According to the method, through aggregation processing of the total number of construction node stages, the number of follow-up dependency levels and risk exposure values, node key levels are ranked through an analytic hierarchy process model, a construction task priority basis is constructed, a continuity index is formed in combination with task interval duration, a task switching time ratio and an equipment idle period, and state calibration is carried out; a node coupling imbalance relationship is judged through a continuity index difference value and a threshold value, a resource utilization rate is calculated based on a resource calling number, a path load and a neutral position number to judge a compression and release state, a conflict is identified based on a task parallelism degree and an equipment occupation conflict rate, and a task sequence structure is recombined by utilizing a genetic algorithm. And the adaptive capacity of the path to resource configuration is enhanced.

The invention provides a remote control method and device for intelligent multimedia interaction equipment, equipment and a medium, which are used for improving the real-time performance and the execution success rate of remote control and enabling complex multimedia interaction to be reliably operated on a heterogeneous network and heterogeneous equipment. The method comprises the following steps: carrying out hierarchical analysis processing on interaction data input by a user to generate perception command data, and carrying out execution planning and resource allocation on the perception command data according to equipment performance characteristics to generate an executable operation sequence, and performing transmission quality evaluation and path selection on the propagation path network according to the executable operation sequence to obtain a target transmission path, and performing protocol encapsulation on the executable operation sequence according to the target transmission path to obtain a remote control instruction data stream.

The invention discloses a contact network state evaluation method and system based on adaptive empowerment optimization. The method comprises the following steps: collecting dynamic and static parameters of a contact network, calculating subjective and objective weights of each parameter by adopting an analytic hierarchy process and an entropy weight method, and calculating a static composite weight by adopting a combined weight method; historical degradation data of dynamic and static parameters of the overhead line system are collected, and the expected degradation rate of each parameter is obtained by adopting a time sequence neural network; calculating a parameter real-time degradation rate, and dynamically optimizing the static composite weight by adopting an adaptive method based on the real-time degradation rate and an expected degradation rate to obtain a dynamic composite weight optimization result; optimizing and updating the gain in the adaptive method; based on a dynamic composite weight optimization result, performing state evaluation on the contact network; the method and the system provided by the invention are not only suitable for contact network state evaluation, but also can be popularized and applied to other state evaluation fields, and have wide application prospects.