181 results about "Function optimization" patented technology

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

The invention provides a three-dimensional point cloud data segmentation method and system based on multi-scale point features, and relates to the technical field of point cloud processing, and the method comprises the steps: obtaining original point cloud data, constructing a three-dimensional Hash index table, carrying out the self-adaptive hierarchical division through a local maximum value of a density distribution curve, and obtaining a local maximum value of a density distribution curve; noise point elimination is carried out based on a region density count value and a geometric distribution coefficient, an enhanced feature vector containing a main feature direction, a main curvature value and the like is constructed for non-eliminated points, a local geometric descriptor is constructed by adopting four spherical search regions with increasing radiuses, a three-branch space attention network is input to extract spatial features, and the spatial features are extracted; and fused feature data are obtained through a multi-level geometric feature extraction network and a bidirectional feature propagation network. And obtaining a final segmentation result through segmentation energy function optimization and a region merging strategy.

The invention relates to a task scheduling optimization method and device based on reinforcement learning, equipment and a medium. The method comprises the steps that firstly, system resource state data are collected in real time, dynamic environment characteristics are determined through preprocessing and time sequence analysis, task characteristic data are analyzed at the same time, and a task priority sequence and a resource demand vector are generated through a priority ranking algorithm and a resource evaluation model; and then a state space and an action space are constructed by adopting a reinforcement learning algorithm, an optimal task allocation scheme is generated through strategy iteration and reward function optimization, and if the scheme meets a resource balance threshold, scheduling is executed, and performance indexes are collected. And finally, fusing real-time indexes with historical data, and updating parameters of the reinforcement learning model through experience playback and gradient descent to form a closed-loop optimized improved scheduling strategy. By adopting the method, the accurate mapping of the resource state and the task requirement can be realized, and the problem of insufficient adaptability of the traditional static scheduling to a complex scene is solved.

The invention relates to a model predictive control charging optimization method based on a dynamic power state, which initiates a'dynamic power state collaborative optimization 'mechanism, takes a real-time power upper limit as an active optimization target instead of a fixed constraint condition, and breaks through the technical bottleneck of power limitation passive response in a traditional charging strategy. The method specifically comprises the following steps: constructing an electric-thermal-aging multi-physics field coupling model of the lithium ion battery, updating electric-thermal characteristic parameters in real time through an online parameter identification algorithm, and synchronously estimating a core temperature and an aging state in combination with a double-Kalman filtering state observer; innovatively establishing a four-dimensional objective function optimization model containing a dynamic power state, and performing multi-objective collaborative optimization on a power upper limit, a charging speed, a capacity fading rate and a current fluctuation rate; and designing a dynamic rolling optimization algorithm based on a model prediction control framework, and solving the optimal charging current meeting the dynamic power distribution requirement of the power grid in real time under the hard constraint of ensuring the maximum core temperature and terminal voltage.

The invention relates to the technical field of electric power system control, in particular to an electric power resource intelligent control system based on digital twinning, which comprises a data acquisition module, a digital twinning model module, an electric power dispatching module, a fault early warning module and a three-dimensional visualization module. According to the method, the load demand prediction model is established based on the hybrid RNN, the RNN and full-connection network hybrid neural network model is constructed, the historical time sequence, the time feature, the historical statistical feature and the similarity feature are learned, and the problem that the power load demand is difficult to predict due to renewable energy source grid connection and power demand increase is solved; a power dispatching decision is made through a multi-objective function optimization algorithm based on an improved particle swarm, the particle swarm optimization algorithm is improved by adding a variation strategy for high-fitness particles, the problem that the particle swarm optimization algorithm is prone to falling into a local optimal solution in the practice process is solved, and therefore the problem that power dispatching of multi-objective optimization is difficult to achieve is solved.

The invention discloses a bank loan business risk control system and method based on big data analysis, and relates to the technical field of financial risk control, and the method comprises the steps: collecting and preprocessing real-time behavior data, and obtaining a user behavior feature set; based on the user behavior feature set, calling a behavior map modeling engine to carry out structured mapping, matching with a risk anchor point rule base, identifying a potential risk mode and labeling an initial anchor point risk label; correcting the deviation between the initial risk anchor point tag and the actual default record by adopting a value function optimization method, and predicting the risk grade score of the current behavior of each user in combination with the historical behavior sample data and loan feedback data of the user; predicting probability distribution of migrating to a default state in the future through user risk grade scores and historical state evolution data; and in combination with the potential loss under each behavior path, evaluating the current loan business risk, and generating a risk control strategy through a risk level mapping rule and a strategy decision engine.

The invention discloses an intelligent optimization system based on a self-adaptive control hydraulic cylinder, and relates to the technical field of hydraulic cylinder control, and the system comprises a monitoring data collection module which collects data of a flow field, a temperature field and a stress field in real time by means of various sensors arranged at different parts of the hydraulic cylinder, synchronously carries out the preprocessing and coding of the collected data, and transmits the data to a data processing module; the data is transmitted to the data processing and analyzing module in real time; detailed data of the hydraulic cylinder in different physical fields are acquired in real time through the monitoring data acquisition module, and the coupling coefficient is accurately calculated by using the multi-physical field coupling model, so that the system can deeply understand the specific influence of each physical field on the performance of the hydraulic cylinder; according to the method, the system can construct a more accurate hydraulic cylinder dynamic model in the model prediction control module, so that the future state of the hydraulic cylinder is accurately predicted, further, the system can obtain an optimal control input sequence through an objective function optimization algorithm, and the pertinence and effectiveness of a control strategy are remarkably improved.

The invention discloses a distributed energy system source load coordinated optimization method based on a quasi-potential game method, and the method comprises the steps: constructing a distributed energy system model, inputting system parameters, and predicting renewable energy power generation and initial load demands. In a source side optimization stage, a leader layer potential function of a quasi-potential game is established by taking minimization of source side cost # imgabs0 # as a target, and an initial power generation plan is generated by comprehensively considering economical efficiency and carbon emission constraints; and then, based on a scheduling result, calculating a carbon potential epsilon t of each node and a dynamic carbon emission factor # imgabs1 # of each stage, optimizing a load side response based on an LCDR scheme, constructing a local potential function of a follower layer, reflecting a relationship between a user profit maximization target and carbon emission, and adjusting user behaviors through a distributed decision. And the updated load demand is fed back to the source side, and the source side optimizes the output plan of each unit based on the updated load, so that an iterative process of source side potential function optimization-load side equilibrium response is formed, and an optimal scheduling strategy and scheduling result of the energy supply side are obtained. According to the framework, the global consistency requirement of a traditional potential game is relaxed, independent optimization of source-load two sides under the guidance of respective potential functions is allowed, and a Nash equilibrium state is finally achieved only by ensuring monotonous convergence of total potential energy of a system in an iteration process. According to the method, the convergence advantage of the potential game is reserved, the method is also adapted to the characteristics of a source-load heterogeneous decision subject, efficient consumption of renewable energy and collaborative optimization of carbon emission are realized through bidirectional transmission of the carbon potential signal, and the overall efficiency of the system is remarkably improved.

The invention relates to the field of high-altitude operation equipment and automatic control, and discloses a control system and method for a self-walking insulating lifting platform, and the system comprises a posture sensing module which is used for collecting the inclination angle and angular velocity information of the platform in real time; the dynamic modeling module is used for establishing a multi-degree-of-freedom dynamic model of the platform; the optimal control module is used for calculating optimal control input through objective function optimization; and the robust control module is used for realizing anti-interference leveling of the platform under an external disturbance condition based on a sliding mode control algorithm and in combination with adaptive gain adjustment. The method comprises the steps of obtaining real-time attitude information of a platform, establishing a multi-degree-of-freedom dynamic model, defining a weighted objective function and solving optimal control input, calculating control input based on a sliding mode control algorithm, and adjusting the positions of platform supporting legs to achieve a stable leveling state. According to the invention, the leveling precision and the anti-interference capability of the platform are improved, and the method is suitable for safe and stable operation under various complex terrain conditions.

The invention belongs to the field of thermal management systems, and provides a multi-heat-source thermal management system self-adaptive control method based on reinforcement learning model predictive control, which comprises the steps of environmental state receiving, reward function calculation, strategy optimization, decision correction, objective function optimization and control law construction, temperature error calculation, action optimization and device regulation and control. By fusing strategy optimization of the intelligent agent and action optimization in traditional model predictive control, real-time optimization of control strategies of equipment such as a fan, a water pump and a valve in the cooling system is achieved, and adaptability of reinforcement learning and stability of model predictive control are achieved; by arranging the intelligent agent and the feedback correction module, the prediction model parameters are adjusted according to the actual state of the system, and the robustness and the adaptive capacity of the control system are improved; the control law of rolling optimization output is combined with multiple times of real-time feedback and prediction correction, and efficient temperature control of the system in dynamic change is achieved.

The invention provides a process window optimization method and device, a medium, a program product and a terminal, and solves the problems of low light intensity contrast ratio, limited process window and high finished product defect rate of weak point and dead pixel areas through an objective function optimization and numerical programming solution method. And establishing a reasonable optimization model by taking the sum of squares of light intensity logarithmic slopes of weak and dead pixel areas as an optimization target and combining an edge section moving range, an edge position error and mutual influence factors. And meanwhile, a plurality of matrix methods such as a sparse matrix and a dense matrix are introduced, so that the optimization efficiency and precision are improved, the accurate optimization of the mask pattern is realized, the process window is expanded, the defect rate is reduced, the process stability and adaptability are enhanced, and the production efficiency and the product quality are remarkably improved.

The invention discloses a large model driven automobile model modeling method based on a Modelica language, and belongs to the technical field of intelligent modeling and automobile simulation. The method comprises the following steps: firstly, accurately analyzing a natural language demand into a structured triple by adopting a BERT-CRF (domain knowledge enhanced) multi-task model; matching an optimal component combination through a multi-objective optimization algorithm driven by a graph neural network, and cooperatively predicting an interdisciplinary parameter feasible region in combination with symbolic mathematical derivation and machine learning; a topological connection matrix is innovatively optimized by using a graph attention network, and intelligent generation and dynamic verification of simulation codes are realized by fusing a template engine and syntax tree analysis; and finally, constructing a multi-target reward function optimization control strategy through reinforcement learning, and establishing a closed-loop knowledge iteration mechanism. Compared with a traditional modeling method, through deep combination of the large model and Modelica, the technical difficulty of automobile system modeling is remarkably reduced while the preciseness of physical modeling is kept, and the method is particularly suitable for complex scenes such as new energy vehicle model development and intelligent driving system integration.

The invention relates to the technical field of intelligent traffic, in particular to an On-Ramp scene trajectory planning method based on deep inverse reinforcement learning, and the method comprises the following steps: S100, candidate trajectory generation: generating a plurality of candidate trajectories based on vehicle state information and traffic environment information; s200, track authenticity filtering, wherein the generated candidate tracks are evaluated and screened through a track authenticity filtering framework, so that abnormal tracks are eliminated, and credible tracks are reserved; and S300, reward function training: utilizing reward function optimization of deep inverse reinforcement learning to realize continuous adjustment and improvement of a trajectory evaluation standard so as to carry out quantitative analysis on rationality and optimality of the trajectory. By constructing a trajectory authenticity filtering framework, optimizing a reward function design and training strategy, and introducing a multi-dimensional scoring mechanism, the trajectory authenticity filtering framework is optimized, so that the accuracy of the trajectory authenticity filtering framework is improved. According to the invention, efficient, safe and dynamically adaptive trajectory planning is realized, and reliable support is provided for real-time decision making of an automatic driving vehicle in a complex scene.

The invention discloses an underground carry-scraper path planning method and system, and the method comprises the steps: obtaining environment data information of a target underground mine roadway, constructing an environment map, and carrying out the preprocessing of the environment map, and obtaining a binary grid map; constructing a generalized Voronoi diagram, extracting a roadway center line and carrying out region division; performing path guidance and segmentation by adopting an algorithm to obtain a reference state set of the target underground mine roadway; and based on a hybrid algorithm, a regional dynamic node expansion strategy and a multi-target heuristic cost function, realizing path planning of the underground carry-scraper, and realizing path planning of the underground carry-scraper. According to the invention, through data acquisition and data processing of a target underground mine roadway, a roadway center line and divided areas are extracted, then path guidance is carried out through an algorithm, and based on a divided area dynamic node expansion strategy and a multi-target heuristic cost function optimization hybrid algorithm, path guidance is carried out. By combining a hybrid algorithm, a Dubins curve, a collision detection scheme, path smoothness punishment and path distance punishment, the path planning of the underground carry-scraper is realized, the reliability is higher, the accuracy is better, and the effect is better.

The invention relates to the technical field of measurement, in particular to an integrated sensing module for a dynamic weighing system, which comprises a physical domain, an environmental domain and a virtual domain, and realizes dynamic adjustment of parameters through cross-domain interaction so as to deal with high-frequency vibration and rapid temperature difference change in a complex environment. The physical domain is responsible for collecting multi-source data, the environment domain senses external interference in real time, and the virtual domain realizes time sequence feature extraction and dynamic model updating by combining the optimization capability of a recurrent neural network and reinforcement learning. According to the technical scheme, the real-time performance and adaptability of the system are remarkably improved, the environment change trend can be accurately predicted, the weighing strategy is adjusted in advance, and the error rate is controlled to be at an extremely low level. Meanwhile, by combining a negative feedback mechanism and objective function optimization, the sampling frequency is dynamically adjusted, and the energy consumption is reduced on the basis of guaranteeing the data processing efficiency. And deep fusion of wavelet decomposition, a dynamic normalization algorithm and a neural network model is adopted, so that the accuracy and stability of data processing are greatly enhanced.

The invention discloses an intelligent vibration isolation control method and system based on vibration monitoring, and the method comprises the steps: collecting vibration data, carrying out the preprocessing, carrying out the fuzzy conversion according to the preprocessed data, and calculating a preliminary control quantity; constructing an optimization model and defining a target function, using a locust optimization algorithm to iteratively optimize the population position until the target function value converges, and using the initial control quantity as the final optimization model input to obtain the final control quantity; driving force signals are calculated based on the final control quantity, a difference equation is further constructed based on accumulation of the driving force signals, then a predicted value is output, conversion is carried out according to the obtained predicted value, and instantaneous data are obtained. According to the method, efficient adaptive control over a complex nonlinear vibration system is achieved, the optimization process is rapidly converged, the target function optimization result is directly used for dynamic adjustment of the control quantity, the time for calculation of the control quantity is remarkably shortened, the future vibration trend is predicted through a difference equation, and the calculation efficiency is improved. And the dynamic response capability is further improved.

The invention discloses an automatic driving carrying equipment scheduling system and method for an industrial robot. The system comprises a plurality of automatic driving carrying devices, a central dispatching device and corresponding communication modules. The system adopts a multi-agent reinforcement learning framework and combines a graph neural network processing environment topological structure to realize dynamic path planning and multi-device collaborative scheduling; integrating an energy consumption prediction mechanism based on a Kalman filter, and bringing energy consumption factors into a task allocation decision; meanwhile, a fault-tolerant management mechanism based on a distributed account book and federated learning is established, and fault detection and rapid recovery are achieved. The technical modules are deeply coupled, and a unified collaborative optimization framework is formed through reward function design, utility function optimization and fault probability calculation of multi-agent reinforcement learning. According to the method, the problems of poor dynamic environment adaptability, isolated decision making of each module, extensive energy consumption management and the like in the prior art are effectively solved, and the overall efficiency, energy efficiency and reliability of a scheduling system are remarkably improved.

The invention discloses a multi-target integrated production plan scheduling method based on improved NSGA-II, which considers multiple constraints such as materials, productivity and inventory on the basis of the existing production plan, and constructs a multi-target integrated production plan scheduling method based on the multiple constraints by taking the materials, the productivity and the inventory as the constraints. According to the method, an integrated production plan model with minimization of tardiness cost, minimization of inventory cost and maximization of balanced production as targets is established, an improved non-dominated sorting genetic algorithm is designed to effectively solve a multi-target optimization problem, the accuracy of a production plan is improved, and compared with an original solving method, the method has the advantages that the cost is reduced, and the production efficiency is improved. The improved NSGA-II algorithm has the advantages of being high in solving precision, high in convergence speed and the like, and is very suitable for solving the problem of complex function optimization under the multi-constraint condition.

The invention provides a social robot detection method and system, computer equipment and a storage medium, and belongs to the technical field of social robot detection.The method comprises the steps that user multi-source data on a social platform and the social relation between users are collected; a hybrid encoder is adopted to capture local dependence and global time sequence dynamic states of behaviors, and user behavior characteristics are generated; aggregating structure attention features between the initial node features of the target account and the initial node features of the social relation account to obtain multi-modal relation aggregated structure features; introducing a multi-modal adversarial training strategy, adaptively adjusting the disturbance intensity of each modal based on gradient sensitivity, and aligning the characterization of the clean sample and the adversarial sample in combination with a content discriminator and a behavior discriminator; and outputting a target user classification result through the multi-task target function optimization model. According to the method, the problems of insufficient modal fusion and insufficient adversarial robustness of an existing method can be effectively solved, and the accuracy and stability of social robot detection in a complex and adversarial scene are improved.

The invention discloses a traffic flow prediction method and device based on a space-time diagram convolutional network, and the method comprises the steps: collecting road traffic flow data, and constructing a traffic congestion index matrix model; calculating a road traffic congestion index based on the collected road traffic flow data, and constructing a traffic flow adjacency matrix by using the road traffic congestion index; performing fast time convolution processing on the traffic flow adjacency matrix to generate a traffic flow data sequence containing spatio-temporal information; inputting the traffic flow data sequence into a space-time synchronization graph convolutional network model STSGCN, further learning a spatial topological structure, and deeply extracting space-time features by stacking multiple layers of space-time synchronization graph convolutional layers; and inputting the spatial-temporal characteristics into a wave loss function-based random vector function link network model Wave-RVFL optimized by an index and trigonometric function optimization algorithm, and predicting the traffic flow. The method can improve the precision and efficiency of traffic flow prediction.

The invention discloses a three-dimensional model wall thickness measuring method, device and equipment combining a ray method and a spherical surface method, and a storage medium, and is applied to the field of three-dimensional models. Processing the input three-dimensional model by adopting a self-adaptive subdivision algorithm of a quadtree to generate an optimized triangular mesh; the method comprises the following steps: constructing an axial alignment bounding box (AABB) hierarchical tree structure optimized based on a superficial area heuristic (SAH) cost function, and in a wall thickness calculation stage, combining the advantages of a ray method and a spherical method: performing dynamic judgment according to an included angle between a normal vector at an intersection point of a ray and the inner surface of a model and an initial ray direction; the hybrid strategy ensures that reliable and efficient measurement results can be obtained in different geometric feature regions; a visual distribution diagram of color coding can be generated; according to the method, through adaptive grid optimization, efficient SAH-AABB acceleration and ray / spherical surface method mixed measurement, high-precision and efficient global measurement of the wall thickness of any three-dimensional model, especially a complex structure model, is realized.

The invention relates to the field of shared control, and discloses an intelligent shared control method and system of a plasma waterless washing machine, which are used for solving the core pain points of the traditional shared washing machine in the dimensions of prediction, energy consumption, maintenance and control. According to the method, through fusion of multi-model cooperative calculation and interdisciplinary theory, full-link optimization control is combined with an evolutionary game theory incentive mechanism and a Lyapunov energy function optimization algorithm, an equipment start-stop strategy and a user integral rule are dynamically generated, and energy consumption and service quality are balanced; and constructing a thermodynamic entropy increase optimization model to dynamically update an environment coupling coefficient and a weight parameter, and integrating meteorological and power grid data to form a self-adaptive negative feedback control ring. According to the invention, the limitation of traditional single-point optimization is broken through, the cleaning uniformity error is improved, the fault early warning response speed is accelerated, and the energy efficiency and reliability of the shared washing machine are remarkably improved.

The invention provides a cascade flow field prediction method and device based on sparse promotion modal feature prediction, and belongs to the technical field of gas turbine flow field prediction. According to the method, a flow field snapshot is decomposed into a DMD mode, a mode amplitude matrix and a DMD eigenvalue matrix, and a flow field reconstruction expression is constructed; constructing an objective function, including promoting a sparse term to produce a more sparse solution; the optimal position of the non-zero modal amplitude is solved based on target function optimization, and a modal order corresponding to the non-zero modal amplitude forms a dominant modal representing the dynamic characteristics of the flow field; the modal amplitude and the DMD characteristic value of the unknown working condition are predicted based on the modal amplitude and the DMD characteristic value of the dominant modal of the flow field under the known working condition, and then the flow field of the unknown working condition is predicted by using the inverse process of the DMD. According to the method, control over the number of sparse modes is added into the target function, the mode sparsity is enhanced, and then the mode subset which has remarkable influence on the flow field is better identified.

A hydraulic power plant water-collecting well and pool semi-supervised flooding detection method based on DINO constructs a semi-supervised target detection network through the steps of network construction, model construction and function optimization, and can realize high-quality detection of flooding detection tasks without performing complete manual annotation on unannotated images collected by a hydraulic power plant. The learning device is constructed by a target detection network DINO based on Transform, deep information contained in an image can be efficiently extracted by utilizing the strong long-range modeling capability of the learning device, and then object query with strong representation capability is generated. A cross-view-angle object query unification module designed based on a mean square error obtains effective features with an invariable extraction view angle by aligning the features extracted after executing different image enhancement operations on the same image, and enhances the generalization ability of the model to a flood detection task.

The invention relates to the technical field of AI, in particular to a digital scene design and compiling drawing system based on AI features, which comprises a feature extraction and user portrait construction module, a tourism scene planning module, a user experience feedback module and an intelligent evaluation and optimization adjustment module. A basis is provided for accurate recommendation through user data feature extraction and user portrait construction; the scene recommendation list is generated by calculating the user similarity and establishing the similarity matrix, and the requirement of the user for obtaining recommendation information is met; a satisfaction degree analysis result is obtained by analyzing the satisfaction degree of the user on the generated scene recommendation list, and a direction is provided for subsequent optimization and adjustment; by identifying the problems and improvement points existing in the scene recommendation list and constructing the optimization target function optimization parameters, intelligent optimization is realized, and the user experience is improved. The method and the device are used for solving the technical problem that tourism scene recommendation in current virtual scene management is inaccurate.

The invention discloses a virtual power plant optimal scheduling method and device, a medium and a product, and relates to the technical field of power plant electric energy resource scheduling and allocation, and the method comprises the steps: obtaining basic data of a virtual power plant; respectively carrying out clustering analysis on the temperature control load data and the electric vehicle data by adopting a density clustering algorithm and a K-means + + algorithm based on an HHACOA algorithm to obtain a clustering result; according to the basic data of the virtual power plant, an internal approximation constraint space integration method is adopted, and adjustable power domain data of the whole temperature control load cluster and adjustable power domain data of the whole electric vehicle cluster are determined; establishing a planning function optimization model according to the adjustable power domain data of the temperature control load cluster, the adjustable power domain data of the electric vehicle cluster and the clustering result; and solving the planning function optimization model to obtain an optimization strategy result of the virtual power plant. The method can improve the adjustment capability of the virtual power plant to the power grid, and guarantees the safety and stability of the power system.

A method includes ingesting by a set of computing nodes of a parallelized database, a dataset for storage therein. The method further includes formatting, by a lead computing node of the set, the dataset in a primary data format to produce a primary data formatted dataset, and storing the primary data formatted dataset in system state data. The method further includes receiving, by a first computing node of the set, a query request involving the dataset and a first data function, determining that the first data function triggers a first data conversion optimization, accessing the primary data formatted dataset from the system state data, converting the primary data formatted dataset from the primary data format to a secondary data format to produce a secondary data formatted dataset, and processing the secondary data formatted dataset in accordance with the first data function to produce a first function output.

The invention relates to an aircraft air-dropped material recycling method and device and computer equipment. The method comprises the following steps: constructing a design variable of an optimal release point of the materials released by the aircraft; the design variables comprise release point coordinates and course angles; according to a motion equation of a material landing process and the design variable, establishing a relationship between the design variable and a landing point position vector; determining a drop point evaluation function according to a pre-acquired launching mode, simulating and calculating a drop point position vector corresponding to each course angle by adopting a course angle traversing mode, and selecting a corresponding course angle and a corresponding drop point position vector according to a drop point evaluation function optimization principle; and solving a traveling salesman problem by adopting a greedy algorithm, and traversing landing points of all the landing point position vectors to obtain an optimal path plan of material recovery. By adopting the method, the course angle can be dynamically adjusted according to the real-time environment data, and the material delivery requirement is met.

The invention discloses a trajectory planning method based on double sampling optimization, and belongs to the technical field of automatic driving. The method aims at solving the technical problems that in a multi-branch unstructured scene, a traditional planning method is prone to falling into local optimum, planning fails or calculation time is too long, and system reliability is affected. According to the scheme, sampling optimization is carried out on a guide path and a smooth path at the same time, an improved WRRT *-S algorithm is adopted for searching, and an optimal guide path is selected in combination with a risk potential field, path distance cost and steering cost; generating a smooth path candidate cluster by using a quintic Bezier curve, and selecting an optimal smooth path through multi-objective function optimization of path smoothness, economy, collision penalty and the like; and discretizing a speed planning problem into a nonlinear planning problem for solving by taking the maximum speed as a constraint and utilizing a Gaussian pseudo-spectral method, so as to realize collaborative optimization of the path and the speed. According to the invention, the vehicle can plan a safe, stable and efficient track in a complex scene.

The invention discloses an SAT solver heuristic function optimization method based on a large language model, and belongs to the technical field of function optimization methods.The method comprises the steps that the large language model analyzes SAT solver source codes, and heuristic functions possibly optimized in the SAT solver source codes are extracted; selecting a heuristic function optimized in the step according to a formulated heuristic function selection strategy; obtaining a to-be-optimized original heuristic function, preparing a training set and a verification set, and determining experimental parameters, such as an overtime threshold of a test SAT solver and a current sample number limit; initializing a code database, running an original SAT solver on a verification set, and initializing a score table and scores; and generating prompt words, providing the prompt words to the large language model, and obtaining feedback of the large language model.