49 results about "Pheromone matrix" patented technology

The invention provides a wind and light storage micro-grid dispatching method based on an ant colony algorithm, and relates to the technical field of micro-grid dispatching, and the method comprises the following steps: S1, building a wind and light storage micro-grid system, and building a model of each device in a micro-grid; s2, establishing a comprehensive cost model, and forming a target function considering both the economic cost and the environmental cost of the system; s3, various constraint conditions of the system are established; s4, establishing a mathematical model of microgrid optimization scheduling based on S1-S3, and obtaining various basic parameters; s5, initializing related parameters and a pheromone matrix of the ant colony algorithm; according to the method, the comprehensive energy system micro-grid model is constructed, the economic cost and the environmental cost are taken as indexes, the output condition of each device in the system is reasonably configured, so that the cost is reasonably dispatched and reduced, the micro-grid is taken as a small system capable of realizing power generation and distribution, the utilization rate of clean energy can be increased, and the energy utilization rate is increased. The method plays an important role in energy conservation and environmental protection.

The invention relates to the technical field of autonomous navigation, in particular to an AUV (Autonomous Underwater Vehicle) multi-sensor fusion autonomous navigation system based on ant colony-particle swarm collaboration.The AUV multi-sensor fusion autonomous navigation system is characterized in that a sensor weight decision module constructs a pheromone matrix based on an ant colony algorithm, retrieves a mapping relation between historical errors and pheromone concentration in real time and dynamically generates a sensor priority sequence; adaptive weight distribution in a data conflict scene is realized; the dynamic path correction module is combined with a particle swarm algorithm and pheromone concentration gradient constraint to balance global path optimality and real-time obstacle avoidance requirements in optical camera obstacle deviation correction; and the closed-loop coupling module is used for converting sonar and geomagnetic residual errors into pheromone concentration correction values and confidence coefficient regulation factors by fusing residual error compensation and path tracking error feedback, so that an error traceability-weight calibration-path optimization closed loop is formed, sensor drift is inhibited, and the long-endurance navigation precision is improved.

The invention discloses a path planning method based on a pheromone enhanced ant colony algorithm, and particularly relates to the technical field of path planning. By constructing an initial pheromone matrix, the ratio of the linear Euclidean distance between a path starting point and a path end point to the sum of the Euclidean distance from the path starting point to a candidate grid position and the Euclidean distance from the path end point to the candidate grid position is calculated, and the initial pheromone concentration is distributed near a theoretical optimal path; an elite solution pheromone enhancement strategy is adopted, all generated paths are sorted according to path quality in each iteration, and a high-quality path is selected, copied and then combined with a global optimal path to be used for updating a pheromone matrix; a prospective steering optimization strategy is utilized, punishment is applied to redundant turning in the searching process, and the pheromone updating amount is determined through the path length and the turning frequency; through multi-round iteration, stable pheromone distribution is realized, so that a final optimized path is obtained, the search efficiency is improved, and the method is suitable for a path planning task in a complex environment.

The invention discloses a distributed energy resource intelligent scheduling method based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: collecting operation data of distributed energy equipment, carrying out the preprocessing of the operation data, obtaining an equipment operation data set, carrying out the group intelligent initialization of the equipment operation data set, and generating an initial pheromone matrix; carrying out local optimization on the equipment operation data set and the initial pheromone matrix to generate an updated pheromone matrix and an equipment candidate scheduling scheme, and carrying out screening and verification to generate a local optimal scheduling scheme; receiving a local optimal scheduling scheme by adopting a quantum computing node, taking the local optimal scheduling scheme as an initial solution, constructing a global optimization problem, outputting a global optimal scheduling scheme, and converting the global optimal scheduling scheme into a global scheduling instruction; according to the method, by formulating a global optimization problem, equipment interaction, economic factors and multi-dimensional constraints in a network range are captured, coordination and constraint compliance among equipment are realized, and the problem of instability of a power grid is prevented.

The invention discloses a business processing method and device based on an ant colony algorithm, electronic equipment and a medium. The method comprises the following steps: acquiring a business service request, and analyzing the business service request to obtain a business task; determining each agent matched with the business task; wherein the intelligent agents are used for cooperative processing of business tasks; planning the cooperation path of each agent based on an ant colony algorithm to obtain a target path; and according to the cooperation sequence of the intelligent agents in the target path, executing the business task by using the intelligent agents in sequence. According to the technical scheme, efficient collaboration between agents is achieved through the improved ant colony algorithm. According to the system, a multi-dimensional pheromone matrix is adopted to record and transmit collaborative experience among departments, a knowledge verification mechanism based on swarm intelligence is established, and dynamic optimization and rapid convergence of a cross-department business process are realized.

The invention discloses an electric vehicle scheduling method based on a multi-pheromone channel ant colony system, and the method comprises the steps: firstly building a scheduling model on the basis of an electric vehicle service scene, and enabling the scheduling model to be related with the multi-source traffic data, such as the vehicle electric quantity and a charging pile, different from a traditional fuel vehicle scheduling model; in order to solve the proposed scheduling model, a multi-pheromone channel ant colony system algorithm is applied. According to the algorithm, on the basis of an ant colony system, a plurality of pheromone channels are established, and a pheromone updating factor is calculated based on an average absolute characteristic value of a pheromone matrix, so that the search capability of the algorithm is enhanced, and the matching degree is improved or the average waiting time is shortened. Experimental results under different scheduling scenes prove that compared with other existing scheduling methods, the electric vehicle scheduling optimization method can obtain a better matching scheme and is an effective method for solving the problem of electric vehicle scheduling.

The invention discloses a flexible job shop energy-saving scheduling optimization method considering a light storage condition and a load characteristic, and relates to the technical field of industrial scheduling, and the method comprises the steps: building a flexible job shop energy-saving scheduling problem model based on mixed integer programming, and setting an optimization target and a constraint condition; an ant colony algorithm is improved, an ant colony is divided into dynamic multi-level search, and a pheromone matrix is optimized; the updating effect of pheromones in the ant colony algorithm in the iteration process is optimized; optimizing the optimal solution of each generation of the ant colony algorithm by adopting a graph neural network off-line learning neighborhood search method; and stopping iteration when a preset termination condition is met, and outputting an optimal scheduling scheme. According to the method, a more efficient and flexible energy-saving scheduling strategy is developed to effectively coordinate the productivity and the energy efficiency, energy optimization and cost reduction in the production process are achieved, actual technical support is provided for energy-saving scheduling of the flexible job shop, and a method system for the workshop scheduling problem is enriched.

The invention provides an ant colony algorithm initialization method and system based on an ISIS protocol, and the method comprises the steps: collecting network topology information in real time based on the ISIS protocol, including a node set, a link set and cost parameters of each link, the cost parameters including transmission time delay, bandwidth utilization rate and packet loss rate; constructing a comprehensive cost evaluation model based on weighted summation of the cost parameters of the links; a pheromone matrix of the ant colony algorithm is initialized based on the result of the comprehensive cost evaluation model, and the link comprehensive cost is inversely proportional to the initial pheromone value; constructing a heuristic information model according to the network topology information, wherein the heuristic information model is the reciprocal of the comprehensive cost; and based on the pheromone matrix and the heuristic information model, initializing a node selection probability function of an ant colony algorithm, and executing the ant colony algorithm to iteratively solve an optimal routing path. According to the method, the real-time network information obtained by the ISIS protocol is used for the initialization of the ant colony algorithm, so that the initialization precision can be improved.

The invention discloses a path planning method, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining a preset straight line between an initial position and a target position in a space grid, determining the vertical distance between each space node in the space grid and the preset straight line, the space grid being a discrete unit structure generated based on a preset space, and the preset straight line being a preset straight line; the space nodes are discrete points in the space grid; constructing an initial pheromone matrix of the space grid according to the vertical distance, wherein the initial pheromone matrix at least comprises the initial pheromone concentration of each space node in the space grid; and iteratively generating a planned path between the initial position and the target position in the space grid according to an ant colony algorithm based on the initial pheromone matrix. Therefore, according to the technical scheme of the invention, the initial pheromone concentration of each space node in the space grid is optimized based on the preset straight line, and ants are guided to preferentially search the region close to the ideal path, so that the iterative convergence speed is accelerated, and the precision and efficiency of path planning are improved.

The invention discloses a multi-SoC cooperative task allocation method based on quantum ant colony optimization. The method comprises the following steps: acquiring a node resource portrait set and a task attribute set for each system-on-chip node in a multi-SoC system; constructing a task-resource mapping graph; obtaining a quantum coding task set; loading the quantum coding task set to a search state space of quantum ant individuals, setting a pheromone matrix and a quantum rotation angle matrix, and obtaining an initial population of a quantum ant colony; obtaining an updated quantum state population; calculating a delay index, a power consumption index and a reliability index of each deterministic task allocation result according to the node resource portrait set, and screening out a candidate feasible solution set meeting real-time constraint; and obtaining a global scheduling scheme. According to the method, the modeling granularity and the distribution precision of task distribution under multi-domain heterogeneous resources are remarkably improved, the problems of bandwidth contention and delay mutation in cross-node and cross-domain migration are effectively avoided, and the real-time performance and the high adaptability of task distribution are achieved.

The invention discloses a parking lot recommendation method based on an improved ant colony algorithm, and the method comprises the steps: S1, setting the number of ants and a pheromone matrix, and determining a weight parameter of a heuristic function; s2, in the current iteration, each ant starts from a starting point, a next node is selected by adopting a roulette strategy according to the pheromone concentration between the nodes and a heuristic function value until the next node reaches an end point, a complete path is formed, and in the path construction process, part of ants execute a random walking strategy to perform global exploration; s3, after all the ants complete path construction, a pheromone matrix is updated uniformly according to the path advantages and disadvantages of all the ants, and the updating process does not include a pheromone volatilization step; and S4, the steps S2 to S3 are executed repeatedly until the iteration termination condition is met, the global optimal path in all iterations is output as a recommendation result, and the recommendation result at least comprises the target parking lot, the distance and the passing time information. The method is suitable for parking lot recommendation.

The application discloses a PLC program automatic debugging method based on an ant colony optimization algorithm, parses information and constructs a multi-PLC controller debugging action collaborative work graph and a constraint set; generates a discrete debugging action sequence; generates a first candidate parallel debugging scheme; performs a debugging action exchange, a debugging port rearrangement and a network bandwidth redistribution operation based on a time axis; inputs a second candidate parallel debugging scheme into a consistency and safety checker for checking, deploys a shadow breakpoint and a shadow forced write in a corresponding controller to construct a minimum disturbance debugging environment; updates a pheromone matrix and a tabu attribute table according to collected debugging data to form an adaptively adjusted third candidate parallel debugging scheme; and a debugging terminal condition is satisfied and a final parallel PLC controller collaborative debugging scheme is output. The application greatly improves the ability of the scheme to jump out of a local optimum and cope with multiple constraint field disturbances.

The invention discloses an interactive ant colony optimization method for multi-cabin vehicle scheduling, and the method comprises the steps: constructing a multi-cabin vehicle scheduling problem optimization model according to a multi-cabin vehicle transportation process; respectively generating a population 1 and a population 2 by adopting a preset encoding and decoding mode according to multiple rules; initializing pheromone matrixes 1 and 2 according to the populations 1 and 2; generating a first population and a second population based on the pheromone matrixes 1 and 2; evaluating the first population according to a first optimization target in the optimization model to obtain an external file set 1; evaluating the second population according to a second optimization target in the optimization model to obtain an external file set 2; interacting the external file set 1 and the external file set 2 to obtain a third population; evaluating the third population according to a first optimization target and a second optimization target in the optimization model to determine a non-inferior solution set and performing local operation to obtain an external file set 3; respectively updating the pheromone matrix 1 and the pheromone matrix 2 by using the individuals of the external file set 3; and outputting the current optimal non-inferior solution until the end condition is reached. Compared with a traditional optimization method, the method has remarkable advantages.

This application relates to methods and systems for optimizing logistics scheduling. A method implemented by a computer is provided, including: constructing inputs for an ant colony algorithm, including order data related to the materials of orders to be delivered within a predetermined time period, available storage location data, and logistics efficiency data; initializing parameters of the ant colony algorithm, including the number of ants based on the quantity of materials included in the order data, an iteration stop condition, and a pheromone matrix, where the elements of the pheromone matrix indicate the corresponding pheromone concentrations on all possible paths from each material in each order to each storage location; iteratively executing the ant colony algorithm when the iteration stop condition is not met; and stopping the iteration and outputting the optimal solution at that time when it is met. For each iteration, a solution corresponding to each ant is generated, which is a set of paths selected by this ant from each material in each order to each storage location; and the optimal solution for each iteration is determined based on an objective function indicating the storage location utilization rate of all materials.

The invention discloses a cold chain low-carbon transport vehicle scheduling method under fuzzy driving time. The method comprises the following steps: initializing a main population and an auxiliary population; fusing the main population and the auxiliary population into a temporary population; according to a plurality of optimization target values in the scheduling model, niche reservation operation is carried out on individuals in the temporary population, ps elite individuals reserved in the temporary population are adopted to replace individuals in the main population, and an updated main population is obtained; determining a non-inferior solution set for the updated main population by using a Pareto non-dominated relationship according to a plurality of optimization target values in the scheduling model; updating a pheromone matrix by using the updated individuals in the non-inferior solution set of the main population, and sampling the pheromone matrix to update the auxiliary population; interacting individuals in the updated primary and auxiliary populations; performing local search on individuals in a non-inferior solution set of the main population and the auxiliary population after interaction; and judging a termination condition. By means of the method, the high-quality non-inferior solution set of the cold-chain low-carbon transport vehicle scheduling problem under the fuzzy driving time can be obtained within a short time.

The application provides a ship steel plate yard storage operation planning method considering pretreatment time, which comprises the following steps: collecting the storage sequence of the steel plate, the pretreatment time of the storage steel plate, grouping according to the manufacturing date of the section where the steel plate is located, determining the parameters of the yard, and determining the moving time of the travelling crane between different stacking positions; determining the initial storage sequence of the steel plate and the plate turning result through a heuristic algorithm; setting the pheromone matrix value of the ant colony and the weight of the pheromone value and the heuristic value, adopting the ant colony algorithm, and iteratively obtaining the steel plate storage operation scheme of each stage; determining the plate turning result of the storage operation scheme obtained at each iteration, and updating the optimal storage operation scheme, the pheromone matrix and the weight of the pheromone value and the heuristic value; and judging whether the stopping condition of the method is reached. The method fully considers the pretreatment time information of the steel plate, reasonably plans the storage stacking position of the steel plate, and reduces the plate turning times when the steel plate is discharged.

The invention discloses a distribution network power line fault positioning method, which comprises the following steps of S1, triggering a power failure fault positioning function, determining a to-be-detected target range, and obtaining a distribution network topology of the to-be-detected target range; s2, a historical database is introduced, and an evaluation state data set is obtained; s3, acquiring meteorological data by using a meteorological database, and acquiring an evaluation state grade index; s4, constructing an ant colony model; s5, constructing an ant colony matching function, and generating an ant colony matching matrix based on the evaluation state data set and the evaluation state grade index; s6, adjusting the putting proportion of the ant colony in the ant colony model according to the ant colony proportion matrix; step S7, obtaining a final pheromone matrix in the ant colony model, wherein the maximum value of the elements is a power failure fault point; therefore, the dynamic ant colony putting strategy is constructed by combining the historical operation data and the environmental factors, the fault positioning efficiency and accuracy are remarkably improved, and meanwhile the computing resource consumption is reduced.

The invention provides an unmanned aerial vehicle sharing management method and system, and relates to the technical field of unmanned aerial vehicle sharing management, and the method comprises the steps: calculating heuristic information according to the time and place of reservation, the type of an unmanned aerial vehicle, and the position, state and maintenance condition of the unmanned aerial vehicle, and the heuristic information is the distance from the unmanned aerial vehicle to the reservation; for each ant, the next food source to be served is determined according to the pheromone concentration and heuristic information, each ant constructs a path, and the path represents a reservation sequence served by the ant; after all ants complete path construction, according to the total cost of each path, the pheromone matrix is updated, iteration is repeated to obtain a finally determined distribution scheme, and according to the finally determined distribution scheme, a corresponding unmanned aerial vehicle is distributed to each reservation; and according to the final unmanned aerial vehicle distribution scheme, the sharing platform sets use rules and cost standards of the unmanned aerial vehicles. The use efficiency of the unmanned aerial vehicle is improved, and the operation cost is reduced.

The application provides a method and system for optimizing SNP interaction selection precision based on an ant colony algorithm, and the method comprises the following steps: each artificial ant selects an SNP interaction in a search space, all artificial ants are divided into several populations, and an adaptive function is allocated to each population, and the adaptive function is used to evaluate the correlation between the SNP interaction and a disease phenotype; an information matrix is updated by using any pheromone updating strategy; in each iteration process of the ant colony algorithm, a self-learning mechanism is set by taking the maximum cumulative reward of the ants as a target, and the pheromone updating strategy is changed through the self-learning mechanism. Based on the method, a system for optimizing SNP interaction selection precision based on the ant colony algorithm is also provided. The application is used for accurately selecting SNP interactions related to a disease phenotype, several different path selection strategies are designed, and the path selection strategy which can bring the maximum selection ability to the algorithm is adaptively selected in the search process of the algorithm based on reinforcement learning.

This invention discloses a scheduling and control method and system for the co-production of bioproteins and ethanol from syngas, relating to the field of scheduling and control technology. The method includes: calculating the relative biomass bias of ethanol based on the operating data of the syngas co-production fermentation system; calculating a comprehensive evaluation quantity based on the relative biomass bias of ethanol; constructing a set of gas injection path rearrangement actions based on the configuration data of the controllable actuators of the fermentation reactor, and obtaining a structural intervention action library based on the set of gas injection path rearrangement actions; obtaining an updated pheromone matrix based on the comprehensive evaluation quantity and the structural intervention action library, and obtaining a target action sequence based on the updated pheromone matrix. This invention improves the ability to regulate complex gas-liquid distribution states.

The present invention discloses an electric vehicle path planning method supporting different charging strategies, including: generating an initial solution using a scanning algorithm; constructing a comprehensive objective function considering vehicle fixed costs, distance costs, energy consumption costs, and customer time window penalties; guiding the search of each ant through a pseudo-random state transition rule and an optimal charging station insertion criterion; performing local search on the iterative optimal ants within a threshold range, and further optimizing the iterative optimal ants using a variable neighborhood descent algorithm; using a charging station adjustment strategy to find a better charging plan and update the iterative optimal ants; updating the global optimal solution; dynamically selecting the global optimal solution or the iterative optimal solution to update the pheromone matrix; using the global optimal solution to update the maximum and minimum values of the pheromone; finally, after the iteration ends, outputting the planned path. This method can effectively implement vehicle path planning under full charging strategies and partial charging strategies, thereby reducing operating costs.

The invention discloses a path planning method and system based on an improved self-adaptive ant colony algorithm, and the method comprises the following steps: initializing, loading traveling salesman problem instance data of a plurality of places based on basic parameters and dynamic parameters of the improved self-adaptive ant colony algorithm, constructing a distance matrix between the places based on Euclidean distance, and performing path planning on the distance matrix. Initializing a symmetric pheromone matrix; iterative optimization is carried out, each ant starts from a randomly selected initial site, a complete path is constructed step by step through an improved state transition probability rule, the path length of each ant is calculated, and the current iterative optimal path and the length thereof are recorded; iteration is terminated, and when the number of iterations reaches the preset value, the algorithm stops running; according to the method, statistics is carried out on results of multiple rounds of operation, the average optimal distance of the multiple rounds of operation and the minimum distance in the multiple rounds of operation are obtained, namely, the optimal planning path is obtained, a self-adaptive volatilization mechanism is introduced, the pheromone volatilization rate is dynamically adjusted according to the number of iterations, and the local optimum problem is solved.

The present invention relates to the technical field of production path planning, and discloses a production order and production path scheduling method based on the ant colony algorithm, including the following steps: S1: Obtain order data and equipment data, construct and initialize a pheromone matrix, and set the ant colony size and the maximum number of iterations; S2: Perform one iteration of the ant colony based on the order data and the pheromone matrix; S3: Update the pheromone matrix based on the production paths explored by each ant in the ant colony and the fitness function; S4: Repeat steps S2-S3 until the maximum number of iterations is reached; S5: Obtain the production path with the highest fitness in all iterations as the optimal production path, and output the production schedule and the corresponding equipment plan Gantt chart; By introducing an improved ant colony algorithm and combining with actual production constraints, the present invention provides efficient, flexible and low-cost scheduling for production orders, which helps to improve the efficiency of order production.

The present invention proposes a ship stowage optimization method based on the ant colony algorithm, including a voyage cost function. The method includes: obtaining a ship set and a set of goods to be stowed to construct a pheromone matrix; establishing a solution item for each ant; establishing a single voyage in the solution item, randomly selecting the carrier ship for the voyage, and calculating the available deck area of the carrier ship according to the floating box plan; selecting and establishing an alternative goods set from the goods to be stowed based on the node transfer strategy, and stowing the alternative goods set on the deck; deleting the alternative goods set from the goods to be stowed, replacing the carrier ship and stowing again until the goods to be stowed are empty, and calculating the voyage cost function of the solution item; obtaining the optimal solution item set through comparison operations and updating the pheromone matrix, and repeating the iteration until the number of iterations reaches a preset value. This method can obtain the optimal matching scheme for ships, ports and goods, and effectively reduce the transportation cost.

The present invention relates to the field of software engineering, and specifically to an experimental implementation method for chaos engineering application scenarios based on an ant colony algorithm; performing structured processing and time series modeling on the collected complex raw data; importing the processed information into the ant colony algorithm, determining parameters such as the number of ant colonies, pheromone concentration, evaporation rate, and initializing the pheromone matrix during initialization, and utilizing resource usage and relevant business information to assist in decision-making. During the execution of the algorithm, ants select paths based on a probability formula, determine the weights of pheromones and heuristic indicators based on the fault history and business information, and adjust the pheromone matrix according to the volatilization, addition, and update rules after traversal, and repeat until the conditions are met. The algorithm outputs an adjacency matrix containing pheromone, node, and edge information, which is used to analyze fault paths, impacts, propagation, and generate visual charts. This method can improve experimental efficiency, accuracy, and dynamic adaptability, and enhance system reliability and resilience.

The invention provides a test case dynamic scheduling method and system based on an ant colony algorithm, and relates to the technical field of software test optimization. The method comprises the steps that core variables and parameters are initialized; analyzing the dependency relationship of the test cases; estimating the execution time of the test case according to the dependency relationship of the test case; selecting a to-be-executed test node according to the test case execution time; selecting a test case to be executed according to the task-node distribution table; according to the to-be-executed test case, updating a node operation state; according to the task execution result, the total completion time and the pheromone matrix, updating a test case execution state and the pheromone matrix; and obtaining an optimal scheduling scheme according to the current number of iterations or the total completion time fluctuation of continuous multiple iterations. According to the method, a load balancing problem is converted into an integer programming problem through mathematical modeling, optimal task allocation is realized by utilizing a heuristic search mechanism of the ant colony algorithm, and the efficiency and stability of a test system facing uncertain factors are remarkably improved.

The application discloses a cold-chain low-carbon transport vehicle scheduling method under fuzzy driving time, which comprises the following steps: main and auxiliary population initialization; fusing the main population and the auxiliary population into a temporary population; performing niche reservation operation on individuals in the temporary population according to multiple optimization target values in a scheduling model, replacing individuals in the main population with the reserved ps elite individuals in the temporary population, and obtaining an updated main population; determining a non-inferior solution set of the updated main population according to the multiple optimization target values in the scheduling model by using a Pareto non-dominated relationship; updating a pheromone matrix by using individuals in the non-inferior solution set of the updated main population, sampling the pheromone matrix to update an auxiliary population; interacting individuals in the updated main and auxiliary populations; performing local search on individuals in the non-inferior solution set of the main and auxiliary populations after the interaction; and judging a termination condition. By the method, a high-quality non-inferior solution set of the cold-chain low-carbon transport vehicle scheduling problem under fuzzy driving time can be obtained in a short time.

The invention provides an ultra-high-speed optical module production line scheduling method. The method comprises the following steps: S1, establishing a scheduling mathematical model according to a set optimization target and constraint conditions; s2, setting parameters of an ant colony algorithm and reinforcement learning; s3, constructing a state transition rule and heuristic information of the ant colony algorithm, and initializing a pheromone matrix of the ant colony algorithm; s4, initializing the ant colony position and path of the ant colony algorithm; s5, the ant colony algorithm runs according to the pheromone matrix to obtain a scheduling scheme and the maximum completion time of the scheduling scheme; s6, reinforcement learning updates the pheromone matrix based on the maximum completion time, and the ant colony algorithm updates the scheduling scheme according to the updated pheromone matrix; and when the number of iterations of reinforcement learning reaches a set number, outputting a scheduling scheme, otherwise, returning to S4. According to the method, the production efficiency of an ultra-high-speed optical module production line is effectively improved.

This application provides a trajectory planning method and system for defect detection robots, relating to the field of equipment planning technology. The method includes: constructing a digital twin of a production line based on various digital models; determining the starting measurement point and the robot to be visited by the ant in the current iteration from a set of measurement points arranged for the body-in-white model; calculating the selection probability of all possible decisions based on the current pheromone matrix and heuristic information matrix; the elements in the heuristic information matrix are obtained based on the simulation results of the digital twin of the production line; selecting the target decision from these decisions and updating the heuristic information matrix in the current iteration accordingly; constructing the corresponding solution when all measurement points have been visited; evaluating each solution through an objective function to obtain the current optimal solution, and updating the pheromone matrix elements based on this and the global optimal solution; and determining the latest global optimal solution as the optimal trajectory for defect detection when the iteration meets the termination condition. The aim is to improve the efficiency of defect detection trajectory planning.