57results about How to "Improve global search performance" patented technology

The invention discloses an industrial robot motion pose error calibration method based on constraint solution. The industrial robot motion pose error calibration method comprises the steps that S1, joint angles, tail end coordinates and temperature data of multiple measurement points are collected; s2, correcting the kinematic model by using the thermal elongation and constructing a parameter identification model; s3, constructing a mixed integer nonlinear constraint pool according to mechanical limitation; s4, introducing a semi-definite programming slack variable to convert the non-convex constraint into a linear matrix inequality; s5, combining a primal-dual interior point method and an alternating direction multiplier method to iteratively solve the optimal kinematics parameter correction; s6, introducing a square root information filtering improvement mechanism to correct a state vector square root factor, and calculating an updated nominal kinematics parameter; and S7, based on the condition number of the error Jacobian matrix, judging observability and repeatedly calibrating. According to the method, the problem of precision reduction caused by thermal errors is solved, and the identification precision, the numerical stability and the self-adaptive calibration capability of the kinematics parameters of the robot are improved.

The invention provides a cooperative path planning method for searching and tracking an underwater target by a multi-USV unmanned cluster, and the method comprises the steps: introducing a pose-control quantity-time to carry out the three-dimensional double-chain coding of a population in an improved genetic algorithm, enabling a control instruction and an accurate execution timestamp to serve as a genetic gene, and enabling the control instruction and the precise execution timestamp to serve as a genetic gene in the crossover and mutation operation, timestamps, control quantities and poses are transmitted synchronously, and it is ensured that offspring individuals can inherit an excellent cooperation mode of a parent; during population initialization, USV is guided to preferentially cover a high-value region through region division and weighted Gaussian distribution, so that blindness caused by random initialization of a traditional genetic algorithm is overcome, and invalid search is avoided; a target for avoiding secondary search is designed in a fitness function, and repeated access to recently searched areas is quantified by introducing a concept of confidence time intervals, so that an algorithm actively explores unexplored water areas, waste of search resources is avoided, and accuracy and planning efficiency of multi-USV collaborative path planning are improved.

The invention relates to an unmanned aerial vehicle path planning method and device, electronic equipment and a readable storage medium. The method comprises the steps that environment information of a task area and constraint parameters of an unmanned aerial vehicle are acquired; initializing a population of the improved beaver optimization algorithm; determining a fitness value for each individual in the population based on a preset cost function; based on the fitness value, dynamically allocating a role to each individual in the population, updating the position of the individual by adopting a corresponding updating strategy according to the allocated role, and executing information exchange between roles in the updating process; and based on a preset termination condition, determining an individual with the lowest fitness value from the population after iteration updating, and outputting a path point sequence represented by the individual as a planned unmanned aerial vehicle flight path. Through the method provided by the invention, the local optimal escape ability and the quality of the solution are enhanced, the flight safety and the terrain adaptability are improved, and the overall performance and the reliability of unmanned aerial vehicle path planning are improved.

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.

This invention discloses a method, device, and electronic device for evaluating the rationality of obstacle avoidance paths, relating to the field of UAV path planning and intelligent optimization technology. The method includes: acquiring information on the location and influence range of obstacles in the environment; constructing an obstacle avoidance path objective function, dividing the influence range into three levels to model a cost function, and dynamically adjusting weight factors based on task completion; repeatedly solving for multiple low-cost obstacle avoidance paths based on an improved whale swarm optimization algorithm to form a rational path sample set, balancing global exploration and local development, and refining the optimal solution in the later stages through pattern search; acquiring the path point sequence of the actual UAV flight path; using a Gaussian mixture model to fit the probability distribution of the offline-generated low-cost obstacle avoidance path feature vectors, extracting the common features of the actual flight paths and calculating their negative log-likelihood, and outputting the obstacle avoidance rationality level according to a preset threshold. This solves the problem of the lack of rationality evaluation for UAV obstacle avoidance paths in complex environments.

The invention relates to the technical field of power grids, in particular to a distributed new energy acceptance capacity dynamic calculation method and system, and the method comprises the steps: constructing a combined small-disturbance dynamic model for distributed new energy access, and obtaining a stable constraint boundary; establishing an optimization model by taking distributed new energy acceptance capacity maximization as a target function; and solving the optimization model by adopting a self-adaptive particle swarm algorithm to obtain the maximum acceptance capacity of the distributed new energy in the current operation state of the power distribution network. According to the invention, based on the equivalent dynamic model of the network-forming inverter and the state-space equation of the power distribution network, the combined small-disturbance dynamic model is constructed and the stability analysis is carried out, the stability constraint is directly introduced into the acceptance capacity optimization, and the adaptive particle swarm algorithm is adopted for solving, so that the maximum access capacity of the distributed new energy can be accurately evaluated. Meanwhile, small-interference stability of the system and operation constraints of the power distribution network are considered, and calculation precision, efficiency and engineering application value are improved.

This invention belongs to the technical field of aircraft manufacturing engineering and intelligent scheduling optimization. It discloses an aircraft final assembly scheduling method and equipment based on an improved genetic algorithm. The steps are as follows: (1) Construct a scheduling model based on process and worker data with the optimization objectives of minimizing the final assembly completion time and minimizing the number of workers involved; (2) Encode the process priority and worker skill slots obtained from the scheduling model using an improved genetic algorithm in the encoding stage, and use the encoding as chromosomes to form an initial population; (3) Perform event-driven decoding on the individuals in the initial population to obtain an initial scheduling scheme; (4) Calculate the fitness value of each chromosome according to the total completion time and the number of workers involved corresponding to the initial scheduling scheme, and screen all chromosomes according to the fitness value to obtain a new population, and then finally obtain the aircraft final assembly scheduling scheme. This invention realizes the collaborative optimization of completion time and the number of workers involved.

The application relates to the technical field of path planning, in particular to a crowd-sensing cooperative optimization method and system based on evolutionary fusion two-stage mixing. The method comprises the following steps: constructing a multi-agent cooperative optimization model of a heterogeneous space based on a mobile crowd-sensing operation scene; adopting a stage-type evolutionary fusion strategy to deeply fuse MOPSO and NSGA-II, and constructing an EF-DH algorithm; using the EF-DH algorithm to perform unmanned aerial vehicle multi-target path planning based on the constructed multi-agent cooperative optimization model, including first-stage unmanned aerial vehicle cluster path optimization and second-stage ground operation personnel task optimization; and performing air-ground cooperative execution and dynamic re-optimization based on the path planning. The evolutionary fusion two-stage optimization algorithm fusing MOPSO and NSGA-II is constructed, and the global search capability and local convergence performance of the multi-target optimization problem are effectively improved.

A multi-objective optimization method for heating section parameters of an electromagnetic induction ethylene cracking furnace belongs to the field of ethylene cracking furnace optimization technology. It includes: determining key structural and electrical parameters of the heating section as optimization variables and setting their value ranges to construct a multi-dimensional design variable space; generating an initial sample set and initializing the particle swarm optimization using Latin hypercube sampling; establishing an electromagnetic field-heat conduction coupled simulation model to calculate the Joule heat distribution and furnace tube temperature field under different parameter combinations; constructing a multi-objective evaluation function based on temperature uniformity, electromagnetic heat transfer efficiency, and coil loss; using a multi-objective particle swarm optimization algorithm combined with non-dominated sorting and external archive mechanisms for iterative search to form a Pareto optimal solution set; and selecting the final heating section design parameter scheme according to engineering requirements. This invention can achieve multi-parameter collaborative optimization, significantly improving heating uniformity and energy utilization efficiency.

The invention discloses an operation state regulation and optimization method for a power distribution network containing a distributed power supply and a flexible load, and the method comprises the following steps: obtaining a topological structure and operation parameters of the power distribution network, and constructing a power distribution network operation state regulation model according to the topological structure and the operation parameters; on the basis of a power distribution network operation state regulation and control model, a traditional knowledge acquisition sharing algorithm is improved, and a spiral updating strategy is introduced to guide a search individual to approach a current candidate optimal solution along a spiral trajectory; and performing iterative solution on the operation state regulation and control model by using an improved knowledge acquisition sharing algorithm to obtain operation optimization configuration of the power distribution network. According to the method, the global search capability and the local convergence precision of the algorithm are remarkably improved, so that the operation optimization configuration can be quickly and accurately obtained, and the stability, the adaptability and the overall operation performance of the power distribution network when the power distribution network deals with renewable energy output fluctuation and load change are enhanced.

The present application relates to a kind of nuclear power process twin system simulation model parameter optimization method based on PSO-TD3 hybrid framework, comprising: based on demand, construct nuclear power process twin system simulation model, determine the optimization parameter contained in nuclear power process twin system simulation model;Loss function for measuring the combination performance of current each parameter of nuclear power process twin system simulation model is constructed, with the value of loss function minimization as optimization goal;With particle, each parameter of nuclear power process twin system simulation model is represented, based on particle swarm optimization algorithm, the PSO update function for updating the speed and position of particle is constructed;With the state parameter of PSO update function, TD3 state vector is constructed, and the output action of TD3 is mapped as the weight parameter of PSO update function;Based on optimal particle group, the parameter of nuclear power process twin system simulation model is updated and optimized.For solving the problem that nuclear power simulation model parameter space dimension is high, nonlinear is strong, traditional static optimization algorithm is easy to fall into local optimum.

This invention discloses a method for monitoring the state trends of hydropower units based on two-stage signal decomposition and the IBiLSTM model. The method involves collecting vibration signals from the hydropower units for preprocessing; constructing the ITGCOA optimization algorithm and designing a fitness function; using the ITGCOA optimization algorithm to adaptively optimize the SVMD and BAACMD models, achieving initial decomposition of the preprocessed signal and secondary decomposition of the sub-mode component with the highest center frequency obtained from the initial decomposition; calculating the fuzzy entropy values ​​of the remaining sub-mode components for reconstruction; and fusing the high-frequency feature sub-sequences obtained from the secondary decomposition with the reconstructed feature sub-sequences to construct the input sequence of the prediction model. This input sequence is then used to construct the IBiLSTM prediction model for monitoring the state trends of hydropower units, achieving high-precision prediction. Compared with existing technologies, this invention improves the efficiency and accuracy of hydropower unit state trend prediction, accurately warns of abnormal unit operating conditions, ensures the safe and stable operation of the units, and improves the overall efficiency of the power plant.

The application discloses an optimization method for a video transmission network CDR. The method comprises the following steps: acquiring an environment characteristic and a control parameter vector, wherein the control parameter vector is constructed based on a fuzzy controller of the video transmission network CDR; generating an initial value of a spore population based on the control parameter vector; iteratively optimizing the spore population by using an optimization method with an adaptive exploration and utilization strategy, so that the spore population is adapted to optimal video transmission quality under the environment characteristic; each spore of the spore population corresponds to a value of the control parameter vector; and after the iterative optimization is completed, the spore with the highest fitness in the spore population is taken as a value of a target control parameter vector and is issued to the fuzzy controller. The application can realize adaptive optimization of clock data recovery in a complex and dynamically changing video transmission environment, and improve the robustness of clock data recovery.

A method for determining the association and continuity combination of top-side-rock structural surfaces based on unsupervised learning is proposed. This method involves investigating the structural surfaces of the roof and side-rock masses in underground engineering projects to obtain geometric and statistical information. A unified encoding is performed to construct a joint feature set of roof-side-rock structural surfaces. An unsupervised density-based clustering algorithm is used for initial cluster analysis of the joint feature set. A swarm intelligence-based Lüperfox optimization algorithm is introduced to optimize key parameters of the DBSCAN clustering algorithm. A Latin hypercube sampling method is used to improve the population initialization process of the Lüperfox optimization algorithm, resulting in better DBSCAN hyperparameters and optimized structural surface clustering results. The method determines the structural surface system affiliation of structural surfaces in different locations, quantitatively calculates the spatial continuity and combination relationship of structural surfaces, and outputs the spatial association determination results and continuity levels of the roof-side-rock structural surfaces. This method enables intelligent identification and quantitative analysis of the spatial relationship between roof and side-rock rock structural surfaces.

The application discloses a multi-virtual local area network spanning tree optimization method based on a large language model and an adaptive temperature genetic algorithm, and belongs to the technical field of network topology optimization, and comprises the following steps: acquiring physical topology information of a target network to construct a network graph model; initializing a population of the genetic algorithm and allocating a use quota of high-value edges to each virtual local area network based on the network graph model; iteratively optimizing the population by using an enhanced genetic algorithm framework integrated with a large language model; dynamically recommending a genetic operation strategy of the current generation by the large language model according to multi-dimensional state indexes of the population, and performing multi-objective fitness evaluation on individuals in the population; and dynamically adjusting a temperature parameter by using a bidirectional state-aware adaptive temperature scheduling mechanism; and collecting optimal edge selection schemes of each virtual local area network spanning tree obtained through final optimization as a multi-virtual local area network network configuration scheme to perform network configuration. The method can realize intelligent global optimal configuration of multi-VLAN spanning trees.

The invention relates to a WEEE recycling supply chain inventory optimization method based on evolutionary reinforcement learning, and belongs to the technical field of supply chain management and artificial intelligence. According to the method, a multi-level inventory cooperative control model is constructed, an inventory optimization problem is modeled as a Markov decision process, and an evolution reinforcement learning algorithm is utilized to train an intelligent agent to learn an optimal strategy. Core innovations include design state space capture inventory dynamics, action space processing continuation to discrete decision mapping, and cost-based minimization of reward functions. According to the algorithm, a population evolution mechanism and experience playback optimization are fused, and the learning efficiency and stability are improved through parallel exploration and sample screening. According to the method, the uncertainty of the recovery amount and demand can be effectively dealt with, the inventory cost is reduced, the supply chain robustness and sustainability are enhanced, and a self-adaptive intelligent solution is provided for WEEE management.

This invention discloses a method and apparatus for optimizing the feeding frequency of particulate friction stir additive manufacturing. First, feeding experiments are conducted under different operating conditions to collect pressure and displacement data. A mechanism-empirical hybrid model of the dynamic coupling relationship between pressure, displacement, and feeding frequency is established. An uncertainty set is defined. A robust optimization model for the feeding frequency is established. The dual objective is transformed into a single objective through weighted summation. The inner worst-case scenario is solved. The outer optimization employs adsorption kinetics-enhanced gradient descent. This invention uses an improved gradient descent algorithm as the core optimizer, requiring only one calculation of the inner worst-case scenario and gradient per generation. The computational load is significantly less than that of population-based algorithms, making it suitable for embedding in real-time control systems for online optimization. Combined with adaptive learning rate, dynamic diffusion intensity, and desorption probability adjustment mechanisms, the algorithm achieves rapid convergence while maintaining global search capability, providing an efficient and feasible technical solution for online dynamic tuning of the feeding frequency.

The invention discloses an antenna pointing correction method based on a theoretical trajectory and an improved genetic algorithm, and the method comprises the steps: constructing an antenna pointing error correction model, extracting correction parameters, carrying out the global optimization of the correction parameters through employing the improved genetic algorithm, and taking a residual matrix as a target value, taking error minimization between azimuth angle and pitch angle deviation predicted values output by the antenna pointing error correction model and a theoretical matrix as a fitness function, performing iterative solution through selection, intersection and mutation operation to obtain an optimal correction parameter group, and completing parameter calibration of the antenna pointing error model; the optimized parameters are fed back to the antenna pointing error correction model, and the antenna pointing error is corrected in real time. According to the method, the convergence speed of the algorithm is higher, a better solution can be found in a shorter time, the global search capability is enhanced, the pointing precision of the antenna is improved, and rapid tower-free calibration of the antenna is realized.

The invention discloses a deep tight reservoir crustal stress field boundary condition inversion method integrated with machine learning, and belongs to the field of petroleum and natural gas engineering. The method comprises the following steps: firstly, constructing a fine three-dimensional heterogeneous modeling model through high-precision scanning and image processing; then, multi-source data such as hydraulic fracturing, acoustic emission and logging are fused, and the real ground stress of a target point is determined; an integrated machine learning model trained by an optimization algorithm is utilized to intelligently invert and generate a high-precision boundary load required by the crustal stress site; and finally realizing reliable prediction of the global crustal stress field. The method is used for providing key and reliable geomechanical basis and boundary condition input for well location trajectory optimization, hydraulic fracturing scheme design and development scheme adjustment of a deep tight reservoir, and effectively supporting safe and efficient drilling and effective utilization of reserves.

The invention provides an energy interconnection framework parameter matching design method and system and a medium, and belongs to the technical field of power systems, and the method comprises the steps: determining energy interconnection framework parameters, including a framework type, a port type and a port number; determining constraint conditions of the energy interconnection framework parameters, constructing full life cycle use efficiency as a target function, calculating a fitness value according to the target function, optimizing the energy interconnection framework parameters through an improved litsea rotundifolia optimization algorithm, and obtaining the energy interconnection framework parameters with the optimal fitness value; and determining an energy interconnection framework parameter matching design scheme according to the optimal energy interconnection framework parameter. According to the method, the optimal energy interconnection framework parameter matching design scheme can be quickly generated, and the energy interconnection framework formed by the design scheme can realize the optimal use efficiency.

This invention discloses a parameter estimation method, system, device, and storage medium for a nonlinear diffusion model. The parameter estimation method includes: constructing a spatial basis function based on system data snapshots for a nonlinear Fisher-type diffusion system applied to the heating and cooling process of an iron rod, and separating spatiotemporal variables using an orthogonal decomposition method; sparsely sampling the nonlinear terms using a discrete empirical interpolation method to obtain the optimal low-order approximation of the high-order system; initializing a particle swarm to obtain n particles corresponding to the m-dimensional solution vector of the low-order time series model, and calculating the fitness of each particle according to the calculation formula of the parameters to be identified and the objective function; iterating and optimizing each particle, and outputting the position of the particle with the global optimum value; adding a non-Gaussian Levy process to the traditional particle swarm algorithm, and avoiding premature concentration of the particle swarm in the same direction through the random jump of the Levy process, thereby increasing the mutual learning ability between particles.

This invention relates to a dual-modal flexible temperature-strain sensor and its fabrication method. The sensor achieves efficient detection of temperature and strain signals by combining direct ink writing and screen printing processes. The sensor features a unique design, comprising a rigid substrate and a flexible substrate, housing a thermal resistance temperature sensor and a piezoresistive strain sensor respectively, ensuring effective separation and accurate measurement of temperature and strain signals. The use of flexible materials and a FA-BP neural network algorithm based on an improved firefly algorithm enhances stability and measurement accuracy under varying environments. Furthermore, the sensor can still operate normally at high temperatures, outperforming common flexible thin-film materials on the market. This sensor demonstrates significant application potential in smart wearable devices, health monitoring, and industrial automation, providing a new solution for achieving high-precision and high-stability temperature and strain monitoring.

The invention discloses a network intrusion detection feature selection method based on a multi-strategy improved neural network algorithm, and the method comprises the steps: firstly carrying out the cleaning, normalization and feature coding processing of original network flow data, constructing an initial feature space, and representing a feature selection scheme in a binary vector form; then Tent chaotic mapping is introduced to initialize a population, and the diversity of an initial solution is enhanced by using the ergodicity and uniformity of a chaotic sequence; in the feature optimization process, the Levy flight strategy is adopted to update the position of a leader, the global search ability is improved through a search mechanism combining long and short steps, local optimum is avoided, meanwhile, a follower update strategy based on the adaptive inertia weight is designed, global exploration and local development ability is dynamically balanced, and the algorithm convergence speed is increased; and further constructing a weighted fitness function taking a classification error rate and a feature subset scale as double targets, evaluating candidate feature subsets, and determining a global optimal feature subset through an elitist retention and iteration termination mechanism.

The invention discloses a correction method and system of a rock gas permeability tester, and relates to the technical field of tester error correction and intelligent calculation, and the correction method comprises the following steps: obtaining measurement data of a rock sample, preprocessing, executing division and splicing operation, generating a measurement vector, and performing population initialization; quantization and binding operations are executed, and a fitness function is defined; a fitness function value is calculated, a sorting list is generated, Levy flight is used for position iteration-updating, a search space boundary is set, constraint adjustment of the position is executed in combination with the iteration-updating position, and a detection position is obtained; on the basis of the detection position, the corresponding data is intercepted to correct the measurement data, the obtained corrected data is stored, the rock gas permeability measurement data can be efficiently and accurately corrected, and the accuracy and stability of the measurement result are greatly improved.

The invention discloses an irrigation area water volume scheduling optimization method based on an index triangular optimization algorithm, and relates to the field of water conservancy informatization. By constructing an optimization model with the branch channel water supply amount as a decision variable and combining an index triangular optimization algorithm, efficient distribution and refined regulation and control of the water amount of the irrigation area are achieved. The water demand of each irrigation piece, a branch channel and different crops in each irrigation period in an irrigation area is integrated, constraint conditions such as the maximum water supply amount of a water source, the water delivery capacity of the channels and the irrigation pieces and the like are introduced, and a multi-objective function system for minimizing the overall water shortage rate and maximizing the crop yield is constructed. Through an index triangular perturbation mechanism, the global exploration capability is enhanced at the initial stage of search, the local search precision is optimized at the later stage, the stability and convergence of solutions are ensured, compared with a traditional method, the water utilization efficiency and the crop yield are improved, the stability of multi-target optimization in a complex environment is enhanced, and the method is suitable for large-scale popularization and application. And a reliable technical support is provided for the construction of an intelligent irrigation area water volume scheduling system.

The invention provides a fuzzy self-adaptive Q learning control method and system for sewage treatment, and the method comprises the steps: obtaining the dissolved oxygen concentration as a system state, and constructing a nonlinear optimization problem; performing dynamic adjustment on proportion, integral and differential coefficients by adopting a Mamdani type fuzzy inference rule in combination with fuzzy logic, performing defuzzification through a centroid method to obtain adaptive PID parameters, and forming a fuzzy adaptive control strategy; an online Q learning framework is further constructed, a Q function is approximated by using the evaluation network, a network generation strategy adjustment amount is executed, and network weight is optimized based on a Bellman equation and a particle swarm algorithm; finally, fuzzy control and Q learning output are fused, control input is generated through a coupling coefficient to adjust an oxygen transfer coefficient, and tracking control over the dissolved oxygen concentration is achieved. According to the invention, the tracking control precision of the dissolved oxygen concentration and the system operation stability can be improved.

The application provides a low-orbit satellite NOMA-OTFS system power distribution joint signal detection method for user fairness, which encapsulates a signal detection process as a fitness function in power distribution optimization under a total transmission power constraint, realizes collaborative design of power distribution and signal detection through construction of a closed-loop optimization mechanism based on detection feedback, and solves the problems of mutual independence of power distribution and signal detection, difficulty in giving consideration to user fairness and system capacity, and high resource consumption of a detection process in the prior art. Through introduction of an optimization mechanism based on a detection result, power distribution can be directly adjusted according to actual detection performance, thereby effectively improving weak user communication quality, enhancing system fairness, reducing calculation complexity, and improving overall system transmission performance.

The invention discloses an unmanned aerial vehicle inspection path planning method and system based on an improved artificial bee colony algorithm, and the method comprises the steps: firstly determining an unmanned aerial vehicle inspection region, a take-off and landing point and a task point of a power transmission line, constructing a topological structure, and then taking the maximum ratio of the inspection coverage rate to the total energy consumption as a target, and combining with various constraints such as voyage, task point priority and flight safety. Constructing a multi-target path planning model, and finally solving through an improved artificial bee colony algorithm to obtain an optimal inspection path. According to the method, the coverage rate and the energy consumption ratio are taken as core targets, multiple types of actual constraints are taken into consideration, the actual requirements of the inspection scene are met, the improved artificial bee colony algorithm improves the initial population diversity, the search efficiency and the global optimization ability through multi-stage optimization, the path planning efficiency is improved, the unmanned aerial vehicle resource utilization is optimized, and the unmanned aerial vehicle resource utilization rate is improved. The method is suitable for unmanned aerial vehicle inspection tasks in a complex environment.

The application discloses a source-load-storage collaborative optimization control method and system for a new energy main body, and relates to the technical field of power grid control.The method comprises the following steps: constructing an energy storage device aggregation feasible region and combining a preset adjustment demand vector to generate a minimum adjustment capacity constraint; constructing a multi-time scale collaborative optimization model comprising a day-ahead scheduling layer, an intra-day rolling layer and a real-time control layer, and optimizing and solving based on the capacity constraint; when solving, extracting the day-ahead reserve margin and the intra-day power feasible region of the day-ahead scheduling layer and the intra-day rolling layer as the boundary constraints of the intra-day rolling layer and the real-time control layer; calculating the deviation penalty cost of the intra-day rolling layer and the real-time control layer and superimposing the objective functions of the day-ahead scheduling layer and the intra-day rolling layer to iteratively solve, and issuing the collaborative control instruction after convergence.The application is used to solve the problem that the prior art cannot quantize the boundary limited degree, resulting in the problem that the reserve margin cannot be adaptively adjusted under extreme fluctuations and the collaborative control capacity is limited.

The application discloses to belong to the technical field of power system planning and optimization, and particularly relates to an optimization method for distributed power generation and energy storage joint planning in an active distribution network, comprising: a double-layer robust planning model considering new energy output uncertainty is established, SOT is used to discretize the probability distribution of wind power and photovoltaic into a probability sequence and calculate the expected output, and an uncertainty processing mechanism is embedded to guide DG and energy storage capacity configuration; a DG and energy storage joint optimization model based on a double-layer planning framework is constructed, the upper layer optimizes site selection and capacity determination with the minimum annual total cost as the target, the lower layer optimizes energy storage scheduling with the minimum daily operation cost as the target, and planning and operation are coordinated; an improved BPSO monitoring population fitness variance based on chaos optimization is proposed, and Tent mapping chaos disturbance is dynamically introduced to realize efficient solution and global optimization of the model. On the PG&E 69 bus system, line loss can be reduced, voltage distribution can be improved, and new energy consumption capacity can be significantly improved.