40 results about "Bat algorithm" patented technology

The invention discloses an engineering project full-cycle cost intelligent accounting system based on a BIM technology, and relates to the technical field of constructional engineering informatization, and the system comprises a BIM visualization platform which is in communication connection with the following modules: a dynamic optimization scheduling module which is used for fusing a target engineering project BIM model and a real-time digital twinborn body, and introducing a bat algorithm to carry out dynamic optimization on construction resource nodes influenced by risks. According to the method, a dynamic resource topology network is constructed by fusing a BIM model and real-time digital twinborn data, accurate tracking and state perception of construction resources are realized, on the basis, a bat algorithm and a graph neural network are introduced to perform dynamic optimization and conflict coordination on resource configuration in a multi-risk concurrent scene, and the resource allocation efficiency is improved. And a rescheduling scheme and an optimal cost increment which conform to actual engineering logic are generated, the limitation of traditional static risk conduction simulation is broken through, and the adaptability of resource scheduling and the refinement level of cost control are remarkably improved.

The invention discloses a cathode protection test pile potential adaptive regulation and control system, device and method. The method comprises the following steps: S1, generating a standardized underground structure potential data set; s2, constructing a three-dimensional sparse convolutional neural network model based on the standardized underground structure potential data set and completing training; s3, performing real-time prediction on underground structure potential distribution under different cathode protection control parameter combinations by using the trained three-dimensional sparse convolutional neural network model; s4, initializing a bat algorithm population, and searching a globally optimal cathode protection control parameter combination; s5, inputting the globally optimal cathode protection control parameter combination into the three-dimensional sparse convolutional neural network model; and S6, applying the combination to the operation of an actual cathode protection test pile system. According to the method, priority learning of key nodes around the test pile is realized, and the problem of large potential prediction deviation of a traditional network in a deep buried region is effectively avoided.

The application discloses a double-reliability RVFL bat optimization wind power interval prediction method and system, specifically for: preprocessing wind farm historical power data, constructing a data set; establishing a basic RVFL, a cascaded enhanced RVFL and a direct connection enhanced RVFL candidate model, expressing the upper and lower bounds of the prediction interval as a linear combination of enhanced features and output weights; in the first stage, the hidden layer parameters are fixed, the output weights are solved by using an opportunity constraint optimization model containing a training set and a validation set coverage constraint, and the optimal basic model is selected according to the validation set SCORE index; in the second stage, the model parameters are used as elite prior solutions, a mixed initialization bat population is constructed by combining random exploration individuals, and the global network parameters are optimized; and the prediction interval is output on the test set and evaluated. The application improves the generalization coverage ability by double-reliability constraint, enhances the scene adaptability, and optimizes the network parameters by using a mixed initialization bat algorithm.

The present application relates to the field of geological exploration, especially to a drilling speed modeling method based on improved bat algorithm and support vector regression. The method first identifies and corrects abnormal data according to the interval anomaly detection method. Then, the support vector regression method is used to build a drilling speed model. Finally, an improved bat algorithm is designed to determine the optimal parameter value of the drilling speed model. The drilling speed modeling method described in the present application can effectively eliminate abnormal drilling data and ensure drilling characteristics, while building a high-precision drilling speed model to accurately predict drilling speed and provide a reliable basis for drilling operation and adjustment.

The invention discloses an optical fiber coupling efficiency improving method based on a bat algorithm. The method comprises the following steps that optical power data are collected and preprocessed; constructing a coupling parameter search space model; initializing an improved submerged space enhancement-bat algorithm, and outputting an initial coupling parameter vector; introducing countercurrent coding to generate a submerged space vector; calculating and outputting an optimal action vector through a differentiable strategy flow mapping mechanism; obtaining a current iteration optimal bat individual; outputting a coupling parameter vector of the global optimal bat individual; and the improved submerged space enhancement-bat algorithm is recalled to perform parameter optimization calculation, so that the optimization speed and the global optimal stability of optical fiber coupling are effectively improved, the coupling loss is remarkably reduced, and the automatic adjustment precision and the optical power stability of the system are improved.

The invention discloses an agricultural machinery operation plan adjustment method considering dynamic weather change, belongs to the field of agricultural machinery scheduling, and aims to solve the problems of frequent failure, cost increase and agricultural time delay in actual execution of an operation plan due to the fact that the dynamic weather change is not fully considered in an existing agricultural machinery scheduling method. The method comprises the steps that firstly, an initial scheduling model with the minimum total cost as the target is constructed based on predicted weather, the model is fused with a farmland operation time window and path continuity constraint, an improved bat algorithm is adopted for solving, and an initial optimal scheme containing the agricultural machinery operation path, sequence and time is output; in the operation process, the system monitors weather in real time, and when an original time window loses efficacy or a path is not feasible due to changes, dynamic adjustment is automatically triggered. And then, based on the real-time position of the agricultural machine and the updated weather information, rapidly reconstructing and solving a dynamic optimization model, and outputting an optimal adjustment scheme of the remaining tasks.

The application provides a terminal counter cabinet foot compression strength optimization method and device and electronic equipment, and relates to the technical field of network communication. The method comprises the following steps: constructing a compression strength model of a terminal counter cabinet foot; and solving the compression strength model of the terminal counter cabinet foot by using a new bat algorithm to obtain optimal parameters of the terminal counter cabinet foot. The bat algorithm is improved in the application, the compression strength of the terminal counter cabinet foot is optimized, and the compression strength of the terminal counter cabinet foot is ensured to be strong.

The invention provides a new energy power generation parameter prediction model training method and a parameter prediction method, and the training method comprises the following steps: obtaining wind turbine generator operation data collected by a data collection and monitoring system, and carrying out the preprocessing and feature extraction of the wind turbine generator operation data; on the basis of the LSTNet model, replacing the existing convolution layer with a double-attention convolution module, and introducing a time attention coefficient to improve a loss function of an output layer to obtain an improved LSTNet model; wherein the double-attention convolution module comprises a deep convolution unit, a channel attention unit, a point-by-point convolution unit and a time attention unit; training the improved LSTNet model based on the extracted features to obtain a new energy power generation parameter prediction model; in the training process, the improved LSTNet model is optimized based on a bat algorithm.

The application provides a marine oil spill detection method and system based on reinforcement learning and an improved bat algorithm, and relates to the technical field of target detection.The technical points of the application include: preprocessing the collected radar image;generating a pseudo label for the preprocessed image using a K-means clustering algorithm;extracting features of the preprocessed image using a self-encoder;classifying the preprocessed image using reinforcement learning, and extracting a region with an oil spill category in the classification result as an interesting region, thereby obtaining an interesting image;obtaining an optimal segmentation threshold value using an improved bat algorithm, and segmenting the interesting image using the optimal segmentation threshold value to obtain a segmented image based on the optimal segmentation threshold value, thereby realizing oil spill detection.The application solves the technical pain points of weak robustness and insufficient precision of traditional algorithms in oil spill segmentation, and provides reliable technical support for rapid monitoring and emergency response of marine oil spills.

The application discloses a kind of based on bat algorithm's optical fiber coupling efficiency promotion method, including the following steps: collecting optical power data and preprocessing;Coupling parameter search space model is constructed;Improved latent space reinforcement-bat algorithm is initialized, and initial coupling parameter vector is output;Introduce reversible flow coding to generate latent space vector;The optimal action vector is calculated by differentiable strategy flow mapping mechanism and output;Current iteration optimal bat individual is obtained;The coupling parameter vector of global optimal bat individual is output;Improved latent space reinforcement-bat algorithm is recalled again to carry out parameter optimization calculation, effectively improves the optimization speed and global optimal stability of optical fiber coupling, significantly reduces coupling loss, improves system automation adjustment precision and optical power stability.

The invention relates to an Internet of Things time series data prediction method and system based on intelligent group optimization deep learning, and belongs to the technical field of Internet of Things. The method comprises the following steps: standardizing time series data of the Internet of Things; decomposing the standardized data into high-frequency, intermediate-frequency and low-frequency component groups by adopting an empirical mode decomposition method; an improved bat algorithm is constructed, wherein the improvement comprises the steps of introducing a dynamically changing inertia weight, adopting a Levy flight strategy, introducing Cauchy random disturbance and adopting linear progressive decrease adjustment on loudness and pulse emissivity; performing adaptive optimization on hyper-parameters of a deep learning prediction model by using the improved bat algorithm; and finally, training independent prediction models for the high-frequency component group, the medium-frequency component group and the low-frequency component group by using the optimal hyper-parameter combination, performing linear superposition reconstruction on the prediction results of the components, and executing a destandardization operation to obtain a final prediction value. According to the method, the accuracy of time series data prediction of the Internet of Things and the optimization efficiency of the model are remarkably improved.

The invention discloses an underwater robot lithium ion battery state collaborative estimation and service life optimization management system and method, and relates to the field of underwater robot battery management, and the method comprises the steps: building an Rint equivalent circuit model, calibrating the nonlinear function relation and internal resistance characteristics between an open-circuit voltage and a state of charge through an experiment, and calculating the service life of an underwater robot lithium ion battery. Adopting an ampere-hour integral method fused with dynamic capacity correction to realize state-of-charge estimation; performing collaborative optimization on parameters of the deep extreme learning machine by using a BA-PSO hybrid optimization algorithm fused by a bat algorithm and a particle swarm algorithm, constructing a battery health state prediction model, and performing health state estimation; the battery capacity attenuation is quantized into economic cost, and the economic cost and system operation energy consumption cost jointly form a multi-objective optimization function; designing a constraint processing mechanism based on a penalty function, and ensuring a battery power safety boundary; and an optimal power distribution strategy is solved by adopting a BA-PSO hybrid optimization algorithm, and cooperative control of the lithium ion battery and the super capacitor is realized.

The application discloses a pure phase array pattern synthesis method and system based on an improved bat algorithm, which comprises the following steps: setting an array model constructed by a plurality of antenna units; constructing an optimization objective function of the array model; minimizing the objective function according to a bat algorithm to complete optimization training of the array model; and performing pure phase array pattern synthesis according to the optimized array model. The embodiment of the application is aimed at pure phase symmetric pattern synthesis, an initial phase distribution is created by using a parabolic function, the distribution has the characteristic of an approximately flat-top mode, thereby accelerating the optimization process, in addition, the excitation distribution is expressed in a polynomial form, the optimization problem is converted into optimization of polynomial coefficients, thereby realizing antenna pattern synthesis, and the application can be widely applied to the technical field of computers.

The invention relates to the technical field of bat algorithms, and particularly discloses a radar emission angle adjusting method for a radar level meter based on the bat algorithm, which comprises the following steps: S1, acquiring an emission angle range of the radar level meter, and presetting a bat population and a maximum number of iterations; s2, performing iterative optimization on the bat to adjust the frequency, calculating the fitness value, the updating speed and the angle of the echo signal, and generating a local new solution; s3, issuing the local new solution, collecting echo data, calculating a fitness value corresponding to the local new solution, and calculating the pulse emissivity and loudness of the bats after iteration; and S4, updating the globally optimal solution and the fitness value, outputting the globally optimal solution and setting parameters of the radar level meter. According to the scheme, the optimization model is constructed based on the bat algorithm, the emission angle is converted into the algorithm search space, the search efficiency is balanced by dynamically adjusting the frequency, the emissivity and the loudness, the optimization parameters are verified in combination with a hardware closed loop, and the optimal parameters are solidified to realize stable measurement, so that the precision is improved.

The invention provides a high-voltage circuit breaker mechanical fault diagnosis method based on an improved bat algorithm optimized double-subsupport vector machine, and belongs to the technical field of high-voltage circuit breaker mechanical fault diagnosis. In order to solve the technical problems of low execution efficiency and low accuracy of diagnosing the fault of the high-voltage circuit breaker by adopting an expert system, a neural network or a support vector machine at present, the adopted technical scheme is as follows: collecting sample data of the mechanical fault of the high-voltage circuit breaker; the characteristic quantity of the sample data comprises a current time point and a current value in a circuit breaker opening and closing coil current curve, and the collected sample data is used as an initial sample; transforming the initial sample by using a principal component analysis method, extracting key features to form an optimal feature sample, and dividing the optimal feature sample into a training sample and a test sample; establishing two hyperplane equations by taking a Gaussian function as a kernel function, and solving to obtain a classification decision function so as to construct a gemini support vector machine model; the method is applied to mechanical fault diagnosis of the high-voltage circuit breaker.

The embodiment of the invention discloses an intelligent decision-making system and method for Mongolian medicine equisetum extraction and separation and preparation development, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing a multi-source heterogeneous data set, and generating a standardized data set; the data processing module is used for generating an optimal extraction parameter combination according to an improved multi-target bat algorithm based on the standardized data set, and generating an optimal preparation prescription scheme according to a pharmacodynamic simulation feedback algorithm; the strategy execution module is used for converting the optimal extraction parameter combination and the optimal preparation prescription scheme into a control instruction, and driving equipment to execute the optimal preparation prescription scheme through the control instruction; the monitoring feedback module is used for collecting actual operation parameters of the equipment, calculating deviation values of the actual operation parameters and target parameters and correcting the control instruction when the deviation values exceed a preset threshold value; and the real-time interaction module is used for displaying the extracted and developed data of each link and executing each instruction operation of an operator. The extraction rate, purity and preparation success rate of Mongolian medicine equisetum are improved.

The invention discloses an unmanned aerial vehicle intelligent spraying method and system based on LiDAR mapping and tree core path optimization, a storage medium and a computer device.The spraying method comprises the steps that firstly, a high-precision orchard three-dimensional point cloud map is constructed based on LiDAR data, and then the spatial position and crown parameters of a single fruit tree are accurately extracted through a semantic segmentation algorithm; the path is optimized through an improved bat algorithm, smooth and efficient spraying path planning is achieved, and therefore precise hovering and targeted pesticide applying operation can be executed, the deposition effect of fog drops on the middle and lower layers of a tree crown is improved, and pollution to a non-target area is reduced.

The invention relates to the technical field of bridge structure health monitoring, in particular to a highway bridge damage identification method and system, and the method comprises the following steps: S1, collecting a surface image and structure physical response data of a bridge structure through an unmanned plane and a multi-type sensor, and carrying out the standardization and noise suppression of the collected data; according to the method, bridge damage identification is converted into an unsupervised multi-objective optimization problem fusing physical constraints and prior knowledge, and dynamic parameter regulation and control and hierarchical search strategies of an improved bat algorithm are combined, so that accurate inversion and optimal solution output of a real damage state of a bridge are realized, the accuracy and efficiency of damage identification are effectively improved, and the method is suitable for large-scale popularization and application. And the credibility quantification of the identification result is completed based on historical data and maintenance records, the core parameters of the bat algorithm are dynamically optimized through a hierarchical feedback mechanism, closed-loop iterative optimization is formed, and the stability, the adaptability and the engineering reliability of the identification performance of the system are continuously improved.

The application provides a path planning optimization method and device for a mobile robot, and relates to the field of path planning. The method comprises the following steps: obtaining a starting position and a terminal position, performing parameter initialization and population initialization, and obtaining a current particle state and a current bat state; determining a current group optimal position and a current individual optimal position based on the current particle state and a fitness function; performing an iteration on an adaptive particle swarm algorithm to update the current particle state; obtaining search parameters corresponding to the bat according to the current individual position, performing an iteration on the bat algorithm based on the search parameters, updating the current bat state to obtain a current best bat; updating the current group optimal position based on the best search parameters corresponding to the current best bat, returning to the step of updating the current particle state, and generating a target path when a second preset iteration number is reached. The adaptive particle swarm algorithm and the bat algorithm are organically combined, and problems such as slow convergence speed and falling into a local optimal solution are avoided.

The invention provides a navigation oil spill detection method and system based on reinforcement learning and an improved bat algorithm, and relates to the technical field of target detection. According to the technical key points, the method comprises the following steps: preprocessing a collected radar image; using a K-means clustering algorithm to generate a pseudo label for the preprocessed image; performing feature extraction on the preprocessed image by using an auto-encoder; using reinforcement learning to classify the preprocessed image, and extracting an oil spill region in a classification result as an interest region to obtain an interest image; and obtaining an optimal segmentation threshold value by using an improved bat algorithm, segmenting the image of interest by using the optimal segmentation threshold value, and obtaining a segmented image based on the optimal segmentation threshold value, thereby realizing oil spill detection. According to the method, the technical problems of weak robustness and insufficient precision of a traditional algorithm in oil spill segmentation are solved, and reliable technical support is provided for rapid monitoring and emergency response of offshore oil spill.

The application provides an AI algorithm-based power grid maintenance plan intelligent arrangement method, and belongs to the technical field of power system automation and artificial intelligence. The method comprises the following steps: obtaining power grid related data to construct a knowledge graph and encapsulating the knowledge graph into a legality judgment function; obtaining a maintenance task reported by a maintenance application unit, extracting maintenance elements by using a large language model, and translating the maintenance elements into structured constraint data; constructing a multi-objective optimization function with workload balance degree, power loss cost and plan execution deviation degree minimization as the target; performing global solution by using an improved adaptive ant colony algorithm with periodic change of pheromone evaporation coefficient, and calling the legality judgment function for real-time pruning in the solution process; when the switching condition is met, switching to an improved bat algorithm with search frequency and pheromone concentration correlation for local optimization; and finally outputting an optimal maintenance plan meeting all constraint conditions after full constraint verification. The application realizes intelligent arrangement of the maintenance plan, and improves the arrangement efficiency and optimization quality.

The invention discloses a program loop structure safety verification method based on optimal inductive loop invariant generation. A BII generation problem is reexpressed as a declarative constraint optimization problem. The new normal form completely bypasses the dependence on symbol abstraction and BAT, and the calculation of BAT is the most expensive step with the maximum calculation cost in the prior art. According to the method, the calculation bottleneck that a traditional method depends on expensive primitives is fundamentally solved. BII solving is carried out through a constraint solving method, and expensive BAT calculation is avoided. Experiments prove that the speed of the method is 4.9 times higher than that of the existing BAT algorithm with the best performance on average, and 86% more reference examples can be solved within 60 seconds of timeout. The high efficiency is derived from the proposed bit-level greedy refinement algorithm and key optimization strategies thereof, and the strategies can reduce the total calling times of the SMT solver by a plurality of orders of magnitude, so that the solving speed is greatly improved.

This invention provides a method and system for concurrent distribution of multiple video streams and weak network optimization for unmanned aerial vehicles (UAVs). The method includes: reading multiple video streams, flight path data, and link status data to generate a video stream set; dividing the video stream set into candidate segments and using the Bat Algorithm to jointly optimize the flight path segment boundaries, key content pre-sending windows, and concurrent transmission weights of multiple video streams to generate a link fate graph; classifying and segmenting each video stream according to the video stream set and the link fate graph to generate segmented transmission queues; performing pre-concurrent distribution of key content data in low-risk segments and generating a recovery index set, and performing sustained transmission in high-risk segments and generating a segmented transmission result table; generating queue correction requests based on link feedback data to obtain the corrected segmented transmission queues, and completing multi-terminal concurrent distribution and weak network recovery.

This invention provides a navigation method for heavy-duty equipment based on the fusion of neural networks and filtering, comprising: when pure electric heavy-duty equipment performs a transfer task, reading the target driving path, safe passage boundary, vehicle structural parameters, and vehicle actuator response parameters, and collecting the load basic state, vehicle dynamic response, attitude response, and vehicle observed pose to form heavy-duty navigation synchronization data; extracting the load offset response feature sequence based on the heavy-duty navigation synchronization data, and inverting the load shadow centroid offset and load offset trend through a neural network model and a bat algorithm; inputting the load shadow centroid offset and load offset trend into a filtered state estimation model to output a fused navigation state; generating the vehicle predicted driving trajectory, vehicle predicted sweep range, and final navigation compensation control quantity based on the fused navigation state; and forming a closed-loop update through compensation execution feedback data.

This invention provides a method and system for concurrent distribution of multiple video streams and weak network optimization for unmanned aerial vehicles (UAVs). The method includes: reading multiple video streams, flight path data, and link status data to generate a video stream set; dividing the video stream set into candidate segments and using the Bat Algorithm to jointly optimize the flight path segment boundaries, key content pre-sending windows, and concurrent transmission weights of multiple video streams to generate a link fate graph; classifying and segmenting each video stream according to the video stream set and the link fate graph to generate segmented transmission queues; performing pre-concurrent distribution of key content data in low-risk segments and generating a recovery index set, and performing sustained transmission in high-risk segments and generating a segmented transmission result table; generating queue correction requests based on link feedback data to obtain the corrected segmented transmission queues, and completing multi-terminal concurrent distribution and weak network recovery.

The invention relates to the technical field of precision measurement, and particularly provides a circular arc diameter measurement method, which comprises the following steps of: obtaining three-dimensional point cloud data of a preset length range of a circular arc section, and performing three-dimensional point cloud correction on the obtained three-dimensional point cloud data to obtain a circular arc diameter; extracting a two-dimensional section contour line of an arc formed by the three-dimensional point cloud data and two-dimensional point cloud data intersecting with a space plane, performing noise point removal and differential calculation on the two-dimensional section contour line by using a conformal filter, and identifying a target arc section in the two-dimensional section contour line through curvature and slope fusion; and performing optimal circle fitting on the target arc section by using a dynamic frequency search mechanism and a bat algorithm of a collaborative variation mode to realize diameter measurement of the target arc section. According to the method, the fitting precision and convergence reliability of the incomplete small arc are remarkably improved, and the problem that a traditional method is prone to falling into local optimum is effectively avoided.

The application discloses a thermal power unit deep peak regulation control system, relates to the technical field of thermal power unit deep peak regulation, and comprises a deep peak regulation control center, wherein the deep peak regulation control center is communicatively connected with a data acquisition module, a load prediction module, a working condition monitoring module, a distributed control module and a safety monitoring and early warning module. The application divides each subsystem of the thermal power unit into multiple local control units by introducing a distributed control architecture and a bat algorithm optimization, realizes independent and collaborative control, significantly improves the flexibility and response speed of the thermal power unit in the deep peak regulation process, ensures that each subsystem can quickly adjust operation parameters according to actual load demand through the distributed control, and the global optimization capability of the bat algorithm further improves the adaptability and decision efficiency of the system when facing complex working conditions, thereby effectively solving the problem of response lag under the traditional centralized control mode.

The invention relates to a cold chain path optimization method based on an improved bat algorithm, and the method comprises the following steps: data collection: obtaining the basic information of each distribution point in a cold chain logistics system, and target function construction: building a target function of a cold chain path optimization problem, according to the characteristics of the cold chain path optimization problem, the bat algorithm is initialized, and the bat algorithm optimization is improved; the improved bat algorithm is adopted to carry out global search and path selection and scheduling; according to an optimization result, an optimal path is selected and distribution scheduling is carried out, and it is ensured that the temperature control requirement is met, and the distribution time efficiency is optimal; and result output: outputting the optimal path and the corresponding distribution scheme as an execution plan of the cold-chain logistics path. The improved bat algorithm can effectively solve the multi-objective optimization problem in the cold-chain logistics, the multi-objective optimization problem comprises multiple objectives such as temperature control, transportation time efficiency and cost minimization, and the method has high practical application value.

The application discloses a database sensitive data sending behavior marking and traceability tracking system and method, and the system comprises six units, i.e., a data feature perception and acquisition unit, a behavior feature extraction unit of an optimized bat algorithm and the like. The data sending parameters are acquired through the data feature perception and acquisition unit, the behavior features are extracted through the optimized bat algorithm, the state transition is analyzed by improving the Markov chain model, the data marking code is generated, the traceability index is constructed, and the tracking feedback is performed. The method comprises six steps, i.e., data acquisition, feature extraction, state analysis, code generation, index construction and tracking feedback. Through the cooperation of the units of the system and the specific method, the application realizes accurate marking and efficient traceability tracking of the database sensitive data sending behavior, and provides a reliable technical means for data security protection.

The present application relates to photovoltaic clustering and cluster division technical field, especially in multi-dimensional photovoltaic clustering method based on bat algorithm, method includes: collecting photovoltaic historical time series data, based on the improved Rayid criterion, the data is pretreated, the data after pretreatment is extracted through SSAE network structure deep feature, and low-dimensional feature data set is generated;Construct a multi-dimensional clustering model, based on the low-dimensional feature data set, through the distance formula of four-dimensional comprehensive measurement, the comprehensive similarity between different photovoltaic scenes is calculated;The photovoltaic scene clustering problem is converted into a clustering center optimization problem, and the multi-dimensional clustering model is iteratively optimized and solved by the bat algorithm, to obtain the globally optimal clustering division result and typical scene;Based on the double-layer optimization model, the clustering division result and the typical scene are closed-loop verified, and the final photovoltaic clustering result is output. Through the present application, the problem that the traditional photovoltaic clustering algorithm has single feature measurement dimension, is easy to fall into local optimum, the clustering result has poor robustness, and is difficult to adapt to the planning and operation demand of distribution network under high proportion distributed photovoltaic access is effectively solved.