14585results about How to "Avoid wasting" patented technology

The invention provides a big data platform scheduling task and data collaborative smooth migration method and system, and relates to the technical field of big data, and the method comprises the steps: constructing a digital twin environment of a target system, collecting task operation state data of a source system, extracting task features through a deep neural network, and carrying out the clustering and grouping. And deploying the task group to a digital twin environment, analyzing a task association relationship and a resource competition relationship based on a graph neural network, and generating a task stability score in combination with a task operation index. And constructing a migration risk prediction model to perform quantitative evaluation on the migration risk, and determining a migration priority and an execution sequence according to the quantitative evaluation. And making corresponding resource configuration, data synchronization and fault recovery plans for each task group, carrying out migration drilling in a digital twinning environment, and optimizing a migration scheme by utilizing a reinforcement learning algorithm. The migration risk can be effectively reduced, the smoothness and stability of the migration process are guaranteed, and the migration efficiency is improved.

The invention provides a building construction potential safety hazard management method and system, and relates to the technical field of construction safety management, and the method comprises the steps: dividing a plurality of sub-regions, collecting data, classifying known potential safety hazard events, and obtaining a first classification; through comparative analysis of the reference features and the contrast features, corresponding data features and fusion features are selected, and risk assessment models of the first classification are established respectively; obtaining a first risk probability and a comprehensive risk index of each sub-region according to real-time monitoring data and the risk assessment model; according to the first risk probability, the comprehensive risk index and / or the risk change, performing early warning on the sub-region; according to an early warning processing result and field feedback; adjusting an early warning threshold value and / or optimizing the risk assessment model; real-time monitoring, early warning and management of potential safety hazards of the construction site are achieved, the efficiency and accuracy of potential safety hazard management can be improved, the labor cost can be reduced, and the safety level of the construction site can be improved.

The invention discloses an expressway service area sewage treatment system based on the Internet of Things, and relates to the technical field of sewage treatment.The expressway service area sewage treatment system is provided with a multi-modal sensor and an edge computing unit, real-time monitoring and data processing of sewage flow and water quality parameters are achieved, the water quality change trend and the parameter change rate are rapidly recognized, and the sewage treatment efficiency is improved. The sensing capacity of the system to load changes is remarkably improved, the system conducts real-time adjustment according to the water quality changes, fluctuation of the treatment effect and waste of energy consumption are avoided, an LSTM model is adopted for conducting dynamic prediction on water quality parameters, in combination with real-time monitoring data, the aeration strategy is optimized through intelligent self-adaptive adjustment and closed-loop fine control, and the aeration efficiency is improved. The LSTM model can predict the future sewage load and ammonia nitrogen concentration change, the aeration intensity and time are adjusted in advance, the fluctuation of the ammonia nitrogen concentration is quickly responded through PID control and closed-loop control of a feedback mechanism, and the ammonia nitrogen removal efficiency and the aeration accuracy are improved.

The invention discloses a logistics park vehicle path planning method and system, and the method comprises the steps: obtaining the real-time position and task state information of a vehicle in a park, dynamically constructing a vehicle path planning network model, dividing a road into nodes and edges, and forming a directed weighted graph; according to the directed weighted graph, an improved multi-lane algorithm is adopted, the path length, the congestion degree and the vehicle characteristic factors are comprehensively considered, and the optimal path of each vehicle is calculated; for the optimal path, introducing a virtual lane and a time dimension, pre-judging occupation conditions of multiple vehicles on the virtual lane for each time slice, and constructing a conflict constraint matrix; obtaining park high-precision map data, extracting road network topology, lane lines and traffic sign marking elements, performing lane-level refinement on the optimal path, and generating a driving track; and through a multi-sensor fusion positioning technology, obtaining high-precision position and attitude information of the vehicle, matching the high-precision position and attitude information with the driving track, and calculating a track tracking error.

The invention provides a computing system and method for GPU computing power scheduling, and the method comprises the following steps: 1, obtaining computing task data, and carrying out the information collection of each task through receiving a to-be-processed computing task queue; according to the invention, through task information collection and deep analysis, and GPU resource omnibearing monitoring and workload prediction, deep understanding of tasks and GPU resources is realized, highly accurate computing power distribution can be carried out, resource waste is avoided, and the resource utilization rate is improved; advanced technologies such as a knowledge graph, mixed integer programming and deep reinforcement learning are fused through the intelligent scheduling model, the fusion mode is innovative in the GPU computing power scheduling field, the limitation of singleness of a traditional scheduling model is overcome, the method can better adapt to a complex and changeable computing environment, the task execution efficiency is optimized, and the task scheduling efficiency is improved. The adaptability and competitiveness of the whole system in a complex environment are remarkably improved, and an excellent solution is provided for the GPU computing power scheduling problem in the field of high-performance computing.

The invention discloses a tunnel segmented exhaust method and system based on train working conditions, a medium and a product, and relates to the technical field of ventilation. The method comprises the steps that a target tunnel is divided into a plurality of control sections according to fan installation configuration of the target tunnel; acquiring train passing data and train configuration information of the control section, and calculating a flue gas growth value of the control section in a preset time period; collecting flue gas concentration data of the control section, and calculating a ventilation demand index of the control section in combination with the flue gas growth value; collecting wind direction parameters of the control sections, determining a ventilation control relationship between the adjacent control sections based on the ventilation demand indexes and the wind direction parameters, and generating a ventilation cooperation scheme; according to the ventilation cooperation scheme, operation parameters of the tunnel fans in all the control sections are calculated, and control instructions corresponding to the operation parameters are sent to the tunnel fans. By implementing the application, the fan operation can be adjusted according to the actual train operation condition, and the ventilation effect is optimized.

The invention discloses an intelligent server management method and system in an edge computing environment, and relates to the field of electrical digital data process.The method comprises the steps that a dynamic load buffer database is established; calculating a load stability coefficient of each server node; when the load stability coefficient of the server node is smaller than a first preset threshold value, marking the server node as a fluctuation node; calculating a node cooperation efficiency value; determining a target server candidate set, and calculating to obtain a task allocation weight; distributing tasks on the fluctuation nodes to server nodes in the target server candidate set; when the continuous preset duration of the real-time load value of the fluctuation node is lower than a third preset threshold value, calculating a processing efficiency improvement proportion; for the task of which the processing efficiency improvement proportion is greater than a fourth preset threshold value, keeping continuous processing at the current server node; and for the tasks of which the processing efficiency improvement proportion is smaller than or equal to a fourth preset threshold value, returning to the original fluctuation node. The method and the device are used for reducing communication overhead and service processing delay between nodes.

The invention provides a dynamic resource scheduling method and device for multi-GPU cluster reasoning service, and belongs to the field of computing resource scheduling. Comprising the steps of obtaining load levels, request queue lengths and resource utilization rates of a multi-GPU cluster system at a current moment and a historical moment, and determining a resource allocation strategy of a GPU cluster by utilizing a reinforcement learning model based on the load levels, the request queue lengths and the resource utilization rates of the multi-GPU cluster system at the current moment and the historical moment; predicting a future load state by using a time sequence prediction model based on the load level, the request queue length and the resource utilization rate of the current moment and the historical moment, and adjusting a resource allocation strategy based on a prediction result; and after the resource allocation strategy is adjusted, updating the reinforcement learning model based on an online learning model according to a feedback result of actual operation of the GPU cluster. According to the method, the scheduling strategy is optimized through reinforcement learning, and the capacity expansion and shrinkage scheme is dynamically adjusted according to the system actual load, the resource utilization rate and the like.

The invention relates to the technical field of big data analysis and artificial intelligence, in particular to a multi-source heterogeneous big data fusion and reasoning method and system based on a knowledge graph, and the method comprises the following steps: constructing mapping interfaces of various data sources, collecting and storing entities in the data sources, and constructing nodes in the knowledge graph; performing representation learning on a graph structure of the knowledge graph based on a graph neural network, extracting an embedded vector of a node, and extracting a fusion node through the embedded vector and a mapping interface; performing knowledge completion on the fusion nodes by using a neural network based on symbolic logic; generating candidate reasoning paths by adopting a depth-first search algorithm based on the knowledge graph; reasoning is carried out based on an attention mechanism and path similarity, candidate reasoning paths are screened and sorted to obtain an optimal reasoning path, a graph neural network is adopted to carry out representation learning of a knowledge graph, symbolic logic and the neural network are combined, the interpretability of the reasoning process is guaranteed, and the learning ability is high.

The invention discloses a nursing decision scheme optimization analysis system based on artificial intelligence, and relates to the technical field of data processing. The system comprises a nursing knowledge base module, a data acquisition and preprocessing module, a nursing scheme matching degree analysis module, a nursing demand prediction module, a nursing scheme optimization module and a nursing scheme monitoring feedback module. According to the nursing decision scheme optimization analysis system based on artificial intelligence, the medical history, the diagnosis result, the physiological parameter data and the real-time nursing record multi-source information of the patient are integrated, comprehensive and dynamic evaluation of the health condition of the patient is achieved, deep mining and analysis are conducted on the data, and therefore more accurate nursing decision suggestions are obtained; individual nursing schemes are provided according to individual differences of patients, pertinence and effectiveness of nursing measures are ensured, in addition, execution conditions of the nursing schemes are monitored in real time, deviation is found and corrected in time, and scientificity and accuracy of nursing decision making are further improved.

The invention provides an unstructured database federated learning collaboration method and system based on swarm intelligence, and relates to the technical field of structured data, and the method comprises the steps: training a query pattern recognition sub-model, a path planning sub-model and a resource allocation sub-model through federated learning, and constructing a swarm intelligence collaboration network. After a user query request is analyzed by the query mode recognition sub-model, candidate query paths are generated and optimized by fusing an ant colony algorithm and a particle swarm algorithm, and a query execution scheme is generated. And the resource allocation sub-model performs resource scheduling accordingly. And generating a performance optimization target for updating local model parameters of each node, and fusing the updated parameters through a security aggregation algorithm to guide the next query optimization. According to the method, the query efficiency of the unstructured database is improved, and the resource consumption is reduced.

The invention discloses a thermal power plant equipment management method and system based on data fusion, and relates to the technical field of equipment management, and the method comprises the steps: collecting the real-time data of equipment through a sensor, and evaluating the health state of the equipment through a data fusion algorithm; based on the real-time operation data of the equipment, the intelligent rule engine dynamically adjusts the evaluation standard; predicting a potential fault and generating a maintenance priority through an intelligent fault prediction algorithm by using historical data and a health state evaluation result; and according to fault prediction and an equipment state, scheduling maintenance resources are optimized, and timely maintenance and repair of key equipment are ensured. According to the thermal power plant equipment management method based on data fusion provided by the invention, comprehensive monitoring of the equipment operation state is realized, and the accuracy and comprehensiveness of data are remarkably improved by combining a multi-stage data fusion technology based on dynamic weighted average and principal component analysis.

The invention relates to the technical field of big data storage, in particular to a storage control method and device based on big data. The method comprises the following steps: acquiring an original data stream and application demand information; performing data dynamic correlation analysis on the original data flow according to the application demand information to obtain a dynamic update data correlation graph; performing feature vector generation on the dynamic update data association graph to obtain a data feature vector; performing intelligent quality evaluation on the data feature vector to obtain a data quality label; performing timeliness evaluation according to the data feature vector, and performing access popularity evaluation to obtain a timeliness score and an access popularity score; and acquiring system resource load condition information. The storage resource utilization rate, the data access performance and the overall efficiency of the system are improved by solving the limitation of a traditional method in the aspects of static data management and resource allocation.

The invention discloses a distributed new energy storage optimal configuration method and system for a power distribution network, and the method comprises the following steps: building a load prediction model through employing a probability discretization method according to historical power utilization data and real-time weather data; according to load demand data output by the load prediction model, a multi-target optimization model is constructed by taking maximization of distributed new energy power generation utilization rate, minimization of distributed new energy storage energy loss and minimization of operation cost of energy storage and scheduling as optimization targets; according to the multi-objective optimization model, adjusting weight distribution of the three optimization objectives, gradually optimizing power distribution between new energy power generation and energy storage, and generating an optimal new energy complementation strategy; charging and discharging are adjusted according to the optimal new energy complementation strategy, the health state, the battery temperature, the number of charging and discharging cycles, the environment temperature and the load demand change of energy storage equipment are collected in real time, and new energy power generation and energy storage configuration is optimized by dynamically comparing multiple indexes under different energy storage configurations in combination with power grid frequency fluctuation.

The invention provides a first-aid platform work order management method based on digital twinning and artificial intelligence, and belongs to the technical field of information technology application innovation, and the method comprises the steps: collecting historical first-aid data and real-time data of a first-aid scene, and constructing a task scheduling system based on a knowledge graph; decomposing the task into a plurality of sub-tasks according to the work order data, and allocating an optimal first-aid resource combination for the sub-tasks; an optimization strategy is set to carry out collaborative allocation control on each resource, and task allocation is optimized; the optimized task allocation is monitored in real time, and meanwhile, in combination with real-time data flow monitored in real time, it is ensured that the change of the dynamic environment is responded at any time in the task execution process; and re-inputting the optimized task allocation and resource scheduling strategy into the knowledge graph, and carrying out closed-loop optimization. According to the invention, the problems of inaccurate task scheduling, weak real-time response capability, low resource utilization rate and the like in the existing work order management system of the emergency platform are effectively overcome, and the overall efficiency and the automation level of the emergency platform are greatly improved.

The invention discloses a robot pose estimation method based on laser point cloud and visual SLAM, and relates to the technical field of robot sensing and positioning, and the method comprises the steps: in a data collection and integration link, installing a specific camera and a radar to collect and compress data; a data preprocessing and feature strengthening stage: processing the image and the point cloud to improve the quality and extract the features; a cross-modal network is constructed through feature fusion and accurate matching, and a special matching mode is adopted; space-time calibration and coordinate unification are realized by means of a high-precision circuit; multi-modal fusion and reinforcement learning are applied to loop detection and intelligent optimization; correcting filtering and visualizing uncertainty by combining pose smoothing and intelligent output with a prediction model; according to the method, the pose estimation precision, reliability and environmental adaptability of the robot are effectively improved, resource utilization is optimized, the pose uncertainty is visually evaluated, and the overall performance and the intelligent level of the robot are improved.

The invention provides an OracIeRAC-based CDC synchronization method and system, and relates to the technical field of OracleRAC, and the method comprises the steps: deploying an incremental log analyzer, a session state collector and a resource monitor, and respectively collecting atomic operation, concurrent transaction information and node performance indexes; and identifying data stream features by using an intelligent classification model, constructing a priority matrix, and performing fine-grained acquisition on high-priority data based on an adaptive sampling strategy. According to the method, multi-dimensional evaluation indexes are further utilized, a time sequence prediction model is constructed to predict node loads, data synchronization tasks are distributed to low-load nodes based on a load balancing matrix, and a two-stage submission protocol is adopted to ensure data consistency. By monitoring the synchronization state in real time and dynamically adjusting the allocation strategy, the CDC synchronization efficiency and stability in the Oracle RAC environment are effectively improved, and the synchronization delay is reduced.

The invention provides a data fusion sharing method for multi-source heterogeneous power big data, which comprises the following steps: when receiving multi-source heterogeneous power data, carrying out dynamic protocol adaptation, and converting multi-source heterogeneous power data formats of different protocols into a current system internal standard format; performing data quality evaluation on the data converted into the standard format in the current system, and when a quality evaluation result does not meet a quality requirement, performing data restoration; analyzing the data passing the data quality evaluation or restoration, determining the correlation among different data sources, and fusing the data according to the correlation among the different data sources; and performing distributed storage on the fused data for distributed indexing and access. By implementing the method and the system, efficient fusion and sharing of multi-source heterogeneous data can be realized, the intelligent level of data management and analysis of a power system is improved, and the requirements of an intelligent power grid on data instantaneity, accuracy and safety are met.

The invention provides an integrated metadata management method and system based on data source expandability, and relates to the technical field of data sources, and the method comprises the steps: executing real-time detection on a plurality of heterogeneous data sources through a PF4J plug-in framework, collecting connection information, and establishing a dynamic connection pool; obtaining a table structure, a field attribute and an association relationship list, and assembling the table structure, the field attribute and the association relationship list into JSON format data; performing analysis and standardization processing on the data, and constructing an association degree calculation matrix; generating a standardized data structure description file; establishing a three-layer index directory, and detecting structure change and updating; and calculating a data table reference dependency relationship, and generating a DAG data flow path. According to the method, efficient integration and management of heterogeneous data sources are realized, and the metadata processing efficiency is improved.

The invention provides a self-adaptive power supply energy-saving configuration method and system based on energy gradient utilization, and relates to the technical field of power supply management, and the method comprises the steps: collecting the operation parameters of electric equipment, building a power utilization model, and carrying out the self-adaptive correction; constructing a multi-stage energy distribution matrix according to the corrected power utilization model, and performing energy efficiency grading on the power utilization equipment; performing spatial-temporal feature extraction by adopting a bidirectional attention mechanism, generating an electric equipment priority strategy in combination with a self-calibration network, and further calculating an energy distribution proportion of each level of electric equipment; and finally, generating a control signal according to the energy distribution proportion, adjusting the power supply power of each stage of electric equipment, distributing the exceeded power to the electric equipment with the secondary high energy conversion efficiency when the power value exceeds a target power threshold value, and outputting a self-adaptive energy balance control instruction according to the prediction control model to adjust the operation state of the electric equipment.

The invention discloses a multi-underwater-robot cooperative control system and method based on predictive control, and relates to the technical field of cooperative control. During operation of the system, underwater environment data are collected through sensor equipment, an underwater three-dimensional dynamic environment model is generated by using a data fusion technology, and the underwater three-dimensional dynamic environment model is established based on task requirements; tasks are decomposed and distributed to all the robots, through an optimization algorithm, optimal paths of the robots and the mechanical arms are planned, the motion trails of the underwater robot mechanical arms are dynamically optimized based on model predictive control MPC, a real-time control strategy is provided for task division among the multiple mechanical arms, and the real-time control strategy of the underwater robot mechanical arms is provided based on environment modeling and the real-time state. Future motion states of the robot and the mechanical arm of the robot are predicted, an optimization objective function including task completion time, energy consumption and mechanical arm operation precision is constructed, a rolling optimization method is utilized, a control instruction is updated in real time, and real-time data sharing among multiple underwater robots is supported based on an acoustic communication technology.

The invention discloses a cloud computing task storage and computing resource intelligent scheduling method based on an adaptive graph neural network. The method comprises the following steps: S1, constructing a cloud computing basic information set; s2, constructing a graph neural network model of tasks and resources; s3, using the cloud computing basic information set to initialize the graph neural network model; s4, gradually optimizing a resource allocation strategy of the task; s5, updating the embedded vector of the task node based on the optimized graph neural network model; s6, scheduling computing resources and storage resources in real time according to the task priority and the generated resource allocation decision; s7, dynamically updating the embedding vector and the resource allocation decision of the task node; and S8, optimizing the distribution efficiency of storage and computing resources in the cloud computing environment. The resource utilization rate of the system is improved by about 18%, and meanwhile the task execution success rate is improved by about 9%.

The invention relates to the technical field of smart power grids, and discloses a distributed renewable energy source and energy storage cooperative scheduling system of Internet of Things data, comprising a data acquisition and monitoring module used for acquiring the running states of distributed energy sources and energy storage units through the Internet of Things technology; the energy supply and demand prediction module is used for predicting energy supply and load demand according to historical data, real-time data and meteorological information and generating basic data of a scheduling strategy; the collaborative scheduling strategy module formulates a collaborative scheduling scheme of the renewable energy and the energy storage unit based on energy supply and demand prediction, and adjusts the charging and discharging time of the energy storage unit; the double-layer optimization decision module is responsible for long-period energy storage planning and short-period real-time scheduling optimization; and the real-time monitoring and feedback module dynamically adjusts a scheduling strategy based on feedback data. According to the invention, through real-time monitoring, supply and demand prediction and cooperative scheduling of renewable energy sources and energy storage equipment, the system can optimize distribution and use of energy to the greatest extent, and energy waste is reduced.

The invention belongs to the technical field of condensation prevention of power equipment, and discloses an intelligent induction type condensation prevention system and method for the power equipment, and the system comprises a multivariate data collection unit, an intelligent control unit and an execution unit, and achieves the comprehensive real-time monitoring of the internal and external environments of the equipment through the integration of the multivariate data collection unit; the intelligent control unit can accurately predict the condensation risk based on real-time and historical data by using a pre-trained machine learning model, and dynamically adjust the condensation prevention strategy according to the condensation risk, thereby ensuring that condensation can be effectively prevented under various complex environmental conditions; the accurate execution of the execution unit further guarantees the condensation prevention effect, the operation stability and safety of the power equipment are integrally improved, the equipment fault risk caused by condensation is reduced, the service life of the equipment is prolonged, and the unnecessary maintenance cost is reduced; the system has self-learning and self-adaptive capabilities, can continuously optimize a control strategy to adapt to different environmental conditions, and ensures an optimal anti-condensation effect.

The invention discloses a disaster emergency rescue command system and method, and relates to the technical field of rescue command systems. Comprising an information collection module which collects information, including disaster situations, personnel distribution and environmental conditions, of a disaster site through sensors and communication equipment; the collected information is input into the system, so that accurate disaster assessment can be conveniently obtained; the decision support module determines optimal allocation of rescue resources by using a linear programming model according to disaster assessment provided by the information collection module, and plans a rescue path by using a graph theory algorithm; and the command control module is used for converting the rescue decision of the decision support module into a specific rescue instruction. According to the system, the disaster assessment time can be shortened through real-time monitoring and rapid data processing, and the rescue decision-making speed is increased; optimal configuration of resources is realized through an algorithm, resource waste is avoided, and resource availability at a critical moment is ensured.

The invention relates to the technical field of industrial internet, in particular to a smart energy management method and system based on the industrial internet, and the method comprises the following steps: S1, data collection: collecting energy consumption data and environment data in a production process in real time; s2, data fusion: carrying out fusion processing on multi-source data by adopting a data fusion technology; s3, distributed energy optimization: optimizing the energy consumption of each node on the distributed nodes in real time by using an edge computing technology; s4, intelligent load balance optimization: dynamically adjusting the operation load of each industrial production device; s5, multi-level fault prediction: performing fault prediction on the industrial production equipment; and S6, control strategy generation: generating a self-adaptive control strategy in combination with the fault prediction result of the industrial production equipment. According to the method, the lowest energy consumption of the optimization result is ensured, the load is uniformly distributed, local overload and energy waste are avoided, and the overall energy utilization efficiency is improved.

The invention relates to the technical field of real-time prediction of three-dimensional forms, in particular to a real-time dynamic prediction system and method for the three-dimensional form of a high-pressure jet grouting pile. The method comprises the following steps: carrying out stratum characteristic identification on a construction site to obtain stratum distribution data; key monitoring point positions are determined according to the stratum distribution data, and a multi-source sensor monitoring network is established; carrying out anti-interference optimization processing on the multi-source sensor monitoring network, and carrying out real-time acquisition on high-pressure jet grouting parameters to obtain real-time high-pressure jet grouting data; and spatio-temporal evolution processing based on injection pressure, lifting speed and rotating speed is conducted on the real-time high-pressure jet grouting data, and parameter change characteristic data are generated. According to the method, the construction stratum distribution and the high-pressure jet grouting parameters are obtained and analyzed in real time, the parameter-stratum response relation model is established, grout diffusion and pile body forming dynamic prediction are conducted on different stratum characteristics in a targeted mode, and the precise construction capacity under the complex stratum condition is greatly improved.

The invention relates to an adaptive control method and device for a supplementary light source of an industrial camera, and the method comprises the steps: obtaining scene perception data of the industrial camera, carrying out the real-time classification and environment parameter analysis of a shooting scene based on the scene perception data, and obtaining scene feature information; performing image quality evaluation on the scene feature information, and comparing with a preset image quality standard to obtain an image quality matching degree; performing control parameter analysis based on the image quality matching degree and the scene feature information to obtain an initial light supplementing control parameter; acquiring image data of the industrial camera, and performing brightness distribution and detail feature analysis on the image data to obtain an image analysis result; performing dynamic light source simulation on the scene feature information based on an image analysis result to obtain a corresponding dynamic supplementary light source parameter; and performing comprehensive processing on the initial light supplement control parameter, the image analysis result and the dynamic light supplement source parameter to obtain a light supplement source adjustment strategy. The problem that a traditional fixed light supplementing method is difficult to adapt to a complex environment can be effectively solved.

The invention provides a high-altitude energy storage monitoring system. The system comprises an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy scheduling module, a health monitoring module, a self-adaptive adjustment module and a system optimization module. The invention provides an intelligent and environment-adaptive energy storage system monitoring and maintenance strategy, the temperature control strategy, the energy distribution and the maintenance plan can be dynamically adjusted according to the real-time change of the high-altitude environment, the efficient operation of the energy storage system is realized, and the service life of equipment is prolonged. A sensor technology is used for collecting multi-dimensional environment data, and an intelligent algorithm is combined to monitor the operation state of the equipment in real time, predict the health state of the equipment, optimize energy scheduling and formulate a self-adaptive maintenance strategy, so that the fault risk and the maintenance cost are reduced, and the stable operation of the system in a complex environment is ensured.

The invention provides a chip production logistics network optimization method and system based on a graph neural network model, and relates to the technical field of chip production logistics optimizing.The method comprises the steps that logistics data of a plurality of process nodes of a chip production line are collected, node criticality scores are calculated, node types are determined, and the node types are determined; constructing a multi-level logistics network diagram comprising a material transmission edge and a priority-based resource competition edge; the method comprises the following steps: dividing logistics sub-networks, determining association strength among the sub-networks, constructing a sub-network state predictor and a cooperative scheduling module, and inputting a network topology structure and node features into a graph neural network for training to obtain an optimization model; and predicting a sub-network state change based on the optimization model to generate a resource allocation scheme and a node scheduling sequence, triggering an adjustment mechanism when a prediction state exceeds a threshold value, re-planning a material allocation and transmission path, and finally converting the scheme into an equipment control instruction and issuing the equipment control instruction to a process node.