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

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 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 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 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 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 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 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.

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 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 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 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 discloses a parallel evolutionary solution method for search space segmentation, comprising: S1, randomly generating solution schemes via a search space sampling and segmentation module, calculating fitness values by combining initial sample schemes with an optimization objective function, statistically screening sample schemes, determining the dimension direction K of the segmented search space, and dividing the search space into subspaces; S2, executing a global search algorithm in the segmented subspaces to obtain a global initial solution scheme; S3, using the global initial solution scheme as a starting point, obtaining a precise solution scheme via a local search algorithm. This method employs evolutionary sampling of the search space, deriving fitness terrain analysis results, and guiding the division direction and step size of the search space. Multiple search subspaces are segmented to enable parallel search for subsequent evolutionary calculations.

The invention belongs to the related technical field of task scheduling optimization, and discloses a transport vehicle operation path and scheduling optimization method and system, and the optimization method comprises the steps: generating a path library, carrying out the global search of a particle swarm search algorithm on the basis of the path library, and during the execution of the particle swarm search algorithm, on the basis of a current global optimal solution, carrying out the global search of the particle swarm search algorithm; circularly executing local search, namely generating a new solution by using a damage and repair strategy and performing conflict adjustment, then updating a global optimal solution based on the new solution, then updating particle positions and speeds, and performing particle swarm iterative search again, namely executing global iteration; and after the number of iterations is reached and an optimal solution is obtained, decoding is carried out to generate a scheduling scheme, that is, tasks in the task set are allocated to each transport vehicle, a vehicle scheduling scheme and path selection are generated, and transport vehicle operation path and scheduling optimization for solving space-time conflicts by multiple vehicles and multiple task points is efficiently realized.

The invention relates to the technical field of wireless communication, and discloses an unloading method in an ultra-dense millimeter wave MEC network. Aiming at the problems of insufficient terminal capability, high energy consumption of an ultra-dense base station, millimeter wave coverage limitation, communication security risk and the like under the condition of sharp increase of a calculation-intensive task, the method comprises the following steps: firstly, obtaining network basic information, constructing a network architecture containing communication, calculation unloading and a security model, and establishing a multi-constraint optimization problem; based on the optimization problem, initializing a multi-strategy black-wing plinuary optimization algorithm population through logic mapping and elite selection; updating individual positions through global and local search in algorithm iteration, and screening historical optimal individuals; and finally, configuring and unloading resources according to the configuration. According to the method, the NOMA technology, a multi-step unloading framework and a hybrid communication mode are combined, the optimization efficiency is improved through a multi-strategy black-wing optimization algorithm, the local energy consumption is reduced, the communication rate is improved, the safety is guaranteed, the optimal scheme meeting time delay and safety constraints is rapidly converged, and the user experience is improved.

The invention discloses a ballastless track plane linear reconstruction method and system under the condition of large foundation deformation, and the method comprises the steps: judging whether a railway line is staggered on a long straight line or a curve, and determining a linear reconstruction mode; determining the intersection position of the railway line and the fault zone, establishing a rectangular plane coordinate system, and collecting basic data of the line; fitting and reconstructing straight line edges on two sides of the line damage center and a clamping straight line between the two straight line edges, and determining a slope range and a change step length of the clamping straight line; determining an optimization variable, a constraint condition, a target function and a fitness function, and searching an optimal plane line shape reconstruction scheme under each linear slope value by adopting a genetic algorithm and a local search algorithm; and summarizing and comparing the fitness values of the optimal plane line shape reconstruction schemes under each straight line clamping slope value, and selecting the scheme with the maximum fitness value. According to the method, the damage form of the large deformation of the railway foundation to the plane line shape of the ballastless track is analyzed, and reference is provided for post-disaster repair of the ballastless track.

The invention relates to the technical field of flexible job shop scheduling in intelligent manufacturing and production scheduling, in particular to a flexible job shop scheduling optimization method for solving and adjusting resource constraints. Comprising the steps of initializing parameters and randomly generating an initial population; sequentially using a crossover operator and a mutation operator to evolve the current population; executing a hybrid decoding strategy on the current population; sorting the individuals in the current population from small to large according to the maximum completion time to form an elite population, and updating the elite population through question specific local search; and judging whether an evolution condition is met or not, if so, executing CP-based mathematical evolution, and outputting a final solution when the running time reaches the total running time. The method has the positive effects of reducing the resource waiting time, improving the machine utilization rate and improving the resource utilization efficiency and the scheduling performance of the whole workshop production.

A marine photovoltaic MPPT control method based on a chaos fishing and perturbation observation method comprises the following steps that the population number of fishers is determined in a preset area through Tent chaos mapping, then the positions of the fishers are initialized, and the positions of the fishers are calculated in combination with different duty ratios to obtain corresponding fitness values; carrying out global search in a preset region according to the fitness value by utilizing a large step length strategy of a fishing optimization algorithm to obtain a region with a global optimal duty ratio, then switching to local tracking, and carrying out local search on the region with the global optimal duty ratio by adopting a small step length strategy to obtain a local optimal duty ratio; the local optimal duty ratio is used as the initial duty ratio of the perturbation and observation method, and then further search is carried out to obtain the optimal duty ratio, namely the maximum power point. The method can track the global maximum power point, and is suitable for different photovoltaic systems and illumination conditions.

The invention provides a multi-center hybrid fleet joint scheduling method based on an improved genetic variable neighborhood algorithm, and the method comprises the steps: building a joint scheduling model with the minimization of vehicle transportation cost, charging cost and overtime penalty cost as a target, and building constraint conditions; a genetic algorithm is adopted for optimization, and a joint scheduling scheme with the lowest total cost under the constraint condition is obtained; performing variable neighborhood local search on each generation of optimal individuals after genetic manipulation; executing a dynamic disturbance operation; and judging whether the maximum number of iterations is reached or not, if so, ending the process, and obtaining a multi-center hybrid fleet joint scheduling scheme, otherwise, repeating the previous steps. According to the invention, a joint scheduling mechanism is introduced to realize transportation resource sharing, and the overall resource configuration is optimized; a joint scheduling model is constructed based on a hybrid motorcade, an electric vehicle charging strategy is optimally designed, an improved genetic algorithm and a variable neighborhood algorithm are used for optimization solution, and customer service satisfaction experience can be improved.

The invention discloses a shale gas well fracturing parameter optimization design method and system based on a depth Q network, and relates to the technical field of shale gas development, and the method comprises the following steps: S1, collecting geological parameters, fracturing construction parameters and productivity data of a shale gas well in advance; s2, constructing an agent model and performing environment simulation; and S3, constructing a DQN model. According to the method, a LightGBM algorithm is adopted to construct a data-driven proxy model to simulate a real fracturing environment, an optimization model is constructed based on a deep Q network (DQN), state, action, reward and epsilon-greedy strategies are defined, and technologies such as a variable step size search mechanism, experience playback and target network soft update are combined, so that the real fracturing environment is simulated. The problems that a traditional optimization method is weak in local search capability and low in convergence speed are effectively solved, multivariable synchronous optimization of the unit perforation length proppant dosage and the fracturing fluid dosage is achieved, and a global optimal parameter combination can be rapidly explored.

The invention discloses a cable operation state fault rapid detection method based on an improved grey wolf algorithm model. The method comprises the following steps: collecting fault data through a detection unit; cleaning, denoising, normalization processing and feature extraction are carried out on the collected fault data; constructing an improved grey wolf algorithm model, wherein the improved grey wolf algorithm model comprises a coding module, an adaptive weight adjustment module, a multi-objective optimization module, a population diversity control module and an optimal path search module; fault detection is carried out on the cable operation state data through a global search module and a local search module; real-time monitoring and early warning are carried out, wherein the real-time monitoring and early warning comprise early warning threshold setting, early warning signal triggering, early warning notification and feedback optimization; according to the invention, the operation state fault of the cable is detected through the improved grey wolf algorithm model, and the detection efficiency and the accuracy of cable fault positioning are greatly improved.

The invention relates to the technical field of wireless communication, in particular to a reachable rate optimization method based on an intelligent reflecting surface, which comprises the following steps of: firstly, constructing a channel model in an application scene, namely designing a system reachable rate optimization model on the basis of establishing a base station-user, base station-IRS and IRS-user three-link model; then, a phase shift matrix of the IRS and an emission covariance matrix of a base station serve as joint optimization variables, a population is divided into an elite subpopulation, a suboptimal population and a common population through a subpopulation grouping strategy of an improved particle swarm algorithm, each particle represents the composed variable, a dynamic cooperation strategy is proposed to calculate the speed in each subpopulation, and the speed in each subpopulation is calculated; and updating individual optimization and group optimization, and continuously carrying out iteration to search a reachable rate optimal solution of the user. According to the method, local search can be finely carried out while large-range global search can be carried out, the calculation complexity is remarkably reduced, and the IRS transmission effect is improved.

The invention relates to the field of control, in particular to a reaction kettle operation control method and system for resin production, real-time operation parameters of a reaction kettle are obtained, a fuzzy neural network model is iteratively trained by adopting a hierarchical collaborative hybrid optimization strategy, a preceding member membership function of the fuzzy neural network model is composed of a Gaussian mixture model, and the preceding member membership function of the fuzzy neural network model is obtained. According to the optimization strategy, an improved quantum particle swarm optimization algorithm is used for carrying out global search to determine Gaussian mixture model parameters, a recursive least square algorithm is used for carrying out local search to determine consequent coefficients after each time of iteration, and in the training process, the parameters of the Gaussian mixture model are subjected to global search to determine the parameters of the Gaussian mixture model. And calculating an importance index according to the average activation degree of the fuzzy rule and the contribution of the fuzzy rule to the prediction error, removing the rule of which the importance is continuously lower than a preset threshold value, and after training is completed, generating and executing a control instruction for controlling the heating system power and the material feeding rate of the reaction kettle at the next moment according to the real-time parameters.

The invention discloses a PCB double-sided four-flying-probe test path optimization method, and belongs to the technical field of path optimization, and the method comprises the following steps: importing double-sided PCB data containing a test network and test points; obtaining an initial matching scheme of a network internal test point corresponding to each test network through the matching strategy, and optimizing each initial matching scheme to obtain a network internal test point path; constructing an initial test sequence between test networks through a recent insertion heuristic algorithm, and then introducing a local search algorithm into an adaptive large-scale neighborhood search algorithm to carry out iterative optimization on the initial test sequence to obtain a test sequence between the networks; and combining the test point paths in the networks and the test sequence between the networks, and outputting a double-sided four-flying-probe test path. According to the PCB double-sided four-flying-probe test path optimization method, the problem that the overall moving cost is difficult to further reduce in an existing method is solved.

The invention relates to the technical field of intelligent traffic management, and discloses a multi-modal time series data analysis-driven public traffic scheduling optimization method, which comprises the following steps of: acquiring multi-modal traffic data, performing time-space alignment preprocessing, and extracting a feature vector; fusing various feature vectors based on a cross-modal interactive learning method; and performing real-time prediction of the public traffic condition based on the multi-modal fusion feature vector to obtain a public traffic flow prediction value, and making a public traffic scheduling strategy based on the obtained public traffic flow prediction value. According to the method, multiple feature vectors are fused based on a cross-modal interactive learning method, the mutual relation between different types of data is effectively captured, the public transport scheduling strategy is formulated based on the particle swarm optimization algorithm, the global search and local search capabilities are effectively balanced, the performance of the PSO algorithm in the public transport vehicle scheduling problem can be effectively improved, and the public transport vehicle scheduling efficiency is improved. And a more efficient scheduling scheme is realized.

The invention relates to the technical field of robot path planning, and discloses a mobile robot path planning method and system based on a rat optimization algorithm, and the method comprises the following steps: environment modeling: carrying out the modeling of a moving environment of a mobile robot, dividing the environment into a plurality of grid units, distinguishing an obstacle area and a passable area; defining a fitness function: converting a path planning problem into an optimization problem, and constructing the fitness function taking a path length and an obstacle avoidance constraint as targets; a rat optimization algorithm is initialized, wherein the position and algorithm parameters of a rat population are initialized; according to the method, the rat optimization algorithm is adopted, local optimization is effectively avoided through diversified behavior modes, the global optimization capability is enhanced, and the convergence speed and the optimization precision are improved while global search is ensured by the algorithm by introducing the adaptive weight and improving the local search strategy.

The invention relates to the technical field of production management, in particular to a color master batch processing energy consumption and efficiency collaborative optimization system based on production data, which comprises a data acquisition and analysis module for integrating production, energy consumption and efficiency data to form a structured data set; the objective function construction module deeply mines production, energy consumption and efficiency characteristics in the data and constructs a multi-objective optimization function set according to the production, energy consumption and efficiency characteristics; the multi-target collaborative optimization module adopts an improved NSGA-II algorithm, introduces a simulated annealing mechanism to enhance the local search ability of an elite solution, combines with a cross, mutation probability and targeted search strategy based on multi-modal feature dynamic adjustment, and applies dynamic process, equipment and security constraints at the same time to solve globally optimal multi-dimensional scheduling parameters. Through combination of deep data analysis and an advanced optimization algorithm, the overall production management efficiency of color master batch production is effectively improved.

The invention relates to the technical field of intelligent manufacturing, in particular to an efficient and energy-saving flexible job shop scheduling method considering adjustment time. Comprising the steps of 1, initializing an initial population of an MTCP-SPEA algorithm; 2, executing a spatial awareness environment selection strategy; step 3, judging whether the current running time of the MTCP-SPEA algorithm reaches half of the total running time, if so, executing step 6, and otherwise, executing step 4; 4, a crossover operator and a mutation operator are adopted to evolve the current population, and current non-dominated individuals are selected to form a Pareto solution set; 5, executing local search operation and returning to the step 2; and step 6, executing a multi-thread-based constrained programming auxiliary optimization method to generate an improved elite set, screening out all non-dominated individuals from the improved elite set to form an optimal solution set, and outputting the optimal solution set. According to the method, the problem of efficient and energy-saving flexible job shop scheduling optimization considering time adjustment is effectively solved.