116 results about "Ant colony optimization algorithms" patented technology

The invention relates to a landslide grading early warning method based on multi-modal data change characteristics. The method comprises the following steps: establishing a mountain digital twinborn body; the method comprises the following steps: deploying a multi-node sensor network in a target area, configuring an edge computing unit, collecting geological data in real time, and screening the geological data based on mountain digital twin to form effective local data; constructing a lightweight multi-modal neural network model at each node, dynamically searching hyper-parameters by using an ant colony optimization algorithm, and generating an encryption model weight update quantity packet; the central server dynamically calculates node weights according to disaster feature vectors output by the digital twins, generates a global model through weighted aggregation, and directionally distributes and updates the global model; real-time monitoring data and a model prediction result are fused, millimeter-level disaster evolution simulation is executed through a variable step size physical engine, an advanced early warning signal is triggered when a deduced prediction risk exceeds a threshold value, and the edge model adaptability, federal aggregation precision and early warning advancement are remarkably improved.

The invention discloses a thermal power plant inspection robot path planning method, device and equipment and a storage medium, and the method comprises the steps: carrying out the semantic analysis and information extraction of an inspection task instruction, an equipment document and a historical inspection log through a large language model; obtaining a semantic importance score, a perception modal demand, recommended residence time and a shooting angle of each inspection target; establishing a multi-target path optimization model based on an improved ant colony optimization algorithm, and inputting the semantic importance score, the perception modal demand, the recommended residence time and the shooting angle into the multi-target path optimization model to obtain a joint scheduling sequence; the thermal power plant inspection robot is controlled to execute the joint scheduling sequence to perform autonomous navigation and perception tasks, the current path planning is adjusted according to the task completion condition, the intelligence, autonomy and adaptability level of the inspection robot can be remarkably improved, and the speed and efficiency of path planning of the thermal power plant inspection robot are improved.

The invention relates to the technical field of intelligent welding systems, in particular to a laser cutting method, system and device suitable for steel structure machining, and the method comprises the steps that a grid map used for planning a cutting path of a to-be-cut steel plate is obtained; acquiring the temperature rise amount of each edge generated by laser cutting; marking the cut edges before each edge is cut as each cut edge, and obtaining the residual heat coefficient and the heat accumulation coefficient of each edge; obtaining the angle influence degree of each edge by comparing the cutting duration of each edge with all previous cut edges and combining the included angle between each edge and the previous cut edge adjacent to each edge with the difference between the included angle and a preset angle threshold value; and heuristic information of each edge of the ant colony optimization algorithm during path planning is obtained, and then the optimal laser cutting path is obtained. According to the method, the cutting path of the steel plate can be optimized by reconstructing the heuristic information in the ant colony optimization algorithm, and then the intelligent welding effect of a subsequent steel structure is improved.

The invention discloses a photovoltaic power generation system maximum power tracking control method, and relates to the technical field of power system operation control, and the method comprises the steps: collecting operation data of a photovoltaic power generation system, and carrying out the preprocessing of the operation data, and obtaining feature data, so as to construct a maximum power point prediction model; training a maximum power point prediction model based on the obtained historical operation data sample, and predicting a target working point by using the trained maximum power point prediction model; and in combination with the target working point and an ant colony optimization algorithm, determining an optimal working point, and obtaining control parameters of maximum power tracking by constructing an adaptive voltage controller. According to the MPPT dynamic prediction method based on deep learning, historical operation data and environmental parameters are fused through the LSTM neural network, tracking lag under the conditions of illumination abrupt change and local shadow is avoided, and the prediction precision and response speed of the maximum power point are improved.

The invention relates to an ocean platform pipeline wiring method and system based on an ant colony optimization algorithm, and a medium, and belongs to the technical field of intelligent optimization algorithms. Comprising the following steps: S11, environment modeling: discretizing a three-dimensional space of an ocean platform into a grid model, marking an obstacle region and a feasible region, and calculating a grid node energy value; s12, parameter initialization: constructing a heuristic information matrix based on pipeline inlet and outlet coordinates and equipment parameters, and determining a node transition probability by the matrix through a direction probability allocation strategy and a distance sensitive function; s13, path optimization: generating candidate paths through dynamic smooth search, local pheromone adaptive adjustment and collaborative ecological niche search, and updating pheromones; and S14, constraint verification and result output: verifying whether the path meets the engineering constraint or not, and outputting the three-dimensional coordinate, length and bend number of the optimal pipeline path after iterative optimization. According to the method, efficient, safe and economical wiring of the offshore platform pipeline is achieved.

The invention provides a workpiece surface target merging and partitioning method, system and device, and relates to the technical field of image processing, and the workpiece surface target merging and partitioning method comprises the steps: obtaining a surface image of a target region of a target maintenance workpiece; performing graph sampling and visual processing on the surface image to generate a binary image of the target area, and analyzing the binary image to obtain a plurality of connected domains of the target area; performing convex hull generation according to the connected domains to obtain a convex hull corresponding to each connected domain; generating a rotation enclosing rectangle set according to the leading edge vector of each convex hull; and through a variant ant colony optimization algorithm, carrying out global optimization on the rotating enclosing rectangle set, and generating a combined partition of the target area. According to the invention, through accurate capturing of the original image and subsequent layer-by-layer optimization, the redundancy of the laser maintenance path is reduced, and the operation cost is reduced; through construction and global optimization of a high-adaptation form carrier, precise coverage of a complex form target is realized, and the maintenance precision is improved.

The invention relates to an MPPT (Maximum Power Point Tracking) method, system and equipment based on distributed photovoltaic and a medium, and relates to the technical field of distributed photovoltaic power generation, and the method comprises the following steps: preprocessing photovoltaic array data, and obtaining irradiance, temperature and MPP voltage data sets; determining the optimal number of hidden layer nodes of the ElmanNN by adopting K-fold cross validation and RMSE evaluation, and dividing a data set into a training set, a validation set and a test set; inputting the training set to firefly algorithm global search and ant colony optimization algorithm local optimization, and defining a stable solution as an FA-EAS optimal parameter after convergence; updating an ElmanNN weight threshold based on the optimal parameter, and deploying a training model to carry out MPP voltage online prediction; and the difference value of the predicted voltage and the reference voltage is input into a PID controller, the control quantity is calculated through incremental PID, a PWM signal is generated to control the duty ratio of the Boost converter, and maximum power point tracking is achieved. According to the invention, the tracking speed, stability and success rate of MPPT can be improved.

A dynamic competition perception workflow scheduling method for a heterogeneous computing system comprises the following steps: constructing a scheduling problem model based on a scheduling object set and bus resources of an obtained system; constructing a dynamic competition perception simulator according to the scheduling problem model, wherein the method comprises the following steps of: decomposing a time-varying behavior of cross-domain bus data transmission into a time-invariant meta-process; each meta-process is mapped into a timeout event carrying a specific timestamp; the completion event indicates that all the timeout events to which the same scheduling object belongs are completed, so that the end of the task or cross-domain bus data transmission is marked; according to an event-driven mechanism, a completion event is arranged on the premise that the preorder constraint of a scheduling object is met, and total completion time estimation is generated while integration of multiple scheduling strategies is supported; according to the total completion time estimation, generating an approximate optimal scheduling solution by using a hybrid earliest completion time genetic algorithm; and optimizing the approximate optimal scheduling solution by adopting a quota weighted ant colony algorithm to generate a final scheduling solution.

The invention discloses a drilling parameter multi-objective optimization method based on an ant colony optimization algorithm-DeepAR model, relates to the technical field of petroleum and natural gas engineering drilling parameter optimization, can predict the mechanical drilling speed in a period of time in the future, and has input and output with adaptive length. Based on a mechanical drilling speed mathematical model, a mechanical specific energy mathematical model and a drill bit wear mathematical model, a multi-objective optimization model is established on the basis of a mechanical drilling speed prediction model, an objective function is optimally solved through a multi-objective optimization algorithm, and three parameters of drilling pressure, rotating speed and displacement are optimized. An intelligent algorithm based on an ant colony optimization algorithm is adopted, and while-drilling multi-target, interactive and dynamic drilling parameter optimization is achieved. The problems that a mechanical drilling speed prediction model is poor in stability, the complex dependency relationship among long-distance drilling data parameters cannot be accurately captured, and a drilling parameter optimization objective function is single and excessively depends on a complex physical drilling speed equation are solved.

The invention discloses a lithium ion battery SOC (state of charge) and SOH (state of health) joint estimation method based on an FOMISUKF-EKF algorithm. The method comprises the following steps: acquiring dynamic response and static characteristic data required by battery modeling; selecting a fractional order equivalent circuit model and establishing a state-space equation; identifying each parameter value of the model by adopting an ant colony algorithm; after the identified initial parameters are injected into the algorithm, SOC estimation under a micro scale is realized based on fractional order unscented Kalman filtering and a multi-innovation sliding mode observer; and when a preset period is met, switching to a macroscale, taking the SOC sequence as input, and performing joint identification on the battery parameters and SOH by adopting an EKF algorithm. According to the method, the multi-information theory and the sliding-mode observation algorithm are introduced on the basis of the traditional UKF algorithm, the sliding-mode observer is used as the state variable of the input end estimation system, the influence of system parameter change and external disturbance can be reduced to a certain extent, and rapid convergence and effective suppression of measurement noise are realized.

The invention relates to the technical field of tax data processing, in particular to a blockchain-based tax audit management system, which comprises a semantic business activity acquisition module for capturing business data from an operating system of a factoring business participant, converting the business data into a semantic unit and storing the semantic unit in a blockchain; the dynamic tax rule evolution module optimizes a tax payment obligation identification model through a federated learning framework; the tax payment event association module is integrated with an oracle machine network to construct a tax payment event atlas; the cash flow tracing and sharing module adopts an ant colony optimization algorithm to generate a cash flow incidence matrix; the auditing deduction and feedback module generates a calibration signal through simulation deduction and feeds the calibration signal back to the rule evolution module. All the modules form a closed-loop system through a data flow and a feedback mechanism, dynamic association and automatic matching of multi-subject sub-link tax payment obligations and cash flow paths are achieved, and the tax auditing efficiency is improved.

The application provides an indoor space environment control method based on an optimization algorithm and related equipment, and the method comprises the following steps: constructing an indoor comprehensive environment model by combining historical equipment control parameters and historical space environment data; constructing a comprehensive target function; creating a state space of an ant colony optimization algorithm and generating constraint conditions of the ant colony optimization algorithm; taking the comprehensive target function as an algorithm optimization target, generating an initial equipment control strategy of an environment control device based on the indoor comprehensive environment model and using the ant colony optimization algorithm; taking an optimal parameter interval as a strategy initial solution of a model predictive control strategy, generating a strategy prediction result by the model predictive control strategy based on the indoor comprehensive environment model; and updating algorithm parameters of the ant colony optimization algorithm according to the strategy prediction result and generating an optimal equipment control strategy of the environment control device. The application has the effect of meeting the environmental experience comfort of users on the basis of the lowest energy consumption.

The invention discloses a multi-robot collaborative welding system and method for an automobile chassis structural component, and the system comprises a sensing layer, a planning layer, a control layer, an execution layer and a monitoring layer, and the sensing layer is used for collecting a welding environment, a working state and robot motion data. The welding spot distribution is optimized through the GA-GSA algorithm, and the path is planned in combination with the improved ant colony algorithm and the DWA algorithm, so that the total welding time is shortened by 20%-30%, the robot load balance degree is improved to 95% or above, and the collaboration efficiency is remarkably improved; multi-source data fusion and fractional order PID closed-loop correction are adopted, the track deviation is controlled within + / -0.05 mm and is reduced by more than 50% compared with a traditional technology, and the positioning precision is greatly improved; through pre-generation of a static mutual exclusion region and trajectory prediction in the future 50 ms, 100% collision-free operation is realized, the deadlock problem is avoided, and the reliability of dynamic collision avoidance is enhanced.

The invention discloses a hydropower station AGC anti-vibration distribution strategy based on a multi-target ant colony optimization algorithm. The strategy comprises the following steps: collecting multi-dimensional data parameters of a unit vibration area; establishing a vibration forbidden zone dynamic model considering the transient change of the water head; pre-screening a feasible solution space by adopting dynamic planning; performing multi-objective optimization through a multi-objective ant colony optimization MOACO algorithm to obtain a Pareto optimal solution set; and based on the objective function, dynamically adjusting the weight coefficient through fuzzy logic. According to the strategy, through a hybrid architecture fusing dynamic planning and a swarm intelligence algorithm and combining a unit vibration area dynamic modeling technology, efficient, safe and economical collaborative optimization of load distribution of a hydropower station is realized, the operation life of a unit is prolonged, and the frequency modulation performance of a power grid is improved.

The application discloses an ant colony optimization algorithm-based layout method for wear-resistant materials of safety shoes, S1, a safety shoe wear data set is constructed; S2, a surface of the safety shoes is divided into multiple priority areas; S3, initial concentrations of pheromones are distributed according to priority area distribution of fractal characteristic values; S4, an optimization objective function is established; S5, an optimal material layout path of a high-priority area is gradually selected; S6, fitness values of each path are calculated according to path search results and the optimization objective function, and path pheromone concentrations are updated by using the fitness values; S7, a final global optimal path is obtained through multiple rounds of iterations of S1-S6; S8, a final optimal layout scheme of the wear-resistant materials of the safety shoes is output; and S9, the final optimal layout scheme is verified. The application can provide an efficient and scientific optimization scheme in the layout design of the wear-resistant materials of the safety shoes, and brings remarkable technical values and economic benefits for actual applications.

The application discloses an automatic driving environment perception method and system based on artificial intelligence, and relates to the technical field of automatic driving environment perception, in particular to an automatic driving environment perception method and system based on artificial intelligence.The application obtains original data through multi-source data collection; adopts a raw data optimization method of sensor calibration, dynamic and static data separation, data normalization, data enhancement and data set segmentation; adopts a double-branch transformer model as an environment perception model, and respectively captures irregular behavior patterns of moving targets and geometric topological constraints of road structures through a double-branch architecture; and adopts an improved ant colony optimization algorithm as a decision optimization algorithm, converts a multi-dimensional risk field into a calculable optimization target, and realizes optimal decision through global exploration of elite ants and local optimization of non-elite ants.

The invention discloses a dynamic equipment layout method based on a quasi-object strategy and a multi-target ant colony optimization algorithm. The method comprises the steps of layout initialization, layout legalization operation, a movement strategy based on reference equipment, configuration optimization operation and Pareto optimal configuration selection based on a maximum and minimum target distance method. N configurations Xl (l = 1, 2,..., n) are randomly generated, each ant l represents one configuration Xl, and initial configurations of the n ants are obtained; performing legalization operation on the configuration X1 by adopting a gradient method based on dynamic step length, executing a movement strategy based on reference equipment to obtain a group of compact and legal configurations, and recording the configurations as a configuration library BL; selecting all non-dominated configurations in the set BL, and storing the non-dominated configurations in an external document CS; and for the current ant l, generating a random number o, and judging the size relationship between the random number o and the parameter p to select an optimization strategy, when o is greater than p, selecting to execute a local search strategy based on improved pseudo-random proportion, otherwise, using a global optimization strategy based on ecological niche.

The invention relates to the technical field of high-end equipment maintenance, and provides a slice integrated maintenance path planning method and device based on a three-dimensional assembly model in order to solve the problems of easy collision, poor accessibility, low efficiency and insufficient refinement of path planning in three-dimensional assembly maintenance. The method comprises the following steps: acquiring point cloud data of a three-dimensional assembly model of to-be-maintained equipment; automatically identifying a to-be-maintained area by using an area expansion algorithm; generating a two-dimensional slice sequence containing the contour of the to-be-maintained area by adopting an adaptive layering algorithm, and constructing a maintenance accessibility scoring matrix; and realizing maintenance path planning through a cross-layer ant colony optimization algorithm based on the accessibility scoring matrix as a path planning environment. According to the method, maintenance constraint is carried out through precise recognition of a maintenance area and self-adaptive layering, and finally global optimization is carried out by using a cross-layer ant colony optimization algorithm, so that a collision-free, high-accessibility, smooth and continuous three-dimensional maintenance path is finely, efficiently and automatically generated.

The application relates to the technical field of offshore wind turbine operation and maintenance, and discloses a system for evaluating the failure of an offshore wind turbine, which comprises a sensor data acquisition module, a data preprocessing module, a failure feature extraction unit, a failure probability calculation module, a maintenance decision module and the like. The system can acquire parameters such as the vibration and temperature of the wind turbine in real time, extract failure features by using a deep convolutional neural network after preprocessing, and calculate the failure probability of components by constructing a dynamic Bayesian network model. An improved ant colony optimization algorithm is used to generate an optimal maintenance path, and a dynamic risk evaluation module, an abnormal signal detection module and a maintenance strategy optimization module are simultaneously provided. The system can accurately evaluate the failure risk of the unit, timely discover abnormalities, optimize maintenance decisions, improve the operation reliability of the offshore wind turbine, reduce operation and maintenance costs, and ensure the stable and efficient operation of offshore wind power generation.

The invention relates to a universal swing-up stabilization control method for a vertical multi-link mechanical arm with a single passive joint, which belongs to the technical field of robot control and comprises the following steps: S1, establishing a mechanical arm dynamic model with a single passive joint configuration by adopting a Lagrangian method; s2, designing a system track based on initial and target states of the system; s3, designing a switching condition through a track error threshold value and a parameter coupling relation; s4, performing off-line optimization on the trajectory parameters by adopting an ant colony optimization algorithm; s5, designing a sliding mode tracking controller for avoiding singular values; and S6, designing a stabilizer.

The invention provides a laser cleaning path planning method and system and electronic equipment, and relates to the technical field of laser surface treatment.The method comprises the steps that imaging collection is conducted on the surface of a to-be-cleaned workpiece, coordinate registration and binarization processing are conducted, and a binarization image of the surface of the to-be-cleaned workpiece is obtained; regularly partitioning the surface of the to-be-cleaned workpiece according to the binary image to obtain a plurality of sub-partitions, and performing path coverage on each sub-partition to obtain a local path of each sub-partition; mapping a starting point of the local path of each sub-partition to obtain a global representative point set of all the local paths, and generating a traveling salesman problem model according to the global representative point set; solving the traveling salesman problem model through a native heterogeneous competitive-cooperative ant colony optimization algorithm to obtain an optimal global access sequence of all local paths; and generating a laser cleaning scanning path of the surface of the to-be-cleaned workpiece. According to the invention, the sensitivity of path planning is improved, and finally a high-quality laser cleaning scanning path is generated.

The application relates to the technical field of high-end equipment maintenance, and provides a three-dimensional assembly body model slicing integrated maintenance path planning method and device to solve the problems of easy collision, poor accessibility, low efficiency and insufficient refinement in three-dimensional assembly body maintenance path planning, the method comprising the following steps: acquiring point cloud data of a three-dimensional assembly body model of equipment to be maintained; automatically identifying a maintenance area to be maintained by using a region expansion algorithm; generating a two-dimensional slice sequence containing the contour of the maintenance area to be maintained by using an adaptive layering algorithm to construct a maintenance accessibility score matrix; and realizing maintenance path planning by using a cross-layer ant colony optimization algorithm based on the accessibility score matrix as a path planning environment. The application can accurately identify the maintenance area, adaptively layer the maintenance constraints, and finally use the cross-layer ant colony optimization algorithm for global optimization, so that a three-dimensional maintenance path without collision, with high accessibility and smooth continuity, can be automatically generated in a refined and efficient manner.

The present invention provides a path planning method for a cage cleaning robot arm, the method comprising: S11, obtaining three-dimensional coordinate data of an area to be cleaned and performing two-dimensional mapping to obtain a two-dimensional area to be cleaned; S12, if the number of two-dimensional areas to be cleaned is less than or equal to a preset threshold, directly performing path planning for the two-dimensional areas to be cleaned using an ant colony optimization algorithm; if the number of two-dimensional areas to be cleaned is greater than the preset threshold, then dividing the two-dimensional areas to be cleaned into blocks using a clustering algorithm to obtain several two-dimensional sub-areas to be cleaned, and then performing path planning for the several two-dimensional sub-areas to be cleaned using an ant colony optimization algorithm; S13, integrating the optimal paths of the two-dimensional areas to be cleaned / two-dimensional sub-areas to be cleaned to generate a global path; S14, inversely mapping the global path to obtain a three-dimensional spatial path planning diagram. The present invention systematically solves the problem of balanced task allocation during collaborative operation of multiple robot arms through a three-level architecture of regional block division, cluster optimization, and global integration.

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.

This invention discloses a method and detection system for determining metal ions in seawater, belonging to the field of image data processing technology. The method includes: obtaining the peak potential, peak height, peak width, and peak area of ​​characteristic peaks corresponding to different metal ions from voltammetric curves; identifying the type of metal ion in seawater based on the peak potential of each characteristic peak; optimizing the hyperparameters in a backpropagation (BP) neural network using an ant colony algorithm to construct an ACO-BP prediction model; and assigning optimal feature weights to the peak height, peak width, and peak area of ​​each characteristic peak, and inputting the weighted average into the trained ACO-BP prediction model to predict the concentration of metal ions in seawater. This invention significantly improves the robustness of the prediction model and the accuracy of the prediction results by using the peak height, peak width, and peak area of ​​the metal ion characteristic peaks as input vectors for the newly constructed ACO-BP prediction model to predict concentration.

The invention provides a video memory and memory fusion distribution method for a large neural network model, and belongs to the technical field of large neural network models, and the method comprises the steps: collecting system state data in real time, including a video memory, a memory utilization rate and related performance indexes; static and dynamic characteristic components are extracted by innovatively applying singular value decomposition, a multi-dimensional mapping relation model of molecular dynamics simulation is constructed, and a coupling state equation set of video memory allocation is established. And solving the equation set by adopting an ant colony optimization algorithm, generating a dynamic storage pointer, and establishing a multi-target storage scheduling equation set through joint characteristic decomposition to optimize resource utilization, access delay, fragment elimination, migration overhead and load balance. Algorithm parameters are updated by utilizing an entropy evaluation method, and finally collaborative scheduling of video memory and memory resources is realized in a unified storage management module, so that the calculation efficiency and the resource utilization rate of a neural network model are remarkably improved, and the problem that allocation of large model memories and video memories is not optimized enough in the prior art is solved.

The invention discloses a medium pulse repetition frequency set selection method based on an ant colony optimization algorithm and a storage medium, an optimal selection problem of a medium pulse repetition frequency set is modeled as a combinatorial optimization problem, and by introducing the ant colony optimization algorithm, on the premise of satisfying distance and speed fuzzy elimination constraints, the optimal selection problem of the medium pulse repetition frequency set can be solved. And the blind area proportion in the radar measurement space is minimized. According to the method, the optimization efficiency of the medium pulse repetition frequency set can be effectively improved, the blind area of the distance-Doppler two-dimensional space is remarkably reduced, the convergence speed and the global optimization capability of the algorithm are improved, and the method has good practical value and popularization prospect and is suitable for improving the target detection performance of a pulse Doppler radar system.

The invention discloses an electronic atomization production line scheduling simulation method based on deep learning, and the method comprises the steps: S1, collecting historical production data of an electronic atomization production line, carrying out the preprocessing, and constructing a standardized data set; s2, constructing and training an electronic atomization production line scheduling model through a multi-dimensional space-time collaborative Transform network; s3, inputting real-time production line state information into the trained electronic atomization production line scheduling model, and evaluating production line operation indexes to generate an optimized candidate scheduling strategy set; s4, constructing a multi-target simulation environment of the electronic atomization production line, and performing simulation execution on the optimized candidate scheduling strategy set; s5, screening an optimal scheduling strategy by adopting an improved multi-target ant colony optimization algorithm; s6, applying the optimal scheduling strategy to an actual production line, and collecting feedback information in real time; and S7, performing continuous iterative optimization on the scheduling model in combination with feedback data. According to the invention, the production line operation efficiency, the resource utilization rate and the scheduling response capability are improved.

The present invention relates to the field of industrial control technology, and in particular to a TSN-based industrial equipment collaborative control method, system, and readable storage medium. The method comprises the following steps: a switch obtains the TSN network from a preset start detection time t1 to the current time t K The present invention adopts a static traffic scheduling strategy to ensure that critical control traffic is transmitted first; it uses an intelligent traffic dynamic scheduling algorithm, combined with the ant colony optimization algorithm, to analyze network status in real time and optimize network resource allocation. This effectively solves the problem that traditional traffic scheduling cannot adapt to network changes in real time.