125 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 a Multi-Point CTS-based chip clock design optimization method, and relates to the technical field of chip clock design optimization, and the method comprises the following steps: using a hardware description language VHDL to compile RTL codes describing circuit behaviors, using a DC EDA tool of Synopsys to convert the RTL codes into a logic netlist, using a standard algorithm H-Tree to construct a preliminary clock tree structure layout, and using the standard algorithm H-Tree to construct the logic netlist. Obtaining a primary clock tree topological graph; a Multi-Point CTS method is introduced, a plurality of clock sources are adopted to optimize the initial clock tree topological graph, and an optimized clock tree topological graph is obtained; a static time sequence analysis tool PrimeTime is adopted to analyze the optimized clock tree topological graph, and a time sequence analysis result is obtained; identifying the longest path from a clock source to a register in the optimized clock tree topological graph according to a time sequence analysis result, marking the longest path as a critical path, and finding the optimal position for placing a buffer in the critical path by adopting a heuristic search algorithm; and calculating a difference value between the actual delay and the maximum allowable delay of the optimized clock tree topological graph by using PrimeTime again.

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 present invention belongs to the field of agricultural machinery, and relates to a multi-sensor fusion adaptive detection method for a stone picker and an intelligent stone picker. The detection data of several sensors on the stone picker are collected, and a pre-trained transfer learning model in different environments is constructed. The transfer learning model is trained based on multi-task learning and adaptive gradient pruning, and the hyperparameters of the transfer learning model are adjusted through Bayesian optimization to obtain the optimal object recognition model under the current soil environment; based on a heuristic search algorithm, the parameter configuration of the sensor and the optimal object recognition model is automatically adjusted in combination with detection accuracy and energy consumption indicators; previous detection data are collected, and a clustering algorithm is used to determine whether there is a new pattern in the object detection result. If so, the optimal strategy of the object recognition model is generated through deep reinforcement learning and an environmental reward mechanism. Accurate detection, classification and efficient cleaning of stones and construction waste in reclaimed land and agricultural land are achieved, and manual labor is reduced.

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 belongs to the field of distributed system and multi-robot formation control, and provides a multi-robot elastic formation control method and system based on a state machine, and the method comprises the steps: initializing and updating an occupation grid map, and generating a Euclidean symbol distance field; on the basis of taking the updated occupied grid map as a search space, searching local path points of each robot by utilizing a heuristic search algorithm, and generating a local path; constructing an optimized objective function by taking dynamics feasibility, obstacle avoidance, avoidance between robots and formation keeping as constraint cost, and optimizing a local path to obtain an optimized path; the method comprises the following steps: periodically detecting potential collision of robots in an optimized path walking process, and carrying out mixed path planning on multiple robots on a Euclidean coincidence distance field by utilizing a cooperative mechanism of leader path offset mapping and local autonomous adjustment according to a detection result; according to the invention, system states can be dynamically switched according to environment changes, and cooperative control of formation maintenance and obstacle crossing is realized.

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 relates to the field of aviation safety, in particular to a low-altitude flight safety co-guidance system, which comprises a flight airspace delimiting module, an image intelligent identification module, a position determination module, a collision risk judgment module, a route planning module and a management database, and is characterized in that an identification model is constructed through a convolutional neural network, and a flight airspace image is acquired; the method comprises the following steps: identifying a moving object and a fixed obstacle in a flight airspace, performing position marking on the moving object and the fixed obstacle in a flight airspace image, predicting a movement track of the moving object in the flight airspace image, further judging whether the aircraft has a collision risk, and determining the collision risk position of the aircraft. A heuristic search algorithm is utilized to output a safe flight path fragment, the whole process from environment perception to risk response is covered, and the possibility of collision accidents of the aircraft is greatly reduced.

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 provides a passive sonar multi-target trajectory reconstruction method and device, and belongs to the technical field related to multi-target tracking, and the method comprises the steps: obtaining sonar signal data of a passive sonar, and carrying out the filtering of the obtained sonar signal data; performing association processing on the multi-target data by adopting a heuristic search algorithm to obtain associated sonar data of different targets; performing multi-target trajectory reconstruction based on the associated sonar data of the plurality of different targets; by introducing an improved heuristic search algorithm, sonar data hopping caused by target crossing can be avoided, continuity and accuracy of a target trajectory are ensured, and trajectory reconstruction failure caused by target hopping is avoided; according to the method, the problem of multi-target data association can be efficiently solved by utilizing the global optimization characteristic of the heuristic search algorithm, and sonar data of different targets can be accurately associated by establishing the cost function of the target trajectory and the heuristic search algorithm, so that accurate reconstruction of the multi-target trajectory is realized.

Techniques are disclosed herein for calibrating a confidence score of a machine learning model trained to convert a natural language into a meaning representation language. The techniques include obtaining one or more original bundling scores generated from one or more bundling levels of a decoder trained as a machine learning model that translates natural language to logical form, each of the one or more original bundling scores is a conditional probability of a sub-tree determined by a heuristic search algorithm of the decoder at one of the one or more bundling levels; classifying, by the calibration model, the logical form output by the machine learning model as correct or incorrect based on the one or more raw bundling scores; and providing a confidence score for the logical form, the confidence score determined based on the classification of the logical form.

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 discloses an old people walking path planning method based on a multi-dimensional old people adaptability index, and the method comprises the following steps: extracting a middle line layer of a walking road network, and carrying out the topological processing, and obtaining a walking topological road network; coordinate system matching is carried out on the elevation point data and the walking topology road network, an elevation interpolation model is generated, and the relative elevation of each topology node is calculated; road section types are marked, and road paving conditions are scored; normalizing the unit weight energy consumption to obtain a physiological burden index; obtaining a psychological burden score based on the road section type score and the road pavement condition score, and carrying out normalization processing to obtain a psychological burden index; performing weighted summation on the physiological burden index and the psychological burden index to obtain a walking burden index; and obtaining road network data containing the burden index, and obtaining a walking path with the optimal old-age adaptability based on a heuristic search algorithm. The method can comprehensively consider the influence of the community terrain on the walking physical characteristics of the old people, and provides accurate path planning.

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 a DTCO circuit structure optimization method and system based on deep learning and heuristic search, and the method comprises the steps: obtaining a circuit netlist of a target circuit for analysis, and dividing the target circuit into a plurality of independent function modules based on function demands; predicting the performance of different devices in the target circuit under a plurality of target process conditions based on a pre-trained deep learning model; searching an optimal circuit connection mode of the target circuit based on a heuristic search algorithm so as to carry out circuit structure layout topology; performing parameter optimization on the target circuit based on the optimal circuit connection mode; and based on the parameter optimization, evaluating the topological circuit through an SPICE simulation tool, verifying whether an optimization result meets a design requirement or not, and if the optimization result meets the design requirement, completing the optimization. Through a deep learning model and a heuristic search algorithm, with the guidance of agile design methodology, rapid and efficient optimization of an advanced process node large-scale circuit structure is realized.