2495 results about "Particle swarm algorithm" patented technology

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 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 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 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 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 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 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 provides a multifunctional integrated cleaning robot for a photovoltaic module of a power station, which belongs to the technical field of photovoltaic module cleaning and comprises a control chip, a robot body device, an automatic cleaning device, a dust collection device, a water spraying system device, a solar charging device, an intelligent navigation device, an environment monitoring device, a communication interface device and a power management device. An intelligent cleaning control module is arranged in the control chip. The method comprises the following steps: constructing a three-dimensional map through environment perception; evaluating the pollution state of the photovoltaic module by using a sensor to generate a thermodynamic diagram; a double-layer optimization model is adopted for path planning and resource allocation, an upper layer adopts an improved ant colony algorithm to take charge of a global path, and a lower layer optimizes cleaning parameters based on a particle swarm algorithm; cleaning operation is executed, and real-time monitoring is carried out; evaluating the cleaning effect; water resource and energy intelligent management is carried out; multi-machine collaboration and task allocation are realized; processing an abnormal condition; the cleaning strategy is continuously optimized through machine learning, and efficient and accurate photovoltaic module cleaning is achieved.

The invention relates to the field of mine ventilation, in particular to an intelligent adjusting method and system for mine ventilation. The method comprises the following steps: constructing a dynamic topology model taking a roadway as an edge and taking a fan and an air door as nodes based on a mine CAD drawing; acquiring the actual wind pressure and the actual wind volume of each roadway branch in real time, performing inversion to obtain the current wind resistance, and updating the wind resistance attribute of the edge in the dynamic topology model by using the current wind resistance; calculating the optimal air volume of the roadway branch by using the updated dynamic topology model; based on the optimal air volume, target regulation and control parameters of the draught fan and the air door are obtained through solving by means of a particle swarm algorithm; and according to the target regulation and control parameters, the fan and the air door are adjusted through a fuzzy PID algorithm. The problem that in the prior art, mine ventilation adjustment is not intelligent and safe enough is solved, the theoretical optimal air volume is obtained through deep analysis of the mine, then ventilation adjustment is conducted step by step, and the scientificity and efficiency of ventilation adjustment are improved.

The invention discloses an automobile front subframe lightweight design method considering modal constraint. The method comprises the following steps: (1) constructing a front subframe parameterized model and a weight-modal simulation model by adopting CATIA (Computer-graphics Aided Three-dimensional Interactive Application) and Altar Optistruct; (2) establishing a population in a design space based on a space filling Latin hypercube sampling method; (3) constructing a classification cooperation speed updating formula based on a particle swarm algorithm to generate offspring particle individuals; (4) constructing a Gaussian process machine learning model and a fitness evaluation function, and screening an optimal offspring particle individual based on a feasibility rule; and (5) constructing a comprehensive score calculation formula according to a statistical ranking method to determine the updating condition of the population individuals, updating the database and skipping to the step (3) until the optimized structure meets the requirement. According to the method, the coverage capability of the filial generation in the target space is enhanced through the classification collaborative speed updating formula, and the prediction precision of the Gaussian process machine learning model is continuously improved, so that the optimization performance of the lightweight of the front subframe of the automobile is improved.

The invention provides a wind power mixed tower damage prediction method and system based on digital twinning. The method comprises the steps of obtaining multi-source data of a wind power mixed tower; wherein the multi-source data comprises structure response data, environment load data and historical operation and maintenance data; based on a building information model and finite element model fusion technology, constructing a digital twinborn model of the wind power mixing tower; inputting the multi-source data into a digital twinborn model to realize real-time mapping of the wind power mixed tower and the digital twinborn model, and obtaining real-time structure state data of the wind power mixed tower through the digital twinborn model; inputting the multi-source data and the real-time structure state data into a pre-trained LSTM-GRU hybrid neural network model, and predicting a damage evolution trend of the wind power hybrid tower in combination with an attention mechanism; and on the basis of the damage evolution trend, a maintenance scheme of the wind power mixed tower is generated by using a multi-target particle swarm algorithm, so that balanced optimization of maintenance cost, power generation loss and risk level is realized, and the operation and maintenance efficiency and safety of the wind power mixed tower are integrally improved.

The invention discloses a building robot multi-machine collaborative operation optimizing and monitoring method and system based on digital twinning, and belongs to the technical field of building construction intellectualization. The method comprises the following steps: constructing a digital twinborn body comprising an environment perception model and a kinematics / dynamics model based on a building information model and robot physical parameters; performing multi-machine task allocation and path planning containing static / dynamic obstacle avoidance in the virtual environment; synchronizing physical environment data in real time through a multi-mode sensor; dynamically optimizing operation parameters by adopting a genetic algorithm or a particle swarm algorithm and realizing closed-loop control; and generating a safety early warning and emergency scheme based on machine learning. According to the method, Markov decision path planning of reinforcement learning and multi-agent game task allocation are creatively fused, laser radar-vision-inertial navigation multi-source data fusion is adopted, the technical problems that in a traditional method, digital twinning precision is insufficient, and dynamic cooperation efficiency is low are solved, and the method is suitable for large-scale popularization and application. And the construction efficiency, the safety and the man-machine interaction experience are remarkably improved.

The invention provides a water supply pipe network optimization scheduling method and system based on multi-model coupling, and the method comprises the steps: collecting the basic data of a regional pipe network, and building a water supply pipe network hydraulic model; obtaining a water supply network operation scheduling target, and establishing a water supply network scheduling scene model; historical monitoring data of a water supply network are acquired, a node water demand prediction model and a node water demand change curve prediction model are constructed by using a neural network algorithm, and node predicted water demand and a node water demand change curve during prediction are respectively output; constructing a water supply pipe network optimization scheduling model based on multi-model coupling, performing optimization by using an improved particle swarm algorithm, outputting a water supply pipe network optimization scheduling scheme, and introducing an optimization scheduling evaluation index to perform scheduling scheme evaluation; and constructing a water supply pipe network optimization scheduling scheme screening model, and obtaining an optimal water supply pipe network optimization scheduling scheme. According to the method, a multi-model coupling mode is adopted, the prediction error is effectively reduced, the optimization efficiency is improved, and intelligent and precise scheduling of the water supply pipe network is realized.

The invention discloses an unmanned aerial vehicle path planning method based on multi-objective optimization and improved particle swarm optimization. The method comprises the steps of 1, constructing a three-dimensional space map model; 2, introducing a multi-objective optimization strategy, and designing an objective function by adopting a weighted objective optimization method for evaluating the advantages and disadvantages of each path; 3, initializing particles by adopting an improved RRT algorithm in combination with a Sobol low-difference sequence, calculating a fitness value of each unmanned aerial vehicle path, and recording an optimal solution; 4, introducing a dynamic inertia weight adjustment strategy, and dynamically adjusting the inertia weight according to the number of iterations; a dive search mechanism in an eagle search algorithm is fused, and a particle restart mechanism is introduced to avoid falling into local optimum; 5, judging whether the set number of iterations is reached or not; if yes, iteration is stopped, and the optimal route of the unmanned aerial vehicle is returned to the environment model; if not, iteration is continued, and the optimal air route is searched. The invention aims to improve the path planning efficiency and robustness of the unmanned aerial vehicle in a complex environment.

The invention relates to the technical field of metasurface antennas, and provides a metasurface antenna parameter optimization method and system based on a convolutional neural network, and the method comprises the steps: collecting metasurface sample data; extracting a comprehensive electromagnetic feature set, and establishing a nonlinear mapping relation model; constructing an antenna performance comprehensive evaluation function, inputting the nonlinear mapping relation model and the antenna performance comprehensive evaluation function into a hybrid optimization algorithm to generate a parameter candidate set, and performing local optimization on the parameter candidate set by using a particle swarm algorithm to obtain a metasurface antenna parameter combination; and extracting electromagnetic characteristics of the metasurface antenna parameter combination, iteratively adjusting the height parameter of the resonant cavity until the height parameter meets a threshold value to obtain an electromagnetic simulation verification result, and feeding back the electromagnetic simulation verification result to the deep Q neural network model for parameter updating to obtain an optimal metasurface antenna parameter combination. According to the method, the optimization of antenna parameters is realized, the design efficiency of the metasurface antenna is improved, and the consumption of electromagnetic simulation calculation resources is reduced.

The invention relates to the technical field of water pump control, and provides a water supply and drainage pump set assembly and a digital control system, which realize self-adaptive control under a complex working condition by fusing multi-source data such as pressure, flow and temperature, break through the dependence of traditional control on a single parameter, and improve the response capability of the control system to water fluctuation and equipment state change. Based on a long-short-term memory network trained by historical multi-source data, a control system can predict a pressure set value in advance, the pressure set value is solved through a particle swarm algorithm, and weight fusion is performed according to the predicted pressure set value and the solved pressure set value to generate an adjustment instruction, so that the starting and stopping times of equipment and mechanical wear are reduced, and the service life of the equipment is prolonged. The maintenance cost is reduced, and water resource waste is avoided through accurate control; and meanwhile, through a redundancy design and a fault-tolerant mechanism, the control system can still operate stably under the scenes of sensor faults, equipment abnormity and the like.

The invention provides an intelligent optimization method and system for tunnel smooth blasting blast holes in layered rock mass, and belongs to the technical field of tunnel engineering and blasting. The method comprises the steps of collecting stratified rock geological data, evaluating and extracting features, constructing a multi-objective optimization function, solving by an intelligent optimization algorithm, performing construction feedback and dynamic optimization and the like. The method comprises the following steps of: acquiring data by using various geological exploration equipment, performing evaluation processing on the data by using a support vector machine model, constructing an optimization function considering multiple indexes, solving an optimal blast hole parameter combination by combining a genetic algorithm and a particle swarm optimization algorithm, and finally feeding back a blasting effect to dynamically adjust an optimization scheme. According to the intelligent optimization method and system for the smooth blasting blast holes of the tunnel in the layered rock mass, the blasting effect can be improved, over-break and under-break and concrete consumption can be reduced, the construction cost can be reduced, the construction safety and efficiency can be enhanced, and remarkable economic and social benefits can be achieved for layered rock mass tunnel engineering construction.

The invention relates to the technical field of robot control, in particular to an energy consumption optimization and automatic charging control system for an intelligent pet robot. The system comprises a robot data acquisition module, an energy consumption state evaluation module, an energy efficiency optimization module, an automatic charging scheduling module and a battery health management module which are in communication connection in sequence. The system is driven by multi-dimensional sensing data, energy consumption characteristics are extracted in combination with potential energy field mapping and sparse modeling, and an energy consumption influence map is constructed; joint optimization of the path and the servo strategy is realized by using a quantum behavior particle swarm algorithm; scheduling a charging path through a graph attention network; and the battery health state is dynamically predicted based on an incremental learning algorithm, and the operation efficiency, the cruising ability and the system intelligence of the robot are improved. According to the method, a closed-loop control process is constructed from energy consumption evaluation to energy efficiency optimization, automatic charging and battery health management, good system integration and intelligent response capability are achieved, and the cruising ability and operation stability of the pet robot in a complex application scene are remarkably improved.

The invention discloses an intelligent rehabilitation guidance system based on machine learning, and relates to the technical field of virtual rehabilitation guidance, and the system comprises an integrated multi-source sensor, collects the physiological and motion data of a user in real time, and constructs health data; fusing multi-modal data, adjusting the resistance difficulty according to the condition of the patient through virtual reality, and calculating a comprehensive health score and an action offset early warning index; setting a rehabilitation cooperation particle swarm algorithm, combining the comprehensive health score and the risk early warning index, combining the resistance difficulty and the muscle-joint cooperation coefficient to compensate action compensation, optimizing rehabilitation action parameters, and returning to the upper layer; and analyzing an abnormal root based on a structural causal model, and generating targeted rehabilitation suggestions. The problem that the risk of secondary injury is increased due to lack of dynamic regulation and control of training intensity and muscle compensation behaviors of a patient in traditional rehabilitation training is solved.

Provided are a cluster air conditioner collaborative control method and apparatus, and a storage medium. The method comprises: under the constraint of a peak regulation market and a carbon market, obtaining the total income of an air conditioner aggregator on the basis of a peak regulation income of a peak regulation response stage, a rebound fluctuation reward and punishment income of a rebound optimization stage, an income of the carbon market and the total compensation cost of an air conditioner user; by using the maximization of the total income of the air conditioner aggregator as an objective, constructing a cluster air conditioner collaborative control model oriented to two stages of peak regulation response-rebound optimization; using a particle swarm algorithm to obtain an optimal solution of the cluster air conditioner collaborative control model to serve as an optimal temperature regulation strategy of cluster air conditioners; and performing collaborative control on all the regulatable cluster air conditioners on the basis of the optimal temperature regulation strategy of the cluster air conditioners.

The invention provides a desert photovoltaic support pile foundation uplift bearing capacity detection and construction parameter optimization method, and relates to the technical field of construction optimization, and the method comprises the following steps: dividing construction subareas according to soil parameter information of a desert target area, carrying out test pile detection in each construction subarea, and establishing a distribution rule of uplift bearing capacity of a pile-soil interface; the pile foundation diameter, the pile foundation burial depth and the pile foundation surface treatment mode serve as variables, a multi-objective optimization function including an uplift bearing capacity stability objective, an uplift bearing capacity redundancy objective and a construction efficiency objective is established, and the weight coefficient of the multi-objective optimization function is dynamically adjusted along with the environment temperature and sand bed migration; solving the multi-objective optimization function by adopting a particle swarm algorithm to obtain an optimal construction parameter combination of each construction subarea; and the optimal construction parameter combination is adopted for pile foundation construction, a pile foundation is selected for uplift bearing capacity rechecking, a rechecking result is fed back to the multi-objective optimization function, and dynamic fine adjustment is conducted on the construction parameters.

The invention relates to the technical field of resource scheduling, and particularly discloses a government affair collaborative emergency resource dynamic scheduling method and system.The method comprises the steps that event spatio-temporal information is obtained through a government affair data sharing platform, multi-source data are fused to construct a three-dimensional situation map, and a dynamic demand list is generated; positioning distributed resource nodes and establishing supply capability portraits; constructing an optimization model with response time efficiency, resource utilization efficiency and demand satisfaction balance degree as targets, and generating a multi-target scheduling scheme set by adopting a particle swarm algorithm; generating a visual decision billboard based on the scheme set, determining a final scheme through multi-party collaborative consultation, and analyzing the final scheme into an operable instruction; monitoring an instruction execution state and a resource track in real time, and automatically triggering scheme re-optimization when deviation or situation change occurs to form a closed-loop scheduling system; according to the invention, the method achieves the precise, intelligent and adaptive dynamic adjustment of resource scheduling, and improves the efficiency and robustness of emergency response.

The invention relates to the technical field of collaborative optimization, and discloses a gas power generation and heat supply collaborative optimization method and system based on a particle swarm algorithm. The method comprises the following steps: acquiring and preprocessing operation parameters of the gas power generation and heat supply system to obtain state characterization data, constructing gas resource limitation and equipment operation constraints based on the data, and generating an optimization objective function; importing the optimized objective function into a particle swarm algorithm for initialization to obtain an initial particle set; a particle swarm is evolved through a search operation based on a gas energy critical path, near-end strategy optimization is carried out, particle swarm parameters are dynamically adjusted, and a target configuration scheme is obtained. And finally, establishing a multi-level cooperative control structure, executing hierarchical scheduling, and generating a cooperative optimization control instruction. The gas power generation and heat supply system is subjected to collaborative optimization control through an intelligent optimization algorithm, the influence of dynamic factors such as gas concentration fluctuation and energy demand change is overcome, and efficient utilization of gas energy is achieved.

The invention provides a turbine blade design method which comprises the following steps: S1, dividing a turbine blade profile into four sections at equal intervals along the radial direction according to the size of a turbine, and calculating section parameters of a middle-section turbine blade profile; s2, connecting the four sections by adopting a third-order Bezier curve, defining a blade front edge angle theta as an included angle between the Bezier curve and a blade front edge radial inlet angle alpha, and generating a three-dimensional blade model by changing the inclination angle theta of each section; s3, performing optimization to obtain an optimized theta parameter; and S4, according to the optimized theta parameter, fitting each section blade profile by using a third-order Bezier curve to generate a turbine stator and rotor three-dimensional model. According to the method, the machine learning method is applied to design of the turbine blade, the three-order Bezier curve, the CFD numerical simulation method and the particle swarm optimization are utilized, the blade parameters are given, the geometric parameters can be quickly and accurately predicted, the calculated amount is reduced, the cost is reduced, the design efficiency is improved, and the quality of the designed blade is guaranteed.

The invention relates to a ship port resource coordinated scheduling method and device, and belongs to the technical field of port resource optimized scheduling, and the method comprises the steps: building a target function with the maximization of the anchor ground utilization rate, the maximization of the berth utilization rate and the minimization of the ship waiting time as targets; solving the objective function by using MILP and a particle swarm algorithm to obtain an initial scheduling scheme meeting constraint conditions; the initial scheduling scheme comprises anchor ground distribution, berth distribution, berthing time and leaving time; clustering the initial scheduling scheme based on the multi-core weighted distance of the initial scheduling scheme to obtain a clustered scheduling scheme; and optimizing the clustered scheduling scheme based on a game theory model to obtain a target scheduling scheme. According to the ship and port resource coordinated scheduling method provided by the invention, global optimization of ship berth allocation, channel use and anchor ground scheduling is realized, and high efficiency and safety of ship operation are ensured while channel resource conflicts are reduced.