887 results about "Simulating annealing" patented technology

The invention provides a multi-source software supply chain intelligent analysis method and system. The method comprises the following steps: analyzing software component identification information submitted by a user, extracting attribute characteristics, positioning node paths among components, and generating a weighted dependency chain list containing direct and indirect dependency chains; a Bayesian network model is combined with a predefined risk index system, the risk score of each dependency relationship chain is calculated, potential supply chain threat points are identified through an anomaly detection algorithm, and a risk report is generated; the report covers the overall risk score and the threat point of the key link or component; based on the report, drawing a comprehensive risk map by using a multi-dimensional visualization algorithm, and displaying component groups with similar risk modes; and a genetic algorithm and a simulated annealing algorithm are fused to explore a high-risk area, an adjustment suggestion is proposed, and a risk mitigation strategy is generated to reduce the overall risk score. According to the technical scheme provided by the embodiment of the invention, the security and integrity of the multi-source software supply chain are improved.

The invention discloses a multi-target task and resource intelligent modeling method, particularly relates to the field of complex adversarial simulation, is used for solving the problems of dynamic constraint optimization and robustness improvement in multi-dimensional task planning, and aims at realizing multi-dimensional coupling of space-time resource parameters by constructing a three-dimensional hypergraph model, mining a parameter association rule by means of tensor decomposition, and realizing multi-dimensional optimization of the space-time resource parameters. Dynamic constraint quantization is supported, multi-dimensional index priority evaluation is fused in a constraint layered injection stage, hard constraints are recognized, a solution domain is compressed, a hybrid optimization strategy regulates and controls balance between global exploration and local optimization, and after annealing is simulated to jump out of a local extreme value, a multi-target particle swarm algorithm is used for screening a space-time resource equilibrium solution in a trimming solution domain. Digital twinborn verification promotes physical and virtual space interaction data closed loop, a parameter correlation degree matrix is corrected, scheme robustness is enhanced, efficient generation and adaptive optimization of a task planning scheme under complex constraints are realized, and system stability and multi-target cooperation capability under sudden disturbance are improved.

The invention relates to the technical field of ecological restoration evaluation, in particular to a surface mine ecological restoration effect evaluation method and system.The surface mine ecological restoration effect evaluation method comprises the following steps that based on ecological restoration targets and standards, a decision tree algorithm is adopted, potential evaluation indexes are analyzed and screened, data in the aspects of biological diversity and soil quality are processed, and the evaluation result is obtained; and classifying the data to generate a refined evaluation index system. According to the method, through application of the decision tree algorithm, screening and classification of evaluation indexes are more scientific and systematic, comprehensiveness and pertinence of an evaluation system are ensured, the genetic algorithm and the simulated annealing algorithm are applied, the global optimization capability is introduced in the weight adjustment process, and the evaluation accuracy is improved. The accuracy of weight configuration and the adaptability of an evaluation system are remarkably improved, the long-term and short-term memory network model and the support vector machine are combined, the long-term dependency relationship in ecological restoration effect time sequence data is captured, and the accuracy of future trend prediction and the reliability of a classification result are improved.

The invention provides a welding defect identification method, system and equipment for an automobile part and a medium, and relates to the technical field of automobile welding.The method comprises the steps that a structured light vision sensor and a high-frequency ultrasonic sensor are used for obtaining three-dimensional structured light data of a to-be-welded area of the automobile part and sound wave signals generated in the welding process; obtaining characteristic frequency information used for identifying welding internal defects; the method comprises the following steps: capturing a heat distribution image of a to-be-welded area of an automobile part through a thermal imaging camera to obtain area feature information used for identifying welding defect thermal anomaly, and obtaining magneto-optical signal feature information used for identifying welding defects; performing defect identification and classification on the fused feature representation by training a deep learning model to obtain a defect identification result; and welding parameters are adjusted through a simulated annealing algorithm, so that the welding defects of the automobile parts are recognized. According to the invention, various welding defects can be identified, so that the accuracy and efficiency of welding quality detection are remarkably improved.

The present invention relates to the technical field of coal mine water inrush disaster prevention and control, and relates to an artificial intelligence-based method for identifying a water inrush point in a mine and parameters of a simulation model, comprising the following steps: step 1: establishing a numerical model on the basis of observation data, and determining prior information of parameters to be identified including position coordinates of a water inrush point; step 2: on the basis of the numerical model and the prior information of the parameters, generating a training sample dataset and a test sample dataset for an alternative model; step 3: constructing and training an alternative model neural network; step 4: testing the accuracy of the alternative model; and step 5: executing a simulated annealing algorithm to identify the position of the water inrush point and parameters of a simulation model. By using the method provided by the present invention, the problem in accurate positioning of water inrush points in underground coal mines can be solved, which is of great significance in carrying out scientific disaster management and rescue efforts in a timely manner.

The invention discloses an electric vehicle charging station layout optimization configuration method and system, and the method comprises the following steps: obtaining the traffic network information and power distribution network information of a planning target area, and determining the charging demand number of electric vehicles and the charging load demand distribution condition; according to the electric vehicle charging load demand distribution condition, determining the service range of each charging station, and constructing a proposed charging station capacity determination model; an upper-layer charging station site selection model is constructed, and the optimal site of each charging station is determined; establishing a lower-layer mixed integer programming model, and determining the configuration number of various charging piles of each charging station; solving the double-layer locating and sizing planning configuration model by using a hybrid genetic simulated annealing algorithm and an analytic hierarchy process to obtain an optimal locating and sizing scheme of the charging station; according to the method, the attraction of the charging station to the user and different types of charging pile configuration schemes are comprehensively considered, a bilevel planning configuration model is established, an optimal solution set is solved by using a hybrid genetic simulated annealing algorithm, and finally an optimal scheme is determined through an analytic hierarchy process.

The invention relates to the technical field of logistics management, in particular to a warehouse material storage location distributing and sorting system based on a genetic algorithm, and the system comprises a data access and analysis module which is connected with a main database of a warehouse management system through a security protocol, synchronously updates the information of materials, orders and employees, carries out the establishment of a data index, and transmits the data index to a server; according to the invention, through the design of the sorting system and the combination of simulated annealing and a genetic algorithm, the intelligent configuration of the material storage position is effectively realized, the sorting path is optimized, the material carrying time and the manual error are reduced, the sorting accuracy is improved, the AGV is managed by adopting an asynchronous algorithm, the task scheduling is optimized, and the sorting efficiency is improved. The material processing speed is accelerated, the response efficiency is improved, the inventory configuration is dynamically adjusted, the market demand change is adapted, the high efficiency and the quick response capability of the inventory are kept, the automation and the intelligence of the inventory management are promoted, and the operation efficiency and the service quality of the whole warehouse are greatly improved.

The invention relates to a metal cutting machining path planning system, and aims to optimize the efficiency and precision of a metal machining process through advanced technical means. The system comprises a plurality of functional modules: an image acquisition module captures a multi-angle high-resolution image of a to-be-processed metal workpiece; the three-dimensional reconstruction module reconstructs a three-dimensional model by using the images; the cutting simulation module simulates the three-dimensional model in a virtual environment and records stress and heat distribution conditions in the cutting process in real time; the intelligent segmentation module divides the machining process into a plurality of stages according to the stress and heat data; the comprehensive optimization module is combined with a genetic algorithm and a simulated annealing algorithm to optimize the cutting path, depth and feeding speed of each machining stage; the feedback adjusting module monitors stress and heat changes in actual machining, and when the stress and heat changes exceed threshold values, cutting parameters are automatically adjusted or machining is paused. According to the system, through comprehensive application of image processing, analog simulation and optimization algorithms, the cutting efficiency and the product quality are remarkably improved.

The invention provides an intelligent inventory robot path optimization method based on environment modeling and collaborative optimization, and relates to the technical field of intelligent warehouse management, and the method comprises the steps: scanning a warehouse region through an unmanned plane platform, and generating three-dimensional warehouse point cloud data; after point cloud denoising, plane fitting, shelf boundary extraction and channel detection processing, a two-dimensional storage plane graph is generated and transmitted to a robot data base; generating an initial inventory planning path by adopting an A * algorithm, optimizing the initial path through a simulated annealing algorithm, and generating a target inventory planning path; based on the dynamic obstacle influence factors, the RFID signal intensity and the robot residual electric quantity are combined, and a target path is corrected through a mixed algorithm of multi-target optimization and PSO dynamic weight; and the operation end checks the checking progress through the AR equipment and manually adjusts the target path, so that the checking path of the intelligent checking robot can be optimized in real time in a dynamic environment, and the checking efficiency, the coverage rate and the cruising ability of the robot are improved.

According to the tray cycle scheduling system and the application method, historical data and a production plan are fused, an improved algorithm is adopted to predict tray requirements, and an attention mechanism is introduced to improve precision; the method comprises the following steps: acquiring multi-dimensional state information of a tray through a multi-modal sensor, eliminating noise by using a data fusion algorithm, and constructing a digital twin model to realize state synchronization; a double-layer optimization architecture is constructed, an upper layer solves a global scheme by combining an improved particle swarm and a simulated annealing algorithm, and a lower layer dynamically adjusts a path through reinforcement learning; an instruction is generated based on a digital twin model, an event triggering mechanism is adopted to reduce communication load, and virtual-real interaction closed-loop control is realized; a real-time evaluation index system is established, a meta-learning algorithm is utilized to quickly adapt to a new environment, and system parameters are continuously optimized. Multi-module collaborative innovation is achieved, the tray scheduling efficiency and the intelligent level are remarkably improved, production logistics whole-process collaborative optimization is achieved, and core support is provided for cost reduction and efficiency improvement of enterprises.

The present disclosure provides an artificial intelligence-based method for enhancing mine safety by identifying and predicting locations of water inrush points in a mine, including the following steps: S1: constructing a numerical model to determine priori information of parameters to be recognized based on observation data, including coordinates of locations of water inrush points; S2: generating a training sample dataset and a test sample dataset of an alternative model based on the numerical model and the priori information of the parameters; S3: constructing and training a neural network of the alternative model; S4: testing an accuracy of the alternative model; and S5: performing a simulated annealing algorithm to identify the locations of water inrush points and simulation model parameters.

The invention discloses a task scheduling method and system for a dense warehousing system, and the method comprises the steps: building an elevator task distribution model, a shuttle vehicle selection model and a warehouse-in and warehouse-out task time mathematical model according to the characteristics of warehousing operation, and preliminarily determining a task distribution scheme of an elevator through employing a heuristic rule on this basis, an improved A * algorithm is used for carrying out path planning on the shuttle vehicle, the time needed by the shuttle vehicle for completing a task is calculated, it is ensured that the elevator and the shuttle vehicle start to run, the scheme is further optimized through an improved PSO-SA algorithm, and the algorithm combines particle swarm optimization and a simulated annealing algorithm, so that the algorithm is optimized. A specific scheduling result of cooperative task execution of the elevator and the shuttle vehicle can be obtained, a scheduling scheme which enables the cooperative work effect of the whole system to be optimal is finally obtained, and maximization of the work efficiency is ensured.

The invention discloses a monitoring system and method for working data of an AGV robot, and belongs to the technical field of artificial intelligence. The method comprises the following steps: preprocessing and integrating the driving track, freight weight, running state and task execution data of the AGV robot to form a robot working data set; clustering analysis is carried out based on driving tracks of all robots, and similar tracks are clustered into one class; constructing a dynamic driving track planning model by using an A * algorithm, and generating a plurality of candidate optimal driving tracks for each task combination; constructing a task demand list, and carding a task sequence logic relationship; performing task allocation on the AGV robot by using a greedy algorithm; generating a task allocation list by using a simulated annealing mechanism; a task instruction is issued to a corresponding AGV robot control system, a task execution process is started, and whole-course tracking is started; and after the AGV robot completes a single task and a round of task sequence, a model self-learning updating mechanism is triggered.

The invention relates to a network routing optimization method and device based on particle swarm optimization and simulated annealing, and the method comprises the steps: taking a network device as a node, taking a communication link as an edge, representing a distributed network as a graph, endowing each edge with a corresponding weight, and enabling the weights to represent the parameters of delay, bandwidth and packet loss rate; a plurality of particles are created, a particle swarm is initialized, and each particle of the particle swarm represents a path combination from a source node to a destination node; adjusting the state of each particle based on a speed and position updating rule of a particle swarm optimization algorithm; and carrying out local optimization on the current global optimal particle according to a simulated annealing algorithm. According to the network routing optimization method based on particle swarm optimization and simulated annealing, through a hybrid optimization method combining a particle swarm optimization algorithm and a simulated annealing algorithm, the problem of local optimization and the problem of insufficient adaptability in existing network routing are solved.

The invention discloses a dynamic short-term and temporary intelligent forecasting system for mine time-delay rockburst, and belongs to the field of short-term and temporary forecasting of rockburst disasters. The system comprises a deep well three-dimensional micro-seismic field global sensing module, a micro-seismic characteristic parameter extraction module, a rockburst catastrophe evolution data set construction module, a data preprocessing module, a deep learning prediction module, an interpretability analysis module and a dynamic short-term and temporary intelligent forecasting module. A dynamic tracking type micro-seismic sensing network is constructed through a three-level annular nested layout strategy, micro-seismic event data are collected in real time, spatial-temporal characteristic parameters are extracted, a rockburst time sequence data set is constructed in combination with a sliding time window method, and the occurrence probability and intensity level of rockburst are predicted. The system adopts a grey wolf-simulated annealing hybrid algorithm to optimize model parameters, and improves the credibility of a prediction result through SHAP value analysis. The system can realize dynamic short-term and temporary early warning of rockburst disasters, and provides scientific decision support for mine safety management.

The invention discloses a multi-satellite task scheduling method based on a genetic algorithm, and belongs to the technical field of satellite task scheduling. Establishing a scheduling model based on satellite and ground station visible window data, and integrating a scheduling period constraint, a task uniqueness constraint, an equipment protection time constraint and a frequency band and orbit type matching constraint by taking task income maximization as a target; a simulated annealing local search mechanism is introduced into the genetic algorithm, local optimum is jumped out through a probability acceptance inferior solution, and a self-adaptive strategy of mutation probability is dynamically adjusted according to population fitness change; setting an early stop mechanism to terminate iteration in advance when the fitness is continuously not improved; and finally generating a scheduling scheme. According to the method, the problems that a traditional genetic algorithm is prone to falling into local optimum and constraint processing is rigid are solved.

The invention discloses a production scheduling method, a production scheduling system and electronic equipment, and belongs to the technical field of production management and scheduling. The production scheduling method comprises the steps that according to production requirements and a preset production correlation model, a fitness function and an initial production scheduling scheme set are determined, and the fitness function comprises at least two of the following production scheduling strategies: total delay time minimization, resource utilization rate maximization, production line load balancing and intermediate product minimization; at least two preset algorithms are fused, the initial production scheduling scheme set is optimized based on the fitness function, the optimal production scheduling scheme is obtained, and the preset algorithms comprise any one of a genetic algorithm, a simulated annealing algorithm and a tabu search algorithm. The method can solve the problems of insufficient processing of resource constraints, low production scheduling efficiency and incapability of effectively finding the optimal production scheduling scheme in related technologies.

The invention relates to the technical field of aircraft optimization, and discloses an expressway low-altitude aircraft optimization arrangement method, which comprises the following steps: constructing an evaluation index system, determining the comprehensive weight of each evaluation index by using an analytic hierarchy process and an entropy weight method, and identifying the importance of the evaluation indexes; constructing a low-altitude aircraft optimization arrangement model, and solving by utilizing a simulated annealing algorithm to obtain static optimal quantity configuration of the low-altitude aircrafts of the service site; dynamically adjusting the static optimal number configuration of the low-altitude aircrafts of the service site by using a social emotion resonance avoidance algorithm; and according to the final number configuration of the low-altitude aircrafts of the service site, combining the social emotion data to optimize the flight path of the low-altitude aircrafts. According to the method, various complex factors are comprehensively considered, efficient arrangement and path optimization of the low-altitude aircraft are achieved, investment of the low-altitude aircraft is more global and economical, and important technical support is provided for application of the low-altitude aircraft industry.

The invention relates to the technical field of network communication, provides a network self-healing device based on node state data, and aims to improve the stability and self-healing capability of network communication. Through real-time monitoring of the network state, state estimation and filtering are carried out in combination with an extended Kalman filtering algorithm, and fault prediction, path selection and resource allocation are carried out through an ant colony algorithm and a simulated annealing algorithm, so that prediction and rapid recovery of potential faults are realized. The device comprises a state monitoring module, a state estimation and filtering module, a fault prediction and prevention module, a fault recovery module and a resource redistribution module, forms a closed-loop optimization mechanism, and is particularly suitable for a complex large-scale network environment, such as a data center network.

The invention provides an electrocardiosignal reconstruction method based on hybrid optimization and multi-modal feature fusion, and the method comprises the steps: obtaining a 12-lead electrocardiosignal, processing the 12-lead electrocardiosignal through a linear regression model, a genetic algorithm and a simulated annealing algorithm in sequence to obtain an optimal three-lead electrocardiosignal, and reconstructing the optimal three-lead electrocardiosignal according to the optimal three-lead electrocardiosignal. The optimal three-lead electrocardiosignal is subjected to one-dimensional convolution layer and maximum pooling processing in sequence to obtain time domain features, the time domain features, the frequency domain features and expert features are subjected to multi-modal feature fusion to obtain fusion features, and the fusion features are input into a Transform mechanism to be processed to obtain a reconstructed electrocardiosignal. The method has remarkable effects in long-range time sequence modeling and local waveform detail optimization. The traditional CNN / RNN is limited by the problem of local receptive field or gradient disappearance, the global rhythm and the local form are difficult to balance, the QRS is adopted to perceive a Transform architecture, and a multi-head self-attention and nonlinear feed-forward network is combined, so that the width error of a QRS wave group is smaller.

The invention provides an urban rainfall emergency processing method and system, and relates to the technical field of rainfall disaster emergency processing, and the method comprises the steps: obtaining historical urban rainfall data and drainage information, and generating an urban rainfall simulation model through the three-dimensional modeling and rainfall simulation technology; furthermore, a disaster tree model is constructed in combination with a machine learning algorithm and a fault tree analysis method, and association and influence relationships of historical rainfall disasters are identified. And based on the disaster template data and a preset historical emergency processing scheme, systematically analyzing and optimizing a disaster prediction model, and realizing real-time prediction of disaster information and generation of the emergency processing scheme. An emergency processing scheme is optimized by adopting a simulated annealing algorithm, and optimal response under different disaster levels is ensured. Through multi-level data analysis and algorithm optimization, the accuracy and efficiency of emergency response are improved, and the occurrence probability and loss of disasters such as urban waterlogging are reduced.

The invention discloses a ship power system optimization control method and system based on a simulated annealing algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining operation parameters and historical energy consumption data in real time, and constructing a multi-objective optimization function of dynamic weight distribution; generating an initial temperature parameter and a solution set in combination with the navigation state and the environment data; a neighborhood search strategy disturbance solution set is improved, a new solution is evaluated by using a dynamic acceptance probability function, temperature parameters are adaptively adjusted for iterative optimization, and an optimal control parameter combination is output; and an adjustment instruction set is generated after multi-dimensional efficiency verification, and a propulsion device, a generator set and an energy storage module are cooperatively controlled, so that global energy consumption optimization is realized. According to the method, through data driving and intelligent algorithm fusion, energy efficiency and environmental adaptability are improved, and stable operation under complex working conditions is guaranteed. According to the ship power system optimization control method and system based on the simulated annealing algorithm, the energy efficiency level and the environmental adaptability of the ship power system are improved.

The invention relates to an ecological system carbon sink evaluation method, which comprises the steps of generating a continuous climate distribution grid based on a global ecological system type and climate element data, extracting multi-dimensional characteristic parameters of temperature, rainfall and a carbon sink value through an improved weighted K-means clustering algorithm, quantifying climate transition zone boundary fuzziness in combination with a covariance matrix, and evaluating the ecological system carbon sink evaluation result. Dynamically adjusting the weight of the spatial variation coefficient; a simulated annealing algorithm is utilized to optimize correlation between the division scale and the carbon sink response, and a multi-scale regression model is constructed to generate a high-precision carbon sink distribution diagram; further fusing the remote sensing vegetation index and the ground actual measurement data, and eliminating the evaluation error caused by the conflict between the boundary fuzziness and heterogeneity of the transition zone; according to the method, the problems of insufficient climate-ecology interaction dynamic response modeling and low boundary division precision in the prior art can be solved, the reliability of carbon sink evaluation in complex areas such as forest-grassland interlaced areas and coastal wetlands is remarkably improved, and scientific data support is provided for ecological restoration project site selection and carbon trading markets.

The invention discloses a system and a method for multi-parameter cooperative control in PVC steel wire hose production, and relates to the technical field of plastic hose production, the system comprises a parameter monitoring module, a central processing module, a parameter adjusting module and a quality detection module, the parameter monitoring module is a high-precision intelligent sensor and has a self-adaptive filtering function, and key parameters are measured and transmitted to the central processing module; the central processing module uses an improved multi-objective optimization algorithm to calculate an optimal cooperation value, a simulated annealing-genetic hybrid algorithm is used for iteration, the parameter adjustment module uses intelligent PID, adaptive variable structure control and model prediction control adjustment equipment, and the quality detection module uses multi-modal fusion deep learning and high-precision physical performance detection equipment. And if not, recalculating the adjustment parameters. The production quality and efficiency of the PVC steel wire hose are improved, the cost is reduced, parameters are precisely and cooperatively controlled, the hose is stable in physical performance and few in surface defect, the raw material and equipment maintenance and energy consumption cost is reduced, and the enterprise revenue and market competitiveness are improved.

The invention discloses an unmanned aerial vehicle cluster formation configuration and spectrum resource joint optimization method, system, device, medium and product, and relates to the field of unmanned aerial vehicle planning control, and the method comprises the steps: dispatching a plurality of unmanned aerial vehicle formations to execute a plurality of tasks under the conditions of given spectrum resources and limited number of unmanned aerial vehicles, constructing an optimization problem model by using a coupling relationship among the number of the unmanned aerial vehicle formations, a target sensing sequence and spectrum resource allocation; solving the optimization problem model by using an improved genetic algorithm, and outputting an optimal unmanned aerial vehicle cluster formation configuration and spectrum resource joint optimization scheme; according to the improved genetic algorithm, a simulated annealing algorithm is embedded into the genetic algorithm, a crossover operator of the genetic algorithm is adjusted, and an elite strategy and a dynamically adjustable mutation rate are introduced into mutation operation. According to the invention, joint planning of unmanned aerial vehicle task planning, unmanned aerial vehicle formation number distribution and bandwidth selection can be realized, and the method has higher global optimization capability.

The invention discloses a runoff prediction method for optimizing a neural network based on a hybrid optimization algorithm, and belongs to the technical field of hydrological forecasting. Neural network parameters are optimized through an SAPSO algorithm (simulated annealing particle swarm optimization), and daily runoff prediction is further carried out. The method comprises the following steps: setting parameters of a neural network and an optimization algorithm, including the number of input layer nodes, the number of hidden layer nodes, the number of training times, the population scale and the like; the method comprises the following steps: dividing ten-year daily runoff actual measurement data of a Taojiang station into a training set and a test set, and carrying out normalization processing to prepare for training and testing of a neural network; the weight and the threshold of the neural network are randomly initialized, parameters are transmitted to a simulated annealing particle swarm optimization algorithm for iterative optimization, optimal network parameters are obtained, and a prediction network model is established; and training the network model by using the grouped training set data, and predicting the runoff volume of the Taojiang station by using the training model. And the prediction precision of the runoff volume is obviously improved.

The invention provides a low-orbit satellite beam scheduling optimization method and system based on a simulated annealing algorithm, and belongs to the technical field of satellite communication. Firstly, modeling is carried out on a determined satellite communication system model and communication service distribution in a satellite coverage range, then a channel model between a satellite and a communication terminal is established, then constraint conditions of a problem are determined, mathematical modeling is carried out on the optimal beam scheduling problem, and the optimal beam scheduling problem is obtained. In order to simultaneously improve the coverage communication service volume and reduce the inter-beam interference, the beam scheduling problem is solved by using the simulated annealing algorithm, the optimal design of the beam pattern is realized, and the comprehensive performance of the low-orbit satellite communication system is improved.

The invention discloses an intelligent optimization system and method for logistics transportation cost management, and relates to the technical field of intelligent logistics management, and the system comprises a data collection and analysis module which is responsible for collecting basic data of goods and analyzing the data; the constraint condition and transportation characteristic analysis module analyzes the characteristics of various constraint conditions and actual transportation environments in the cargo transportation process; the initial loading scheme generation module is responsible for generating an initial loading scheme according to characteristics and constraint conditions of goods; when an initial loading scheme is generated, three heuristic methods of first adaptation, optimal adaptation and worst adaptation are adopted, historical data are combined to guide generation of the scheme, and meanwhile, a genetic algorithm and a simulated annealing algorithm are introduced to further optimize the scheme, so that an approximate optimal solution is efficiently searched in a solution space.

The invention belongs to the technical field of power supply recovery, and particularly relates to a rapid power supply recovery method and device based on source-load-storage cooperative power grid partition optimization, and the method comprises the steps: S1, respectively constructing an electrical coupling degree index, a source-load-storage power balance index, a grid structure index and a node scale index, and comprehensively forming a power grid partition comprehensive index; s2, taking the comprehensive index of the power grid partition as a fitness function, designing an energy entropy driven hybrid selection mechanism to improve a simulated annealing genetic algorithm, and solving the power grid partition by using the improved simulated annealing genetic algorithm; s3, on the basis of the power grid partitions, taking each partition as a unit, and establishing an optimal scheduling model taking the maximum recovery of the important load as a target; and S4, solving the optimal scheduling model by adopting a particle swarm algorithm to obtain a rapid power supply recovery scheme. Priority recovery of the key load can be realized, and rapid recovery capability and reliability of the power grid are enhanced.

The invention relates to the field of automatic crane control, in particular to a gantry crane remote control platform access system and method, and the system comprises a task prediction module, a task distribution module, a task execution module and an exception handling module. The task prediction module generates task prediction data by collecting the task pool and the equipment state data; the task allocation module dynamically generates a task allocation scheme by using a reinforcement learning optimization algorithm and a simulated annealing algorithm; the task execution module generates control path data through real-time data acquisition and multi-modal data fusion, and controls equipment to execute tasks; and the exception handling module predicts a fault through a time sequence model and implements a task relay and dynamic recovery mechanism. According to the method, the efficiency of task scheduling, the precision of a control path and the stability of system operation are improved, and the execution efficiency of a remote control task is remarkably improved.