31results about How to "Strong global search capability" patented technology

The invention discloses a regional risk prediction method and system based on toxic gas diffusion analysis, and provides a regional risk prediction method based on toxic gas diffusion analysis on the basis of source item traceability prediction of a leakage source. The content of the invention mainly relates to development and improvement of a gas diffusion model, data acquisition and processing, construction of a risk prediction model, uncertainty analysis, research of an emergency response strategy, case analysis and verification and system integration and demonstration application, and through comprehensive consideration of factors such as gas properties, meteorological conditions and topographic features, prediction accuracy is improved, and the risk of the gas diffusion model is predicted. Scientific basis is provided for risk management, and practicability and popularization of the prediction system are finally realized.

The invention belongs to the technical field of intelligent control, and discloses a building energy-saving intelligent integrated control system and method. Comprising the steps of collecting building comprehensive data of a building internal space area; preprocessing the building comprehensive data to obtain standard building data; establishing a building dynamic model according to the standard building data, and predicting by using the dynamic model to obtain an energy consumption change curve and a comfort change curve of the internal space area of the building in a future fixed time period; solving to obtain a series of optimal control strategy sequences by taking the energy consumption change curve and the comfort change curve of the internal space area of the building in the future fixed time period as constraints; the optimal control strategy sequence obtained through solving is distributed to all actuators, and integrated intelligent control over heating equipment, refrigeration equipment, ventilation equipment, illumination equipment and the like is achieved; not only is the energy-saving level of the building improved, but also a comfortable environment is created for the indoor space, and the comprehensive performance of the building is comprehensively enhanced.

The invention discloses a structure optimization design method based on a particle swarm optimization algorithm, aiming at a super high-rise building, an initial structure parameter matrix is generated by establishing an optimization model, setting a target function and taking structure layer displacement, an inter-layer displacement angle and top layer peak resultant acceleration as constraint conditions; a wind vibration response characteristic matrix is obtained by adopting a frequency domain calculation method in combination with structural dynamic characteristics and a wind tunnel test result, and an equivalent static wind load matrix and an acceleration response matrix are calculated. A combined algorithm of an optimization criterion method and a particle swarm algorithm is used for performing multiple iterations, wind load and acceleration response data are recalculated by initializing and updating related parameters of a particle swarm, calculating a fitness value, iteratively updating a position and a speed matrix, and finally an optimal solution matrix is output according to convergence judgment. Therefore, a final design scheme of the structure is determined, and comprehensive optimization of wind resistance and economic benefits of the structure is realized.

The invention relates to a micro-grid multi-objective optimization scheduling method and device, and belongs to the technical field of micro-grid optimization scheduling, and the method comprises the following steps: determining the structure of a micro-grid system, and collecting the equipment parameters of each distributed power supply; establishing a micro-grid optimization scheduling model according to the characteristics of the micro-grid and the collected equipment parameters, wherein the micro-grid optimization scheduling model takes minimization of user operation cost and environment cost as an objective function; generating an initial population of the MOPSO algorithm by using a chaotic equation, and improving the value of a learning factor by using a dynamic adjustment parameter to obtain an improved MOPSO algorithm; and performing optimization solution on the micro-grid optimization scheduling model by using an improved MOPSO algorithm to obtain the output power of each distributed power supply. According to the invention, the cost and environmental factors of the micro-grid are considered at the same time, and effective scheduling and management of the micro-grid are realized.

The invention relates to a special assembly workshop machine and AGV combined scheduling optimization method considering optimal path selection, and the method comprises the following steps: initializing configuration parameters according to a workshop assembly process; a general assembly workshop machine and AGV scheduling scheme is taken as a solution, and a machine and AGV combined scheduling multi-target optimization model considering an AGV transportation optimal path is established; designing a multi-objective learning type artificial bee colony algorithm; and optimizing the scheduling scheme through iterative computation, and finally obtaining a plurality of optimized scheduling schemes which accord with actual production. Compared with a traditional scheduling mode, the method considers the selection of the optimal path during AGV transportation, greatly improves the scheduling efficiency and accuracy of the special assembly workshop, reduces the scheduling time cost and energy cost while providing a reasonable and effective scheduling scheme for workshop scheduling, and improves the scheduling efficiency. The overall production efficiency of an assembly workshop is improved, the order delay condition is reduced, and consumption reduction and efficiency improvement of enterprises are achieved.

The invention discloses a bridge point cloud extraction method based on evolution gradient search, and relates to the technical field of data processing, and the method comprises the steps: employing a clustering algorithm to carry out the denoising of a bridge surface point cloud; preliminarily extracting the de-noised bridge floor point cloud based on a multi-thread and minimum heap data structure, and meanwhile, setting a perpendicularity density constraint to further extract the bridge floor point cloud; a gradient-based optimization algorithm is combined with an evolutionary strategy to serve as a complementary algorithm to be incorporated into a unified framework, and the extracted bridge deck point cloud is optimized by alternately adopting a gradient updating method and an evolutionary updating method, so that the finally extracted bridge deck point cloud is obtained. According to the method, the processing precision of the bridge deck point cloud is improved, the main body structure of the bridge can be more effectively identified and separated from the complex point cloud data by introducing the evolution gradient search strategy, and meanwhile, the interference of noise and redundant information on a final result is remarkably reduced.

The invention discloses a sensor layout optimization method based on an NSGA-II algorithm, and the method comprises the following steps: firstly, dividing a population into different frontier layers through a fast non-dominated sorting NSGA-II algorithm, and determining and calculating the degree of congestion or the degree of congestion distance to maintain the diversity of frontier layer individuals; secondly, the operation of the NSGA-II algorithm is dynamically adjusted by adopting self-adaptive crossover and mutation operation, so that the adaptability and efficiency of the algorithm are improved; and finally, selecting a better Pareto frontier solution set through selection operation, and providing a group of multi-objective optimal solutions. According to the sensor layout optimization method based on the NSGA-II algorithm, the global search capability is high, exploration in a solution space can be wide, and a potential optimal solution can be found.

The invention relates to an optimization design method of a multi-winding regulation and control transformer for flexible interconnection, which comprises the following steps: 1, according to a single-target optimization design mathematical model, constructing an optimization target function with the purpose of minimizing the sum of the material cost of the multi-winding transformer and the ten-year operation cost; 2, determining required constraint conditions according to basic electrical parameters of the multi-winding regulation and control transformer for flexible interconnection; under the action of the obtained optimization objective function, punishing a trend violating constraints by adopting a punishment function, so that non-constraint optimization is gradually close to a feasible region, a constraint optimization problem of the distribution transformer is adjusted into an unconstraint optimization problem, and an optimization design model of the amplitude-phase adjustment type multi-winding transformer for flexible interconnection is obtained; and 3, solving the optimal design model, obtained in the step 2, of the multi-winding regulation and control transformer for flexible interconnection by using a particle swarm optimization algorithm. According to the method, the local optimal solution and the global optimal solution of the individual can be solved, and higher solution precision can be obtained.

The invention discloses a medium and low voltage power distribution network fault reconstruction method based on a particle swarm algorithm. The method comprises the following steps: S101, obtaining a target power distribution network management data set; step S102, determining an island division range by taking each DG as a center and taking the capacity of the DG as a constraint condition, dividing load grades into a first-level load, a second-level load and a third-level load, and numbering load nodes in each divided area from high to low; step S103, according to the target power distribution network management data set, using an optimization algorithm based on a particle swarm, taking the minimum network loss as a target, and taking the bearing capacity of the DG as a constraint condition to carry out islanding solving on the DG, and obtaining load points contained in each islanding and a switch required to be disconnected; and S104, fault reconstruction is carried out according to the island division solving result, and a reconstructed network topology structure and a system fault reconstruction result are obtained.

The invention relates to a variable-density high-vacuum multi-layer heat insulation structure and an optimization method thereof, and the optimization method comprises the following steps: determining design parameters and performance indexes of the variable-density high-vacuum multi-layer heat insulation structure, and carrying out the functional expression of each performance index based on the design parameters, so as to obtain a simulation function of each performance index; and based on the simulation function of each performance index, establishing an optimal arrangement problem of the variable-density high-vacuum multi-layer heat insulation structure, and solving the optimal arrangement problem by adopting a genetic algorithm to realize optimization of the variable-density high-vacuum multi-layer heat insulation structure. When the genetic algorithm is adopted to solve the optimal arrangement problem, population individuals are generated based on the number of heat insulation layers among all reflecting screens of the variable-density high-vacuum multi-layer heat insulation structure, and a heat flow density function of the variable-density high-vacuum multi-layer heat insulation structure serves as a fitness function. Compared with the prior art, the method has the advantages of effectively improving the heat insulation performance, being high in efficiency and the like.

According to the wind-light storage coordinated configuration method based on the K-means and the simulated annealing genetic algorithm, the K-means algorithm is adopted to process initial data, then a wind-light storage configuration model is constructed, and finally the simulated annealing genetic algorithm is adopted to solve. According to the simulated annealing genetic algorithm, the advantage of strong local search capability of the simulated annealing algorithm and the advantage of strong global search capability of the genetic algorithm are combined, and the advantages can be mutually complemented, so that the precision and optimization speed of the algorithm are improved. By testing the wind and light energy storage configuration of a certain park, a good result is finally obtained, the investment cost is the lowest while wind and light curtailment is reduced, and an effective planning scheme is provided for the micro-grid.

The application provides a UWB and PDR fusion positioning method and system in an indoor shielding environment and a medium, and belongs to the technical field of indoor positioning and multi-source information fusion. The method comprises the following steps: calculating a UWB positioning track and a PDR positioning track. Taking translation, rotation, step scaling and scale change as combined parameters, a track alignment error function is constructed by using the Euclidean distance of the two tracks at each timestamp. An improved vole optimization algorithm is used to solve the optimal combined parameters. An initial population with uniform distribution is generated by mapping a Sobol difference sequence. In the iteration, the UWB track is used as the reference, the PDR track is temporarily transformed by using the combined parameters, the Euclidean distance is calculated, and the vole with the smallest distance is selected to breed the next generation. After reaching the maximum number of iterations, the PDR track is corrected by using the optimal combined parameters at one time, and the UWB track is fused and output. The application does not require a priori model, can simultaneously suppress UWB non-line-of-sight drift and PDR cumulative error, and has high positioning accuracy.

The invention discloses a method for identifying thermal runaway reaction kinetic parameters of a high-nickel ternary battery based on DSC (differential scanning calorimetry) multi-exothermic peak heat flow curves, which is characterized by comprising the following steps of: establishing a chemical reaction kinetic model, and identifying the thermal runaway reaction kinetic parameters of the high-nickel ternary battery based on the DSC multi-exothermic peak heat flow curves at different temperature rise rates; and realizing identification of the thermal runaway reaction kinetic parameters of the high-nickel ternary battery by applying a Kissinger equation and a particle swarm optimization algorithm. According to the technical scheme of the method, necessary reaction kinetic parameters can be provided for thermal runaway modeling of the high-nickel ternary battery, compared with a traditional genetic algorithm, the convergence speed is higher, meanwhile, the global search capability is high, thermal contribution decoupling between different reactions of a battery material in the thermal runaway process is achieved, and the thermal runaway modeling efficiency is improved. And improvement means can be researched aiming at high-yield heat reaction among materials, and the risk of thermal runaway of the high-nickel ternary battery is reduced.

The invention provides a consultation service system based on big data collection and analysis, and the system comprises a data collection module which is used for obtaining consultation service data from a plurality of data sources, and obtaining original consultation service data; the data cleaning and preprocessing module is used for cleaning and preprocessing the collected original consultation service data to obtain preprocessed data; the data analysis and mining module is used for analyzing and mining the preprocessed data to obtain feature data; and the decision support module is used for generating a decision suggestion report by utilizing the feature data. According to the technical scheme, the data acquisition module, the data cleaning and preprocessing module, the data analysis and mining module and the decision support module are arranged, so that the quality and efficiency of consultation service are improved.

The invention provides a substation project multi-objective optimization method and device. The method comprises the following steps: acquiring a process set, a process preposition and postposition logic relation, selectable construction modes of each process and corresponding parameter information; introducing the logical relationship as a constraint, taking a construction mode selection result as a discrete decision variable, and constructing a multi-objective optimization model including a construction period, a cost present value, quality and a carbon emission objective; an improved swarm intelligence algorithm is adopted for iterative solution and layered sampling to generate an initial population and establish an external file, a target function is calculated in iteration and feasibility is judged, non-dominated sorting and congestion degree are executed after merging, the external file is updated, and a leader individual is selected; and candidate positions are generated based on nonlinear convergence control and hunting, random search and spiral bubble net updating, discretization and constraint restoration are performed after fusion, and an external file non-dominated solution set is output. According to the technical scheme, multi-target scheme optimization is achieved, and scheme generation efficiency and evaluation quality are improved.

The invention discloses a three-network emergency resource optimization method and system fused with an unmanned aerial vehicle communication relay, and belongs to the technical field of power system planning and disaster defense, and the method comprises the steps: building a communication power supply coupling model based on an unmanned aerial vehicle relay drive, and obtaining a communication recovery basic framework; constructing an outer layer optimization model taking the pre-disaster multi-type emergency resource layout scheme as a decision variable; an improved bald eagle optimization algorithm is adopted, constraint conditions of a communication recovery basic framework are embedded, an outer layer optimization model is iteratively solved, and a pre-disaster resource deployment scheme which is most robust in all scenes is determined; constructing an inner-layer model by adopting mixed integer second-order cone programming, performing refined solution on post-disaster emergency response, and optimizing a pre-disaster resource deployment scheme; and constructing a double-layer robust optimization model to realize three-network emergency resource optimization. According to the method, the cooperative scheduling capability of multiple types of resources in pre-disaster, in-disaster and post-disaster stages can be improved, the guarantee capability of key loads is enhanced, and the method has global search capability and engineering adaptability.

This invention discloses a design method, device, and system for an ultrasonic amplitude transformer based on a genetic algorithm, belonging to the field of manufacturing technology. Considering the strong global search capability and excellent convergence of genetic algorithms, this invention combines the principles of genetic algorithms with a fitness function to construct a fitness optimization algorithm based on genetic algorithms to optimize the contour parameters of the ultrasonic amplitude transformer. This ensures that the expected optimal amplification factor is obtained under different optimization objectives, effectively avoiding the local optimum problem common in traditional design methods and improving design efficiency and performance. By optimizing the contour of the amplitude transformer's geometry, the amplitude transformer can achieve the preset target amplification factor under operating conditions, thereby improving the efficiency of ultrasonic energy transfer and obtaining the expected output displacement amplitude. The final optimization result outputs the optimal geometric contour and parameters of the amplitude transformer, which can then be used for subsequent engineering manufacturing and application according to actual needs.

The invention discloses an improved whale optimization algorithm and SVRM short-term icing prediction method based on a random forest, and the method comprises the steps: (1) obtaining all micrometeorological data and the equivalent icing thickness thereof, carrying out the mapping along with a time sequence, preprocessing the data, and dividing the data into a training set and a test set; (2) analyzing the influence of each meteorological factor on icing growth based on a random forest algorithm, obtaining an icing influence rate through the random forest algorithm, and taking the characteristic quantity with the high influence rate as an input characteristic quantity; (3) initializing a support vector machine regression model; (4) adding stage position updating in the basic whale optimization algorithm, and adding adaptive weight for improvement; (5) optimizing a kernel parameter and a penalty factor of the support vector machine regression model by using an improved whale algorithm; and (6) reconstructing a support vector machine regression model by using the obtained optimal parameters, training the support vector machine regression model by using the training set, testing the input of the test set, obtaining a final icing prediction value, and performing error calculation on a prediction result.

The application is suitable for the technical field of parameter optimization, and provides an optimization method for maximizing power density of a proton exchange membrane fuel cell, comprising the following steps: optimization variable and optimization target selection; adopting an AdaBoost integrated neural network model as a proxy model for calculating fitness function values in an iteration process of a swarm intelligence optimization algorithm; selecting an improved grey wolf optimizer (IGWO) as an optimization tool for iteration optimization in a value range of the optimization variable, with the maximum power density as the target, to obtain an optimal optimization variable combination; and optimization result verification.The application proposes an optimization method for the power density of the proton exchange membrane fuel cell by coupling machine learning and a swarm intelligence algorithm, has a fast convergence speed, can obtain an optimal solution or an approximate optimal solution under a small number of iteration times, effectively reduces the number of target function calls, significantly reduces the calculation time and the calculation cost, and has a strong global search capability and can avoid falling into a local optimum.

The invention provides a hybrid neural network photovoltaic power prediction method based on a VMD-SSA-GA-LSTM algorithm, and relates to the field of photovoltaic power generation network access power prediction. Photovoltaic power signals are decomposed through a VMD algorithm, signal features are effectively extracted, feature extraction and dimension reduction are carried out by using an SSA and GA combined algorithm, optimal fitness is searched, and the optimal power generation network access power prediction efficiency is improved. The method is intersected with a traditional power prediction method, complex interaction between photovoltaic power generation and meteorological factors can be better captured, the prediction capability of instantaneous fluctuation is improved, the prediction accuracy is improved, meanwhile, compared with an existing mixed model, data processing is more perfect, and the method is more suitable for being used in the field of photovoltaic power generation and meteorological factors. The prediction speed is higher, and training is easier.

According to the hydro-generator regulation and control multi-agent modeling and state optimizing method, the mathematical model of the hydro-generator regulation and control system is established based on the characteristics of the hydro-generator regulation and control device and the controlled object, and the step disturbance signal is introduced into the established model; performing model identification on a regulator part of the regulation and control system, a water turbine and a water diversion system part of the water turbine by adopting multiple agents; the method can ensure polynomial time convergence and avoid premature convergence, the improved multi-agent algorithm increases the global search ability in a solution space, breaks a local solution by means of external force, and avoids the problems of premature convergence and genetic stability, and the result proves that the algorithm is effective, and the algorithm is suitable for large-scale popularization and application. The establishment of the accurate mathematical model of the water turbine regulation and control system is of great significance to the analysis and calculation of a power system and the improvement of the control performance of the regulator, the power generation efficiency and quality of the water turbine generator set are improved, and the performance improvement plays a positive role in the quality, economic benefit and safe operation of a hydraulic power plant and the power supply thereof.

The application belongs to the technical field of wireless communication and signal processing, and discloses a quantum heuristic joint optimization method suitable for a flow state antenna ISAC system, which comprises the following steps: 1, a MIMO-FAS model is constructed, and an equivalent downlink channel is given to realize unified expression of a communication link and a sensing link; 2, a weighted sum rate maximization problem P1, a minimum user SINR maximization problem P2 and a sensing signal-to-jamming noise ratio SCNR maximization problem P3 are constructed; 3, the weighted sum rate maximization problem P1 is solved; 4, the minimum user SINR maximization problem P2 is solved; 5, the sensing signal-to-jamming noise ratio SCNR maximization problem P3 is solved; and 6, a quantum heuristic simulation bifurcation method is adopted to perform port selection optimization to obtain a joint optimal port-beam configuration. The application realizes collaborative optimization of port configuration and beam design, effectively improves the communication rate and sensing accuracy of the system while guaranteeing the calculation efficiency and scalability.

The present invention discloses a multi-objective endmember identification method based on meme optimization, comprising: obtaining an original hyperspectral image of an endmember to be identified; constructing a self-dictionary based on the data of the original hyperspectral image and pruning the pixels of the hyperspectral image using vertex component analysis based on a pure pixel hypothesis; constructing a multi-objective optimization model for endmember identification of hyperspectral images based on the self-dictionary; obtaining a non-dominated solution set of the multi-objective optimization problem model based on a meme optimization algorithm; and determining an optimal endmember combination based on a knee method using the non-dominated solution set of the multi-objective optimization problem model. The present invention can integrate the global search capability of an evolutionary algorithm and the local optimization capability of a local search operator to improve the accuracy and efficiency of endmember identification, and can also determine an optimal endmember combination from a non-dominated solution set based on a knee method.

This invention belongs to the field of cutting parameter optimization technology, specifically relating to a multi-objective cutting parameter optimization method, including: acquiring cutting parameters and machine tool spindle power signals, calculating cutting specific energy, and simultaneously acquiring surface roughness; constructing a BP neural network model, optimizing the connection weights and thresholds of the BP neural network model using the Harris Eagle optimization algorithm to minimize the fitness function, and establishing a cutting specific energy prediction model; constructing a surface roughness prediction model with the encoded variables of cutting parameters as input and surface roughness as output, and establishing a multi-objective cutting parameter optimization model; solving the multi-objective cutting parameter optimization model to obtain the Pareto non-dominated solution set, using the entropy weight method for comprehensive decision-making, and selecting the solution with the largest comprehensive evaluation value as the optimal cutting parameter combination. This invention, through methods such as the BP neural network improved by the Harris Eagle optimization algorithm, reduces machining energy consumption, improves surface roughness, and enhances overall machining performance.

The invention belongs to but is not limited to the technical field of harmonic detection, and particularly relates to a harmonic detection method, system equipment and terminal based on a dung beetle optimization algorithm, in order to obtain high-precision harmonic parameters, sampled data are firstly analyzed through discrete Fourier transform (DFT) and then are processed through the dung beetle optimization algorithm, and the harmonic parameters are obtained. A discrete Fourier transform (DFT) Fourier analysis method is the most basic method for signal analysis, Fourier transform is the core of Fourier analysis, signals are transformed from a time domain to a frequency domain through the Fourier transform, and then the frequency spectrum structure and the change rule of the signals are researched. B, judging whether inter-harmonics exist or not; and C, updating the harmonic amplitude, frequency and phase by adopting a dung beetle algorithm. The waveform reconstructed by the algorithm is basically consistent with the original waveform, and the algorithm has the characteristics of high precision and rapid convergence.

This disclosure provides a method and electronic device for detecting geometric error parameters of a surface machining machine tool, relating to the field of surface machining machine tool technology. The method includes: acquiring a pre-constructed machine tool simulation model of the surface machining machine tool; acquiring the measurement vector value of the reference surface corresponding to the error vector; performing coarse-tuning iterative processing on the vector value of the parameter vector formed by the target geometric error parameters based on multiple discrete values ​​of each target geometric error parameter to obtain a first vector value; determining a fine-tuning interval for each target geometric error parameter based on the first vector value obtained from the coarse-tuning iterative processing; performing fine-tuning iterative processing on the vector value of the parameter vector based on the fine-tuning interval to obtain a second vector value; and obtaining the values ​​of multiple target geometric error parameters of the surface machining machine tool based on the second vector value obtained from the fine-tuning iterative processing. This improves the accuracy of the detection results of the geometric error parameters of the surface machining machine tool.

The present application relates to the technical field of tunnel engineering, and particularly relates to a tunnel lining long-term safety evaluation method based on a PSO-SA hybrid algorithm, comprising: determining a surrounding rock creep constitutive model and obtaining initial surrounding rock parameters through an inversion algorithm; introducing the influence of a supporting structure on surrounding rock deformation to obtain corrected surrounding rock parameters, so as to improve authenticity; based on time-varying factors, iteratively updating surrounding rock parameters and lining parameters to realize dynamic evaluation of long-term safety; inputting the iteratively updated surrounding rock parameters and lining parameters into numerical analysis software to obtain stress and deformation characteristics of the lining structure through the numerical analysis software; and evaluating the long-term safety of the tunnel lining structure according to the stress and deformation characteristics and relevant specifications, wherein the application constructs a dynamic evaluation model that truly reflects the time-varying characteristics of the tunnel structure, brings about a qualitative leap in evaluation accuracy, reliability and foresight, and provides strong technical support for the long-term safety of operating tunnels.

The invention relates to the technical field of air traffic flow management in the aerospace field, and discloses an airline and time slot collaborative optimization method considering airline preference, which comprises the following steps: acquiring flight information, time slot information and airspace capacity information; according to the flight information and the time slot information, determining that the target function is the minimum flight total delay cost and the minimum airline fairness coefficient; a flight uniqueness constraint, a time slot uniqueness constraint, a time slot feasibility constraint, a capacity constraint and a maximum threshold constraint are set, and an air route and time slot resource allocation model is constructed and completed; solving the constructed route and time slot resource allocation model by adopting a genetic algorithm; and outputting an air route and time slot resource allocation scheme. According to the method, delay minimization and fairness improvement are taken as a dual-core target, a resource allocation mechanism is innovated, the flight delay rate is effectively reduced, fairness of resource acquisition among airlines is ensured, and an efficient and fair air traffic management system is constructed.

The present invention relates to the field of environmental traceability technology, and specifically to an environmental traceability tracking system for a Juncao production process, comprising a data acquisition module, a data analysis module and a traceability tracking module. The data acquisition module is used to collect environmental data and quality data of Juncao. The data analysis module is used to construct an abnormal data analysis model of an isolated forest algorithm optimized by a wolf pack algorithm, and to construct an SVM-LSTM network model optimized by a dung beetle algorithm. The traceability tracking module is used to mark the Juncao production process, and to input abnormal data into the Juncao traceability analysis model, and to trace the source of the abnormal data. The present invention uses an isolated forest algorithm optimized by a wolf pack algorithm to analyze abnormal data, has better distinguishing ability, is more adaptable, and improves the accuracy of anomaly detection. At the same time, it combines the SVM-LSTM network model optimized by the dung beetle algorithm to perform environmental traceability tracking, which speeds up the convergence of the model, makes the training process more efficient, and improves the overall operating efficiency.

The application provides a kind of GAPSO algorithm-based power station load tracking method, including data acquisition and preprocessing, and related data of power grid and power station are collected and processed;A power grid load forecasting model based on GAPSO algorithm is constructed, variables are determined, and an optimized neural network is constructed;The charge-discharge strategy of energy storage power station is optimized, the objective function is established and solved;Real-time scheduling and response are carried out, data monitoring, decision-making and adjustment of power station state are carried out;Multi-objective optimization adjustment is carried out, function is constructed, solved and continuously improved.The application improves the load forecasting accuracy, optimizes the power station operation strategy, enhances the real-time scheduling and response capability, realizes the multi-objective comprehensive optimization, effectively improves the operation efficiency and adaptability of energy storage power station in power system.