523 results about "Simulated annealing" patented technology

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.

The invention relates to the technical field of liquid cooling servers, and discloses a liquid cooling server safety management system and method, and the method employs a multi-mode sensor network to synchronously collect the thermodynamic parameters of a liquid cooling system at 100 Hz, and constructs a three-dimensional thermal field digital twinborn model after the processing of an extended Kalman filtering algorithm. A distributed cooling strategy is designed based on a federated learning framework, and each node locally trains a thermal dynamic prediction model and is optimized by a central aggregator. A dynamic control instruction is generated by using a near-end strategy optimization algorithm, and cooling liquid flow distribution is optimized in combination with a quantum derivative simulated annealing algorithm. And designing a dual-threshold phase change control mechanism, establishing a block chain log to ensure traceability and tamper resistance of the instruction, and realizing closed-loop feedback control through a CAN bus. The system and the method can accurately monitor and intelligently control the liquid cooling system, improve the heat dissipation efficiency, reduce the power consumption, and guarantee the data safety and the system stability.

The invention relates to the technical field of intelligent laser processing path planning, and discloses a laser flight processing path planning method and device for a complex curved surface, and the method comprises the steps: obtaining the geometric continuity features of the curved surface, carrying out the region division and boundary feature extraction, obtaining the geometric features of the boundary points of each region, and obtaining the geometric features of the boundary points of each region; performing track generation based on boundary geometric features to obtain an initial processing track path, then performing heat accumulation analysis and track screening, identifying a track section needing to be adjusted, executing multi-target path scheduling optimization, and obtaining adjusted track execution feature data; processing sequence optimization is carried out based on the feature data, and an optimized processing sequence is obtained; performing thermal input analysis and parameter correction according to the processing sequence to obtain an updated processing parameter set; and finally, performing global coordination based on a simulated annealing algorithm, generating a final processing track set, completing track optimization and instruction set generation, and outputting a complete processing scheme. According to the method, the problem of insufficient track fitting degree can be solved.

The invention discloses a ship shore-end intelligent management method, system and device and a storage medium, and the method comprises the steps: carrying out the ship position prediction based on an electronic chart, the current navigation state data of a ship, and the current environment data, and obtaining the information of a to-be-docked ship according to the future position information of the ship, constructing a port operation knowledge graph based on the information of the ships to arrive at the port, the equipment data of the ships approaching and leaving the port, the historical tug operation data and the port data, and predicting the tug workload demand based on the port operation knowledge graph; taking tug operation data as a state space, taking a tug scheduling strategy and a cargo stacking layout adjustment strategy as an action space, taking maximization of wharf operation efficiency as a reward target, and adopting a reinforcement learning method and a simulated annealing method to carry out wharf operation scheduling optimization to obtain an optimal wharf operation scheduling strategy. According to the method, the port working efficiency is improved, manual interference is not needed when the wharf operation scheduling strategy is obtained, and autonomous decision-making can be completely achieved.

The invention discloses a filling body multi-age strength prediction and material inversion model construction method, and belongs to the technical field of metal mine paste filling, and the method comprises the steps: carrying out the preprocessing operation according to the characteristic variables of a material ratio parameter and a tailing characteristic parameter, and obtaining a characteristic variable data set; performing data division on the characteristic variable data set according to a preset proportion in a preset multi-age period; constructing a multi-age strength prediction model, and training the model by using the training set to obtain an unconfined compressive strength prediction result in a preset multi-age; verifying the constructed multi-age strength prediction model by using a test set by adopting a Shapley addition explanation method; and adding a simulated annealing algorithm to the verified multi-age strength prediction model to obtain a reverse optimization model. The method has higher generalization ability and universality, and the filling design period is greatly shortened by combining model prediction of the multi-age strength prediction model with the simulated annealing optimization algorithm.

The invention provides a charging pile energy consumption scheduling method based on a load prediction model, and the method comprises the steps: carrying out the data collection and preprocessing, obtaining multi-source data, such as charging, equipment, environment and electricity price, from a charging station management system, and cleaning abnormal values; and then using grey correlation analysis to determine weights of factors influencing energy consumption, constructing a grey prediction model, and combining preprocessing and weight distribution data to predict future charging pile load demands. And the control center receives a load prediction result and vehicle reservation information, preliminarily plans a charging distribution scheme according to the real-time state of the charging pile, and optimizes scheduling by taking the minimization of the total charging cost and consideration of the user satisfaction and the stability of the power system as targets through a simulated annealing algorithm. During operation, an actual state and an external environment are continuously monitored, a charging plan is rapidly adjusted when deviation occurs, finally, vehicle charging bills of all departments are generated according to an execution scheme, and system performance indexes are regularly evaluated to realize continuous optimization. The method can effectively reduce energy consumption cost and guarantee efficient and stable operation of the charging station.

The invention discloses a dynamic equipment management service system and method for deep learning. The method comprises the following steps: S1, collecting real-time operation data of equipment and preprocessing the real-time operation data; s2, constructing a long-short-term memory network model, and outputting comprehensive early warning indexes of the equipment; s3, constructing an optimization problem based on the comprehensive early warning index of the equipment, and setting an objective function and constraint conditions; s4, performing global search on the optimization problem by adopting an improved dragon fly algorithm, and generating a plurality of resource scheduling and maintenance candidate schemes; s5, performing local optimization on the candidate scheme by using a simulated annealing algorithm, and determining an optimal resource scheduling and maintenance decision scheme; and S6, feeding back the real-time prediction result and the optimal decision result to form closed-loop control. Through fusion of deep learning prediction, the improved dragon fly algorithm and the simulated annealing algorithm, real-time closed-loop control of accurate early warning of the equipment state and dynamic resource scheduling decision is realized, so that the failure rate is effectively reduced, and the equipment management efficiency is improved.

The invention discloses a positioning method and system based on an RSSI fingerprint database and a generalized regression neural network, and relates to the technical field of indoor positioning. The method comprises the following steps: acquiring RSSI values and corresponding position information of a plurality of sampling points in a target space, and constructing a fingerprint database based on the RSSI values and the position information; establishing a generalized regression neural network prediction model based on the fingerprint database, and globally optimizing model parameters of the generalized regression neural network prediction model by adopting a quantum particle swarm algorithm to obtain preliminary model parameters; performing local optimization on the preliminary model parameters by adopting a simulated annealing algorithm to obtain target model parameters, and training based on the target model parameters to obtain a target generalized regression neural network prediction model; and collecting a real-time RSSI value of a to-be-positioned target, inputting the real-time RSSI value into the target generalized regression neural network prediction model, and outputting a target position coordinate of the to-be-positioned target. By implementing the technical scheme provided by the invention, parameter optimization can be prevented from falling into a locally optimal solution, and the positioning precision is improved.

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

The invention relates to the technical field of machine learning optimization, in particular to a machine learning optimization method based on a QUBO model and quantum annealing, and the method comprises the following steps: firstly, discretizing continuous parameters of the machine learning model into binary variables, and constructing a QUBO objective function; then optimizing the QUBO model by using a quantum annealing algorithm; and finally, solving a globally optimal solution by adjusting annealing parameters, wherein the annealing parameters comprise the initial temperature, the cooling coefficient and the number of iterations. The QUBO model can discretize continuous variables (including parameters such as weights and offsets) in models such as AR, SVM and CNN in machine learning into binary variables, so that a nonlinear relation is better processed, and the calculation complexity in the training process is reduced. Through the QUBO model, the regularization item can be better controlled, and the problem of overfitting is avoided. Meanwhile, the QUBO model can be solved in parallel through quantum calculation or a simulated annealing algorithm, and the calculation efficiency under large-scale data is remarkably improved.

The invention discloses a wing design optimization method based on agent-assisted multi-initial-point simulated annealing, and belongs to the technical field of optimization design. Comprising the steps of 1, initializing algorithm parameters and a training data set; 2, constructing an agent model; 3, executing multi-initial-point parallel simulated annealing, and generating a batch of candidate new solution sets; 4, executing a double-elite active learning strategy based on the new solution set, screening the most potential sample to carry out real evaluation, and updating the agent model; 5, cooling the temperature and reducing the step length; 6, judging whether the cumulative evaluation times of the expensive objective function reach the set maximum evaluation times or not; if not, returning to the step 2; if yes, optimization is stopped; and finally, traversing the training data set, and selecting a sample point with the minimum real objective function value as a global optimal solution. According to the method, a multi-initial-point parallel simulated annealing search mechanism and a double-elite active learning strategy are combined, and the global optimal solution is quickly approached under the limited simulation times.

The invention provides an aviation composite material hot-press forming workpiece loading method based on a hybrid optimization algorithm. The method comprises the steps that workpiece demand information and autoclave platform information in composite material hot-press forming manufacturing are obtained; minimizing the opening times of the autoclave as a target function; establishing a two-dimensional multi-layer platform hot press molding workpiece loading model, defining space constraints, distribution constraints, resource constraints and type constraints among workpieces, and incorporating the constraints into model constraint conditions; a GA-SP is adopted to optimize a workpiece loading sequence and a rotation state, an optimal layout is generated through chromosome coding, decoding, selection, intersection and mutation operations, a hybrid optimization algorithm is constructed in combination with a neighborhood search strategy, efficient arrangement of workpieces on different platforms is realized in combination with a simulated annealing criterion, and an optimized loading scheme is output. The production efficiency of the hot press molding procedure can be improved, the manufacturing cost is reduced, and efficient technical support is provided for intelligent manufacturing of aviation composite materials.

The invention provides an LLM reasoning-oriented heterogeneous core particle architecture simulation and search method and system, and the method comprises the steps: constructing a heterogeneous core particle joint simulation platform which integrates behavior-level modeling aiming at various core particle types, simulates the calculation and data transmission behaviors of a heterogeneous core particle architecture and power consumption area characteristics, forms a simulation architecture, and carries out the simulation of the heterogeneous core particle architecture; carrying out performance evaluation on the simulation architecture; an improved simulated annealing search strategy is adopted to explore the design space of the simulation architecture, and the strategy comprises the following steps: configuring core particles of different types or scales for core particle groups executing tasks of different layers based on calculation and memory access characteristics of different network layers of LLM by adopting packet heterogeneous search, optimizing the search process by adopting a simulated annealing algorithm integrated with Pareto frontier optimization; a hybrid parallel strategy of TP, PP, DP and EP and grouping heterogeneous search are subjected to collaborative optimization, and optimal parallelism combination and task mapping based on core particle grouping are automatically explored.

The invention provides a steel cutting and production scheduling method and device based on dynamic risk assessment, and relates to the field of steel processing pre-production management. Order, equipment and inventory data are collected in real time, and a multi-dimensional numerical matrix is constructed to achieve data structured expression; adopting a Monte Carlo method to generate a candidate production scheduling sequence, combining leftover material loss and delay risk dynamic evaluation, and fusing simulated annealing and an adaptive threshold mechanism to carry out iterative optimization; and through probability similarity screening and regression fitting, an optimal production scheduling scheme is determined in an error allowable range. The method effectively balances the production efficiency and the resource utilization rate, reduces the cutting loss and the delivery risk, and is suitable for the intelligent production scheduling decision of a complex manufacturing scene.

The invention discloses an intelligent cloud computing resource scheduling method and system based on a digital technology, and relates to the technical field of cloud computing, and the method comprises the steps: collecting and preprocessing multi-dimensional resource use data, constructing a resource demand prediction model to carry out resource scheduling prediction, extracting feature factors based on predicted resource demands, and carrying out resource scheduling prediction. And calculating a comprehensive priority score, designing a scheduling strategy, optimizing a resource scheduling strategy by adopting an adaptive strategy combining cat group optimization and ant colony algorithm and simulated annealing, and implementing resource scheduling. Through combination of cat group optimization, ant colony optimization and simulated annealing algorithms, efficient combination of global search capability and local optimization capability is realized in a scheduling process, an efficient adaptive task scheduling mechanism is formed, the problems that priority division is unreasonable, a scheduling path is easy to fall into local optimum and the like in a traditional method are avoided, and the scheduling efficiency is improved. Therefore, the scheduling efficiency and the global resource utilization are optimized.

The invention discloses a data security situation intelligent monitoring method and system based on a power service chain. The method comprises the following steps: S1, forming a power service chain data set; s2, forming a preprocessed power business chain data set; s3, extracting a key feature vector reflecting the running state and the abnormal feature of each link; s4, identifying and marking a power business chain data sample with potential abnormal behaviors; s5, using a power business chain data sample with potential abnormal behaviors as verification data, constructing an anomaly detection performance evaluation index, and performing global optimization on key hyper-parameters in the improved isolated forest model by using a simulated annealing algorithm; and S6, applying the optimized improved isolated forest model to the power service chain data flow acquired in real time, and performing anomaly detection on the newly acquired power service chain data. According to the method, the recognition sensitivity of the model to the key power abnormal signal can be remarkably improved, so that the path division of the abnormal behavior better fits the business risk reality.

The invention discloses a carbon neutralization target setting and tracking system based on big data and an artificial intelligence technology, and relates to the technical field of carbon neutralization. The system comprises a plurality of key modules, a data acquisition module widely collects multi-source data of energy, industry, traffic, building, weather and the like, a satellite remote sensing data acquisition technology is introduced, and the carbon emission condition is comprehensively reflected; the data preprocessing module adopts a transfer learning technology to improve the processing efficiency; the big data storage module is combined with a block chain technology to guarantee data credibility; a reinforcement learning mechanism is integrated into a target setting sub-module of the artificial intelligence analysis module, a knowledge graph technology is introduced into a tracking analysis sub-module, and a carbon neutralization target is accurately set and tracked; the visual display module provides immersive experience by applying VR and A technologies; and the decision support module gives comprehensive decision suggestions in combination with a simulated annealing algorithm and a multi-objective decision theory. The system can scientifically set targets, accurately track the progress and intelligently assist in decision making, and provides powerful technical support for carbon neutralization.

The invention provides an FPGA parallel Isin model optimization method and system based on a GHMC simulated annealing algorithm, relates to the field of power system combination optimization, and solves the technical problem that an existing FPGA-based Isin model solver adopts a traditional Monte Carlo method and is low in convergence precision and solution quality. The method comprises the following steps: receiving a question input by a user through an upper computer, and mapping the received question into a Hamiltonian parameter of an Isin model; the Hamiltonian parameter is transmitted to an FPGA through a communication interface, and a mixed GHMC sampler and a temperature scheduling module are arranged in the FPGA; the hybrid GHMC sampler carries out parallel solution on Hamiltonian parameters; and the temperature scheduling module gradually reduces the system temperature according to a preset annealing curve, and drives the Isin model to converge to a global optimal solution. The method is used in the power system combination optimization process.

The invention discloses a kitchen waste collection and transportation path optimization method based on an improved simulated annealing algorithm, belongs to the technical field of intelligent optimization and path planning, and mainly solves the technical bottlenecks of an existing kitchen waste collection and transportation path planning method in the aspects of dynamics, multi-objective optimization and algorithm efficiency. The method comprises an application layer, an algorithm layer and a data layer, and by fusing mixed neighborhood search, a dynamic punishment mechanism and a parallel computing architecture, the timeliness, the economical efficiency and the environmental protection property of path planning are remarkably improved. According to the method, real-time traffic and corruption risks can be dynamically responded, multi-target optimization weights are quantified, different city management strategies are adapted, the problems that a traditional algorithm is low in efficiency and prone to falling into local optimum are solved through multi-chain parallel acceleration and self-adaptive temperature control, and city-level large-scale node real-time scheduling requirements are supported.

The invention belongs to the technical field of engineering mechanical structure health monitoring, and particularly relates to a dynamic prediction method for multi-working-condition stress of a tower crane based on a constraint space Kriging agent model. Comprising the steps of system parameter acquisition and constraint space construction, physical constraint sample generation, efficient stress sample calculation, agent model training and real-time stress distribution prediction. According to the method, the Kriging agent model and the variable grid density optimization strategy are organically combined, the calculation efficiency is high, the efficient dynamic prediction capability is achieved, and the real-time prediction requirement under the dynamic working condition is met; a Latin hypercube sampling method in a physical constraint space is adopted, and a simulated annealing optimization technology is combined, so that invalid sample points are effectively eliminated, and the utilization rate of a sample space is improved; a nonlinear mapping relation between multiple parameters of hoisting weight-amplitude-speed and full-beam long stress distribution is established through a Kriging agent model, the limitation that a traditional method only supports fixed working conditions is broken through, and an accurate and rapid response model is provided for structural health monitoring under complex working conditions.

The invention provides an improved DBSCAN dry-type transformer vibration signal fault early warning method and system, and belongs to the technical field of dry-type transformer fault diagnosis, and the method comprises the steps: obtaining a traction rectifier transformer vibration signal of a to-be-detected urban rail power supply system; processing the acquired vibration signal by using a pre-trained fault diagnosis model to obtain a fault diagnosis result; and if the number of the abnormal points in the fault diagnosis result exceeds a set threshold value, performing early warning. According to the method, the DBSCAN parameters are automatically optimized through multi-objective optimization and a simulated annealing algorithm, so that the judgment of abnormal points reaches a most accurate state, and the workload is reduced.

The invention discloses a high-efficiency automatic parking system and method based on turning radius optimization, and the system comprises a sensor group which is used for obtaining the state information of a vehicle; the ECU is used for carrying out path planning operation based on a variable radius according to the vehicle state information and carrying out path tracking control operation based on preview distance self-adaption according to a planned path; the actuator is used for responding to the control operation of the ECU; according to the method, multiple parking strategies can be fused, rapid and accurate parking is achieved, and based on the R-S curve algorithm with the variable radius, garage rubbing or parking failure caused by the fixed radius is avoided; search is accelerated based on a simulated annealing algorithm, and a pure tracking algorithm is improved, so that the preview distance is adaptive according to the vehicle speed, the tracking precision and the parking stability are improved, and the automatic parking experience is optimized in all directions.

The invention discloses a five-axis machining cutter location linear interpolation optimization control method, which relates to the technical field of numerical control machining, and comprises the following steps: fitting a machining contour to generate a curve expression, determining an operation point sequence based on curvature distribution, and converting the operation point sequence into an actuator pose sequence; based on the model of a five-axis machining machine tool, a total transformation matrix from a base coordinate system to an actuator coordinate system is established through a D-H method, motion parameter changes caused by five-axis elastic deformation and inertia force are established based on the nonlinear beam theory and the Alembert principle, and a nonlinear axis body coupling motion equation is established; a correction expression is generated in combination with the Jacobian matrix, optimal unknown parameters in the correction expression are determined by introducing a quantum fluctuation simulated annealing algorithm, and a correction Jacobian matrix is generated; according to the actuator pose change of the adjacent operation points in the actuator pose sequence, the corrected Jacobian matrix is combined, the inverse kinematics algorithm is adopted to derive the motion parameter vector of the previous operation point, and higher-precision machining control is achieved.

The invention relates to an automatic driving test scene simulation generalization generation method and system based on knowledge distillation. The method comprises the following steps: acquiring real trajectory data, constructing a scene-level multi-agent trajectory generation framework based on a denoising diffusion probability model, and generating diversified and vivid agent trajectories through an iterative denoising process so as to realize modeling of multi-agent interaction behaviors in a complex traffic environment; a mixed knowledge self-distillation framework is further proposed, multi-level knowledge of a teacher model is migrated in a student model, and task loss and distillation loss are dynamically balanced in combination with a simulated annealing strategy, so that the cross-scene adaptability and generation stability of the model are improved; simulation verification shows that the generated intelligent agent track can effectively reduce the collision event rate and the retrograde event rate, and interaction coordination and traffic rule constraints are ensured. According to the system, the authenticity, diversity and cross-scene adaptive capacity of an automatic driving simulation scene can be improved.

The present invention relates to the technical field of data processing, and is a sewage treatment intelligent dosing adjustment system based on fuzzy logic, comprising: a data acquisition module, which collects water quality data of sewage and optimizes the data acquisition process, including chemical oxygen demand, ammonia nitrogen content, total phosphorus content and sewage flow; a data analysis module, which is used to receive real-time data and perform small batch gradient descent analysis on the data to obtain analysis results; an optimization module, which is used to optimize the parameters and rules of the fuzzy logic controller through differential evolution according to the analysis results to obtain optimized parameters and rules; a fuzzy logic control module, which performs fuzzy logic calculation of dosing amount through simulated annealing according to the received water quality parameters and optimized rules, and converts it into a dosing control signal. The present invention realizes comprehensive intelligent management from data acquisition to dosing adjustment by integrating advanced data acquisition, analysis, optimization and control technologies.

The invention discloses a lithium battery residual life prediction method and system based on an electrochemical model, and relates to the field of lithium ion battery life prediction, and the method comprises the steps: constructing an initial electrochemical model according to battery powder attribute parameters, internal structure parameters and empirical parameters of a lithium ion battery; the method comprises the following steps: respectively taking new lithium ion batteries of the same model and old batteries at different aging stages as experimental objects, obtaining respective corresponding battery aging characteristic data through experimental measurement and electrochemical model simulation, and then taking the minimum value of the difference between the measurement data and the simulation data as an optimization target; empirical parameters and battery recession parameters in the initial electrochemical model are identified through a simulated annealing intelligent optimization algorithm; and according to the identified empirical parameters and the battery recession parameters, constructing a lithium ion battery full-life-cycle electrochemical model, performing working condition analog simulation on the lithium ion battery full-life-cycle electrochemical model, and predicting the remaining life of the battery. The battery is modeled from the internal electrochemical reaction level of the battery, and the prediction precision of the residual life of the battery is improved.

The invention provides a marine emergency rescue station site selection and resource presetting method considering a shipborne unmanned aerial vehicle. According to the method, two-stage algorithms of the self-adaptive K-means clustering method, the gradient search, the simulated annealing and the Bellman-ford algorithm are combined to solve the site selection and resource presetting problems in the marine emergency, and the obtained scheme significantly reduces the resource allocation cost in the rescue process on the premise of ensuring efficient rescue. Besides, in the process of formulating a site selection and resource presetting scheme, the cooperative advantage of the shipborne unmanned aerial vehicle is fully considered, the response speed and the resource utilization rate of the maritime emergency rescue system are effectively improved, and powerful support is provided for efficient operation of the maritime emergency rescue system.

The invention relates to the technical field of cloud computing, and provides a cloud native assembly type application construction method and device supporting dynamic combination. According to the method, through component standardized packaging and distributed warehouse management, the high component reuse rate is achieved, the repeated development workload is reduced, a visual arrangement tool and a process template are reused, the application construction period is shortened by 50% or above, business personnel can directly participate in development, the'business-technology 'communication cost is reduced, and the development efficiency is improved. And during dynamic combination, an optimal loading path is calculated by adopting an optimization particle swarm algorithm of global search of a particle swarm algorithm and local kick of a simulated annealing algorithm, so that intelligent dependency analysis and optimal path calculation are realized, and the complexity of manual dependency sorting is avoided.

The invention discloses an unmanned aerial vehicle arrival and departure joint scheduling method under a conical airspace structure, which solves the arrival and departure joint scheduling optimization problem by adopting a simulated annealing algorithm of a double-disturbance mechanism, balances the solving quality and convergence speed, provides a theoretical basis for vertical take-off and landing field unmanned aerial vehicle arrival and departure joint scheduling, and improves the unmanned aerial vehicle arrival and departure joint scheduling efficiency. The total operation cost of the vertical take-off and landing field is reduced, the guarantee efficiency is improved, and delay is reduced. Meanwhile, the optimal airspace structure design scheme and scheduling strategy are obtained by comparing the total cost under different airspace structure parameters, safety interval time and check time, a theoretical basis is provided for vertical take-off and landing field airspace arrangement and operation scheduling, and certain practical significance is achieved.

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.