491 results about "Ant colony" patented technology

The invention discloses a multispectral pollution analysis method and system for a photovoltaic cleaning unmanned aerial vehicle, and belongs to the technical field of spectrum detection.The multispectral pollution analysis method comprises the steps that a multispectral image is obtained, dark current-radiation-atmosphere-shadow full-link preprocessing is carried out, multiband weighted threshold segmentation and morphological post-processing are constructed, and a pollution area is extracted; establishing a self-learning pollution spectral feature library, and identifying pollution types by using cosine similarity; generating a pollution degree distribution diagram in combination with the pollution type-efficiency attenuation mapping table; and when the overall efficiency loss exceeds a preset value, planning a three-dimensional track based on a cluster priority score and an ant colony-genetic fusion algorithm, and returning an image to perform closed-loop verification and complementary cleaning after the unmanned aerial vehicle performs cleaning. According to the invention, the pollution identification precision, the efficiency evaluation accuracy and the cleaning intelligence level are significantly improved, and the method can be widely applied to unattended operation and maintenance of the photovoltaic power station.

The invention discloses a double-layer and double-stage heterogeneous unmanned aerial vehicle task allocation and flight path planning method, and relates to the technical field of unmanned aerial vehicles. The method comprises a dual-stage task allocation method and a dual-stage path planning method. The beneficial effects of the invention are that the dual-stage task allocation method and the dual-stage path planning method are provided for improving the efficiency of the multi-unmanned aerial vehicle cooperative execution of the search rescue task; according to the dual-stage task allocation method, a joint optimization framework combining mixed integer linear programming and an improved ant colony algorithm is provided, so that the task load balance and the total flight distance can be optimized, and a better task allocation effect is realized; the dual-stage path planning method provides a dual-stage path planning scheme in which global task sequence optimization and local obstacle avoidance planning are coordinated, and a better path planning effect is obtained by combining the global and local dual-stage optimization scheme.

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention provides a rounded-corner container transportation path dynamic optimization scheduling method and system, and the method comprises the steps: quantifying three constraints of the size, the gravity center and the loading and unloading priority of a rounded-corner container, converting the three constraints into a matching formula, a path constraint threshold value and a weight rule, and constructing a weighted multi-objective optimization function in combination with the transportation cost, the time and the cargo damage risk; a genetic algorithm and ant colony algorithm mixed framework is built, a constraint adaptation layer is embedded to filter invalid solutions, and the iteration efficiency is improved; two types of algorithm operators are improved, and a constraint satisfaction degree, a loading and unloading priority and a dynamic parameter adjustment mechanism are fused; dividing multiple regions into sub-region optimization by adopting a divide-and-conquer strategy, and adapting to a large-scale dynamic scene through cross-region collaboration and local re-optimization; a full-dimension verification scheduling scheme in iteration is carried out, algorithm parameters are automatically adjusted based on constraint violation information, and iteration is terminated or constraint relaxation is started according to preset conditions; an improved algorithm is integrated to a dynamic scheduling system, real-time data are connected, parameters are optimized through a self-learning module, and a manual intervention interface is reserved.

The invention provides a path planning method and system based on HER-ACO, and belongs to the technical field of robot path planning. The method comprises the following steps: S1, acquiring an environment grid map, wherein the grid map comprises a starting point, a target point and obstacle distribution information; s2, introducing an HER algorithm to improve a traditional ant colony algorithm ACO, and searching a feasible path from a starting point to a target point in the grid map by using the improved ant colony algorithm as an initial planning path; the improvement comprises the following steps: introducing an HER thought and a dynamic pheromone volatilization mechanism; increasing a direction weight factor, a neighborhood obstacle factor and a future obstacle factor; and S3, smoothing the initial planning path based on the feasibility check of the linear path, removing redundant intermediate nodes, and outputting a final planning path. A simulation result shows that the scheme has remarkable advantages in a complex environment, particularly has excellent performance in the aspects of path quality and convergence speed, and is suitable for scenes such as robot navigation and concrete robot path planning.

The invention discloses a temperature self-adaptive adjustment control method and system for an automobile sensor, and relates to the technical field of automobile sensors, and the temperature self-adaptive adjustment control method for the automobile sensor comprises the following steps: S1, obtaining data; s2, performing classification and label addition on the data to form a data set; s3, extracting core temperature characteristic parameters, and constructing a four-dimensional temperature mapping matrix; s4, introducing an ant colony algorithm to iteratively optimize a temperature dynamic weighting rule, and obtaining an adjustment target and a change trend; s5, generating a regulation control strategy, and constructing a temperature fusion regulation feedback model at the same time; s6, performing iterative verification on the model, and generating a sensor three-dimensional verification report; and S7, performing security judgment on the three-dimensional verification report of the sensor. According to the method, through multi-source sensing data acquisition and segmented labeling preprocessing, temperature local mutation features and long-time time sequence dependence features are captured, and temperature data heterogeneity of traditional single-model feature extraction is broken.

The invention is suitable for the technical field of crowd simulation and emergency evacuation, and provides an emotional infection and social force model coupling-based crowd evacuation method, which comprises the following steps of: constructing an emotional infection model based on an SEIRS framework, dividing individual emotional states into a susceptible state, an exposed state, an infected state and a recovery state, the infection rate and the recovery rate are dynamically adjusted through a feedback function; establishing a social force model, and embedding an emotion value into calculation of a target driving force and a repulsive force between individuals to realize real-time adjustment of the motion behavior by the emotion; global path planning and an ant colony pheromone mechanism are fused, a density perception cost function and pheromone guiding force are introduced, and dynamic blockage avoidance is achieved; a bidirectional feedback mechanism is designed, and local path reconstruction is triggered when congestion is detected. According to the method, bidirectional coupling of emotion propagation and behavior decision is realized, the authenticity and adaptability of evacuation simulation are improved, and a scientific basis can be provided for emergency management and evacuation strategy optimization in public places.

The invention provides an intelligent detection method and system for an urban sewer pipeline, and the method comprises the steps: synchronously collecting pipeline data through laser, vision, ultrasound and a pressure sensing array, and generating aligned multi-source data through space-time registration; edge end defect preliminary screening is realized by using a lightweight AI model, and high-risk data is screened and uploaded to a cloud end; a pipeline digital twin model is constructed based on point cloud splicing and feature recognition, a multi-modal fusion neural network is adopted to realize accurate defect diagnosis, and a detection path is dynamically optimized in combination with an ant colony algorithm. According to the method, the technical problems that the adaptability is poor and the omission ratio is high in a complex sewer pipeline environment and multi-source data fusion analysis and active early warning cannot be realized by a traditional method can be solved.

The invention relates to a power transmission line unmanned aerial vehicle airport optimal configuration method. The method comprises the steps of determining an inspection area and unmanned aerial vehicle inspection task requirements; constructing an unmanned aerial vehicle energy consumption model; based on the unmanned aerial vehicle energy consumption model, combining a neural network and a Kalman filtering algorithm to predict the energy consumption of the unmanned aerial vehicle arriving at the tower; an improved ant colony algorithm is adopted, and optimal energy consumption path planning of unmanned aerial vehicle inspection is carried out; based on the planned optimal energy consumption path, the tower distance and the energy consumption prediction of the unmanned aerial vehicle reaching the towers, determining the position and the number of optimal supply points with the goal that the remaining electric quantity of the unmanned aerial vehicle reaching the supply points is not lower than a safety threshold value; in the inspection process, according to real-time energy consumption prediction, a flight path and a supply strategy are dynamically adjusted, and it is ensured that the unmanned aerial vehicle arrives at the nearest supply point for supply before the remaining electric quantity is lower than a safety threshold value. The method can achieve the minimization of the total energy consumption of the inspection and the maximization of the inspection efficiency, and improves the cruising ability in the inspection of the unmanned aerial vehicle through the reasonable configuration of the supply point of the unmanned aerial vehicle airport.

The invention provides a cable path intelligent optimization method fusing a cellular automaton and an ant colony algorithm, and the method comprises the steps: obtaining a three-dimensional boundary range of a target region, constructing a three-dimensional underground space voxel model, and dividing the target region into voxel units containing five-dimensional attribute tensors; constructing and inputting the five-dimensional attribute tensor into a physical constructable potential field model, and generating a corresponding three-dimensional physical constructable potential field; performing construction scoring according to the three-dimensional physical constructable potential field and the five-dimensional attribute tensor; constructing a composite cost function in combination with the three-dimensional physical constructable potential field and the construction score, generating a composite passing cost of each voxel, and generating an optimal cable path in combination with a path search algorithm which takes an improved ant colony algorithm as a control mechanism and introduces a rule of a cellular automaton on a path state propagation structure; and performing three-dimensional geographic mapping, structure reconstruction and geometric fairing on the optimal cable path, and outputting a standardized path result.

The invention discloses an IDC intelligent calculation center energy efficiency optimization system and method based on an intelligent algorithm, and relates to the technical field of computer system resource collaborative management. Comprising a swarm intelligent decision center module, an ant colony optimization LSTM temperature prediction module, an ant colony collaborative task scheduling module, a path optimization cooling control module and a fault energy consumption linkage sensing module. According to the IDC intelligent calculation center energy efficiency optimization system and method based on the intelligent algorithm, a swarm intelligent decision center serves as a core, the ant colony algorithm penetrates through the whole process of temperature prediction, task scheduling and cooling control, the local optimization limitation of a single algorithm in a traditional scheme is broken through, and the optimization efficiency of the IDC intelligent calculation center is improved. According to the method, data of all modules can be integrated through a pheromone transmission mechanism, global strategy overall planning of prediction, scheduling and cooling is achieved, decision deviation caused by data splitting of an independent algorithm is avoided, and integrity and coordination of energy efficiency management are improved from the top design level of computer system resource configuration.

The invention belongs to the technical field of unmanned aerial vehicle path planning. Aiming at the problems of two-dimensional limitation, low search efficiency, high probability of local optimum and the like of a traditional ant colony algorithm during unmanned aerial vehicle path planning, Q-Learning is integrated into the ant colony algorithm based on DEM modeling, a heuristic function and a pheromone strategy are optimized, and the unmanned aerial vehicle path planning method and system suitable for the three-dimensional terrain are provided. And the optimal path is fused and smoothed, so that the efficiency and safety of path planning under the complex terrain are improved, and the method is suitable for mountainous area inspection, agricultural plant protection and other scenes.

The invention discloses an unmanned aerial vehicle battery replacement scheduling system and method based on multi-state battery prediction, particularly relates to the technical field of unmanned aerial vehicle battery management, and is used for solving the problems of insufficient energy of an inspection unmanned aerial vehicle and mismatching of battery replacement decisions. According to the method, multi-source data such as voltage, current, temperature, internal resistance and environment wind speed are collected, key states such as charge, health, power, energy and temperature are extracted, state feature vectors related to task context are formed, and task energy consumption and return energy are estimated based on a prediction model. A scheduling instruction for continuing tasks, going to a charging station or a battery swap station, maintaining charging and cooling fast charging is generated, in a battery swap scene, an improved ant colony algorithm of a battery health factor is introduced to construct a path cost function, distance, energy consumption and time are considered, optimal battery swap station selection and safe path planning are achieved, and the optimal battery swap efficiency is improved. And the energy management precision and the task stability of the unmanned aerial vehicle inspection task are obviously improved.

The invention discloses an unmanned aerial vehicle path planning method based on an intelligent optimization algorithm. The method aims to overcome the defects that a traditional ant colony algorithm is low in convergence speed, prone to falling into local optimum and prone to falling into blind search in the initial stage of the algorithm. The unmanned aerial vehicle path planning method suitable for the complex environment is provided from the characteristics that a fitness function and a heuristic function of an ant colony algorithm can be customized to meet actual requirements, a search strategy can be customized to adapt to a specific environment, and the ant colony algorithm is easily combined with other intelligent optimization algorithms. Based on a search mode of an unmanned aerial vehicle, three-dimensional grid topographic data and a basic unmanned aerial vehicle kinematics constraint and path evaluation principle, after environment modeling and topographic data preparation are carried out on a three-dimensional mountainous area scene based on a grid method, a classical ant colony algorithm is improved; comprising the steps of introducing an improved heuristic function, a self-adaptive step length search strategy based on a visual area and a pheromone updating strategy comprehensively considering path smoothness.

The invention relates to the technical field of scenic spot route planning, and provides a scenic spot global collaborative touring planning method and system, and the method comprises the steps: constructing a scenic spot environment depth model, and deploying an ant colony algorithm on the constructed scenic spot environment depth model through key parameter initialization and tabu table initialization; path selection is carried out by executing ant colony movement, and real-time information updating and ant colony collaborative obstacle avoidance are carried out in the movement process; constructing a pheromone updating rule to update global pheromones, and judging and outputting an optimal path through iterative convergence; and performing multi-dimensional verification on the output optimal path, and generating a personalized touring scheme according to the path passing the verification. According to the invention, collaborative planning and dynamic optimization of the scenic spot sightseeing path are realized, the sightseeing efficiency of tourists is effectively improved, the queuing time is reduced, the information interaction comprehensiveness is enhanced, and the overall operation efficiency and tourist experience of the scenic spot are remarkably improved.

The invention provides a mobile robot path planning method based on an improved ant colony algorithm, and relates to the technical field of robot path planning, and the method is technically characterized in that the method comprises the following steps: carrying out environment modeling by adopting a grid method, and constructing a multi-target performance evaluation function; an improved ant colony algorithm is adopted to carry out optimal path search, path planning is carried out on a grid map from a starting node to a target node, and the improvement of the ant colony algorithm comprises the steps of distributing pheromone concentration by adopting a non-uniform initialization method; improving a state transition rule; differentiating a pheromone updating strategy; and smoothing the path. An initial pheromone distribution optimization strategy is introduced, heuristic information based on a triangular inequality, a steering angle suppression function and a state transition rule of artificial potential field force are combined, a differential pheromone updating strategy based on a multi-target performance evaluation index is adopted, and finally a planned path is smoothed. Therefore, the convergence speed, the path quality and the environment adaptability of the algorithm are remarkably improved.

The invention discloses a three-mode switching type sewage plant biochemical pool inspection robot control system, and belongs to the technical field of robot inspection, the three-mode switching type sewage plant biochemical pool inspection robot control system comprises a composite moving system, an integrated crawler walking module, a buoy module and a propeller module, and the three-mode switching type sewage plant biochemical pool inspection robot control system is used for controlling and switching walking modes of an inspection robot; the mode switching triggering mechanism is used for switching the composite moving system to the crawler walking module, the buoy module or the propeller module according to the water depth; the multi-source heterogeneous sensor monitoring module is used for detecting environmental data in real time through a multi-source heterogeneous sensor; and the path planning module is used for fusing monitoring data of the multi-source heterogeneous sensor, optimizing a global path by adopting an improved ant colony algorithm, and carrying out dynamic obstacle avoidance in combination with a D * Lite algorithm. Switching of multiple propelling modes is supported, and limitation of a single moving mode of a traditional robot is broken through; and meanwhile, monitoring data of heterogeneous sensors are aligned, and dynamic obstacle avoidance is performed by adopting an improved ant colony algorithm in combination with a D * Lite algorithm, so that the accuracy of path planning is improved.

The invention provides an NoC low-delay data transmission method based on a dynamic routing algorithm, relates to the technical field of data transmission, and aims to solve the technical problems that an existing NoC routing algorithm cannot adapt to a link dynamic load, is lack of congestion trend prejudgment, is slow in path search convergence and is insufficient in QoS differential scheduling. The method comprises the following steps: constructing a dynamic sensing module containing a double-branch LSTM time sequence prediction model, collecting states such as link bandwidth and queue length in real time, and predicting a congestion trend in 50-100ms in the future; constructing a multi-objective evaluation function taking delay as a core, and dynamically adjusting the weight according to the QoS level; solving an optimal path by adopting an improved ant colony algorithm which introduces a congestion penalty and pruning strategy; during transmission, dynamic reselection is triggered through a pre-calculation + fast matching mechanism; and iteratively optimizing the prediction model based on incremental learning. According to the method, congestion is avoided in advance, the path search efficiency and the transmission reliability are improved, the delay is remarkably reduced compared with a traditional algorithm, and the method is adaptive to delay sensitive scenes such as a high-performance processor and an AI chip.

The invention relates to the technical field of intelligent power grids, in particular to an intelligent generation method and system for a substation switching operation ticket, and the method comprises the steps: constructing a weighted topological graph with switching equipment as nodes and electrical connection as edges according to a power line of a substation; assigning weights to the edges of the weighted topological graph; wherein the weight of the edge at least comprises direct operation cost and power grid disturbance cost; receiving a switching operation target task, and determining a search starting point and an end point in the weighted topological graph based on the target task; performing path search in the weighted topological graph by adopting an improved ant colony algorithm determined on the basis of a weight and an adaptive volatility coefficient based on the search starting point and the search ending point to obtain an optimal path; and converting the optimal path into a switching operation switch action sequence, and generating a switching operation ticket. Through the improved ant colony algorithm, the method comprehensively considers the number of operation steps and the impact influence of power grid operation, and facilitates the determination of a global comprehensive optimal path.

The invention discloses a substation inspection method and equipment based on an inspection robot and a storage medium, and the method comprises the steps: collecting environment data through an environment sensing module after the robot is started, carrying out the fusion processing through a positioning and calculation module, and constructing a substation high-precision semantic map containing the equipment position and obstacle information; based on the semantic map and a preset inspection task, adopting an improved ant colony-deep reinforcement learning algorithm to plan an optimal path covering all inspection target points, and supporting dynamic optimization according to environmental changes; according to the planned path, high-precision navigation is realized through a multi-source fusion positioning scheme, the mobile execution module is controlled to move to each inspection point, and a data acquisition task is executed; in the cruising process, front obstacles are sensed in real time, and a layered obstacle avoidance strategy is executed according to the types and danger levels of the obstacles; and when all inspection tasks are completed or the electric quantity is lower than a threshold value, automatically planning a path and returning to the charging base station for charging.

The invention relates to the technical field of path planning, in particular to a path planning method and device for a construction site, computer equipment and a storage medium, and the path planning method comprises the steps: constructing a static three-dimensional model according to a building model and topographic data of the construction site; acquiring obstacle data in the construction site in real time, and mapping the obstacle data into the static three-dimensional model to obtain a dynamic three-dimensional model; establishing a path search graph model according to the dynamic three-dimensional model; performing path planning through the path search graph model and a preset ant colony algorithm; in the path planning process, the probability of selecting the next path is determined by the pheromone concentration, the heuristic factor and the straight line guide factor at the same time. According to the embodiment, the transportation path can be dynamically adjusted according to the construction environment, so that the construction site transportation efficiency is improved.

The invention relates to the technical field of pipeline layout, in particular to a single-pipeline layout method based on an intelligent optimization hybrid algorithm. On the basis of a pipeline layout mathematical model, oil and gas treatment system pipeline layout engineering constraint conditions are considered, and an intelligent optimization hybrid algorithm fusing an A * algorithm and an artificial bee colony is provided for the defects that an ant colony algorithm is low in convergence speed, premature and prone to falling into local optimum. The ant colony is divided into four kinds of ant types of different working mechanisms: A * ant, leading ant, following ant and investigation ant, the A * ant improves the ant colony search speed, the leading ant and the following ant improve the ant colony search breadth, and the investigation ant further enhances the ant colony search depth.

The invention discloses an ant colony optimization energy focusing cable insulation damage positioning method and device, and relates to the technical field of cable detection engineering, and the method comprises the steps: generating a Gaussian envelope linear frequency modulation chirp detection signal, injecting the detection signal into a to-be-detected cable, and collecting an incident signal and an echo signal containing a reflection component; constructing a parameterized time-frequency analysis kernel function family under a Cohen type time-frequency distribution framework, and searching and optimizing kernel parameters by using an ant colony optimization algorithm; and calculating the time frequency distribution and energy-time curve of the signal based on the energy focusing time frequency analysis kernel function obtained through optimization, extracting the arrival time corresponding to an energy peak value, and realizing cable insulation damage positioning according to the reflection time delay and the propagation wave velocity. According to the method, energy peak focusing and peak position stability are enhanced while cross terms are restrained, defect positioning precision and robustness are remarkably improved, and the method is suitable for engineering online detection and automatic implementation of cable insulation damage.

The invention provides an unmanned aerial vehicle cluster weapon target collaborative allocation method and system under space-time constraint, and the method comprises the steps: defining a collaborative allocation object, constructing a collaborative combat scene target function, defining a collaborative combat scene constraint condition, and finally solving the target function through employing a multi-gene population parallel ant colony algorithm, encoding the weapon set, the target set and the unmanned aerial vehicle set into a weapon gene sequence, a target gene sequence and an unmanned aerial vehicle gene sequence respectively to generate an initial population, and performing staged joint optimization by fusing a pheromone guiding mechanism of an ant colony algorithm and crossover mutation operation of a genetic algorithm, the optimal cooperative allocation scheme is searched when the constraint condition is satisfied, the unmanned aerial vehicle task allocation result can be better obtained and optimized through the cooperative allocation mode, and the unmanned aerial vehicle cooperative allocation efficiency is improved.

The invention discloses a power distribution network fault positioning method and device based on a quantum ant colony and a PSO algorithm, and belongs to the technical field of power system fault positioning. The method comprises the steps of obtaining topological structure data and real-time operation data of a power distribution network, and performing preprocessing; parameters of the quantum ant colony algorithm are initialized, path selection is carried out based on the quantum bit state transition probability, global search is carried out on the whole power distribution network, and a suspected fault area is obtained; and taking the suspected fault region obtained by the quantum ant colony algorithm as a search space of a PSO algorithm, initializing PSO algorithm parameters, performing local fine search through particle position and speed updating by using the PSO algorithm, and determining a fault position. Through organic fusion and cooperative work of the two algorithms, the global search capability of the quantum ant colony algorithm and the local search advantage of the PSO algorithm are fully played, the accuracy and efficiency of power distribution network fault positioning are effectively improved, and the adaptability of the algorithms to a complex topological structure is enhanced.

The invention provides a material collaborative distribution method in an electric forklift assembly process, and relates to the technical field of intelligent logistics and production management, and the method comprises the steps: obtaining the material demand prediction data of each station through employing a time-phased material consumption prediction model based on the material demand information of an electric forklift assembly station; and in combination with the material storage position information, a cooperative distribution path of multiple distribution vehicles is calculated by adopting an improved ant colony algorithm, the cooperative distribution path is decomposed according to the material distribution emergency degree, a material distribution task sequence is generated, and distribution task adjustment is performed in a task interaction area by adopting a bidirectional matching algorithm. According to the method, distribution path planning and task dynamic adjustment are optimized, and task interaction area resources are fully utilized, so that the problems of insufficient prediction precision, poor path optimization effect and low distribution resource utilization rate in the prior art are effectively solved.

The invention discloses a multi-logistics vehicle collaborative scheduling method based on a guided customer selection ant colony system, and belongs to the technical field of computing intelligence. According to the method, a feasible solution scheme is constructed for a multi-logistics-vehicle cooperative scheduling optimization problem by maintaining ant teams with the same quantity as the logistics vehicles, wherein each ant is responsible for constructing a service path of one logistics vehicle. After each ant randomly selects the initial customer of the respective service path, selecting the customer which is finally served for each ant based on a final customer selection method of the minimum service cost; then, the ant colony system fuses the current customer information and the customer selection mechanism of the customer information of the final service, and constructs the service path of the logistics vehicle path by path and customer by customer, so that the constructed path is more directional; and finally, in combination with 2-opt and a point insertion local search strategy, optimizing service paths of all logistics vehicles. According to the method, the optimal multi-logistics-vehicle cooperative scheduling scheme is constructed, and meanwhile, the service cost between the logistics vehicles can be well balanced.

The invention relates to a dynamic self-adaptive obstacle avoidance path planning method for a hoisting operation area, and belongs to the technical field of path planning in hoisting operation. The method comprises the following steps: scanning point cloud data by using three-dimensional laser, and establishing a static three-dimensional working environment model with a known environment; dividing a space by using a grid method, and dispersing a continuous space into a limited three-dimensional point set; setting a safe distance d; redefining a heuristic function eta; defining a volatilization coefficient; constructing a self-adaptive adjustment function considering the path length, the hoisting height and the safety factor; and updating pheromones, carrying out iteration, stopping iteration after an optimal effect is achieved, and obtaining a three-dimensional path. The method is improved on the basis of the ant colony algorithm, and efficient and safe path planning of the hoisting equipment in a complex environment is realized by combining strategies such as environment precise modeling, dynamic visual area definition, three-dimensional space rasterization processing and algorithm core optimization.

The invention relates to the field of data processing, in particular to an automatic optimization method for a laser drilling path of a circuit board, and introduces a dual-pheromone ant colony algorithm to overcome the defect that a traditional algorithm only pursues a shortest path but neglects a heat accumulation effect. Two independent pheromones of path length and heat distribution are constructed, a hole site neighborhood heat influence factor and a path global heat entropy are defined to quantify the heat dissipation performance, and an algorithm is guided to consider the distance and heat during path selection, so that the processing efficiency and heat control are effectively balanced, the algorithm is prevented from falling into a suboptimal solution due to local heat concentration, and the processing efficiency is improved. And finally, a comprehensive optimal path considering the path length and the heat dissipation effect is found. According to the method, the path length pheromone and the heat distribution pheromone are constructed and independently updated by adopting the dual-pheromone ant colony algorithm, so that the drilling path length and heat control are balanced, and the optimal solution is found.

The invention provides a starting time determination method of a substrate management controller and electronic equipment, which can be applied to the technical field of servers. The method comprises the following steps: determining respective target starting time intervals of a plurality of substrate management controllers by using an ant colony algorithm according to current ant colony parameters, and obtaining a plurality of target starting time intervals; periodically updating the ant colony parameters of each ant colony, and using the updated ant colony parameters for determining the target starting time interval next time; the step of periodically updating the ant colony parameters of each ant colony comprises the sub-steps of: according to respective interval duration of a plurality of target starting time intervals and the moment when a corresponding server reaches a maximum power value in the target starting time intervals, updating the ant colony parameters of each ant colony; the baseboard management controller determines fitness information corresponding to the maximum instantaneous power of the server in the starting process of the target starting time interval; and taking the fitness information as an evaluation standard of parameter optimization, and optimizing the current ant colony parameters of any ant colony based on a particle swarm algorithm to obtain updated ant colony parameters.