1974 results about "Particle swarm optimization" patented technology

The invention discloses a cloud computing resource optimization method based on intelligent scheduling, and belongs to the technical field of cloud computing resource processing. The method comprises the steps of obtaining real-time operation data of target data in a data optimization detection range, collecting historical resource scheduling records and task execution logs, and constructing a multi-dimensional resource state data set; according to the method, multi-objective optimization, simulation verification and reinforcement learning feedback in the step S5 are carried out, a perception-prediction-scheduling-monitoring-optimization closed-loop mechanism is constructed, the resource utilization rate, the response time and the energy consumption cost of a multi-objective optimization function are balanced, and a particle swarm optimization algorithm is combined with simulation verification to generate a global optimal strategy; and reinforcement learning dynamically adjusts model parameters by taking the execution deviation as a reward signal, continuously updates a resource perception dimension and a prediction model, realizes continuous iterative upgrade of a resource optimization effect, and performs optimization processing on cloud computing resource optimization based on intelligent scheduling.

A path planning system for an unmanned aerial vehicle in a network of unmanned aerial vehicles is disclosed. The system includes the unmanned aerial vehicles (UAVs). The system further includes a first processing circuitry configured with a particle swarm optimization component to offline generate paths for each of the UAVs by PSO to minimize path length and avoid static obstacles. The system further includes a second processing circuitry configured with a deep reinforcement learning (RL)-based planner component for each UAV, to perform real-time path planning to navigate the UAV through dynamic environmental conditions using a particular path generated by the PSO for the UAV as a consistent reference for the UAV. The system further includes a reward component to calculate a reward as part of the path planning by the deep RL-based planner component to determine potential paths and converge to an optimal path for the UAV.

The invention provides a tunnel grouting dynamic adaptive simulation method and system based on multi-physics field coupling, and belongs to the technical field of grouting simulation, and the method comprises the steps: obtaining original geological information, constructing a three-dimensional geological geometric model and a fracture network, constructing a multi-physics field coupling mechanism, and carrying out discrete solution; further predicting slurry diffusion and crack filling to obtain an isobaric envelope diagram and an early warning area, and simulating a grouting process; acquiring real-time sensor data, inputting the isobaric envelope diagram, the early warning area and the real-time sensor data into an adaptive parameter prediction model, dynamically updating the weight of the adaptive parameter prediction model according to the change of the real-time sensor data by adopting rolling training to obtain a prediction index, and constructing a feedback control chain based on the prediction index. Determining an optimal grouting parameter combination by adopting a particle swarm optimization algorithm; and the optimal grouting parameters are fed back to the simulated grouting process for automatic parameter adjustment, and the optimized grouting strategy is executed. The intelligent numerical values of the grouting parameters can be dynamically and adaptively adjusted.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a task allocation and conflict resolution system and method for multi-unmanned aerial vehicle collaborative operation, and provides the following scheme: dividing an initial operation area and generating a response weight by constructing a crop growth state map and a three-dimensional plot model; based on path planning and resource adaptation, a flight route is dynamically generated, and the crop state and the unmanned aerial vehicle state are monitored in real time; when adjustment conditions are met, a multi-dimensional dynamic task evaluation model is constructed, and task migration and conflict decoupling are completed in combination with particle swarm optimization and an autonomous negotiation mechanism. The method is suitable for a precision agriculture scene, the unmanned aerial vehicle path dynamic scheduling in the operation area and the high-priority area precision coverage are realized, and the operation efficiency and the resource cooperation capability are improved.

The invention discloses an unmanned aerial vehicle optimal path self-adaptive planning method adopting particle swarm optimization, and relates to the field of unmanned aerial vehicle path planning, and the method comprises the steps: constructing a space constraint model of an unmanned aerial vehicle flight task; initializing a particle swarm based on the spatial constraint model; combining the path smoothness, the path threat probability and the voyage efficiency to establish a multi-target fitness function, and calculating the fitness value of each particle based on a particle swarm; hierarchical particle swarm optimization iteration is executed, in each iteration, control parameters are adaptively adjusted based on the current particle swarm distribution characteristics, and the path node positions of particles are updated; and when a convergence condition is satisfied, outputting a global optimal path as a final flight path of the unmanned aerial vehicle, and controlling the unmanned aerial vehicle to execute. According to the unmanned aerial vehicle optimal path self-adaptive planning method based on particle swarm optimization, the problems that the unmanned aerial vehicle route path optimization target is single and difficult to cooperate, and the route planning effect is poor are solved.

The invention belongs to the technical field of vehicle control, and particularly relates to a vehicle-mounted image recognition and target detection system based on deep learning, and the system comprises a distributed monitoring module which collects the operation, obstacle and traffic signal information of a target vehicle through multi-modal classification and scene matching, completes the marking of a shielding region and the matching of information through the combination of shared data, and achieves the recognition of the target vehicle. Forming an enhanced monitoring set; the label planning module constructs an enhanced topological space based on the enhanced monitoring set, and adjusts moving tracks in different scenes by combining with vehicle and pedestrian track probability distribution fed back by dynamic intention recognition; the action recognition module predicts trajectory parameters and collision probabilities of non-target vehicles and pedestrians by using Bayesian and multi-modal algorithms; the decision-making module generates a real-time control instruction through particle swarm optimization and fuzzy control, and optimal control parameters are fed back through simulation; according to the invention, intelligent track planning and real-time control in a complex scene are realized, and the detection precision and control robustness of the shielded and label-free area are improved.

The invention relates to the technical field of high-voltage circuit breaker fault diagnosis, and discloses a multi-feature optimization fusion high-voltage circuit breaker fault diagnosis method. The method comprises the following steps: adaptively optimizing variational mode decomposition parameters by adopting a particle swarm optimization algorithm, and accurately decomposing an original vibration signal; performing noise dominant and fault feature dominant classification on the intrinsic mode function based on permutation entropy; aiming at the two types of modes, respectively taking signal-to-noise ratio maximization and kurtosis maximization as targets, and implementing differential wavelet threshold denoising; after reconstructing the signal, extracting an energy entropy, a singular value entropy and a power spectrum entropy to form a multi-dimensional feature vector; and inputting the data into a support vector machine classifier subjected to particle swarm optimization hyper-parameter for state diagnosis. According to the invention, through full-chain collaborative optimization, the accuracy and robustness of fault diagnosis in a strong noise environment are significantly improved.

The invention discloses a self-adaptive fault reflection method and device based on adjustable excitation frequency, and belongs to the technical field of cable fault detection and positioning. The method comprises the following steps: injecting an excitation signal which can be adjusted in a range of 1kHz-10MHz through an adjustable impedance matching network; collecting a reflected signal and calculating an SNR (f) curve; optimizing by adopting a particle swarm optimization algorithm and taking maximization of a signal-to-noise ratio as a target, and determining an optimal excitation frequency; and finally, calculating the fault position and type based on the optimal frequency. The particle swarm optimization algorithm is adopted to perform adaptive optimization on the excitation frequency, and the adjustable impedance matching network is combined to form a closed-loop adaptive detection framework of excitation-acquisition-analysis-optimization-re-excitation, so that the excitation frequency can be automatically adjusted according to a real-time detection result, and the detection accuracy is improved. And the fault detection sensitivity, the positioning precision and the energy efficiency are obviously improved.

The invention discloses an intelligent optimization method for a multi-type well seam joint control fine injection-production mode, and relates to the technical field of oil-gas field development. The method comprises the following steps: setting a well seam joint control fine injection-production mode, establishing an oil reservoir numerical simulation model in oil reservoir numerical simulation software, obtaining multiple groups of oil reservoir injection-production schemes based on a Latin hypercube sampling method, performing simulation according to each group of oil reservoir injection-production schemes by utilizing the oil reservoir numerical simulation model, generating multiple pieces of sample data, and establishing a sample database; a deep learning agent model is established, after the sample database is utilized to train and train the deep learning agent model, a particle swarm optimization algorithm is adopted to carry out single-target pre-search global optimization to obtain a preferred reference strategy, a reinforcement learning dynamic decision model is established, and a reinforcement learning agent is obtained through training based on a PPO near-end strategy optimization algorithm; and the optimal injection-production development scheme of the oil reservoir is obtained by utilizing the reinforcement learning agent, so that rapid optimization and decision support of the oil reservoir injection-production scheme in a new multi-type well seam joint control mode are realized.

The embodiment of the invention discloses a resource scheduling method and device, equipment and a medium, and relates to the technical field of resource scheduling. The method comprises the following steps: acquiring a service quality constraint index of a data processing task, and resource states of a cloud center and an edge node; constructing a three-dimensional dynamic resource feature space according to the resource state fluctuation information of the edge node, the spatio-temporal information of the cloud computing and the edge node and the historical occurrence probability of the service quality constraint index; and performing particle swarm optimization based on the three-dimensional dynamic resource feature space to obtain an initial strategy set, scheduling computing resources in the cloud center and the edge node based on the initial strategy set, and processing the data processing task based on the scheduled computing resources. According to the technical scheme, scheduling is flexibly carried out according to the condition of the data processing task and the resource condition of the cloud center and the edge node, and the actual requirement of the data processing task is accurately met.

The invention provides a new energy station multi-type inspection equipment cooperative control method and a comprehensive management and control platform, and the method comprises the steps: generating a global inspection path comprising an unmanned plane, a cabin inspection robot and a ground inspection vehicle based on a particle swarm optimization algorithm through obtaining a station topological structure, an equipment state, and meteorological and historical defect data; a priority inspection area is determined through time sequence analysis of equipment state data, and a path is dynamically adjusted according to obstacle information. According to the method, the instruction synchronization and the data sharing of multiple devices are realized by adopting a user-defined New Entry Protocol, and the collision of the devices is avoided by combining time window scheduling and space partitioning. And carrying out data fusion analysis by using a YOLOv5 algorithm and the like, generating a maintenance work order and optimizing an equipment inspection period. And meanwhile, a reinforcement learning model optimization cooperation strategy is constructed, so that multi-equipment'air-ground-cabin 'intelligent cooperation is realized.

The invention belongs to the technical field of reusable aircraft control, and relates to an intelligent fault diagnosis and control method for a reusable aircraft. According to the method, a control-oriented aircraft dynamics model is established, a neural network adaptive fault observer and a particle swarm optimization algorithm are fused, and high-precision tracking control over the attitude and trajectory of the aircraft is achieved. When faults such as performance loss of an actuator and unmeasurable deviation occur, fault information can be estimated quickly and accurately, the reconstructed fault information is integrated into control law design, and it is guaranteed that the aircraft still keeps stable flight in a serious fault state. Meanwhile, the method can effectively deal with composite disturbances such as pneumatic uncertainty, elastic modal coupling and external environment interference, and improves the real-time performance and accuracy of fault diagnosis by means of high-precision joint online estimation. In addition, the particle swarm optimization algorithm further enhances the overall performance and adaptive ability of the control system, so that the aircraft operates more efficiently and stably in the flight process.

The invention discloses a dynamic trajectory control method for an SCARA (selective compliance assembly robot arm) robot. According to the kinematics model, a homogeneous transformation matrix is used for analyzing the coordinate transformation relation between each joint and the connecting rod, and an initial pose and a target pose are determined; establishing a kinetic model based on a Lagrange equation, and determining kinetic parameters needing to be identified; performing parameter identification by adopting a random weight particle swarm optimization algorithm; according to an identification result, a sliding mode robust item RBF neural network adaptive controller is constructed, real-time track control is achieved, and the end manipulator is made to move to a target pose along a preset dynamic track; a real-time pose is detected through a sensor and compared with a target pose, and arrival is judged when the error is smaller than a threshold value. Model precision and identification efficiency are improved through modeling and efficient parameter identification, the composite controller effectively compensates uncertainty, disturbance and nonlinear factors of the model, closed-loop control and online adjustment are achieved, and trajectory tracking precision, system stability and dynamic response speed are improved.

The invention relates to the technical field of tunnel engineering and intelligent construction, in particular to a shield tunneling real-time control method based on a random forest and a particle swarm optimization algorithm. The method comprises the steps that initial tunneling parameters are generated through a parameter recommendation random forest model according to geology and tunnel geometric parameters, and model hyper-parameters are optimized through a sparrow optimization algorithm; carrying out settlement prediction by utilizing the settlement prediction random forest model; if the predicted value exceeds the limit, carrying out iterative optimization by adopting a particle swarm optimization algorithm and taking the initial parameter as a starting point, and searching a global optimal tunneling parameter combination meeting the settlement requirement; finally, the optimized parameters are issued to the shield tunneling machine to be executed, the model is continuously updated based on real-time construction data, and closed-loop control is formed. According to the method, intelligent recommendation and real-time optimization of tunneling parameters can be realized, the ground surface settlement control precision and the system response speed are improved, the dependence on artificial experience is effectively reduced, and the self-adaptive capability and the intelligent level of shield construction under complex geological conditions are enhanced.

An active-reactive voltage cooperative control method for multiple reactive compensation devices considering energy storage comprises the steps that 1, simulation analysis is conducted on time sequence voltage fluctuation characteristics of a high-permeability photovoltaic grid-connected system, and dynamic influences of photovoltaic output fluctuation in different time periods on system node voltage are quantitatively evaluated; 2, solving a non-dominated solution set of the model by adopting a multi-target particle swarm optimization algorithm, realizing collaborative optimization and trade-off analysis of multi-dimensional performance indexes, and establishing a reactive power regulation model and an energy storage active power support model; and organically coupling the reactive power regulation model and the energy storage active power support model. By means of the method, high photovoltaic area voltage treatment can be effectively carried out in real time, and the method has high engineering application value and wide popularization prospects.

The invention relates to a distributed source-load collaborative optimization method based on high-order topology and multi-scale attention, and the method comprises the steps: firstly providing a high-order graph construction method driven by structural interaction, and achieving the structural embedded expression of a physical interaction relation between multi-source equipment through a hyperedge-line graph mapping mechanism and functional attribute coding; secondly, a graph feature extraction method based on a multi-scale joint attention mechanism is designed, topology and state information are fused, and the inter-node adjustment collaboration recognition capability is improved; further constructing a source-load collaborative optimization scheduling model, introducing a particle swarm optimization algorithm to obtain an initial feasible strategy, and establishing a state-action mapping relation based on a deep reinforcement learning framework driven by graph embedding to realize autonomous learning and rolling optimization of a distributed control strategy; and finally, constructing an operation feedback closed loop mechanism, and introducing a graph structure migration and strategy adaptive updating method to enhance the response capability of the system to topological change and dynamic disturbance.

The invention relates to the technical field of power dispatching management and control, and discloses a power distribution network power dispatching method based on a virtual power plant AI large model and demand response, and the method comprises the steps: collecting the operation data of a power distribution network, and constructing a feature vector; an AI large model is adopted to calculate and predict load output, and joint uncertainty information is output; calculating a system power unbalance amount, and generating a scheduling strategy; issuing a scheduling instruction corresponding to the scheduling strategy and executing the scheduling instruction; comprehensive performance evaluation indexes are calculated, and whether a performance reduction reason diagnosis mechanism is started or not is judged; according to the method, the AI large model is adopted, the load power, the photovoltaic output and the wind power output are predicted at the same time through a multi-task learning strategy, and the correlation among multiple variables is fully utilized; by constructing a multi-objective optimization model, comprehensively considering economy, safety and reliability and adopting an improved particle swarm optimization algorithm for solving, coordinated optimization configuration of demand response resources is realized, power grid fluctuation is effectively reduced, and power supply reliability is improved.

The invention provides a reinforcement corrosion electrochemical parameter inversion method based on LSTM (Long Short Term Memory) time sequence prediction, which comprises the following steps: S1, acquiring electrochemical time sequence data in a reinforcement corrosion process through an electrochemical workstation to form an original reinforcement corrosion electrochemical time sequence data set; s2, preprocessing is carried out to obtain a training set, a verification set, a test set and normalization coefficients of all parameters; s3, constructing and training an LSTM time sequence prediction model; s4, constructing and calibrating a steel bar corrosion electrochemical parameter forward modeling model; and S5, constructing an inversion framework fusing a particle swarm optimization algorithm, a simulated annealing algorithm and an Adam optimization algorithm, forming closed-loop cooperation by the particle swarm optimization algorithm, the simulated annealing algorithm and the Adam optimization algorithm so as to minimize an error between a target electrochemical response parameter and a theoretical electrochemical response parameter, and outputting an inversion result. According to the method, through organic combination of time sequence prediction and multi-algorithm cooperation, the problems that a traditional inversion method is low in precision and poor in stability are solved, and a reliable technical means is provided for reinforced concrete structure health monitoring.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance and dynamic path planning method and system based on multi-modal perception and hybrid intelligent decision. Environmental parameters such as wind speed, illuminance, temperature and humidity and image quality indexes are collected in real time through an airborne weather station, an IMU and a visual sensor, a flight parameter-image quality coupling model is established, and multi-target optimization is achieved through support vector regression (SVR) and particle swarm optimization (PSO). And designing a dynamic strategy optimization module based on Q-learning, and designing a reward function in combination with image quality, obstacle avoidance safety and energy consumption. An obstacle three-dimensional model is constructed in real time through ORB-SLAM3, a dynamic danger coefficient is calculated, and an obstacle avoidance track is generated by adopting an improved APF-RRT * algorithm. And the online cooperative control module optimizes the control quantity by using a BFGS algorithm so as to ensure the flight stability and the task efficiency. The method realizes high-precision obstacle avoidance and path planning of the unmanned aerial vehicle in a complex environment, has the characteristics of high robustness and wide adaptability, and is suitable for practical application scenes such as routing inspection, surveying and mapping and the like.

The invention discloses a pavement maintenance decision-making method, system and equipment, a medium and a product, and relates to the field of highway engineering management. The method comprises the following steps: firstly, collecting performance data of a target road section, and identifying a to-be-optimized pavement maintenance unit through a threshold judgment method or a K-means clustering algorithm; encoding each maintenance measure type and the corresponding maintenance opportunity into a real number vector, and taking the real number vector as a maintenance scheme code; constructing a multi-target fitness function covering pavement performance, maintenance cost and carbon emission; carrying out iterative optimization on the maintenance scheme through a particle swarm optimization algorithm on the basis, and outputting a particle swarm optimization solution set; performing rapid non-dominated sorting and congestion degree distance calculation on the particle swarm optimization solution set, and extracting a Pareto optimal solution set; and fusing the Pareto optimal solution and the full-life-cycle comparison data of the target road section, and outputting a maintenance decision scheme for each pavement maintenance unit, so that the decision efficiency and the scientificity, accuracy, sustainability and refinement degree of the maintenance decision scheme are improved.

The invention provides a wind power prediction method and system based on federated learning and aggregation weight optimization, and relates to the technical field of new energy power prediction, and the method comprises the steps: enabling each wind power plant to serve as an independent client, obtaining the historical wind power data and meteorological parameters of each client, and constructing a local training data set; constructing a CNN-BiLSTM-ATT model as a local model of each wind power plant, and performing local model training based on the local training data set to obtain local model parameters; a client uploads local model parameters to a central server to participate in federated learning training, a dynamically adjustable coefficient is introduced to construct a composite weight, a particle swarm optimization algorithm is used to optimize the dynamically adjustable coefficient, a differential fine tuning method is introduced to adjust a target wind power plant personalized CNN-BiLSTM-ATT model, and a target wind power plant personalized CNN-BiLSTM-ATT model is obtained. And enabling the personalized model to adapt to the unique power fluctuation mode of the target wind power plant, and finally obtaining a wind power prediction model for the target wind power plant for power prediction of the target wind power plant.

A system for classifying apple leaf diseases using deep learning and feature fusion, consisting of: A data input module, which includes a data storage module, is configured to store a data set created using various data sources, with the data set containing apple leaf images being derived from the data sets "Apple Leaf 9", "Kashmiri Apple Plant Disease" and "Plant Village Apple Leaf"; a data preprocessing module configured to perform preprocessing of the newly prepared dataset of apple leaves, wherein the data preprocessing module is configured to perform data decoding, expansion, resizing, segmentation, scaling and color conversion of the input image data; a feature extraction module that is operationally connected to the data processing module and is configured to receive preprocessed data and transfer the preprocessed data to one or more convolutional neural network models for feature extraction; a feature fusion module configured to combine the extracted features from the Convolutional Neural Networks to develop a fused feature vector; a hyperparameter optimization module configured to optimize the hyperparameters of the feature extraction models by implementing a particle swarm optimization algorithm; a classification module configured to classify the fused trait vector into 13 apple leaf disease classes using a random forest classifier; and a user interface connected to the classification module, configured to display the classification results.

The invention discloses a multi-objective optimization method and system for a regional integrated energy system, and the method comprises the steps: constructing a multi-source input sequence sample; inputting a multi-source input sequence sample into the wind power and photovoltaic prediction model for processing, obtaining a current wind power and photovoltaic output optimization prediction result, calculating the current wind power and photovoltaic output optimization prediction result and a really constructed sample, obtaining a combined loss function value to train the model, obtaining the trained wind power and photovoltaic prediction model, processing the sample collected in real time, and obtaining a wind power and photovoltaic output prediction result. Outputting current wind power and photovoltaic output optimal values; and constructing an online optimization model of the integrated energy system, performing online optimization solution on the constructed model by an accelerated particle swarm optimization algorithm to obtain a control strategy of the energy system, issuing the control strategy to the energy equipment, and performing multi-target optimization scheduling. According to the method, the wind power and photovoltaic prediction model is combined with the accelerated particle swarm optimization algorithm, prediction errors are fully considered, and the stability and flexibility of the regional integrated energy system are enhanced.

The invention provides an adaptive stochastic resonance weak fault detection method based on kurtosis optimization, and belongs to the technical field of fault detection, and the method comprises the steps: collecting and preprocessing a transient signal on a cable line; constructing a bistable stochastic resonance model; kurtosis of the output signal is used as a fitness function, and potential well parameters of the bistable stochastic resonance model are optimized based on a particle swarm optimization algorithm; and substituting the optimized potential well parameter into the bistable stochastic resonance model, and processing the preprocessed transient signal to obtain an enhanced output signal for fault detection. The method has the beneficial effects that the stochastic resonance system is constructed on the basis of the classical bistable model, the kurtosis of the output signal is used as the fitness function of the particle swarm optimization algorithm, the potential well parameters are adaptively optimized, the potential well parameters are driven to automatically converge to the optimal state, and the robustness of the system is improved. The high-impedance grounding fault traveling wave signal can be extracted and enhanced from a strong noise background, and the detection sensitivity and reliability are improved.

The invention discloses a high-dimensional data feature selection method and system based on a multi-strategy improved whale optimization algorithm, and the method guarantees the uniform distribution of populations through a good point set initialization strategy, and solves a search blind area problem caused by conventional random initialization. A whale optimization and particle swarm optimization double-population cooperation mechanism is adopted, and dynamic balance of global exploration and local development is achieved; and a tangential flight disturbance strategy is introduced, so that the capability of jumping out of local optimum of the algorithm is effectively enhanced. Finally, binary feature selection vectors are output and directly applied to machine learning model training, the classification precision is remarkably improved in the fields of medical diagnosis, image recognition and the like, the calculation complexity is reduced, and an efficient and reliable solution is provided for high-dimensional data feature selection.

The invention discloses a distribution-micro collaborative operation simulation optimization method, which belongs to the technical field of simulation optimization, and comprises the steps of preprocessing grid-connected point voltage data, tie line power data and communication time delay data, constructing a power distribution network power flow physical network following a Kirchhoff's law, outputting a source load power prediction curve by using a long short-term memory network algorithm, and calculating the distribution-micro collaborative operation according to the source load power prediction curve. And a distribution-micro collaborative simulation optimization model is obtained based on residual error rolling correction tie line impedance parameters, a delay penalty term is set in a target function in combination with the preprocessed communication delay data, a power regulation instruction is obtained by using a particle swarm optimization algorithm, and a dynamic simulation video stream is generated. According to the invention, through rolling correction of the tie line impedance parameters and setting of the delay penalty term positively correlated with the time delay, the problem of control failure caused by physical deviation caused by model parameter solidification and communication time delay accumulation is solved, and the defects of voltage deviation calculation distortion and inaccurate network loss evaluation are eliminated. And the simulation precision and the operation stability of distribution-micro cooperation are improved.

The invention belongs to the technical field of novel power system photovoltaic and energy storage system optimization configuration, and discloses an optical storage system optimization method based on carbon emission and line loss rate collaborative modeling, which comprises the following steps: constructing a double-layer planning structure of an upper layer planning model and a lower layer operation model, a carbon emission calculation model and an improved line loss rate calculation model are introduced into the lower layer, joint optimization of configuration and operation is realized through parameter coupling, and the charge and discharge efficiency loss power consumption of the energy storage device is introduced as an independent parameter in line loss rate calculation, so that the line loss calculation precision is improved; and solving by adopting an improved particle swarm optimization algorithm, and objectively sorting candidate schemes in combination with an information entropy method and a TOPSIS comprehensive evaluation method. A simulation result based on an IEEE33 node power distribution system shows that the model can effectively reduce carbon emission and line loss rate, improves node voltage level, and has good convergence and engineering applicability.