58 results about "Ant colony optimization algorithms" patented technology

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 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 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 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 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 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.

This application provides a data center load balancing method and apparatus based on temperature sensing and path selection, belonging to the field of data center load balancing technology. The method provided in this application: determines virtual machines to be migrated from overheated servers based on resource utilization; determines candidate target servers from servers in the data center based on resource requirements; calculates a comprehensive score for each candidate target server based on its temperature factor, load utilization factor, and heat accumulation factor; calculates the migration probability of each virtual machine to be migrated to each candidate target server based on pheromone concentration and comprehensive score; iteratively solves for the optimal solution based on the migration probability and ant colony optimization algorithm; determines the target server based on the optimal solution; and migrates the set of virtual machines to be migrated. The method and apparatus provided in this application are used to achieve synergy between data center thermal management and energy consumption optimization in ultra-dense deployment environments.

The invention relates to the technical field of laboratory personnel training, and discloses a laboratory personnel training closed-loop management method based on standard specifications, and the method comprises the steps of knowledge graph construction, intelligent analysis, personalized path planning, dynamic learning and assessment, and traceable recording and output. According to the invention, by introducing the pre-post test diagnosis matrix, the enhanced ant colony optimization algorithm and the reinforcement learning feedback mechanism based on the real effect, the technical problems that the existing laboratory training path is fixed, the intelligent degree is low and the personnel ability change cannot be dynamically adapted are systematically solved.

The application discloses a MEMS gyroscope north-seeking method and device based on a hybrid algorithm, which comprises the following steps: firstly, collecting three-axis acceleration and gyroscope data of a north seeker at any two positions; calculating the pitch angle and the roll angle of the two positions according to the acceleration data; then, establishing a function relation model containing the azimuth angle and the gyroscope zero offset based on the coordinate transformation and the earth rotation angular velocity, and constructing a nonlinear optimization equation with the two azimuth angles as optimization variables by using the characteristic that the zero offset is short-time invariable; finally, performing global search by using an ant colony optimization algorithm to obtain a rough solution, taking the solution as an initial value, and performing local accurate solution by using the steepest descent method to obtain a high-precision azimuth angle result. The application can realize north-seeking at any two positions without relying on a precise rotating device, and has the advantages of simple structure, convenient operation, high north-seeking precision and high calculation efficiency.

The invention discloses a Bluetooth tag-based non-inductive routing inspection and path planning method and system, and the method comprises the steps: collecting historical routing inspection data to generate a routing inspection task, automatically connecting to a server through routing inspection equipment, and downloading the routing inspection task; when the inspection equipment enters the Bluetooth range of the electronic tag, connection is automatically established, and risk levels are divided according to the connection time of the inspection equipment and the electronic tag; the MCU of the electronic tag automatically records inspection data and uploads the inspection data to a server through WIFI, an ant colony optimization algorithm is used to optimize an inspection path, and risk assessment and historical inspection data are integrated to provide an inspection path. According to the method, the operation efficiency of a factory is improved through automatic non-inductive inspection and intelligent path planning; human errors and omission are reduced through an intelligent system, so that higher reliability and safety are ensured; the system reduces deployment and maintenance costs by utilizing existing low-cost techniques and intelligent algorithms.

The application discloses a routing method, medium, equipment and product of a low-orbit satellite constellation, relates to the technical field of satellite routing, and comprises the following steps: according to a quality of service parameter, performing snapshot freezing on a current network topology to construct a static topology graph structure; constructing a basic quality of service parameter cost function for each link; performing an offline ant colony optimization algorithm to globally evaluate a satellite topology library constructed in advance, constructing a heuristic function according to a link basic quality of service parameter cost; initializing a pheromone value for each link, and updating the pheromone in an iteration process according to the heuristic function; adjusting the cost function based on the pheromone; taking the cost of the discounted pheromone as the final link weight of the satellite network, constructing a weighted graph structure, and adopting a shortest path search algorithm to calculate a routing path between a source node and a target node, so that an optimal routing path is obtained. The application solves the problems of high-speed time-varying satellite link state adaptability and poor quality of service.

The present application relates to underwater acoustic equipment design and manufacturing technical field, especially a kind of passive sonar target positioning method based on double sensor, comprising the following steps: step 1, the coordinate information of all candidate targets relative to each array at t1 Time is calculated;Suppose the coordinate of a certain array is (x, y), the motion equation of the target about orientation is calculated according to the orientation (θ) of the target y=θ*x+b;The motion equation of all targets about a certain array is calculated, and the intersection point is solved by combining two equations;Step 2, initialize parameters: parameters include: the number of tracking ants required for tracking and positioning, the initial value of objective function, convergence threshold and divergence coefficient;Step 3, randomly place tracking ants on candidate targets at t1 Time.The method of the present application simulates ant colony optimization algorithm, makes reasonable assumption on the motion of target, i.e. assuming uniform linear motion of target in a short time, and the positioning problem of target is abstracted as optimal path problem.

The present application relates to a distributed photovoltaic MPPT method, system, device and medium, and relates to the technical field of distributed photovoltaic power generation, which comprises the following steps: preprocessing photovoltaic array data, obtaining irradiance, temperature and MPP voltage dataset; determining the optimal ElmanNN hidden layer node number by K-fold cross-validation and RMSE evaluation, and dividing the dataset into a training set, a validation set and a test set; inputting the training set into global search of the firefly algorithm and local optimization of the ant colony optimization algorithm, defining the stable solution as the FA-EAS optimal parameters after convergence; updating the ElmanNN weight threshold based on the optimal parameters, deploying the training model to perform MPP voltage online prediction; inputting the difference between the predicted voltage and the reference voltage into a PID controller, calculating the control amount through the incremental PID, generating a PWM signal to control the duty cycle of the Boost converter, and realizing maximum power point tracking. The present application can improve the tracking speed, stability and success rate of MPPT.

This invention discloses a deep learning-based method for extracting multi-layer semantic features, specifically relating to the field of multi-layer semantic feature extraction technology. The method includes: acquiring semantic sample data, standardizing it, and mapping it to multiple semantic levels; performing semantic representation calculations on each semantic level to determine optional cross-layer connections; applying hierarchical order constraints to the optional cross-layer connections to construct a set of candidate semantic paths, which serve as candidate paths in an ant colony algorithm; initializing pheromone values ​​for each candidate path to form a candidate path set and a corresponding pheromone set; and using cross-layer semantic transmission paths as evaluable objects and continuously selecting, strengthening, and eliminating semantic paths based on prediction error feedback, thereby dynamically adjusting the generation process of multi-layer semantic features according to changes in data semantics, thus solving the problem of hidden semantic distortion caused by static solidification of semantic paths.

The invention discloses an ad hoc network data transmission optimization method, device, equipment and medium, and the method comprises the steps: calculating the signal-to-noise ratio of a legal node and the signal-to-noise ratio of an eavesdropper of a target network according to the network topology data and channel parameters of the target network, and carrying out the optimization of the data transmission of the target network according to the signal-to-noise ratio of the legal node and the signal-to-noise ratio of the eavesdropper; calculating a confidential connection probability; calculating the end-to-end average delay of each path in the target network according to the real-time path information and the traffic data; and calculating an optimal bandwidth allocation scheme of the target network by taking the end-to-end average delay as a delay constraint through a preset double-layer ant colony optimization algorithm, and obtaining an optimal path set by taking the confidential connection probability as a confidentiality constraint and combining with the optimal bandwidth allocation scheme. And performing data transmission according to the optimal bandwidth allocation scheme and the optimal path set. According to the invention, the high efficiency and security of ad hoc network data transmission can be improved.

This invention discloses a robot movement path planning method based on ant colony optimization algorithm. It includes the following steps: environmental modeling of the robot's movement space using a grid method to obtain a grid map, with grid coordinates representing the robot's starting and ending positions; generating an initial path on the grid map using a jump-point search algorithm, and calculating the non-uniform initial pheromone concentration distribution along the initial path; searching for the optimal path on the grid map using the ant colony optimization algorithm; eliminating redundant nodes on the optimal path and performing smoothing processing to obtain the robot's movement path. This invention improves convergence speed and search efficiency by using a non-uniform distribution of the initial pheromone concentration in the grid map, and shortens the planned path length by eliminating redundant nodes on the optimal path and performing smoothing processing.

The invention relates to the technical field of data processing, in particular to a shop site selection recommendation method and system based on big data. The method comprises the following steps: acquiring positions of a plurality of candidate shops and passenger flow data of a plurality of points of interest; for any candidate shop position, determining the passenger flow toughness strength between the point of interest and the candidate shop; performing clustering processing on the passenger flow toughness strength to obtain a plurality of commercial clusters; performing link breaking processing on association between points of interest in the business cluster and candidate shops, and ensuring that each point of interest is associated with at most one candidate shop; calculating the collaborative stability of the business cluster after the link breaking processing, and constructing an objective function of an ant colony optimization algorithm based on the collaborative stability; and performing optimization processing on the candidate shops by using an ant colony optimization algorithm to obtain a final shop site selection result. The method has the effect of improving the accuracy and reliability of the shop site selection result.

This invention provides a three-dimensional quantitative analysis method, medium, and system for millimeter-level cracks on infrastructure surfaces, belonging to the field of crack detection technology. The invention identifies crack contours using a minimum cut-maximum flow-based crack-background segmentation algorithm, extracts the crack skeleton centerline using an ant colony optimization algorithm, and performs network connectivity and branch identification of the crack network through a crack topology connectivity optimization model. Cracks are classified according to their direction angle and length characteristics. Inclined laser scanning lines are set in the crack cross-section area to form cross-scan coverage. Weak echo signals are amplified by adaptive gain and supplemented with neighborhood depth interpolation to complete missing depth data. This accurately calculates the cross-sectional crack width and depth, solving the technical problem of inaccurate crack depth measurement due to missing bottom point cloud data caused by laser occlusion in deep and narrow cracks.

The invention provides a wave energy electric field dynamic scheduling method based on a game, and the method comprises the steps: 1, distributing a batch of different types of tasks submitted by a service demander to a service provider in a pre-decision scheduling layer for a local fault problem of a wave energy power generation field; respectively putting the unallocated task set and the corresponding candidate service set into a virtual manufacturing static task pool and a virtual manufacturing service pool; and step 2, constructing a static scheduling game, searching Nash equilibrium of the static scheduling game by using a double ant colony optimization algorithm, and when a local fault occurs in the wave power plant, performing simulation and solving. When a local fault occurs in the wave power plant, the algorithm model provided by the invention can perform efficient simulation and solution.

The application belongs to the technical field of wireless communication, and specifically discloses a node position optimization method and device based on an ant colony optimization algorithm, which comprises the following steps: setting ant colony optimization algorithm parameters, randomly generating an initial solution, and extending to generate a plurality of ants, wherein each ant respectively represents a different three-dimensional node layout vector; repeatedly performing an iteration process until a preset iteration number is reached; taking the three-dimensional node layout scheme corresponding to the optimal solution after iteration as an optimal node layout scheme, and optimizing the node position based on the optimal node layout scheme; the iteration process comprises the following steps: calculating the fitness of all current ants respectively; comparing the fitness of the ant with the lowest fitness with the original optimal solution, and taking the ant with lower fitness as the current optimal solution; calculating the pheromone intensity based on the fitness of all ants respectively to obtain pheromone distribution; updating the ant position based on the pheromone distribution and performing boundary constraint processing on the new ant position.