136results about How to "Improve search capabilities" patented technology

The invention discloses an unmanned aerial vehicle path planning method based on global search and meta-heuristic optimization, and the method comprises the steps: firstly building an unmanned aerial vehicle path planning model under complex constraints, determining a fitness function and a constraint condition thereof, setting algorithm initialization parameters, carrying out the chaotic mapping of an initialization population position, and carrying out the optimization of the algorithm; and performing loop iteration solution by using a meta-heuristic optimization algorithm, outputting a global optimal path, finally realizing the global optimal path on the unmanned aerial vehicle, and calculating a flight distance corresponding to the optimal path to complete unmanned aerial vehicle path planning. According to the method, a meta-heuristic algorithm, namely a crown porcupine algorithm, is combined with tabu search, the global search capability is enhanced, and meanwhile, population initialization is optimized in combination with chaotic mapping, so that the randomness and unpredictability are increased, the algorithm can converge more quickly, and the robustness of the algorithm is improved. The method has obvious advantages in solving the problem of path planning with complex environment and complex constraint, and solves the problem of path planning of the unmanned aerial vehicle in the three-dimensional complex mountain environment.

The invention provides an unmanned aerial vehicle three-dimensional path planning method based on an improved sparrow algorithm, and the method comprises the steps: carrying out the initialization of a population through fusing Tent chaotic mapping and Sine chaotic mapping, thereby improving the diversity of the population, and improving the search range of the population; secondly, introducing a butterfly optimization algorithm, updating the position of a discoverer in a sparrow search algorithm, and enhancing the search capability of the algorithm for a globally optimal solution; then, position updating is carried out on the participant by using adaptive T distribution, the adaptability and flexibility of the algorithm are increased, K-means clustering is introduced to select the alert, and the path planning capability of the unmanned aerial vehicle in a complex environment is enhanced; finally, in order to prevent the algorithm from falling into local optimum, optimal individual Gaussian disturbance variation is introduced, and the algorithm is prevented from falling into local optimum in the search process. A simulation experiment result shows that compared with a traditional sparrow algorithm, the improved sparrow algorithm can find a safe and feasible path, and has relatively stable optimization capacity.

The invention relates to the field of PID control, and provides a tread calender rotating speed PID control method and system, a storage medium and equipment. The tread calender rotating speed PID control method comprises the following steps: processing an initial solution space of a plurality of to-be-optimized parameters based on Logistic-Tent chaotic mapping and dynamic reverse learning to obtain an initial population matrix of a Sabot swarm algorithm; according to the evaluation function, calculating the initial fitness of the soloat group individuals, and updating the global optimal position of the soloat individual with the optimal fitness to the current optimal position; executing a salmons swarm algorithm, and searching the to-be-optimized parameters by simulating a search behavior or an attack behavior of a salmons; in the process of executing the sodat swarm algorithm, if it is judged that the algorithm enters a development stagnation state, executing a transverse crossing strategy and a longitudinal crossing strategy to enable the algorithm to get rid of the development stagnation state; and when a set termination condition is satisfied, outputting a solution corresponding to the current optimal position, and controlling the direct current brushless motor. The method can improve the response speed.

The invention discloses a locating and sizing planning method and system based on improved multi-objective differential evolution, and relates to the technical field of electric power system planning and optimizing.The method comprises the steps that electric power system data are collected and processed, and the electric power system data comprise network topology, line parameters, load characteristics and generator set parameters; the method comprises the following steps: establishing a static var compensator locating and sizing planning model based on power system data, setting an objective function, and formulating a constraint condition set; a self-adaptive variation strategy and an elitism strategy are introduced, and a multi-target differential evolution algorithm is improved; according to the method, a self-adaptive variation strategy and an elitism strategy are introduced, and the improved multi-target differential evolution algorithm is combined, so that the searching capability and the convergence of the algorithm are greatly improved, and the optimal locating and sizing scheme is obtained. And the optimal solution can be more efficiently searched in the complex solution space.

The invention relates to the technical field of path planning, in particular to a path planning method based on a multi-strategy fusion improved dung beetle optimization algorithm. The method comprises the following steps: A, inputting an environment map, and setting a starting point and an ending point; b, initializing parameters of an algorithm; c, an initial population generation process in the dung beetle optimization algorithm is optimized through improved Tent chaotic mapping, and the fitness value of each dung beetle is calculated. The invention provides a path planning method based on a multi-strategy fusion improved dung beetle optimization algorithm. An initial solution of a multi-strategy fusion improved dung beetle optimization algorithm is diversified by adopting a Tent chaos strategy, a penalty term of a turning angle is added into a fitting function, an inertia weight is adaptively updated by using a sine algorithm, and an Ivre flight strategy and a local search mechanism are introduced, so that when a mobile robot moves, the accuracy of the movement of the robot is improved, and the accuracy of the movement of the robot is improved. A simpler and shorter path is obtained, the path efficiency is improved, the search capability in the steering behavior and the smoothness of the planned path are improved, and the convergence speed is accelerated.

The invention discloses a regional school site selection optimization method and system based on an improved black plinuan search algorithm, and the method comprises the steps: building a regional school site selection optimization model through an XGBoost algorithm, and enabling the regional school site selection optimization model to achieve the optimization of a multi-target optimization function composed of school bearing pressure, student school accessibility and school service coverage rate as a target; according to the method, a regional school site selection optimization model is solved through an improved Black-South optimization algorithm, and the improved Black-South optimization algorithm is obtained through a Sine chaotic mapping initialization population, a golden sine search strategy and the search performance of a Gaussian disturbance improvement algorithm, so that the diversity of the population is increased, and the global search and local optimization capability of the algorithm is improved. According to the method, the regional school site selection optimization model can be established through the XGBoost algorithm, the optimization speed can be effectively improved, and the global search and local optimization capability of the algorithm can be effectively improved by improving the Black-Youlinuan optimization algorithm and increasing the diversity of populations.

The invention discloses a PID (Proportion Integration Differentiation) parameter optimization method based on an improved graffiti search algorithm, and relates to the technical field of PID. The invention aims to solve the problems of insufficient local search capability, slow convergence speed and poor system stability in the prior art, thereby realizing a better PID parameter optimization effect. Specifically, the PID parameter optimization method based on the improved graffiti search algorithm comprises the following steps: constructing a PID controller model; a step of replacing a fixed perception probability and a step length in the model with an adaptive perception probability and a Levy flight step length; a step of adding a crow misleading prevention factor into the model; and acquiring preset parameters in the PID controller model based on a particle swarm algorithm. The preset parameters comprise proportional (Kp), integral (Ki) and differential (Kd) parameters. Parameters of the Levy flight step length are dynamically adjusted through a Bayesian optimization algorithm. The method is suitable for being applied to PID parameter optimization work.

The invention relates to a lithium ion battery pack liquid cooling system temperature prediction method and system based on improved PSO-ABC-BP, and the method comprises the steps: carrying out the lumped modeling of a lithium ion battery pack liquid cooling system, and obtaining a lithium ion battery pack liquid cooling system model; acquiring operation data of the liquid cooling system of the lithium ion battery pack, and performing data preprocessing to form an operation data set of the liquid cooling system of the lithium ion battery pack; designing the structure of a BP neural network according to the battery pack temperature and the temperature influence parameters of the lithium ion battery pack liquid cooling system operation data set; using an improved PSO-ABC algorithm to optimize the initial weight and the threshold value of the BP neural network to obtain a PSO-ABC-BP lithium ion battery pack liquid cooling system temperature prediction model; the method comprises the following steps: acquiring the latest acquired actual operation data of the lithium ion battery pack liquid cooling system, inputting temperature influence parameters into a PSO-BP lithium ion battery pack liquid cooling system temperature prediction model to obtain a temperature prediction value, and combining an improved PSO-ABC algorithm with a BP neural network to seek a better parameter combination, thereby improving the accuracy of a prediction result.

The invention belongs to the technical field of array antenna synthesis, and particularly relates to a thin cloth rectangular planar array antenna synthesis method, a program, equipment and a storage medium. According to the method, a thin cloth rectangular planar array antenna comprehensive method is converted into an optimization problem of an array element position in a quadrant, an improved sparrow algorithm is adopted, Kent mapping population initialization is introduced, and diversity of an initialized population is enhanced; the idea of a sine and cosine algorithm is fused in a position updating strategy of a discoverer, and a nonlinear sine learning factor is introduced to enhance all search capabilities; and finally, a crisscross strategy is introduced into a sparrow algorithm, and the optimization precision is improved by improving a search mode of a supervisor.

The invention discloses an unmanned aerial vehicle flight path planning method based on an improved Harris eagle optimization algorithm, and the method comprises the steps: setting peak parameter information, constructing a threat equivalent terrain simulation map through a peak function, and carrying out the modeling of a flight environment; establishing a corresponding cost function according to the length cost, the threat cost and the smoothness cost of the flight path, and converting a flight path planning problem into an optimization problem of minimizing the cost function; and solving the optimization problem of the minimum cost function by using an improved Harris eagle optimization algorithm fusing a labor division mechanism and an elimination selection strategy, and carrying out iterative optimization to obtain the optimal flight path of the unmanned aerial vehicle. According to the invention, the feasible track can be explored and developed as much as possible in the flight space, collision and track jitter can be avoided, and smooth execution of the flight task of the unmanned aerial vehicle is facilitated.

The embodiment of the invention discloses a self-adaptive rapid non-dominated sorting genetic method and system, a storage medium and equipment. The method comprises the following steps: setting a population scale, a maximum iteration generation number and a generation channel parameter; selecting a preset number of individuals from the initial population as a parent population according to the individual fitness on the basis of the population scale and the generational channel parameters; determining a self-adaptive crossover probability and a self-adaptive mutation probability according to the non-dominated level and the congestion degree of the parent population; performing crossover variation according to the adaptive crossover probability and the adaptive variation probability in the progressive stage and the mutation stage; performing non-dominated solution sorting on individuals in the parent population after crossover variation to form a new offspring population; and determining a final filial generation population according to the non-dominated level and the congestion degree of the combined parent population and the new filial generation population until the maximum iteration generation number is reached, and outputting a non-dominated optimal solution set. And multiple crossover and mutation are carried out through the adaptive crossover probability and the adaptive mutation probability, so that the convergence speed is accelerated.

The invention relates to a noise feature vector classification model construction method, a speech enhancement method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: coding penalty parameters and kernel function parameters of a support vector machine; taking the penalty parameter and the kernel function parameter as optimization variables of a genetic algorithm, setting an initial population, and constructing a fitness evaluation function based on classification accuracy; performing selection operation, crossover operation and mutation operation on the initial population, and updating an iterative population according to a fitness evaluation function to obtain an optimal penalty parameter and an optimal kernel function parameter; and training a support vector machine through the optimal penalty parameter and the optimal kernel function parameter, and constructing a noise feature vector classification model. In a noisy environment, the classification effect of the noise feature vector is improved under the condition that nonlinear and non-stationary noise exists, and the noise feature classification requirement in a complex sound signal processing scene is met.

The invention discloses a check point deployment method, system, product and device for a heterogeneous computing system, and relates to the technical field of computers. According to the demand parameter of each heterogeneous computing power device, calculating a fault recovery time-consuming expected value corresponding to each storage device when the check point is deployed on each storage device, so as to balance the influence of the recovery of the check point corresponding to each demand parameter of each heterogeneous computing power device. And determining a preliminary first check point deployment relationship according to the demand parameter of each heterogeneous computing power device and the fault recovery time-consuming expected value, so as to realize the predictability of local optimal selection from the corresponding deployment check point to each storage device for the characteristic parameter of each heterogeneous computing power device in the deployment process. According to the preliminary first check point deployment relation, the second check point deployment relation is determined through optimization of the deployment positions of the heterogeneous computing power devices, the possibility of finding a global optimal solution by effectively jumping out of a local optimal solution is increased, and the execution efficiency of a training task is improved.

The invention relates to the technical field of database data access, and particularly discloses a data access method and system based on multi-dimensional data analysis and a storable storage.The method comprises the steps that S1, after a user accesses a database, a management recommendation module automatically allocates a tourist ID to the user, and an acquisition module acquires user information of different dimensions generated in the database access process of the tourist ID and sends the user information to the management recommendation module; the management recommendation module provides personalized content recommendation for the tourist ID; s2, the tourist ID logs in an account in the process of accessing the database, an acquisition module acquires user information of different dimensions of the account, and a management recommendation module judges whether a corresponding account permission is granted or not and provides personalized content recommendation for the user; according to the invention, the management recommendation module analyzes according to the multi-dimensional information collected by the collection module, and provides corresponding personalized content recommendation information for the user accessing the database, so as to achieve the purpose of further improving the search performance, accuracy and user experience of the user.

The invention relates to the field of friction stir welding process parameter optimization, in particular to an aluminum alloy friction stir welding process parameter optimization method which comprises the steps of obtaining experimental data, training a multi-feature subset collaborative network and finding an optimal process parameter combination. Aiming at the problems that training samples of aluminum alloy friction stir welding are insufficient and a neural network is difficult to accurately predict the quality of a welding joint, a multi-feature subset collaborative network is provided, and the number of the training samples is implicitly increased and the accuracy of a prediction result is improved by constructing multiple feature subsets and a main network and sub network collaborative work method; in order to solve the problems that the change range of technological parameters is large, the precision requirement is high, and the optimal solution is difficult to obtain through a traditional optimization algorithm, the technological parameters are optimized through an improved genetic algorithm, the fitness serves as a selection index of filial generations in interlace operation, a self-adaptive mutation strategy is introduced in mutation operation, and filial generation genes are selectively mutated, so that the optimal solution is obtained. And the practical application effect of process parameter optimization is improved.

The invention relates to the technical field of fire prediction, in particular to an improved GWO-based neural network fire trend prediction method, which comprises the steps of collecting fire detection parameters; the detection parameters are preprocessed; establishing a fitness function according to a mean square error of a predicted value and an actual measurement value of the detection parameter; initializing a hyper-parameter and a fitness function of the deep learning network model, and optimizing the hyper-parameter of the deep learning network model by using an improved GWO algorithm; and inputting fire detection parameters collected in real time into the improved deep learning network model to predict the fire. The problems that an existing fire treatment method is low in treatment speed and poor in accuracy are solved.

The invention relates to the technical field of fault diagnosis, and discloses a fault diagnosis method and system applied to an automobile air conditioner compressor. The method comprises the following steps: firstly, introducing auxiliary noise to decompose a vibration signal, calculating a decomposition result by using a sample entropy method to obtain a sample entropy, and identifying and removing noise; secondly, variational mode decomposition is carried out on the vibration signals after noise elimination, an improved crayfish optimization algorithm is used for optimizing a decomposition scale and a penalty factor in the variational mode decomposition process, and a final IMF component signal set is obtained; mapping the final IMF component signal set to polar coordinates through a symmetric polar coordinate transformation method to form a fault diagnosis image set; and finally, training to obtain a convolutional neural network fault diagnosis model, identifying a fault diagnosis image set, and outputting a fault diagnosis result of the automobile air conditioner compressor. The vibration signal of the automobile air conditioner compressor is processed and analyzed, the purpose of fault diagnosis is achieved, and the method is objective and accurate.

The invention relates to a search processing method and device and a computer readable storage medium, and relates to the technical field of artificial intelligence. The search processing method comprises the following steps: receiving a question input by a user; according to the question, an answer of the question and a plurality of answer videos related to the question are displayed in a search result page, and the answer is a statement which is generated by carrying out machine understanding on the answer videos and is described in a natural language; the description information comprises description information of each answer video generated by performing machine understanding on each answer video in the plurality of answer videos.

The invention discloses a bearing fault diagnosis method, which comprises the following steps of: firstly, acquiring vibration signals of a bearing at different positions and directions under health and various fault states by adopting multiple sensors, denoising original signals of the bearing by utilizing an SG filtering method, and carrying out feature fusion to improve the adaptability and reliability of the signals; secondly, decomposing the denoised bearing vibration signal by using a regenerative phase shift sine-assisted empirical mode decomposition method to obtain a series of vibration signal component data IMF, and selecting an optimal TE-IMF by using transfer entropy to obtain recombined signal data; secondly, improving an initialization stage and a population search stage of a non-monopoly search algorithm NO by using sparse initialization and a probability disturbance strategy to obtain a CNO; and finally, optimizing hyper-parameters in the bearing fault diagnosis model based on SageFormer by using CNO, and carrying out bearing fault diagnosis by using the trained model. According to the invention, the accuracy of the bearing fault diagnosis result can be effectively improved.

The invention relates to the technical field of data processing, and discloses a medicine flow data transmission safety early warning method and system based on big data. Firstly, an improved neighbor sorting algorithm and an improved clustering algorithm are used for carrying out similar repeated data cleaning and missing data filling on an original medicine flow direction data set, and a processed medicine flow direction data set is obtained; secondly, dimension reduction processing is carried out on the processed medicine flow direction data set, and a final medicine flow direction data set is obtained; a kernel parameter and a penalty factor in the support vector machine are optimized by using a parrot optimization algorithm, and Cauchy variation improvement is introduced to obtain a support vector machine early warning model; and finally, establishing a medicine flow direction data transmission safety early warning model, checking the final medicine flow direction data set to obtain an early warning result, and completing safety early warning. By processing and analyzing the medicine flow direction data, the purpose of medicine flow direction data transmission safety early warning is achieved, and the method is accurate and objective.

The invention provides a production maintenance and purchase collaborative scheduling method and system based on an improved dung beetle optimization algorithm, a storage medium and electronic equipment, and relates to the field of production maintenance and purchase collaborative scheduling. According to production characteristics of new energy batteries, after production resources and workpiece processing tasks are obtained, the parallel machine scheduling problem of preventive maintenance and order material purchase expected arrival time is considered at the same time, collaborative optimization is carried out on production, maintenance and purchase, and a mixed integer programming model is constructed with the goal of minimizing the maximum completion time. Besides, a dung beetle optimization algorithm combined with a VND local search algorithm is designed, the convergence speed and the search capability of the algorithm are greatly enhanced, and new energy battery manufacturing enterprises can be effectively helped to reduce the number of preventive maintenance times, so that the maintenance cost is reduced, meanwhile, more efficient production scheduling is realized, and the enterprise competitiveness is improved.

The invention discloses a particle swarm algorithm-based single intelligent metasurface beam forming method, which comprises the following steps of: firstly, generating an IRS position phase shift vector and a BS position beam forming vector by using a phase regulation matrix at an IRS position and a BS position, taking the generated vector as a single particle, then randomly generating a particle swarm, initializing each particle, calculating the fitness of each particle, and finally forming a single intelligent metasurface beam according to the particle swarm. And updating individual experience and group experience from the fitness, continuously carrying out iterative search to enable an optimal solution with the maximum reachable rate at the user, and recording the optimal solution. According to the method, large-scale global search can be carried out, local search can be finely carried out, the possibility of falling into a local optimal solution is reduced, excellent performance is shown in the non-convex optimization problem, and good performance is achieved in solving the joint optimization IRS and BS beam forming problem.

The invention provides a training method of a natural language processing model based on a Transform architecture, and belongs to the related technical field of big data processing, and the method comprises the steps: obtaining and preprocessing input text data, sending the preprocessed text to the natural language processing model based on the Transform network architecture for training, and obtaining a training result; the training of a feedforward network of the network adopts a dynamic self-adaptive neural optimization algorithm, and after a Transform network architecture is used for carrying out context-aware semantic analysis on an input text, a multi-layer extreme learning machine classification algorithm based on path integration is adopted to carry out context-aware semantic analysis on the input text. And establishing a probability amplitude model from feature input to classification result output by using a Feimann path integration strategy. According to the method, the precision of the model in task understanding and semantic analysis is improved by optimizing a core algorithm of the network, and the method can capture a complex semantic relationship, so that the processing of the natural language is more intelligent.

The invention discloses a method for predicting the residual capacity of a vehicle-mounted lithium battery, and the method comprises the steps: achieving the collection of battery data through a battery management system, analyzing the correlation of the multi-dimensional features of the data through a cloud server according to the data of a plurality of electric vehicle battery samples through a Pearson correlation coefficient algorithm, and screening out the strong features which greatly affect the capacity of the battery; performing standardization processing on the data; constructing an LSTM neural network prediction model according to the dimension of the input data; setting parameters of a chaos particle swarm optimization algorithm according to the neural network structure, and applying the optimal parameters obtained by the chaos particle swarm optimization algorithm to the LSTM neural network; and finally completing training and testing of the neural network model to obtain the LSTM neural network model capable of being used for prediction. According to the invention, the Pearson's correlation coefficient algorithm is utilized to carry out battery capacity correlation characteristic analysis, strong characteristics having great influence on the battery capacity are found out, and the accuracy of the model is improved; and the LSTM neural network model optimized by the chaos particle swarm optimization is used for prediction, so that the prediction precision is improved.

The embodiment of the invention discloses a lithium battery capacity prediction method based on short-term charging data and an enhanced whale optimization algorithm, which can accurately predict the battery capacities of different types of batteries, improve the capacity prediction accuracy and reduce the training data volume at the same time. The method comprises the steps that in the cyclic charging and discharging test process of different types of batteries, charging and discharging data of the batteries are collected and recorded in real time, short-term charging data are extracted, and the charging and discharging data comprise charging time, voltage, current and capacity data; the short-term charging data is obtained by performing spline interpolation processing on the charging voltage and current data within a preset short-term charging duration; based on an enhanced whale optimization algorithm, battery aging characteristics are extracted from short-term charging data through iterative optimization, and the enhanced whale optimization algorithm is obtained by introducing a pooling mechanism, a migration search strategy, a preferential selection search strategy and a surrounding prey search strategy into a classic whale optimization algorithm. The battery aging characteristic is the voltage increment of the voltage interval corresponding to the charging time interval with the strongest correlation with the battery capacity; and for different types of batteries or the same type of batteries, charging and discharging data and aging characteristics are input into a Gaussian process regression model to predict a battery capacity prediction value, and the Gaussian process regression model is constructed based on a Gaussian process regression algorithm.

The invention discloses a data-driven urban solid waste incineration process multi-objective operation optimization method, and belongs to the field of intelligent optimization. The method comprises the steps that firstly, nonlinear characteristics of the urban solid waste incineration process are captured through a self-organizing radial basis function neural network, accurate evaluation of operation performance index combustion efficiency and nitrogen oxide emission concentration is achieved, and an accurate optimization target model is provided for follow-up set value optimization solution; then, on the basis of the established optimization target model, a multi-target particle swarm optimization algorithm based on a double-file mechanism is designed, and an optimal solution set of set values of the primary air inlet flow and the secondary air inlet flow is obtained; and according to the optimal solution set, designing a utility function to determine optimal set values of the primary air inlet flow and the secondary air inlet flow, and keeping a relatively high combustion efficiency level while realizing that nitrogen oxide emission reaches the standard. According to the method, the combustion efficiency and the denitration efficiency can be synergistically improved, and efficient and green operation of the urban solid waste incineration process is promoted.

The invention provides a photo album image searching method and device, equipment and a storage medium, and the method comprises the steps: obtaining description information related to a to-be-searched image, transmitting the description information to a semantic recognition model server, receiving a first image tag from the semantic recognition model server, and transmitting the first image tag to a server; a target image label matched with the first image label is determined from second image labels corresponding to each image in the photo album, the second image labels are obtained by recognizing each image through a preset target recognition model, a to-be-searched image is determined based on the target image labels, and the to-be-searched image is searched according to the to-be-searched image. The image recognition model can be deployed on the end side to recognize the tag of the image, the semantic recognition model server is called to recognize the search intention to obtain the corresponding tag, and the to-be-searched image is further obtained through tag matching, so that model miniaturization processing does not need to be performed on the end side to reduce the model performance; therefore, the image searching effect in the photo album is improved.

The invention provides a power line impedance matching method and system based on space division, and belongs to the technical field of power line impedance matching based on deep learning. Acquiring a power line terminal load value; based on a database query function, querying capacitance and inductance element values and an optimal target function value during optimal matching corresponding to the obtained power line terminal load value in a database; wherein the database stores the optimal values of capacitance and inductance devices under the optimal matching condition corresponding to different power line terminal load values obtained by using a deep reinforcement learning algorithm model for optimization. According to the method, the program running time is effectively shortened, the problem of unsatisfactory agent optimization caused by too large search space is improved, and the problem of poor optimization result caused by insufficient training of the agent is avoided; the search space is reduced, and the search accuracy is improved; the space is subjected to non-uniform segmentation, and compared with multi-agent distributed optimization based on uniform segmentation, the search effect of the agents is obviously better.

The invention relates to the technical field of image processing, and discloses a medical image denoising method based on a 3D chaotic mapping dung beetle algorithm, and the method comprises the following steps: 1, inputting a to-be-processed original medical image, carrying out the noise adding of the original medical image, and generating a noisy image; step 2, inputting the noisy image into a hybrid filter used for denoising the image; 3, calculating an optimal parameter combination of each filter in the hybrid filter by adopting a dung beetle optimization algorithm, and configuring the hybrid filter by adopting the optimal parameter combination; step 4, performing denoising processing on the noisy image by using the configured hybrid filter to generate a denoised image; wherein in the step 3, the distribution position of the population is initialized by adopting 3D chaotic mapping; the technical problem of poor medical image denoising effect in the prior art is solved.

The invention discloses an SVC reactive voltage control method and system based on an improved particle swarm active power distribution network, and relates to the technical field of power distribution network planning, and the method comprises the steps: building a distributed photovoltaic output model, and designing a first strategy; establishing a first constraint by taking the minimum voltage deviation of the power distribution network as a target function; and calculating the optimal position and the maximum objective function value of the particles, verifying the number of iterations and outputting the number of iterations. According to the SVC reactive voltage control method based on the improved particle swarm active power distribution network, detailed modeling is carried out on the output characteristics of a distributed photovoltaic power generation system, the adaptability of the whole power distribution network under the photovoltaic access condition is improved, the voltage deviation of the power distribution network is reduced through accurate adjustment of reactive compensation, and the stability of the power distribution network is improved. By improving the random weight in the particle swarm algorithm, the search capability of the algorithm between the global optimal solution and the local optimal solution is improved, and the method achieves better effects in the aspects of adaptability, robustness and response capability.