802 results about "Hierarchical analysis" patented technology

The invention provides an intranet service quality optimization method and system based on deep reinforcement learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting multi-dimensional performance index data, carrying out the preprocessing, constructing an analytic hierarchy model, outputting the performance score of each layer, integrating the performance score of each layer, and outputting an intranet service quality score. Constructing a simulation environment, adjusting hyper-parameters of the adaptive deep reinforcement learning model, continuously learning in an actual intranet environment, determining multi-dimensional rewards, performing multi-target optimization, and generating a multi-target optimization result; and setting a hierarchical performance index threshold, if a hierarchical optimization strategy is triggered, adjusting a network topology structure, adjusting resource allocation and service configuration, adjusting weights of different indexes in the service quality evaluation model, updating a reward function, solving an optimal service decision, generating an optimal service decision sequence, and executing the optimal service decision sequence.

The invention relates to the technical field of equipment remote control, and discloses a remote control method and system for sewage treatment equipment and a storage medium. The method comprises the following steps: performing communication encryption and identity authentication processing on a standard data packet of a sewage treatment system, and constructing an equipment communication link; performing intelligent analysis and state diagnosis processing on the data in the equipment communication link, and generating an equipment operation state report; performing control strategy generation and parameter matching processing according to the equipment operation state report, and outputting an equipment control instruction; performing hierarchical analysis and execution state monitoring on the equipment control instruction, and establishing a control feedback data link; and performing remote optimization treatment according to the data in the control feedback data link so as to realize remote intelligent control of the sewage treatment equipment. Under the condition that the sewage treatment equipment is dispersedly arranged, the real-time monitoring of the running state of the equipment and the intelligent adjustment of the process parameters are realized, and the stability of the treatment effect is ensured.

The invention relates to the technical field of image detection, and discloses an intelligent detection method for the machining process of an automobile decorative plate. The method comprises the following steps: performing high-resolution image acquisition and preprocessing on the automobile decorative plate to obtain standardized image data; performing region segmentation operation on the standardized image data to obtain a decorative plate target region mask image, and extracting to obtain a feature vector set; inputting the feature vector set into a ResNet-v1-50-based transfer learning network for training to obtain a defect classification model, and calculating candidate defect area masks through the defect classification model; and performing multi-scale analytic hierarchy process on the candidate defect area mask to obtain a defect risk heat map, and performing clamping quality analysis on the connection area of the clamping block and the clamping groove according to the defect risk heat map to obtain a clamping quality evaluation result. According to the method, accurate positioning of the defect area is realized, the defect risk heat map is constructed to quantify the risk levels of different areas, and data support is provided for production process optimization.

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 relates to the technical field of plastic production, in particular to an intelligent processing method and system for plastic mold process parameters. The method comprises the following steps: carrying out sensor network deployment on a plastic mold to obtain a mold parameter sensing network; operating parameters of the plastic mold are collected in real time through the mold parameter sensing network, and mold operating state data flow is obtained; performing process state feature extraction on the mold operation state data stream to obtain mold process state feature data; performing spatio-temporal data hierarchical analysis on the mold process state feature data to obtain mold state multi-scale feature data; and performing layered thermal field dynamic modeling on the plastic mold based on the mold state multi-scale characteristic data to obtain a mold thermal field evolution model. The key process parameters of the mold can be identified, and the stability and reliability of the mold performance are enhanced.

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 relates to the technical field of chemical safety management, and discloses an emergency equipment high-efficiency management system and method.The emergency equipment high-efficiency management method comprises the steps that multi-source heterogeneous data of emergency equipment in a chemical industrial park is collected and converted into a standardized data set through a heterogeneous data integration algorithm, and the standardized data set is stored in a database; the expert knowledge is converted into a domain knowledge graph by means of a hierarchical knowledge representation framework. Based on this, a multi-layer causal network model is constructed by using a causal discovery algorithm, analysis results and decision suggestions are extracted through multi-scale causal reasoning, and a dynamic causal knowledge base is generated through combination of a bidirectional calibration algorithm and newly added data; according to the method, the limitation of single analytic hierarchy process is broken through by fusing domain knowledge and multi-source data, explanation is provided for decision making, the method has adaptive evolution capability, and the efficiency and accuracy of emergency equipment management are greatly improved.

The invention relates to the technical field of intelligent teaching, in particular to an intelligent teaching system for traditional Chinese medicine clinical skill cultivation. The system comprises a virtual reality simulation module, an operation interaction module, an operation data analysis module, an adaptive learning feedback module and a teaching evaluation module. The virtual reality simulation module constructs a vivid traditional Chinese medicine skill training scene through three-dimensional modeling, ray tracing and physical simulation technologies, and the vivid traditional Chinese medicine skill training scene covers pulse feeling, acupuncture and moxibustion, massage and other operations. The operation interaction module collects user operation data and extracts features in combination with a variational auto-encoder. The operation data analysis module analyzes user operation deviation by using a graph neural network and a generative adversarial network, the adaptive learning feedback module generates a personalized learning strategy, and the teaching evaluation module evaluates a learning effect through fuzzy evaluation and analytic hierarchy process. According to the invention, intelligence, individuation and high efficiency of traditional Chinese medicine clinical skill cultivation are realized, the fidelity of teaching and the accuracy of feedback are improved, and the skill level and the training effect of learners are also improved.

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 invention relates to the technical field of behavior portrait construction, and discloses a learner behavior portrait construction system and method based on data analysis, and the system comprises a learning behavior collection module, a learning rhythm mutation detection module, a behavior track stability evaluation module, a knowledge point backtracking index calculation module, and a portrait dynamic adjustment module. According to the method, the learning behavior fluctuation rate is calculated, the knowledge point jumping frequency and the task completion interval fluctuation condition are combined, the sudden change characteristics of the learning behaviors are judged, the recognition capacity for sudden learning behavior changes is enhanced, the behavior path overlapping degree of different time periods is compared, and the page switching frequency and the dwell time change range are calculated; the method comprises the steps of analyzing the stability of learning behaviors, performing hierarchical analysis on a knowledge point access mode of a learner according to a backtracking condition of a learning path, and optimizing the sensitivity of a portrait to learning path adjustment and enhancing the portrait adaptability by dynamically adjusting a portrait updating strategy and self-adapting to real-time behavior changes of the learner.

The invention provides a log analysis adaptive optimization method and system based on swarm intelligence, and relates to the technical field of log analysis, and the method comprises the steps: obtaining a database log, and carrying out the feature extraction to generate a multi-dimensional feature matrix; constructing a hierarchical analysis network and deploying an agent analysis unit cluster with a self-learning capability; the agent analysis unit executes an analysis task through a feature recognition module, a parallel computing engine and a decision optimizer; constructing a multi-target evaluation model based on the performance evaluation indexes; and generating an optimization strategy set by adopting a self-adaptive evolution model and updating the optimization strategy set to the analysis network. According to the invention, the accuracy and efficiency of log analysis are improved, and the self-adaptive capability of the system is 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 discloses an English composition evaluation method and device, belongs to the technical field of artificial intelligence in the education industry, and aims to solve the problem that a scoring result of an existing English composition automatic scoring system is greatly different from a manual scoring result. Comprising the following steps: obtaining a to-be-scored composition; semantic analysis is carried out on the to-be-scored composition, wherein the semantic analysis comprises high-level vocabulary analysis, semantic coherence analysis, logic hierarchy analysis, sentence pattern analysis and theme consistency analysis; obtaining a high-level vocabulary score, a semantic coherence score, a logic hierarchy score, a theme consistency score and a high-level sentence pattern score; based on a preset weight coefficient, performing weighted summation on the high-level vocabulary score, the semantic coherence score, the logic hierarchy score, the high-level sentence pattern score and the theme consistency score to obtain a final score of the to-be-scored composition; according to the invention, the accuracy and reliability of English composition scoring can be effectively improved.

The invention discloses a multi-dimensional collaborative AI training processor benchmark evaluation device and method. According to end-to-end performance evaluation, TTA and throughput indexes are obtained through a standardized training process; the operator support degree evaluation adopts a dynamic parameter generation technology to evaluate the speed-up ratio and the floating point performance utilization rate of calculation-intensive and memory-access-intensive operators; the energy consumption evaluation calculates a power consumption delay product based on steady-state power consumption monitoring. And finally, integrating multi-dimensional indexes through an analytic hierarchy process model and a fuzzy theory, realizing level mapping by adopting a four-section membership function, and finishing multi-level aggregation evaluation in combination with matrix operation. According to the method, three dimensions of performance, operator support degree and energy efficiency are creatively integrated, the problems of single evaluation index and high environment dependence of the heterogeneous AI processor are solved through automatic container deployment, dynamic parameter generation and steady-state power consumption monitoring technologies, and a comprehensive and quantifiable evaluation basis is provided for processor model selection.

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 tourist route recommendation, in particular to a tourist route recommendation system and method based on big data. The method comprises the following steps: collecting travel demand data and basic sign information of a user, and performing hierarchical analysis on a demand to obtain master / slave demand data; then, through demand behavior intersection analysis, muscle soreness intensity is simulated in combination with user physical sign data, and the physical degeneration rate is further estimated; based on the physical decline rate, making a rest strategy, and optimizing distribution and adjustment of tourist routes; and finally, transmitting the adjusted tourist route data to a user terminal. According to the invention, the tourist route recommendation technology is improved, so that the tourist route recommendation technology is more perfect.

The invention belongs to the technical field of constructional engineering management, and particularly relates to a green construction evaluation method and system.The method comprises the steps that S101, energy consumption data, material use data and environmental parameters of a construction site are collected in real time through Internet of Things monitoring equipment; s102, constructing a multi-dimensional evaluation index system including resource utilization efficiency, carbon emission intensity and an environmental influence coefficient based on a BP neural network model; s103, calculating a dynamic weight coefficient by adopting an analytic hierarchy process in combination with a time factor; s104, generating a green construction comprehensive score by using a fuzzy comprehensive evaluation algorithm; and S105, carrying out iterative optimization on the evaluation model based on a reinforcement learning algorithm. Different from a traditional static evaluation mode, various data of a construction site are collected in real time by means of Internet of Things monitoring equipment, and dynamic monitoring of the construction process is achieved. From energy consumption data to environmental parameters, each link can be fed back in time, so that a manager can master the green construction condition of a project at any time, and is not limited to one-time evaluation at a completion acceptance stage any more.

The invention discloses a product carbon footprint data quality evaluation method, which comprises the steps that a product carbon footprint data quality evaluation system comprising activity data and emission factors is constructed, activity data evaluation indexes comprise source reliability and statistical accuracy, and emission factor evaluation indexes comprise time representativeness, geographical representativeness and technical representativeness; comparing the evaluation factors in pairs by using a 1-9 scale method, constructing a discrimination matrix of each evaluation index, and obtaining the weight of each evaluation factor; checking and correspondingly adjusting the discrimination matrix based on the random consistency ratio; a carbon emission source of the product in each production stage is determined, and the uncertainty of each carbon emission source is determined according to an Einformation database and the weight; and calculating the carbon footprint overall uncertainty of the product life cycle based on an error transfer method. According to the method, the analytic hierarchy process and the data quality index are combined to carry out product carbon footprint data quality evaluation, and the influence of subjective factors on data quality evaluation is reduced.

The invention is suitable for the field of abnormal data monitoring, and provides an abnormal data monitoring method and system based on artificial intelligence, and the system comprises a real-time data flow collection module, a cross-level correlation analysis module, a dynamic baseline model building module, an abnormal behavior comparison module, and an abnormal traceability module. According to the method, through real-time data acquisition, cross-hierarchy analysis, dynamic modeling, anomaly detection and deep threat tracing, an efficient and intelligent anomaly monitoring closed loop is constructed, and remarkable beneficial effects are presented. According to the method, firstly, real-time data flow capture and cross-level correlation analysis are utilized to ensure that interaction behaviors of multiple protocol levels in a network are comprehensively covered, and a complex abnormal mode is recognized in combination with context information; according to the multi-dimensional analysis, the detection accuracy is remarkably improved, false alarm and missing alarm are effectively reduced, meanwhile, an abnormal mark data set with weights is generated, and high-quality data support is provided for follow-up processes.

The invention discloses a medical equipment maintenance inspection method and system, and relates to the technical field of medical equipment. The system comprises a data acquisition and transmission module, a data processing and analysis module, an inspection task distribution module, a maintenance plan making module and a real-time interaction and feedback module. The method comprises the following steps: step 1, data acquisition and transmission; step 2, data processing and analysis; step 3, making a maintenance plan; step 4, distributing inspection tasks; step 5, inspection execution and feedback; and step 6, system optimization and continuous improvement. Through multi-dimensional data fusion and an advanced machine learning algorithm, faults of the medical equipment can be predicted more accurately, maintenance measures are taken in advance, the influence of the equipment faults on medical services is reduced, multiple factors are comprehensively considered by adopting an analytic hierarchy process, a personalized maintenance plan is made, the utilization efficiency of maintenance resources is improved, and the maintenance cost is reduced. And the maintenance cost is reduced.

The invention discloses a deep learning algorithm comprehensive evaluation method and system based on game theory combination weighting-TOPSIS, and the method comprises the steps: constructing a deep learning algorithm evaluation index system, calculating different evaluation index values of each candidate algorithm, constructing an evaluation matrix, and carrying out the range standardization processing according to a positive or negative index type; obtaining importance weights of four groups of indexes based on an analytic hierarchy process, an optimal sequence diagram method, an entropy weight method and a CRITIC weight method; on the basis of the four groups of index importance weights, a final index importance weight is obtained based on a game theory combination weighting method; and according to the final index importance weight, in combination with a weighted TOPSIS method, obtaining a comprehensive evaluation and selection sorting result of all candidate algorithms. According to the method, the comprehensiveness and accuracy of evaluation are improved, and a decision maker can be better supported to make scientific judgment in the deep learning algorithm selection process.

The invention relates to the technical field of data cache management, in particular to a multilayer data cache dynamic management method and system, and the method comprises the following steps: based on a data access path, extracting a data access path hierarchy identifier, analyzing path hierarchy depth, recording access continuity, classifying the path to be stable or unstable, and obtaining path hierarchy distribution information. In the invention, through hierarchical analysis and stability classification of the data access paths, the cache resource allocation efficiency, the stable paths and the unstable paths are improved, the access delay is reduced, the access continuity is improved, the cache waste caused by hopping is reduced, the space utilization rate is improved, the cache pollution is avoided, the storage sequence is optimized, and the hit rate is improved; through dynamic elimination, resource competition is reduced, storage medium I / O load is monitored, medium load is balanced, throughput capacity is improved, data flow is optimized, cache coordination capacity is improved, cache levels can flexibly adapt to access mode changes, and data storage stability and load balance are enhanced.

The invention discloses a rapid maintenance method for a distribution line, and relates to the technical field of electrical variable measurement, and the method comprises the steps: transmitting a multi-frequency electromagnetic wave to the distribution line through a multi-frequency electromagnetic wave detection device, receiving an echo signal, and extracting the electromagnetic characteristic data of the line; performing hierarchical analysis on the electromagnetic characteristic data to generate a fault preliminary report; based on the characteristics of the fault preliminary report and the echo signal, constructing a three-dimensional fault positioning map, and outputting fault positioning data; based on the repair suggestion report, utilizing an electric field to guide liquid metal to flow to the fault point, and performing filling and curing repair on the fault area; after repairing is completed, secondary detection is carried out on the repairing area and the adjacent area through multi-frequency electromagnetic waves, and the operation characteristics of the repairing point and the adjacent area are verified; according to the method, the electromagnetic characteristic data is extracted by using the echo signals, the three-dimensional fault positioning map is generated, the spatial position, geometric characteristics and material distribution of the fault point are accurately identified, and accurate support is provided for repair.

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 robot motion control method and platform combined with image processing, electronic equipment and a storage medium, and relates to the technical field of mechanical control, and the method comprises the steps: building interaction between ROS2 and a planning open source library through a software interface, constructing a layered modular architecture, and achieving the communication of each layer through a communication protocol; a planning task is received, a front-end image sensor is controlled to collect a vision field image, and the vision field image is transmitted back to the architecture for hierarchical analysis and motion planning; a hardware abstraction layer performs format conversion on image data; the planning layer formulates a control strategy based on image data and constraint conditions; and the execution layer converts the strategy into a control instruction, and performs real-time motion control according to the characteristics of an actuator. The technical problems that in the prior art, a traditional robot system is delayed in response, insufficient in uncertainty processing capacity and low in task distribution efficiency are solved, and the technical effect of improving the adaptivity, task cooperation efficiency and task execution accuracy of the robot in the dynamic environment is achieved.

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.