617 results about "Local search (optimization)" patented technology

The invention discloses a factor multiple optimization-based flood risk dynamic zoning method and system, and the method comprises the steps: constructing an initial flood risk assessment index set, carrying out the screening of the initial flood risk assessment index set, and calculating the frequency value of each index; constructing a comprehensive discrimination index value, and further screening to obtain a key flood risk assessment index set; constructing a risk assessment model and a key flood risk assessment index weight optimization model, and adopting a genetic-local search hybrid algorithm of simulated annealing optimization to calculate and obtain an optimal weight combination scheme; and calculating the similarity between the scene of each time point of the research area and a historical typical flood event, selecting an optimal weight combination scheme, and inputting the optimal weight combination scheme into the risk assessment model for calculation to obtain a flood risk dynamic division of the research area. According to the method, scientific and reasonable dynamic risk assessment can be provided, effective decision support can be provided for related decision makers, and the flood control toughness and the extreme environment coping capacity of the drainage basin area can be improved.

The invention discloses a network edge monitoring and early warning method based on video image AI analysis, and the method comprises the following steps: S1, obtaining video data, processing the video data, and generating an image frame sequence; s2, analyzing an image frame sequence, and extracting space and time features; s3, taking the target as a hypergraph node, constructing a hyperedge based on space and time features, and dynamically adjusting hyperedge connection by using a genetic algorithm; s4, constructing a multi-layer hypergraph Transform network, extracting multi-scale spatial-temporal characteristics, and calculating a semantic relationship between nodes; s5, setting a butterfly optimization algorithm initial population, and dynamically optimizing model parameters through global and local search; s6, constructing an anomaly detection model, identifying an abnormal behavior, and feeding back a result to optimize model parameters and hyperedge selection; and S7, deploying the model at an edge node, triggering early warning when an abnormal behavior is detected, and pushing information to a management platform. According to the invention, through video image AI analysis, accurate detection and real-time early warning of abnormal behaviors in a network edge scene are realized.

The invention discloses a distribution transformer fault diagnosis method based on full-band multi-modal data fusion. The method comprises the following steps: collecting different feature maps of a transformer; performing feature extraction and weighted summation on the collected different feature maps to obtain a multi-source fusion feature vector; performing data preprocessing on the multi-source fusion feature vector, and performing normalization processing after data preprocessing; training an improved probabilistic neural network by using the normalized multi-source fusion features; optimizing the polynomial order of the trained improved probabilistic neural network by adopting an improved parrot optimization algorithm; and carrying out transformer fault diagnosis by adopting the optimized improved probabilistic neural network. According to the method, the improved parrot-shaped optimization algorithm is adopted, the foraging behavior of the parrot-shaped algorithm is simulated, the polynomial order of the improved probabilistic neural network is optimized by effectively combining global search and local search strategies, and the accuracy of transformer data fusion diagnosis and the generalization ability of the probabilistic neural network are improved.

The invention discloses a packaging tin material proportioning optimization method based on a particle swarm optimization algorithm, and the method comprises the following steps: initializing a particle swarm, dynamically adjusting the positions and speeds of particles through a fuzzy logic control system, and generating an initial material proportioning scheme; a fitness function is constructed based on a non-Euclidean geometric method, multiple indexes such as strength, toughness, corrosion resistance and cost are comprehensively considered, and fitness values of all the schemes are calculated; gradually optimizing a material proportioning scheme by using a biologically inspired particle swarm optimization algorithm; a dynamic clustering mechanism is introduced, and different material matching schemes are analyzed and optimized; dynamically switching between global search and local search through a hybrid search strategy, and further optimizing a selected scheme; and finally, outputting an optimized optimal material proportioning scheme, and evaluating the reliability of the optimal material proportioning scheme through experimental verification and simulation. According to the invention, the optimization efficiency and performance of the packaging tin material proportioning are improved, and the method is suitable for the requirements of production automation and high efficiency.

The invention relates to the field of robot target path planning, and discloses a multi-target path planning method and device based on an improved mantis optimization algorithm, and the method comprises the steps: improving the mantis optimization algorithm, and building a grid environment map and a multi-target fitness function, an obstacle avoidance function and a smooth path function corresponding to path planning; the population position optimization corresponds to path nodes in the grid environment map, iteration is performed on all individuals of the population, and fitness values are formed; calculating comprehensive fitness by using a multi-target fitness function to obtain an updated global optimal position and a corresponding optimal fitness value; completing path planning of a multi-target fitness function; and observing the convergence curve graph and the shortest path graph, and verifying the feasibility of multi-target path planning. According to the method, the convergence speed of the algorithm is improved, the global and local search capabilities are balanced, the shortest path length is effectively reduced, path redundant nodes and path inflection points are reduced, and the path quality and efficiency are improved.

The invention discloses an intelligent path planning method and system for an unmanned aerial vehicle. The method comprises the following steps: initializing related parameters and optimization algorithm parameters of the unmanned aerial vehicle; coding the path points of the unmanned aerial vehicle; calculating fitness values of all individuals in the population; generating a new population through selection, crossover and mutation operations; carrying out local search on individuals with better fitness in the population; dynamically adjusting algorithm parameters according to population fitness changes; and outputting the path with the highest fitness value as the optimal flight path of the unmanned aerial vehicle. According to the method, a local search mechanism and a dynamic adjustment strategy are introduced on the basis of a traditional genetic algorithm, the method is suitable for a combinatorial optimization problem, excellent performance is shown in unmanned aerial vehicle path planning, and a path with the shortest time can be efficiently found.

The invention relates to the technical field of medicine purchasing, and provides a hospital easy-to-expire medicine purchasing decision optimization method, which comprises the following steps of: firstly, extracting medicine inventory, consumption records and purchasing management parameters in a hospital information system, and constructing a complete inventory state data set; then, calling a demand prediction model constructed based on a time sequence and external factors, and outputting drug prediction demand values in a plurality of periods in the future; thirdly, constructing a multi-cycle purchase optimization model according to the current inventory and the prediction demand; then, dividing a planning period into rolling windows, and gradually generating an ordering scheme meeting conditions by using a heuristic greedy and local search algorithm; then, the scheme is pushed to a purchase decision-making interface, and a formal order is submitted and generated after the scheme is confirmed by a manager; after a supplier delivers goods and stores the goods, the system updates inventory information and records scrapping and shortage data; and finally, adjusting the model through a feedback mechanism so as to improve the accuracy of subsequent prediction and decision.

The invention provides a charging station dynamic pricing method based on a game grey wolf algorithm, and aims to solve the problem of dynamic pricing of a single charging station. According to the method, modeling is carried out on a charging station dynamic pricing problem, a Markov decision process is combined, a double-layer gradual-reduction population reduction strategy is introduced, the search range is gradually reduced, and the global search efficiency of an algorithm is improved; meanwhile, in combination with a self-adaptive local search strategy, the local search strength is dynamically adjusted to solve the optimal solution as much as possible. In order to further improve the performance of the algorithm, the neural plasticity variation strategy is fused, the capability of the algorithm for jumping out of local optimum is enhanced, a competitive cooperation mechanism based on hierarchical game is designed, and the global search and local convergence capability among populations is balanced. The improvements effectively improve the convergence, stability and robustness of the algorithm. Experimental results show that the method has good solution precision and convergence efficiency under dynamic requirements, the income optimization capability of the charging station and the accuracy of dynamic pricing are remarkably improved, and an efficient and practical solution is provided for the dynamic pricing problem of the charging station.

The invention belongs to the technical field of PID (Proportion Integration Differentiation) control optimization, and particularly relates to a pure fresh meat kitten immune dry food dehydration control method for optimizing PID based on an improved particle swarm optimization algorithm. Firstly, multi-parameter data in the dehydration process are collected, then PID controller parameters are optimized by using an improved particle swarm optimization algorithm, and according to the algorithm, through a specific objective function, particle initialization, fitness function selection, iterative updating of particle speed and position, introduction of local search enhancement factors, dynamic adjustment of inertia weight, updating of step length based on particle historical information and the like are carried out. And when the fitness function reaches a threshold error, optimization is stopped, and finally, optimized parameters are applied to dehydration control, and the precision is improved in combination with improvement of a dehydration delay compensation item. The defects of an existing control method are overcome, the dehydration process control precision is effectively improved, the system stability and the response speed are improved, and a better technical scheme is provided for dehydration control of the pure fresh meat kitten immune dry food.

The invention provides an image enhancement optimization method based on multi-strategy improved sparrow search. The method comprises the following steps: performing normalization processing on a gray value of each pixel point of an original image; initializing a sparrow population and parameters according to an elite chaos reverse learning strategy; the method comprises the following steps: preprocessing an input image by adopting a dual gamma function BiGA; calculating the value of the evaluation function; updating the optimal fitness value of the sparrow population; when an iteration stop condition is not met, updating the position of the sparrow population by adopting a probabilistic following strategy, a self-adaptive local search strategy and a Cauchy Gaussian mutation strategy, and updating parameters of a dual gamma function BiGA; otherwise, ending iteration, and calculating an optimal parameter; processing the image, and outputting an enhanced image; according to the method, the visual effect of the image can be improved, edge details and textures can be richer, and the enhanced image can have higher contrast and better definition. The method is best in overall enhancement effect, and the obtained image is better in quality.

The invention discloses a sprint learning path optimization method based on an improved whale optimization algorithm. The problem that existing sprint path planning is unreliable is solved. The method comprises the following steps: constructing a total learning cost objective function; using a random key coding algorithm based on chaotic mapping to represent the learning paths in a sequence mode, and using serial scheduling to obtain a plurality of candidate sprint learning paths; introducing an adaptive weight mechanism, an examination driving offset item and a Gaussian disturbance item, and adopting an improved white whale optimization algorithm to carry out iterative optimization on the candidate sprint learning paths to obtain a plurality of iteratively optimized sprint learning paths; in any iterative optimization, if the current candidate sprint learning path violates a first repair relation or / and a time window or / and a daily learning duration budget in the learning path, performing feasibility repair on the current candidate sprint learning path; performing neighborhood local search on the iteratively optimized sprint learning path based on a forgetting risk index; and outputting an optimal sprint learning path.

The invention discloses an energy-saving type rescue path dynamic planning system and method for an intelligent networked automobile, and relates to the technical field of intelligent rescue planning. The system comprises an information sensing module, an initial path planning module, a real-time monitoring module, a path adjusting module and a result display module, the method comprises the following steps: acquiring road, traffic, weather and vehicle information, constructing a multi-objective function containing energy-saving and rescue timeliness targets by an initial path planning module, planning an initial path by applying a multi-objective optimization algorithm, continuously monitoring information changes and vehicle states by a real-time monitoring module, calculating path and energy-saving deviation, and performing real-time monitoring on the path and the energy-saving deviation. The path adjusting module is used for triggering an adjusting mechanism according to deviation, searching an alternative path by adopting a local search algorithm and comprehensively evaluating and selecting an optimal path, the result display module is used for displaying the adjusted path and related information, and the system and the method realize energy conservation and high efficiency of a rescue path through multi-source information fusion and dynamic adjustment.

The invention relates to the technical field of intelligent workshop scheduling, and provides a job shop scheduling method and system based on a principal component heterogeneous graph neural network, and the method comprises the following steps: determining a constraint condition of a distributed flexible workshop, taking the minimization of the maximum completion time as a control target, and building a problem model according to the constraint condition; obtaining a current state of workshop operation, performing embedding based on a graph attention network to obtain an embedded representation of the state, and extracting an embedded representation of a key state based on principal component analysis after fusion; the obtained embedded representation of the current key state serves as input, the problem model is solved through a strategy network, and probability distribution, distributed to a specific machine tool, of each working procedure serves as an initial solution; and based on the obtained initial solution, performing iterative optimization by adopting an enhanced local search heuristic algorithm to obtain an optimal solution. According to the method, the high efficiency and accuracy of the final solution can be ensured, the defects of a traditional GNN-based method in a subsequent optimization mechanism are overcome, and the method is suitable for the scheduling problem of various types of distributed flexible workshops and has high expansibility and adaptability.

The invention provides an improved particle swarm optimization (PSO) algorithm for mechanical arm trajectory planning, and aims at solving the problems that a traditional PSO algorithm is low in convergence speed and prone to falling into a local optimal solution. Therefore, the inertia weight and the learning factor of the algorithm are dynamically adjusted, so that the inertia weight and the learning factor change along with the increase of the number of iterations, and the global search capability and the convergence speed are enhanced; meanwhile, an elite reverse learning (EL) strategy is introduced, an optimal trajectory planning scheme is selected according to a fitness function, a new search area is explored through reverse particles, and the local search capability is enhanced; and a Gaussian-Cauchy variation (GC) strategy is combined, so that the global search capability is further improved, and a local optimal solution is avoided. In the optimization process, a single-target optimization method is adopted to carry out hierarchical classification on population solutions, and the performance of the algorithm under single-target optimization and constraint conditions is improved. Finally, the trajectory of the mechanical arm is optimized based on the algorithm, and particularly, each time period in a 3-5-3 polynomial interpolation method is finely optimized, so that the time from an initial point to a target point is shortened, a trajectory planning scheme with the optimal time is obtained, and the working efficiency of the mechanical arm is improved.

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 multi-unmanned aerial vehicle search and rescue task planning method based on ABC-DQN hierarchical optimization, and belongs to the technical field of multi-unmanned aerial vehicle cooperative search and rescue. According to the method, task planning is carried out on a search and rescue area applying multiple unmanned aerial vehicles based on an artificial bee colony algorithm and a deep Q network, and adaptive cooperative search and rescue of the multiple unmanned aerial vehicles is completed. According to the method, global planning of a search and rescue task and path planning of a local area are both constrained and optimized, scene analysis of a search and rescue area and selection of the number of unmanned aerial vehicles with different performances are firstly carried out, and the unmanned aerial vehicle of a central control node is determined; and then the central unmanned aerial vehicle completes static global region division by using an ABC algorithm, then unmanned aerial vehicles in a local region perform adaptive search and rescue path planning based on a DQN algorithm, and in a local search and rescue process, the local unmanned aerial vehicles and the central unmanned aerial vehicle perform information interaction. Once the target is determined to appear, the central unmanned aerial vehicle further reduces the global area and performs ABC algorithm division again until the search and rescue target is determined to be within the required range.

The invention discloses an improved grey wolf optimization algorithm-based solution for multi-unmanned aerial vehicle cooperative flight path planning, and belongs to the field of unmanned aerial vehicle flight path planning. According to the method, a multi-aircraft cooperative flight path planning model is established in a three-dimensional space, and the provided algorithm not only needs to consider self-factor limitations such as a pitch angle, a yaw angle, a navigation height and a navigation distance of an aircraft course, but also needs to consider areas where radars, artillery fire coverage areas and meteorological equipment in avoidance space are located, and external factors such as inter-aircraft cooperation. An original grey wolf optimization algorithm is improved by using a method of combining global search and local search, a fitness function loss value is quickly reduced by adopting multiple strategies, and a local search range can be expanded to a certain extent. The improved grey wolf optimization algorithm adopted by the invention is relatively high in parameter adjustment effectiveness and high in convergence speed, and can plan a track with relatively small loss according to different types of three-dimensional space constraints, so that a collaborative track optimization scheme of the unmanned aerial vehicle group is given.

The invention discloses an image segmentation method and device based on an improved eagle optimization algorithm, and relates to the technical field of image segmentation, and the method comprises the steps: obtaining a to-be-segmented image, determining a color channel of the to-be-segmented image, and initializing the parameters of the eagle optimization algorithm to determine an initial optimal solution. In the global search stage, population positions are updated by using expanded search and differential variation strategies, the global exploration capability is enhanced, and local optimum is avoided. After iteration of the global search stage and the local search stage is completed, the population position is further optimized through random reverse learning, T distribution variation and teaching and learning optimization strategies, and the convergence speed and stability are improved. And finally, according to an optimization result, determining a segmentation threshold value of each color channel and completing image segmentation. Compared with the prior art, the method has the advantages that the image segmentation efficiency and precision are remarkably improved, the noise immunity is enhanced, the method is suitable for complex image processing, and the actual application requirement is met.

A multi-objective optimization method for a distribution network containing distributed power sources taking into account carbon emissions, comprising the following steps: 1) constructing a multi-objective optimization model for the distribution network; 2) using a hierarchical analysis method to determine the weights of each objective function in the above-mentioned multi-objective optimization model; 3) using an improved beetle whisker particle swarm algorithm to improve the global search capability and local search accuracy of the algorithm for solving the above-mentioned multi-objective optimization model. By integrating a multi-objective optimization model and an improved hierarchical analysis method, the present invention not only optimizes economic indicators, but also takes carbon emission costs into consideration for the first time, thereby achieving the dual economic and environmental benefits of distribution network planning. The improved beetle whisker particle swarm algorithm used further improves the search efficiency and solution accuracy, ensuring the robustness and rapid convergence of the algorithm when dealing with high-dimensional complex problems. The method effectively reduces network losses and carbon emissions, improves voltage distribution, and provides technical support for the green transformation and sustainable development of power systems.

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 discloses a departure peak period-oriented flight driving and taking-off collaborative optimization method, which comprises the following steps of: firstly, accurately identifying an optimal release control rate based on historical flight data, secondly, improving the sliding time prediction accuracy by utilizing integrated learning, and then, establishing a departure flight collaborative scheduling model according to an actual operation rule so as to realize the departure flight collaborative scheduling. And establishing a conflict mechanism to set flight priorities. A solving algorithm adopts a hybrid algorithm combining a genetic algorithm and tabu search, effective combination of global search and local search is ensured, and falling into a local optimal trap is avoided. The scheduling scheme covers key indexes such as runway throughput and average taxiing time, the airport scene congestion condition is relieved, the operation efficiency and safety of an airport in the departure peak period are improved, flight delay is reduced, and the utilization rate of runway time slot resources is increased.

The invention discloses an effluent total phosphorus prediction model construction method based on a pigeon flock optimization echo state network, and the method comprises the steps: obtaining sewage treatment data, and carrying out the data preprocessing; determining an auxiliary variable set and a prediction variable according to the preprocessed sewage treatment data; wherein the predictive variable is effluent total phosphorus; determining the number of input nodes and the number of output nodes of the echo state network according to the auxiliary variable set and the prediction variable; and determining optimal parameters of an echo state network reserve pool layer based on a self-adaptive improved pigeon inspired optimization algorithm in combination with a tabu search algorithm. According to the method, the global search capability of the pigeon inspired optimization algorithm and the local search capability of the tabu search algorithm are utilized, the optimization capability of the algorithm is further improved by improving an operator updating formula in the pigeon inspired optimization algorithm, and the parameters and the structure of the echo state network are optimized by applying a self-adaptive adjustment mechanism. The original algorithm gets rid of a local optimal solution, and the model prediction precision can be improved.

The invention discloses a shipborne radar oil spill detection method and system based on an improved whale optimization algorithm, and relates to the technical field of image processing and mode recognition. The method is characterized by comprising the following steps: preprocessing an acquired shipborne radar image; extracting saliency boundary ratio features of the preprocessed image; extracting an ROI image based on the saliency boundary ratio feature; obtaining an optimal segmentation threshold value by using an improved whale optimization algorithm, and segmenting the ROI image by using the optimal segmentation threshold value to obtain an optimal segmentation image; and performing post-processing on the optimal segmentation image to obtain a final oil film image. According to the method, the difference between the oil spill area and the background is highlighted by extracting the significant features; by improving the whale optimization algorithm, the local search capability of the algorithm is enhanced, and premature convergence is avoided. The method achieves the efficient and accurate recognition of the oil film region, can reduce the false detection rate, and is suitable for the detection scenes of different sea conditions and oil spill types.

The invention provides a multi-bucket wheel machine collaborative operation scheduling system and method based on a swarm intelligence algorithm, and relates to the technical field of electronic information. Inputting the equipment information, the stacking information and the environment information into a first model to generate constraint conditions; performing global search based on an artificial bee colony algorithm introducing harmony search, and screening solutions according to constraint conditions in each iteration to obtain a group of feasible solutions; performing local search in each feasible solution neighborhood by adopting mixed integer programming to obtain a globally optimal solution; and generating working parameters of the equipment according to the globally optimal solution, and performing job scheduling. The artificial bee colony algorithm is improved by introducing a harmony search mechanism, the global search capability is enhanced, and premature convergence is avoided; and meanwhile, local optimization is carried out in combination with mixed integer programming, so that the solution accuracy is improved, the algorithm can adapt to yard scheduling requirements of different scales and different constraint conditions, and the overall operation efficiency is improved.

The invention discloses a satellite-ground continuous variable quantum key distribution modulation variance optimization method. The method comprises the following steps: acquiring communication data of a satellite and a ground receiving station; constructing a satellite-ground CV-QKD communication channel transmission model according to the communication data, and simulating the transmission efficiency of a satellite-ground communication link and the transmittance change intensity of a transmission channel; obtaining a key generation rate under a parameter configuration condition based on satellite-ground CV-QKD according to a simulation result, and calculating a system modulation variance; establishing a modulation variance optimization model based on a residual structure of a full-connection layer and inputting a training set; and determining an error of the modulation variance optimization model according to the loss function, and carrying out back propagation on the error to optimize the modulation variance optimization model until the loss function is converged to obtain a final modulation variance optimization model. The method can solve the problems of high computational complexity, high computing power requirement and the like caused by optimizing protocol modulation variance parameters through a traditional local search algorithm or a global search algorithm, and is suitable for a satellite-ground CV-QKD communication system.

The invention relates to the technical field of power distribution network architecture optimization design, and discloses an energy storage architecture optimization method and system, and the method comprises the steps: initializing the particle position and speed of a particle swarm according to the optimization target of a power distribution network architecture, and setting the initial parameter of each particle; constructing a fitness function according to the key indexes of the power distribution network, calculating a fitness value for the current state of each particle, and recording a global optimal position; dynamically adjusting the speed and position of each particle by introducing a linear decreasing inertia weight and an acceleration factor updating mechanism for balancing global and local search capabilities, so that the particle is close to a global optimal solution; and when a preset termination condition is met, outputting an optimal design scheme of the power distribution network architecture. The method is improved in the aspects of improving the optimization efficiency, ensuring clear target orientation, constructing a precise fitness function, realizing dynamic adjustment, ensuring global optimum, setting a reasonable termination condition, outputting a practical result, ensuring algorithm robustness and the like.

The invention discloses a motor equipment intelligent supervision method and system based on big data. The method comprises the following steps: collecting initial multi-dimensional data; extracting semantic features and spatial-temporal features in the initial multi-dimensional data by using a pre-trained BERT language model and an STGN spatial-temporal convolutional network, performing weighted fusion on the physical parameters, the semantic features and the spatial-temporal features through an attention mechanism, constructing a dynamic graph network based on multi-dimensional information feature representation, and performing time sequence modeling in combination with a TCN time convolutional network; and carrying out multi-objective optimization by using an NSGA-III algorithm, carrying out local search by using a quantum annealing algorithm, outputting an equipment operation state report, and carrying out motor equipment fault early warning according to the equipment operation state report. And the fault detection accuracy and the early warning response time are improved.

The invention relates to a multi-target energy-saving optimization method for a propylene rectifying tower based on state simulation and an improved wolf pack algorithm, which comprises the following steps of: S1, on the basis of a rectification process balance-level steady-state model, carrying out production working condition state simulation on the propylene rectifying tower based on a PRO / II process simulation platform, and establishing a propylene rectifying tower state simulation model; s2, on the basis of the state simulation model of the propylene rectifying tower, carrying out sensitivity analysis of single parameters and combined parameters on the operation parameters of the propylene rectifying tower, and establishing a data relation table between the propylene yield of the propylene rectifying tower and the raw material feeding temperature as well as between the energy consumption of the propylene tower and the tower top reflux quantity; s3, performing model parameter identification on the propylene rectifying tower data relation table by adopting a multiple regression analysis method and a least square method, and establishing a propylene tower multi-target energy-saving optimization model; s4, setting penalty factors to construct penalty function items, and converting the constrained multi-objective optimization function of the propylene tower into an unconstrained multi-objective optimization function; s5, in order to improve the local search capability of the optimization algorithm, the convergence speed is accelerated, the algorithm is prevented from falling into local optimum, and a dynamic search factor is added to improve the optimal solution search capability of the wolf pack algorithm; s6, executing an improved multi-objective wolf pack algorithm to solve an unconstrained multi-objective energy-saving optimization function of the propylene rectifying tower; and S7, judging whether the current maximum number of iterations reaches an upper limit value or not, if so, outputting population non-dominated individuals as a propylene tower multi-target energy-saving optimization result, and otherwise, returning to S4 to continue iterative calculation.

The invention provides a battery electrochemical parameter identification method, system and device and a program product, and the method comprises the steps: carrying out the discharge test of a plurality of discharge rates on a battery, and obtaining the experimental data of the discharge test; constructing a battery electrochemical model based on experimental data; performing cross-working-condition sensitivity analysis on the model input parameters of the battery electrochemical model by adopting a global sensitivity analysis method to obtain global sensitivity parameters; constructing a target function based on key region constraint based on experimental data; and alternately adopting a constraint Bayesian optimization method based on a trust domain and a granular self-adaptive local search method to explore the optimized target function, and iteratively optimizing the target function to obtain an optimal solution of the global sensitivity parameter. According to the method, a set of parameter identification system with high precision, cross-working-condition robustness and calculation efficiency is constructed, and cross-working-condition high-precision identification of the electrochemical parameters of the high-capacity lithium ion battery is realized.

The invention relates to a mobile robot path planning method based on an improved eagle optimization algorithm, and belongs to the technical field of robot path planning, and the method comprises the following steps: S1, constructing a two-dimensional grid map according to map information, and marking a starting point, a target point and an obstacle; s2, constructing a multi-objective function, and evaluating the advantages and disadvantages of the path according to the path length and the path smoothness; s3, using uniformly distributed Logistic chaotic mapping and combining with an LOS sight line detection algorithm to initialize a population; s4, improving an egret optimization algorithm by adopting a self-adaptive nonlinear inertia factor and a doliolaria group algorithm, selecting a corresponding global search mode and a local search mode according to different iterations, and updating the position and the target function value of an egret individual; and S5, outputting the optimal path.