104 results about "Heuristic search algorithm" patented technology

The invention discloses a V2X-based global path planning method for a mine unmanned driving cloud control platform, and relates to unmanned driving. The V2X-based global path planning method comprises the following steps: constructing a traffic semantic map data structure # imgabs0 #; according to task issuing or operation plan adjustment, generating path request data R, and performing time constraint, resource constraint and path optimality constraint verification on the path request data R; according to the R and # imgabs1 #, adopting a heuristic search algorithm based on a graph theory to carry out optimal path search on the road topological structure, and generating a global path P containing a node sequence and driving parameters; and acquiring obstacle data detected by the vehicle end through a local sensor, performing obstacle avoidance correction through a local path optimization algorithm according to the obstacle data and the global path P, and generating a local path meeting the safety distance constraint and the path deviation constraint. According to the method, on the premise of meeting multi-dimensional coupling constraints such as time-space, priority-resource, safety-efficiency and the like, the optimal driving path dynamically adapting to the complex environment of the well industry and mining is generated.

The invention provides a spatial heterogeneous vision positioning system and positioning method of a pineapple picking robot, relates to the technical field of picking robot positioning, and realizes high-precision spatial positioning and attitude estimation of the robot in a complex terrain environment by comprehensively utilizing multi-source information such as vision, inertial measurement, multispectrum and structured light. The positioning stability and the real-time performance are obviously improved; by embedding a multi-modal deep learning model, efficient non-destructive detection and intelligent classification of internal defects of pineapple fruits are realized, a mechanical arm is supported to dynamically adjust a picking strategy, fruits with better quality are preferentially selected for picking, and the picking quality and efficiency are improved; and meanwhile, the preset three-dimensional terrain model and the heuristic search algorithm are combined, the motion path of the robot is effectively planned, it is guaranteed that the robot safely and stably moves in the complex environment, and the mechanical damage risk is reduced.

The invention discloses an unmanned aerial vehicle cooperative inspection path planning and airport layout method and system for expressway multi-facility maintenance. According to the method, a multi-dimensional environment characterization system is constructed by collecting highway multi-source data, a comprehensive optimization model with the coverage range, the response time and the total cost as targets is established, an airport layout scheme is generated by adopting an adjustable service radius strategy and a heuristic search algorithm, and an optimal equilibrium solution set is extracted through multi-target sorting and screening. The task priority is determined based on the facility type and maintenance urgency, a multi-unmanned aerial vehicle cooperative route planning model is established, and a conflict-free circular inspection path is planned. And performing online rolling replanning by adopting a prediction control strategy, and integrating inspection data feedback to realize closed-loop self-adaptive optimization of the system. According to the invention, collaborative optimization of the airport layout and the inspection path is realized, the inspection efficiency is improved, the operation cost is reduced, and the adaptive ability of the system to deal with a dynamic environment is enhanced.

The invention provides a service scheme generation method and system, and relates to the technical field of computer application, and the method comprises the steps: obtaining user demand information which comprises a service type, a service scene, a resource constraint condition and an expected effect description; semantic analysis is performed on the user demand information based on the comprehensive domain knowledge graph, core service elements are extracted, and a user demand model is constructed; according to the user demand model, candidate service resources are matched in a service resource library through a heuristic search algorithm, and candidate service schemes are generated; a multi-objective optimization algorithm is adopted, real-time resource state data and user feedback information are combined, the candidate service schemes are dynamically adjusted, and the final service scheme is output, so that user requirements can be accurately analyzed, service resources are efficiently matched, the generated service scheme is optimized, knowledge and resource iteration is realized, and the scheme generation efficiency and quality are improved.

The invention belongs to the field of power distribution network operation control, and discloses a power distribution network global optimization regulation and control method, system and device and a medium, and the method comprises the steps: obtaining multi-dimensional indexes including regional modularity, regional reactive power balance degree and regional active power balance degree in a power distribution network; based on a comprehensive evaluation mechanism of a multi-dimensional index, an optimal power distribution network region division result is obtained by utilizing iterative calculation of a heuristic search algorithm; a power distribution network global optimization model with cost optimization as a target is constructed so as to realize control of the comprehensive cost of the power distribution network; on the basis of a distributed optimization algorithm, a global optimization problem of the power distribution network is decomposed into a cloud main problem and a side terminal problem, and global optimality of resource allocation among different power distribution network regions is realized by solving the cloud main problem; and the optimal control strategy in the power distribution network area is realized by using the control result of the comprehensive cost of the power distribution network and the solution of the edge terminal problem. And the coordination control of the global massive controllable resources of the power distribution network is realized.

The invention discloses an intelligent advertisement resource allocation method based on dynamic planning and heuristic search, and relates to the technical field of advertisements, and the method comprises the steps: S1, obtaining historical flow data and predefined time node data, S2, generating an initial advertisement resource scheduling plan by using a dynamic planning algorithm, and S3, generating an advertisement resource scheduling plan based on a real-time flow trend and a time node. S4, using a heuristic search algorithm to optimize the corrected resource scheduling plan, and relieving the peak load pressure; the intelligent advertisement resource allocation method based on dynamic planning and heuristic search has good expandability and actual landing performance, and the resource scheduling plan is corrected according to the time node and the flow trend to solve the problem of overlarge peak load pressure.

The invention discloses a safe evacuation path planning method under a strong wind condition of island engineering. According to the technical scheme, the method is characterized by comprising the steps that firstly, the physical space of an engineering site is discretized into a plurality of grid units, and a computable graph structure is constructed; then, acquiring multi-source dangerous area information generated in real time, and quantifying the dangerous area information into a dynamic risk value of each grid unit; then, acquiring the real-time position of the constructor as a starting point, and presetting an emergency exit as an ending point; and finally, on the constructed risk grid map, a heuristic search algorithm such as A * is adopted, and the geometric distance and the dynamic risk value between grid units are fused by a cost function of the heuristic search algorithm, so that an optimal evacuation path considering safety and efficiency is quickly searched. According to the method, the safety risk is quantified and fused into the path optimization process, so that the quick response to the dynamic environment and the real-time re-planning of the path are realized, and the efficiency and safety of personnel evacuation under the emergency condition are remarkably improved.

The invention is suitable for the technical field of road construction, and provides a wind power plant road intelligent line selection method based on multi-dimensional constraint dynamic modeling, and the method comprises the steps: obtaining a multi-dimensional constraint condition of wind power plant road line selection, and constructing a constraint model; generating a slope grid map based on a digital elevation model, and fusing the slope grid map with a constraint map layer reflecting geological stability and a land cover type to generate a dynamic cost surface; adopting an improved heuristic search algorithm to perform parallel path search from the road starting point to each target fan position, generating a plurality of initial feasible paths, and calculating a comprehensive cost value; screening a candidate line set according to a comparison result of the comprehensive cost values; receiving real-time updating data in a construction process and environment monitoring, inputting the real-time updating data into the constraint model to trigger model updating, performing iterative optimization on the candidate line set based on the updated constraint model, and outputting a final line selection scheme; and the line selection efficiency, economy and reliability are effectively improved.

The invention relates to the technical field of intelligent security and protection, and discloses an intelligent security and protection alarm method and system based on an intelligent park. The method comprises the following steps: acquiring original security and protection data by using sensing equipment arranged in a smart park; performing standardization and redundancy elimination processing on the original security and protection data, dividing safety areas of the park through a pattern recognition algorithm, calculating a load intensity index of each area, and generating a security and protection situation summary; according to the security situation summary, a heuristic search algorithm is adopted to determine alarm trigger parameter initial values including detection sensitivity and alarm intervals, and a basic alarm parameter group is generated; performing nonlinear correction on the basic alarm parameter group, and outputting an environment perception alarm parameter group; training the historical security data set by adopting a neural network model, and analyzing the interaction between the load and the threat to obtain a threat diffusion model; and comparing the threat diffusion model with the real-time security data points, performing difference measurement, reconstructing alarm trigger logic according to a measurement result, and completing autonomous regulation and control of security and protection of the smart park.

The invention provides a fracture-vug reservoir inter-well three-dimensional communication path multi-target search method and system, and the method comprises the steps: building a three-dimensional space model of a fracture-vug reservoir based on seismic multi-attribute data, mapping a carbonate reservoir to a three-dimensional space, and carrying out the grid division; according to the coherent amplitude data and the well distribution condition of each well pair in the search space, adaptively optimizing a coherent amplitude threshold and a seismic attribute fusion weight by adopting a multi-objective optimization algorithm by taking the communication length and the volume as optimization objectives, and searching to obtain grouped inter-well communication paths meeting set requirements corresponding to the current well pair; according to the method, an evaluation function improved heuristic search algorithm is designed to search a single path in a search space, and a self-adaptive crossover and mutation probability improved multi-objective algorithm is proposed to optimize threshold parameters to obtain a plurality of inter-well communication paths conforming to a real seismic structure, so that the problems of limited path analysis and poor reliability in the prior art can be solved; and efficient and comprehensive search of inter-well communication paths is realized.

The invention discloses a multi-constraint path planning method and device based on intelligent heuristic search, and is suitable for a mobile robot in a complex and narrow environment. According to the method, a multi-source sensor is used for environment perception and fusion mapping, and a grid map is generated; a neural heuristic search algorithm based on a Mama structure and a selective state space model (Selective SSM) is adopted on the cost map, and a guide graph is generated; a continuous and collision-free initial path is generated through self-adaptive step size control in combination with robot kinematics constraints and channel characteristics; further establishing a multi-target constraint model based on a robot center Euclidean symbol distance field (RC-ESDF), and executing path smoothing and safety optimization by using a priori-guided particle swarm optimization algorithm (Prior-guided PSO) to obtain an optimal path meeting dynamic consistency; the system corrects the path in real time through a closed-loop feedback mechanism and triggers dynamic re-planning, so that the path feasibility, safety and real-time adaptive capacity of the robot in a limited environment are remarkably improved.

The invention discloses a carbon flow optimization method and device for edge calculation under vehicle-road cooperation and a medium, and relates to the technical field of traffic carbon emission. The method comprises the steps that a real-time state parameter sequence and environment information of a vehicle are acquired, and at least two candidate driving strategies are generated through a built-in carbon emission digital twinborn model; according to the candidate driving strategies, the corresponding expected carbon emission amount is calculated by inquiring the emission factor mapping table of the specific power of the vehicle and integrating; packaging the candidate driving strategy and the corresponding expected carbon emission into a bidding packet, and sending the bidding packet to an edge computing node on the roadside; based on the received bidding package, carbon right reverse auction is initiated, a combined optimization problem is solved by adopting a heuristic search algorithm, and a bid winning strategy set is determined; and generating a cooperative driving strategy according to the bid winning strategy set, and issuing the cooperative driving strategy to the corresponding vehicle end node. By means of the method, information distortion is restrained, and direct and real-time optimization control over regional total carbon emission is achieved.

The invention discloses a load power balance regulation and control method and device based on knowledge graph and random sequence optimization, a medium and a product, and relates to the field of power system dispatching, and the method comprises the steps: constructing a power grid topology knowledge graph according to power grid physical equipment and corresponding topology connection and operation constraints; performing probability distribution fitting on the power output of the renewable energy according to the output data to obtain the output of the renewable energy; obtaining a plurality of typical output scenes; taking the state of the power grid physical equipment after fault isolation as an initial state, and constructing a random Markov decision process; searching in a state space by using an improved heuristic search algorithm by taking an initial state as a starting point, minimizing an accumulated cost function as a target, taking a hard constraint as a constraint condition and taking a topological distance as a heuristic function to obtain an optimal operation sequence; verifying the optimal operation sequence to obtain a final operation sequence; the power of the power grid is regulated and controlled; according to the invention, the fault response speed can be improved, and rapid adaptive decision in a complex scene can be realized.

The invention relates to a diffusion model reasoning acceleration method based on feature caching and distributed parallelism, and the method comprises the steps: exploring a caching method through a heuristic search algorithm, and confirming features corresponding to time steps needing to be cached; and performing distributed reasoning according to the caching scheme. The efficiency problem of an existing diffusion model in an image generation task in a high-resolution image generation scene is solved; the reasoning process of the diffusion model is efficiently processed based on the feature cache and the distributed reasoning architecture, and the resource utilization rate and the reasoning efficiency are improved.

The invention discloses an unmanned aerial vehicle route planning method, device and equipment and a medium, and the method comprises the steps: obtaining the real-time transient wind field data of an unmanned aerial vehicle, and generating the flow field information of time-space correlation according to the real-time transient wind field data; a flight energy consumption model is established based on the flow field information, the flight energy consumption model is adopted to calculate energy consumption in different stages, node energy consumption attribute values are obtained, and the different stages comprise a level flight stage, a climbing stage and a descending stage; and the node energy consumption attribute value is input into the heuristic search algorithm model to obtain the current path planning with the lowest energy consumption, so that the accuracy of the route planning is improved, and the route energy consumption is saved.

The invention relates to the technical field of laboratory air ventilation, in particular to a ventilation cabinet airflow dynamic response optimization method based on digital twinning. The specific implementation process comprises the following steps: collecting a state vector sequence to predict a window motion track, identifying an operation intention and generating a virtual boundary condition; constructing a fluid mechanics digital twinborn model to simulate airflow dynamic characteristics, calculating and outputting airflow prediction data including a fluid resistance coefficient and turbulence intensity distribution, and generating an airflow optimization instruction for minimizing surface wind speed fluctuation by using a heuristic search algorithm; and monitoring a virtual-real deviation value, triggering a dynamic correction mechanism when a drift interval is exceeded, and carrying out online transfer learning on model parameters by utilizing the updated state vector sequence. According to the method, the risks of response lag and airflow oscillation are reduced through window trajectory prediction and digital twinborn simulation, meanwhile, errors caused by equipment aging are corrected through an online transfer learning mechanism, and the use precision and safety of long-term operation of the laboratory fume hood are ensured.

The invention provides a path planning method, device and equipment and a readable storage medium, and the method is applied to a mobile device, and comprises the steps: obtaining a target map, dividing the target map into a plurality of sub-regions, and enabling the plurality of sub-regions to comprise a starting point region and an ending point region; determining an access point in the starting point area, and determining an outgoing point in the ending point area; a hybrid obstacle avoidance algorithm is utilized to determine an access path between the starting point and the access point and determine an outgoing path between the outgoing point and the terminal point, and the hybrid obstacle avoidance algorithm is a path planning algorithm combining a heuristic search algorithm and vehicle dynamics constraints; determining a main road path between the access point and the outgoing point by using a shortest path search algorithm; and determining a target path of the movable device based on the access path, the main road path and the access-out path. The driving requirements of the movable device can be met while the driving path is optimized.

The invention discloses a heuristic log board cutting optimization method driven by three-dimensional reconstruction, and relates to the field of wood processing and manufacturing. The invention provides a heuristic log board cutting optimization method based on a three-dimensional point cloud reconstruction model. The method specifically comprises the following steps: accurately reconstructing a log three-dimensional shape by using point cloud data acquired by a sensor, and converting the log three-dimensional shape into a two-dimensional plane projection by adopting dimension reduction processing; further, a heuristic search algorithm is constructed based on a graph theory to carry out contour extraction, so that the adverse effect of the complex geometric shape of the log on the outturn percentage is eliminated; on the basis, a simulated annealing algorithm improved on the basis of a skyline constraint rule is provided, rapid optimization of a cutting scheme is achieved through an intelligent search strategy, the calculation speed is greatly increased while the solving quality is guaranteed, and the aging requirement of large-scale production is practically met.

The invention discloses an unmanned aerial vehicle path planning method based on comprehensive risk cost, which can generate a low-risk unmanned aerial vehicle flight path. The method comprises the steps that firstly, an unmanned aerial vehicle path planning model containing comprehensive risks and operation cost is constructed, the model comprises a target function and constraint conditions, and the target function is composed of airspace risk cost and unmanned aerial vehicle transportation cost; then, setting three-dimensional constraint conditions including a voyage constraint, a turning angle constraint and a maximum takeoff weight constraint, and constructing a feasible solution space conforming to physical characteristics of the unmanned aerial vehicle; then, performing initial path planning based on a heuristic search weight-A * algorithm with weight, and generating an initial feasible path meeting a constraint condition by dynamically adjusting a heuristic function weight balance path length and risk cost; and finally, based on a parallelogram rule, optimizing the turning point optimization of the initial planning path. According to the invention, a path planning solution with safety and economical efficiency is provided for unmanned aerial vehicle transportation in a complex low-altitude environment.

The invention provides a global path planning method and system for a wheel-foot robot in multiple motion modes, and relates to the technical field of robot path planning, and the method comprises the steps: obtaining multi-source data of the wheel-foot robot; calculating actual motion cost values of the wheel-foot robot in various motion modes in different terrains; constructing a deep learning network model; outputting a motion cost prediction value through a deep learning network model; constructing a loss function, and optimizing the deep learning network model by taking the minimum function value of the loss function as a target; sampling the global map through a path planning algorithm to generate an undirected graph; through the optimized deep learning network model, motion cost prediction values of all sampling points in the undirected graph in different motion modes are output; determining a plurality of middle sampling points through a heuristic search algorithm; and connecting the initial sampling point, each middle sampling point and the target sampling point in sequence to obtain a global path of the wheel-foot robot in various motion modes.

The invention discloses a multi-sensor fused intelligent AGV path planning method and system, and the method comprises the steps: enabling an AGV to carry a 360-degree laser radar sensor, a binocular vision sensor, an infrared thermal imaging sensor, an ultrasonic array sensor and other sensors, processing data through a low-rank multi-source feature fusion module LMF, and constructing a high-precision two-dimensional grid map through a VF-Cart algorithm; the global path planning uses an improved MRRT * algorithm, and the algorithm combines the dynamic data processing capability of a Markov decision process MDP and the efficient search capability of RRT * to ensure the path optimality; local path optimization adopts an FPA * algorithm fusing an artificial potential field method APF and a dynamic heuristic search algorithm DHPA *, local optimum is avoided, and intelligent obstacle avoidance is realized in combination with deep reinforcement learning DRL; according to the method, centimeter-level obstacle detection and dynamic pedestrian recognition are realized in a complex dynamic environment, the path planning efficiency and the motion stability of the AGV are remarkably improved, and the scheduling time is shortened.

The invention relates to a real-time obstacle avoidance and path optimization method and system of a distributed inertial navigation radar, and belongs to the technical field of robot navigation and control. The method comprises the following steps: acquiring current inertial sensing data, image data and point cloud data of a laser radar, calculating current advancing path information, and generating an environment path data set; based on the environment path data set, correcting an accumulated error of inertial navigation, and constructing an obstacle avoidance path model of the inertial navigation radar; environment information required by obstacle avoidance and path planning is loaded to a dynamic environment prediction model, a real-time obstacle avoidance path is constructed through a dynamic grid map, an obstacle movement track is predicted, and an obstacle avoidance strategy is generated; based on a preset navigation task target, a heuristic search algorithm and a dynamic planning algorithm are adopted to optimize a global path, path parameters are adjusted through distributed calculation to generate a control instruction, and parameters of the control instruction are adjusted through real-time feedback. And real-time obstacle avoidance and path planning of the unmanned equipment in a complex environment are realized.

The invention discloses an intelligent design method for a dual-frequency wide-angle high-transmission metasurface, which comprises the following steps of: performing layered sampling on geometric structure parameters of a metasurface unit, performing equal-interval sampling on incident angles, performing full-wave electromagnetic simulation on a parameter sample group to obtain transmission electromagnetic response data, and constructing a database; a multi-layer perceptron neural network is used for learning a nonlinear mapping relation among geometric structure parameters, incident angles, frequencies and transmission coefficient amplitude values, and an independent test sample is used for evaluating the prediction precision and generalization ability of the trained multi-layer perceptron neural network under wide angles and dual frequency bands. A substitution model is constructed based on the trained multi-layer perceptron neural network, the substitution model is embedded into a global optimization process, structural parameters are iteratively optimized in combination with a heuristic search algorithm, an optimal design meeting a dual-band high-transmittance index is obtained, the precision and efficiency of metasurface design are remarkably improved, and the performance of the metasurface design is improved. While the wide-angle double-frequency high transmission performance is ensured, the design period is greatly shortened.

The invention discloses an unmanned aerial vehicle highway combined transport integrated scheduling method, and relates to the field of cargo transportation, and the method comprises the steps: screening out an initial cargo distribution strategy according to a comparison result of cargo attributes and transportation tool performance parameters, and obtaining an optimized distribution scheme containing unmanned aerial vehicle transportation and highway transportation in combination with a heuristic search algorithm; generating a route for transportation of the unmanned aerial vehicle by using the initial flight path of the unmanned aerial vehicle in combination with a heuristic search algorithm and the flight factors of the unmanned aerial vehicle; generating a route for road transportation by using a basic path algorithm and combining a multi-objective optimization strategy and road transportation factors; according to the route for the unmanned aerial vehicle transportation and the route for the road transportation, determining a conversion node of the unmanned aerial vehicle and the road transportation; and establishing an intermodal node management mechanism at the conversion node. According to the invention, resource configuration can be optimized, transportation efficiency can be improved, transportation cost can be reduced, and real-time sharing and accurate monitoring of information can be realized.

This invention discloses a dynamic obstacle avoidance method for assistive visually impaired scenarios. First, a feature extraction module based on a hierarchical residual structure constructed within residual blocks is built, which expands the receptive field of deep features. Second, a spatial feature recovery module based on bilinear interpolation and transposed convolution upsampling is designed to make segmentation edges more accurate. Third, a discrete sampling strategy is used to extract obstacle category, distance, and contour information, and path planning is performed using a heuristic search algorithm considering safe distance constraints. Finally, priority rules and logical order rules for the obstacle avoidance warning system are assumed, and a decision is generated based on the planned path, effectively guiding blind users to avoid obstacles through auditory and tactile information. This invention solves the problem that single-stage instance segmentation algorithms struggle to simultaneously and accurately segment small target obstacles and background road surfaces, overcoming the difficulty of providing effective information for obstacle avoidance decisions in assistive visually impaired scenarios with instance segmentation results, enabling blind users to avoid obstacles more intelligently and autonomously.

The invention discloses a spiral ship unloader work order processing method and system based on reinforcement learning, and the method comprises the steps: receiving an external work order of a spiral ship unloader, and generating an executable task instruction; real-time environment information of the working environment of the spiral ship unloader is collected; based on a reinforcement learning model of an Actor-Critic architecture, generating a job strategy according to the task instruction and the real-time environment information; and according to the operation strategy, a heuristic search algorithm is adopted, path optimization, obstacle avoidance and efficiency optimization are taken as priorities, an operation path and an action sequence of the screw ship unloader are planned, and the screw ship unloader is controlled to act according to the operation path and the action sequence. Through combination of reinforcement learning intelligent decision and heuristic search path planning, multi-target collaborative optimization of the whole operation process of the unattended screw ship unloader is realized, and the operation efficiency, the safety and the adaptive capacity are remarkably improved.

This invention discloses an energy-saving color matching method and apparatus for electronic maps based on semantic relationships. The method includes: spatializing the colors of the original map; modeling the map colors as discrete point clouds in the CIELab color space according to perceptual uniformity rules; analyzing the discrete point clouds using a clustering algorithm based on color difference rules to extract semantic relationships between map colors, including differences, associations, and orders; establishing a color semantic relationship evaluation model based on the extracted semantic relationships; reorganizing the colors in the RGB color space and establishing an energy consumption estimation model based on the red, green, and blue component values ​​of each color; establishing the map energy-saving problem as a multi-objective, multi-constraint optimization problem; using a heuristic search algorithm to find energy-saving colors; and obtaining the optimal energy-saving color matching scheme by analyzing the Pareto front solution set. This invention is applicable to vector and raster electronic maps of different scales and themes, automatically extracting semantic relationships to reduce map energy consumption.

The application discloses a large-size photovoltaic module hoisting path planning model construction method, and belongs to the technical field of photovoltaic module installation. The method comprises the following steps: acquiring high-precision three-dimensional point cloud data of a complex mountainous terrain, physical parameters of a large-size photovoltaic module and kinematic constraint parameters of a hoisting device; constructing a digital elevation model and superimposing the physical parameters of the module to generate a terrain passability cost map; defining a comprehensive optimization target comprising a total path duration, hoisting energy consumption, module attitude stability and spatial collision risk on the passability cost map; generating an initial collision-free path by using an improved heuristic search algorithm; establishing a multi-objective Pareto optimization model of the initial path, and jointly iteratively optimizing a spatial trajectory and a module attitude sequence in combination with the kinematic constraint parameters of the hoisting device to output an optimal hoisting path planning model.

The invention discloses a multi-source data fusion intelligent path planning method and system based on a machine learning model, and belongs to the technical field of intelligent path planning, and the method comprises the steps: data collection and processing: collecting multi-dimensional data, and carrying out data cleaning, denoising, format unification and feature extraction and selection; model training and optimization: based on the processed data and features, constructing a machine learning model to predict the road condition of an unknown area and perform training of obstacle distribution, and learning historical path planning data by using a machine learning algorithm to optimize a path planning strategy; path planning and implementation: generating a preliminary path by combining a heuristic search algorithm and a global optimization algorithm, monitoring environment change in real time, and adjusting path planning in real time according to the environment change, including re-calculating the path and selecting an alternative path; and performing feedback and optimization. The intelligent level of path planning can be remarkably improved, and the accuracy and practicability of path planning are improved.

The embodiment of the invention discloses a path generation method, device and equipment, a computer readable medium and a program product. A specific embodiment of the method comprises the steps of obtaining a driving starting point and a driving ending point corresponding to a target vehicle; using a target heuristic search algorithm to generate an initial driving path for the driving start point and the driving end point; performing path optimization on node paths among the driving nodes in the initial driving path by using a point insertion method so as to generate an optimized driving path; and generating a to-be-driven path corresponding to the target vehicle according to the optimized driving path. The implementation mode is related to intelligent driving, and the path generation efficiency can be improved on the premise that it is guaranteed that the optimal driving path for the target vehicle is accurately generated.