4227 results about "Particle swarm algorithm" patented technology

The invention discloses an intelligent scheduling method for a comprehensive virtual power plant, and relates to the technical field of power plant intelligent scheduling, and the method comprises the steps: carrying out the redundancy elimination filtering and unified time sequence calibration of multi-node sensing data of a cold and hot energy station, energy storage equipment and the like through the deployment of an edge side high-concurrency collection module, generating a high-quality time sequence fusion sequence, and screening schedulable resources; on-line model correction is realized through an error dynamic evaluation mechanism, a multi-stage scheduling strategy is generated by adopting an improved particle swarm algorithm aiming at the targets of economy, reliability and environmental protection, and an optimal instruction is output in combination with an equipment constraint condition. Through priority control and real-time monitoring index evaluation deviation, a rolling correction and standby strategy is triggered, equipment-level fault risk quantification and self-healing control are realized, abnormity is rapidly isolated, and redundant resources are started. Through data-prediction-scheduling-execution closed-loop cooperation, the resource utilization rate, the scheduling precision and the system fault-tolerant capability are significantly improved, and the scheduling capability of source heterogeneous resources is enhanced.

The invention provides a distributed computing resource smart evolution method and system based on digital twinning, and relates to the technical field of resource evolution.The distributed computing resource smart evolution method comprises the steps that a smart sensing probe is deployed at a computing node to collect operation data, a double-layer attention mechanism is used for extracting features, and a digital twinning model is constructed based on a graph neural network; a resource allocation strategy and a scheduling decision are optimized in combination with a hierarchical reinforcement learning framework, a prediction sequence is generated through an attention-enhanced sequence prediction network, and finally an optimal configuration scheme is solved by adopting a particle swarm algorithm. According to the method, the utilization rate of distributed computing resources can be improved, the energy consumption of the system is reduced, and intelligent scheduling and optimal configuration of the computing resources are realized.

The invention discloses a virtual power plant collaborative optimization operation method based on distributed energy, and the method achieves the real-time monitoring of the operation state of a distributed energy resource through the precise modeling of the distributed energy resource, and predicts the change of the power load demand. A multi-objective optimization model and an advanced optimization algorithm, such as a genetic algorithm or a particle swarm algorithm, are utilized, so that the virtual power plant can dynamically adjust energy supply and load demands, and economic benefit maximization, energy efficiency optimization and carbon emission minimization are realized. The method also considers the energy efficiency and economic benefits of the virtual power plant, and improves the comprehensive benefits of the system through comprehensive analysis and optimization. Through effective interaction with the power grid, the virtual power plant can provide peak regulation, frequency modulation and standby services, a demand response mechanism is implemented, and operation of the power grid is further optimized. The method has the beneficial effects of improving the energy utilization efficiency, enhancing the stability of a power grid, optimizing economic benefits, promoting environmental protection, improving the flexibility and adaptability of the system and supporting policies and market mechanisms.

The invention provides a pure electric vehicle charging control method and system, and relates to the technical field of electric vehicle energy storage, and the method comprises the steps: collecting real-time charging data, combining historical data to construct a battery state evaluation model, and calculating battery state information in real time; a multi-objective optimization model is established based on the battery state information, and an optimal charging strategy is solved by adopting an improved particle swarm algorithm; and a multi-dimensional safety monitoring system is established, a corresponding safety plan is executed when abnormal charging is detected, and the plan is fed back to the multi-target optimization model for strategy correction.

The invention relates to the technical field of power dispatching, in particular to a digital twin energy management method and system for source-network-load-storage cooperative dispatching, and the method comprises the steps: collecting source-network-load-storage multi-dimensional space-time operation data, and extracting space-time coupling features through a graph convolution-long and short-term memory network; establishing a simulation model of a digital twin environment, simulating an uncertain operation condition by using a Monte Carlo scene generator, and processing a power flow constraint by using a second-order cone relaxation technology; training an energy storage scheduling agent in a digital twin environment, and learning an energy storage charging and discharging strategy through a near-end strategy optimization algorithm; designing a source-network-load-storage hierarchical collaborative optimization framework, optimizing power output and load distribution by using an improved particle swarm optimization algorithm on the upper layer, and solving power flow distribution by using an alternating direction multiplier method on the lower layer; and establishing a self-adaptive feedback correction mechanism, and dynamically adjusting a cooperative scheduling strategy. According to the invention, intelligent collaborative scheduling of source network load storage is realized, and the operation efficiency and stability of a power system are improved.

The invention relates to the technical field of abalone peptide production, and discloses an abalone peptide production process optimization and intelligent decision-making method and system, and the method comprises the steps: collecting production data through a multi-source sensor, and extracting features through a convolutional neural network; the genetic algorithm optimization model is combined with an adaptive mechanism and a multi-target fitness function to generate a process parameter optimization scheme; and the multi-objective production optimization model outputs optimal production process parameters by means of an improved particle swarm algorithm. In the hierarchical intelligent decision-making model, the planning layer realizes global resource allocation, the scheduling layer adjusts a process in real time based on a Bayesian network and the like, and the operation layer controls equipment through fuzzy logic and constructs quality closed-loop control. According to the method, the problems that parameter control depends on experience, data processing is difficult, optimization is one-sided, decision making is not intelligent and the like in traditional production are solved, data can be accurately processed, the process is optimized, decision making is intelligent, production efficiency, the raw material utilization rate and product quality are effectively improved, cost is reduced, and the application prospect is wide.

The invention discloses a self-adaptive multi-dimensional adjustment metering box based on the Internet of Things, and relates to the technical field of intelligent monitoring of power equipment. The problem of measurement distortion caused by high fixed threshold false triggering rate, weak anti-aliasing capability and multi-source data asynchronization in a high-dynamic industrial scene in the prior art is solved. Multi-parameter acquisition is realized through the sensing acquisition module and the annular buffer area; a reconfigurable FIR filtering module is adopted to dynamically switch a low-pass / band elimination mode to suppress aliasing interference; noise features are extracted by combining spectral kurtosis analysis and concept drift detection, and a dynamic threshold value is generated by using exponentially weighted moving average and a sliding window standard deviation; a multi-channel time sequence is aligned through an IEEE 1588 protocol, and cloud parameter closed-loop optimization is realized based on extended Kalman filtering and a particle swarm algorithm; according to the method, the threshold fault tolerance, the high-frequency transient signal capturing precision and the multi-source heterogeneous data fusion reliability in a high-noise environment are remarkably improved.

The invention provides a power distribution network reliability cooperative control method and system under distributed power supply access, relates to the technical field of electric power, and designs a power distribution network reliability index system considering a distributed power supply based on a load demand uncertainty model, and adopts a Monte Carlo simulation method. Evaluating the reliability of the power distribution network in combination with the power distribution network reliability index system, and formulating a cooperative control strategy including distributed power supply output control, load demand response management and network topology reconstruction according to an evaluation result; a multi-target cooperative control optimization model with reliability improvement, economical efficiency optimization and environmental benefit maximization as targets is established, the multi-target cooperative control optimization model is solved by adopting an improved particle swarm algorithm, and an optimal cooperative control strategy is generated; and based on the optimal cooperative control strategy, issuing and executing a control instruction to each control object.

The invention provides a building air conditioning system optimization control method and device based on spatio-temporal data, and the method comprises the steps: executing temperature simulation, and obtaining N temperature simulation distribution conditions at different moments in a year; establishing a room temperature distribution prediction model based on a graph attention neural network and a gating circulation unit; executing energy consumption simulation to obtain N pieces of energy consumption simulation data at different moments in a year; setting parameters of the air conditioner are used as input, energy consumption at the next moment is used as output, and an energy consumption prediction model is established based on a BP neural network; based on the room temperature distribution prediction model and the energy consumption prediction model, a multi-target particle swarm algorithm is adopted for optimization, an optimal scheme is determined through a weighted sum method, optimization targets are room thermal comfort and air conditioner energy consumption, and decision variables are setting parameters of an air conditioner; and the air conditioning system is controlled according to the optimal scheme, so that the control reliability of the building air conditioning system is effectively 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 present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

The invention relates to the technical field of town sewage pipe network detection, and discloses a town sewage pipe network leakage detection system and method. The system comprises a data acquisition module which uses a multi-source sensor to acquire real-time operation data such as pipe network pressure, flow and the like; the feature extraction module extracts spatio-temporal features based on the multi-scale convolutional neural network, and generates a pipe network state feature matrix; the anomaly detection module inputs the feature matrix into a pre-training model and marks a potential leakage area; the optimization analysis module constructs a multi-constraint dynamic optimization model, and pipe network pressure parameters are optimized by using an adaptive particle swarm algorithm; the hierarchical execution module generates a global regulation and control sequence, dynamically matches local pressure parameters and adjusts the valve opening and the pump station power through a decision layer, a region coordination layer and an execution layer. The system and the method are accurate in detection and reasonable in regulation and control optimization, leakage risks can be effectively reduced, the operation management level of a pipe network is improved, and water resource waste and environmental pollution are reduced.

The invention discloses a modeling analysis method for optimizing an urban water supply pipe network, and belongs to the technical field of intelligent water affairs, and the method comprises the steps: building a comprehensive pipe network database, collecting hydraulic parameters through Internet of Things equipment, and fusing the hydraulic parameters with GIS topological data; constructing a dynamic hydraulic simulation model, coupling real-time data of a pressure sensor with an EPANET model, and correcting a friction resistance coefficient of a pipe section; a hybrid optimization algorithm architecture is designed, a double-target genetic algorithm is adopted for old pipe network transformation, and a constrained particle swarm algorithm is adopted for new pipe network planning; executing multi-stage optimization calculation, generating a pipe diameter feasible solution space in combination with geographical constraints, and iteratively outputting a scheme meeting a pressure threshold value; and generating a pipe network optimal configuration scheme, and outputting an engineering implementation list through spatial overlay analysis and valve regulation logic. According to the method, the problem of modeling distortion caused by a multi-source data island is solved, the defect that a static model is difficult to adapt to dynamic water demands is overcome, and the economical efficiency and the engineering implementability of a pipe network optimization scheme are remarkably improved.

The invention discloses an automatic test case generation method based on a large model and a particle swarm algorithm, and the method comprises the steps: designing a large language model demand analysis module, carrying out the semantic analysis of a demand document or code annotation of a software system through a large model, and generating a preliminary test case template; designing a particle swarm optimization module, and globally optimizing an input parameter combination and an execution path of the test case through a particle swarm optimization so as to improve the test coverage rate and the defect detection capability; and an automatic test execution and feedback module is designed, the particle swarm optimization evaluates the effectiveness of the test case according to the fitness function, a strategy is iteratively optimized and generated, and the test case is adjusted through a feedback mechanism. According to the method, efficient test cases can be effectively generated, various functions and boundary conditions of the system are covered, dynamic optimization of the test cases is achieved through a feedback mechanism, and comprehensiveness and reliability of system testing are ensured.

The invention discloses an electromechanical equipment noise reduction method based on machine learning, and relates to the field of electromechanical equipment noise reduction, and the method comprises the steps: collecting sound pressure and vibration signals during the operation of electromechanical equipment, and collecting the operation state data of the electromechanical equipment; sparse reconstruction is carried out on the noise features; carrying out sound source localization on the reconstructed noise features by using a beam forming algorithm to obtain a noise distribution diagram; constructing a noise reduction model based on deep learning to obtain noise signals after noise reduction; extracting the frequency of the noise signal in a preset frequency range through a peak search algorithm; analyzing the acquired running state data by using a time-varying linear predictive coding algorithm TVLPC to obtain a noise frequency offset caused by the abnormal running state of the electromechanical equipment; generating an optimal solution by using a particle swarm algorithm; and according to the optimal solution of noise control, noise dominant frequency components are extracted for separation, and a plurality of phase-reversal noise reduction control signals are obtained. Aiming at low noise reduction precision of electromechanical equipment in the prior art, the noise reduction control precision is improved.

The invention relates to the technical field of wireless communication networks, in particular to a vehicle ad hoc network based on intelligent networked vehicle WiFi and a V2V interaction method, a vehicle motion state and network load data are acquired through a vehicle-mounted sensor, AP / STA modes are dynamically switched by combining a deep Q network (DQN) model and an epsilon-greedy strategy, and an adaptive topological structure is constructed. And expanding OLSR protocol message embedding path delay fields, calculating and optimizing multi-hop routing selection in combination with pheromone gradient, and realizing message priority classification and differential privacy protection through a federated learning model. And dynamically adjusting an AP / STA proportion threshold based on a particle swarm algorithm, and triggering node role reconfiguration to balance network load. According to the method, the existing WiFi hardware is reused, the deployment cost is reduced, cross-layer routing optimization and a distributed decision mechanism are combined, and the network real-time performance, the expansibility and the group cooperation capability in a dynamic scene are improved.

The invention relates to a low-altitude logistics unmanned aerial vehicle path planning method, which comprises the following steps of 1, discretizing an urban low-altitude region into computable grid units, a dynamic-static obstacle classification modeling technology is combined to endow a static obstacle with a basic risk degree, a height attenuation factor is introduced to quantify the influence of a high-altitude obstacle on a low-altitude path, and a collision risk of a dynamic obstacle is dynamically evaluated through a time dimension risk degree prediction model; 2, based on an improved genetic algorithm, comprehensively considering flight time, height change, risk degree and dynamic risk cost through a multi-objective optimization objective function, and generating a globally optimal task allocation scheme; 3, generating a local optimal three-dimensional path considering meteorological conditions and dynamic obstacle influence by adopting an improved particle swarm algorithm; and 4, performing path planning to feed back actual flight time and energy consumption to task allocation for optimization.

The invention discloses an unmanned aerial vehicle group cooperative inspection control method for power station inspection, and the method comprises the steps: determining an unmanned aerial vehicle group according to a power station inspection task; performing task allocation of the heterogeneous unmanned aerial vehicle cluster by using a multi-objective optimization algorithm; performing unmanned aerial vehicle path planning by adopting an improved dynamic multi-population particle swarm algorithm; based on a graph theory and master-slave control, unmanned aerial vehicle group attitude consistency control is realized; integrating a target detection technology based on deep learning, and constructing an inspection anomaly detection model; performing remote fault diagnosis analysis on the real-time inspection data through an inspection anomaly detection model to obtain an inspection anomaly result; optimizing the inspection anomaly detection model according to the target condition of the real-time inspection data; and dynamically controlling task allocation and path planning of the unmanned aerial vehicle group according to an abnormal inspection result. Through the scheme, potential problems can be found and processed in time, the intelligent control level of power station inspection is improved, and the efficiency and accuracy of unmanned aerial vehicle group cooperative inspection are improved.

The disclosure provides an energy-saving control system and method for a boiler in a thermal power plant. The system includes: a prediction module, used for predicting current boiler operating efficiency; an optimization module, used for optimizing boiler input parameters based on a particle swarm algorithm, determining an optimal operating condition parameter combination, and setting adjustment steps of the optimal operating condition parameter combination; an emission detection module, used for obtaining NOx emission data of the boiler in thermal power plant and calculating a NOx emission fluctuation degree according to the NOx emission data; a parameter correction module, used for setting emission weight according to the fluctuation degree and setting a correction value of the optimal operating condition parameter combination according to the emission weight; a regulating and controlling module, used for regulating and controlling the boiler of the thermal power plant.

The invention discloses an air conditioner energy-saving control method and system based on multi-objective optimization, a medium and a product, and relates to the field of data processing. The method comprises the steps that optimization targets for conducting energy-saving control on the air conditioner and corresponding constraint conditions are determined, and the optimization targets comprise target air conditioner energy consumption and target comfort degree; acquiring current environment data of the air conditioner; the equipment operation parameter combination of the air conditioner is used as particles in the multi-target particle swarm algorithm; determining an optimal solution through a multi-target particle swarm algorithm; the air conditioner is controlled according to the parameter combination corresponding to the optimal solution; the actual air conditioner energy consumption and the actual comfort degree are obtained; based on the target air conditioner energy consumption, the target comfort level, the actual air conditioner energy consumption and the actual comfort level, the equipment operation parameters of the air conditioner are adjusted through a PID control algorithm, and final equipment operation parameters are obtained; and controlling the air conditioner to work according to the final equipment operation parameters. According to the method, both the energy-saving effect and the comfort level of the air conditioner are balanced.

The invention discloses a carbon emission intensity inversion and traceability method and system based on numerical simulation and a reduced-order model. The method comprises the following steps: constructing a full-size three-dimensional map; numerical simulation of pollutant diffusion is carried out according to map information and known environment conditions; reducing the order of the analog data through an intrinsic orthogonal decomposition method; establishing a carbon emission simulation database under the multi-working-condition variable environment based on the reduced-order model; the carbon dioxide concentration and meteorological data are measured in real time and on site, a simulation database is used as a supplement, and the intensity of a known emission source and the intensity and position of an unknown emission source are calculated by adopting a source item estimation method, a particle swarm algorithm and the like. According to the invention, full-size reduction of a limited area and high-precision small-scale carbon emission monitoring can be realized, and effective emission source tracing, intensity analysis, potential emission source prediction and the like can be carried out according to a small amount of field actual measurement data.

The invention provides a multi-target scheduling optimization method for a smart warehouse logistics system, and belongs to the technical field of smart warehouse logistics distribution. An intelligent decision-making strategy is introduced, firstly, according to an attribute relationship between intelligent warehouse logistics entities, when there is an order, statistics is performed on the order, and related information is counted into a database; a multi-objective optimization model for storage environment shelf stability, operation cargo distribution uniformity, in-out warehouse carrying efficiency and the shortest transportation operation path is established, a good point set is adopted to uniformly initialize a population, a position disturbance factor is introduced to update and adjust the particle position of a particle swarm algorithm, and multi-objective optimization solution is performed. Determining an optimal cargo position storage decision under the current condition; an enhanced A * algorithm is put forward to plan a general path of transportation vehicle-mounted order operation from a storage place to a position below a goods shelf, an output path scheme is taken as an initial path planning solution record of an improved particle swarm algorithm, and accurate optimization is further realized by the improved particle swarm algorithm, so that the lowest consumption is realized.

The invention discloses an autonomous obstacle avoidance and path optimization control method for an unmanned aerial vehicle in a complex environment. The method comprises the following steps: A, establishing a simulation environment containing static obstacles and dynamic obstacles; b, obtaining a group of path schemes approaching an optimal solution and a plurality of candidate paths based on a particle swarm algorithm and the initial position of the unmanned aerial vehicle; c, based on a multi-agent depth deterministic strategy gradient algorithm, constructing a state space, an action space and a reward function used for training an obstacle avoidance motion strategy of multiple unmanned aerial vehicles; d, a multi-agent depth deterministic strategy gradient algorithm is used for training, and a path planning strategy is optimized; and E, evaluating scores of all candidate paths through a reinforcement learning mechanism, and calculating to obtain an unmanned aerial vehicle flight path with the highest comprehensive score as an optimal path. According to the invention, a safe and efficient flight path can be quickly and accurately planned for the unmanned aerial vehicle in a complex dynamic scene, and efficient obstacle avoidance of the unmanned aerial vehicle in the complex dynamic scene is realized.

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 discloses a multi-agent cooperation method based on a large language model, and relates to the field of agent cooperation control, and the method comprises the steps: constructing a multi-agent system; establishing a multi-agent coordination resource allocation model; a task allocation problem in the resource allocation model is converted into a multi-objective optimization problem, and optimization objectives comprise the minimum task completion time and the optimal resource utilization rate; and solving a multi-objective optimization problem by using an improved particle swarm optimization (PSO) to obtain an optimal cooperation scheme among the master control agent, the task creation agent, the intention arrangement agent and the at least one professional agent. The improved particle swarm optimization (PSO) evaluates the uncertainty of each candidate solution through multiple times of random sampling, and when the uncertainty exceeds a threshold value, cross validation is performed through a multi-agent voting mechanism. For uncertainty accumulation of a multi-agent system under a complex cognitive task, the efficiency and stability of collaborative decision and task scheduling of the multi-agent system are improved.

The invention discloses an energy equipment carbon emission scheduling optimization method in an integrated energy system, and the method comprises the steps: determining an energy equipment carbon emission factor, constructing a carbon emission cost function, employing Monte Carlo simulation to generate a carbon transaction price fluctuation scene, and carrying out the carbon transaction price fluctuation scene. The method comprises the following steps: calculating a carbon transaction risk index of equipment in each scene according to a cost function, determining start-stop time by economic benefit optimization, constructing a comprehensive energy system topology network structure, updating a carbon flow path set according to the start-stop time, calculating a comprehensive carbon emission coefficient, establishing an energy flow and carbon flow coupling model between a high emission path and a low emission path, and constructing a comprehensive energy system topology network structure. A scheduling strategy feasible space is obtained through a cooperation strategy, and an optimal cooperation scheduling operation strategy is obtained through optimization of a particle swarm algorithm by taking the minimum carbon emission as a target. The method can effectively reduce the carbon emission / optimize the start and stop of the equipment, improves the economical efficiency and environmental protection property of system operation, achieves the dual goals of energy conservation and emission reduction and benefit improvement, and has good interpretability at the same time.

The invention relates to the technical field of supply chain management, and discloses an intelligent supply chain optimization management and control method. The method comprises the following steps: acquiring full-link real-time operation data of a supply chain through a distributed data acquisition module; extracting topological feature data by using a graph neural network; inputting the multi-target collaborative optimization model, and generating supply chain strategy optimization parameters by adopting a mixed integer linear programming framework and a dynamic constraint relaxation mechanism; constructing an adaptive resource scheduling model, realizing node dynamic scheduling by using an improved particle swarm algorithm, and outputting optimal configuration data; and establishing a layered supply chain management and control model which comprises a strategic planning layer, a dynamic coordination layer and an execution control layer, and realizing full-link intelligent optimization management and control. The invention also relates to risk event processing, strategic planning layer resource network planning and the like. The system can comprehensively collect data, optimize resource configuration, improve response speed, reduce cost, effectively control risks and improve the overall competitiveness of a supply chain.

The invention discloses a multi-region collaborative power grid planning system and method based on an improved multi-target particle swarm optimization algorithm, relates to the technical field of power system planning, and solves the problems of multi-target coupling and cross-region coordination in traditional power grid planning by constructing an economical, environment-friendly and reliable multi-dimensional target function and introducing a game theory method to quantify a multi-target constraint relation. The system comprises a data acquisition module, a multi-objective optimization model construction module, an improved particle swarm algorithm execution module, a collaborative decision module and a result output module, the improved particle swarm algorithm adopts dynamic adaptive inertia weight, time-varying acceleration coefficient and differential mutation operation, and the convergence speed and Pareto frontier distribution quality are remarkably improved; and the collaborative decision-making module realizes cross-regional parameter interaction and scheme optimization through a hierarchical collaborative mechanism and a fuzzy entropy theory. According to the method, collaborative optimization of calculation efficiency and scheme balance is realized in multi-regional power grid collaborative planning, and technical support is provided for scientific planning of a complex power grid system.

The invention relates to the field of liquid crystal display, and discloses a liquid crystal display screen dynamic contrast optimization method and system, and the method comprises the following steps: collecting ambient light intensity and backlight module temperature parameters in real time, and combining with image content entropy analysis to dynamically generate a non-uniform backlight partition grid; a brightness diffusion physical model is constructed based on hardware parameters such as the refractive index of the light guide plate and the LED spacing, and the optimal brightness of each partition is solved by adopting a temperature self-adaptive improved particle swarm algorithm; driving voltage is generated through piecewise nonlinear gamma mapping and inverse function iterative calculation, and the driving voltage is adapted to the nonlinear response characteristics of the backlight module; a hardware accelerator is used for executing a diffusion equation and optimization calculation in parallel, and model parameters are dynamically adjusted in combination with feedback of an optical sensor, so that a closed-loop control link is formed. According to the method, the contrast performance and the dynamic scene adaptability of the display screen are comprehensively improved through dynamic partition adaptive to the environment, an optimization algorithm driven by a physical model and real-time processing accelerated by hardware.

The invention provides an automatic task allocation method and system for an elevator emergency rescue platform, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining elevator operation state data and environment information data, constructing an emergency task model, and obtaining classified emergency tasks; inputting the classified emergency tasks into a deep reinforcement learning model to generate a task allocation scheme; the deep reinforcement learning model is formed by combining a deep Q network structure with a priority scanning algorithm, and a priority experience playback mechanism is introduced; and constructing a multi-objective optimization module comprising a time constraint, a space constraint and a resource constraint, and optimizing the task allocation scheme by the multi-objective optimization module based on an improved particle swarm algorithm to obtain an optimal task allocation scheme.