73 results about "Planning algorithms" patented technology

This invention discloses an energy management system and method for range-extended electric vehicles that considers low-temperature thermal demand. By establishing a dual-model training unit, a dual-model fusion prediction reference generation unit, and an MPC real-time decision and closed-loop feedback unit, it solves the pain points of insufficient collaborative optimization of power and thermal management at low temperatures and the difficulty in balancing global optimization and real-time performance. It achieves global optimization of the two state variables, SOC and temperature, through a state grid adaptive dynamic programming algorithm. The optimal reference trajectory and predicted vehicle speed are generated online by the LSTM+RBF dual model. Combined with MPC rolling optimization, it achieves collaborative control, effectively improving fuel economy, reducing battery life degradation, and taking into account the thermal comfort of the passenger compartment. It has strong engineering practicality.

This invention proposes an integrated beamforming method and system for communication and sensing based on reconfigurable smart surfaces, belonging to the field of wireless communication technology. The aim is to design suitable beamforming to maximize communication and data rate while ensuring adequate radar sensing performance. The method includes: first, constructing an ISAC system containing J RIS elements, a base station (BS), and M single-antenna mobile users (MUs), and completing the modeling of baseband signals and user received signals; second, constructing user communication and data rate models and radar target detection signal-to-noise ratio models, establishing unit mode constraints on RIS reflection coefficients and transmit power constraints; third, employing an improved algorithm combining alternating optimization and fractional programming to decompose the non-convex optimization problem into sub-problems for iterative solution, ultimately maximizing communication and data rate and radar sensing performance, while also meeting the dual functional requirements of communication and sensing. This invention is applicable to providing target detection and communication functions for vehicles.

The application discloses a kind of based on layered multi-agent deep reinforcement learning's unmanned aerial vehicle cluster task planning algorithm and its evaluation method, belong to unmanned aerial vehicle technical field, solve the current unmanned aerial vehicle cluster collaborative task planning problem solving algorithm generally exists online solving efficiency is low, large amount of computation, poor stability, difficult to apply to complex dynamic scene etc.The application first establishes the cluster task allocation and flight path planning problem part Markov decision process model respectively;Second, a layered deep reinforcement learning framework based on multi-agent deep reinforcement learning is proposed, through coupled training, hierarchical solving method, the task allocation and flight path planning associated coupling problem is solved simultaneously;Also based on UE4 and Airsim build unmanned aerial vehicle cluster combat simulation environment, design evaluation index, the result shows that the application has good performance in cluster task planning, can effectively improve the intelligentization and actual combat level of cluster combat.

This invention provides a method and system for cooperative obstacle avoidance and trajectory planning in UAV swarm formation, relating to UAV swarm control technology. By introducing three-dimensional affine transformation parameterization of formation motion, the dimensionality and search space complexity of UAV swarm system modeling are reduced, alleviating computational burden. An affine A-RRT planning algorithm is employed to construct path topology in affine space, improving sampling search efficiency and reducing redundant nodes and ineffective expansion. ESDF gradient information is used for path correction and to construct sequential convex regions, simplifying the construction process of safe regions and ensuring sufficient safety margins. By constructing a quadratic programming model integrating multiple performance indicators and solving it online, a balance is achieved between trajectory smoothness, energy efficiency, and swarm formation maintenance performance, meeting the real-time planning requirements for cooperative obstacle avoidance in complex environments.

The present application relates to a kind of pipeline combination system impact optimization method, device, equipment and medium, belong to structural impact design technical field, its method includes: the three-dimensional finite element model of pipeline combination system is constructed, based on the boundary condition, load condition and material parameter set to three-dimensional finite element model is carried out impact performance simulation, obtains the maximum plastic strain of pipeline under different design scheme;Corresponding design parameter is obtained with different design scheme, based on design parameter and the maximum plastic strain of pipeline neural network model is trained to be constructed, to determine the mapping relationship of design parameter and maximum plastic strain, design parameter includes isolator stiffness configuration, stopper arrangement mode and damper position;Based on the mapping relationship of design parameter and maximum plastic strain, with the minimum as optimization goal of maximum plastic strain, using sequential quadratic programming algorithm to optimize design parameter and obtain optimal design scheme, improve the efficiency and quality of impact design.

The application relates to an unmanned underwater vehicle path planning method based on an improved SAC algorithm, and comprises the following steps: obtaining state data of an unmanned underwater vehicle in an underwater environment; a simulation experiment environment is built based on a Gym framework, physical parameters of the unmanned underwater vehicle, geometric shapes of seabed obstacles and current characteristics are simulated, and an interactive feedback mechanism of an environment state and unmanned underwater vehicle action is constructed; a path planning algorithm based on an improved SAC is constructed, a heuristic exploration mechanism is introduced on the basis of the SAC algorithm, heuristic actions are generated based on Euclidean distance or actions are generated through a strategy network, and a multi-target reward function is designed; state data is input into the path planning algorithm, actions of the unmanned underwater vehicle are generated through the strategy network, the value of the actions is evaluated in combination with a value network, network parameters are updated by using the interactive feedback mechanism, and a path planning strategy is optimized. Compared with the prior art, the application has the advantages of fast convergence in a complex underwater environment, good adaptability and the like.

This application discloses a method, apparatus, device, and storage medium for real-time planning of explanation tasks, relating to the field of robotics. The method includes: acquiring the instruction intent of the explanation task command; determining the explanation task planning result based on the instruction intent, a preset knowledge graph, and a preset task planning algorithm; and during the explanation process according to the explanation task planning result, using a preset acquisition device to scan and acquire current environmental information in real time, which is then combined with a preset path update algorithm to determine whether to update the explanation route. This method determines the explanation task planning result based on the task command using a preset task planning algorithm and a preset knowledge graph, and scans the current environmental information in real time during the explanation process. This enables the determination of whether to update the explanation route based on the current environmental information, avoiding obstacles in the explanation route from interrupting the explanation. This solves the problems of fixed explanation robot modes and poor flexibility, and optimizes the user experience during the explanation process.

This invention discloses a ground-based experimental system and method for onboard autonomous maneuvering mission planning. It utilizes a ground-based telemetry, tracking, and command (TT&C) simulation module to manage TT&C resource constraints, satellite-ground status, and mission requirements based on the test scenario. An onboard computer equivalent module performs autonomous mission planning based on mission instructions generated by the ground-based TT&C simulation module, generating control commands or mission execution sequences. A spacecraft dynamics simulation module determines the satellite's orbit, attitude, and visibility based on the control commands. A space environment simulation module generates sensor simulation data based on the orbit and attitude. A navigation sensor simulation module initiates or terminates the mission based on control commands and generates measurement data based on the sensor simulation data, sending it to the onboard computer equivalent module. A payload execution simulation module simulates the payload's operation process based on the mission execution sequence. This allows for the simulation and comprehensive verification of the entire mission planning algorithm, from mission injection to execution feedback.

This invention discloses a dual-prediction collaborative safety control method and system for shaping energetic materials. The method includes: real-time acquisition of multi-source parameters; extraction of time-series features based on a sliding window mechanism to construct a dynamic temperature prediction model and output the predicted value of the maximum cutting temperature; simultaneously, correction of manual quality scoring data using a large language model to construct a dynamic quality prediction model and output the machining quality score; inputting the dual prediction results into an adaptive control flow; combining a dynamic linear time-varying model and a sequential quadratic programming algorithm to achieve dual-objective collaborative optimization of maximizing quality and temperature safety constraints to generate control commands; and finally, dynamically adjusting the safety threshold and executing a hierarchical response strategy based on tool wear state and material phase transformation characteristics. This method innovatively integrates time-series temperature prediction and large language model quality assessment through the collaborative optimization of temperature and quality dual prediction models, constructing a model-independent adaptive control framework, effectively preventing thermal runaway, improving prediction accuracy, and increasing system efficiency.

The application discloses a logistics cargo batch loading data analysis method, which can effectively avoid subjective deviation and recognition lag problems caused by artificial judgment, improve the accuracy and response efficiency of batch loading time detection by constructing unified and standardized batch loading judgment logic and automatically triggering and real-time judging at key operation nodes of cargo circulation; by correlating multi-dimensional operation data and automatically identifying batch causes through data mining, the key influencing factors causing batch loading can be quickly located, the cumbersome process of artificial one-by-one checking and tracing is saved, and the efficiency and objectivity of problem analysis are greatly improved; based on the attribution analysis result, the routing planning algorithm and standard operation procedure are continuously optimized, a complete intelligent management closed loop from judgment, analysis to optimization execution is formed, the phenomenon of cargo batch loading is reduced from the source, the integrity and continuity of logistics loading are improved, and the overall cargo circulation efficiency and operation control quality are optimized.

This invention discloses a method, system, and equipment for disaster relief using swarm robots. The method includes: simulating a disaster relief scenario; determining the hard rules that each robot in the swarm must follow based on its actions and states in the simulated scenario; constructing a functional relationship between the adoption rate of social rules and the performance indicators of the swarm robots; constructing a compromise decision model based on the performance indicators, the social rules, and the functional relationship; solving the compromise decision model using an adaptive linear programming algorithm and performing multi-objective visual trade-off analysis to obtain the range of social rule adoption rates that satisfies all the performance indicators; and directing the swarm robots to perform disaster relief based on the range of social rule adoption rates. This invention can minimize casualties and property damage in disaster relief.

The application discloses a ground test system and method for on-orbit autonomous mobile task planning, which comprises a ground measurement and operation control simulation module, a satellite computer equivalent module, a spacecraft dynamics simulation module, a space environment simulation module, a navigation sensor simulation module and a load execution simulation module. The ground measurement and operation control simulation module generates a task instruction based on a test scene, a measurement and operation control resource constraint, a satellite-ground state and a task demand. The satellite computer equivalent module generates a control instruction or a task execution sequence based on the task instruction generated by the ground measurement and operation control simulation module. The spacecraft dynamics simulation module determines a satellite orbit, an attitude and visibility based on the control instruction. The space environment simulation module generates sensor simulation data based on the orbit and the attitude. The navigation sensor simulation module starts or terminates a task based on the control instruction and generates measurement data based on the sensor simulation data and sends the measurement data to the satellite computer equivalent module. The load execution simulation module simulates a load working process based on the task execution sequence. The application can simulate and verify the whole process of a task planning algorithm from task injection to execution feedback.

The application discloses a satellite-monitoring vehicle high-precision cooperative emergency method and system. The satellite remote sensing monitoring system constructs a water quality inversion empirical model based on historical data and monitors and alarms in real time. After receiving the alarm information, the intelligent remote sensing monitoring vehicle controls the unmanned aerial vehicle to collect hyperspectral images and the unmanned ship to monitor water quality parameters in situ through unmanned aerial vehicle sampling area and unmanned ship collection point accurate planning algorithm. The data processing system constructs a ground hyperspectral inversion model based on measured data, obtains high-precision water quality parameter layer data of the sampling area, and uses a scale elimination algorithm and a weighted least square method to dynamically correct the satellite inversion model on site. The corrected model is applied to satellite data to obtain high-precision water quality parameters, generate a water quality report and realize real-time linkage between the site and the command center through a decision command system. The application solves the problems of insufficient traditional monitoring precision and response lag, and realizes high-precision and high-efficiency cooperation of water quality monitoring.

The application provides a kind of unmanned aerial vehicle path planning algorithm based on particle swarm optimization and reverse learning, belongs to artificial intelligence optimization algorithm and intelligent control technical field.The technical scheme is: including the following steps: step 1, the success rate parameter is introduced into the traditional linear decreasing adaptive mechanism;Step 2, a particle is randomly generated, and the next particle is initialized based on the particle parameters using Logistic chaotic mapping, and so on, the particle swarm is initialized;Step 3, in the process of algorithm optimization, the particles that converge rapidly in a short time are disturbed using Levy flight;Step 4, the algorithm is optimized using reverse learning.The application reduces the operation time of the algorithm and improves the stability of the unmanned aerial vehicle in space operation.

This invention discloses an auto-scaling system and method for cloud-native intelligent typesetting services. The system includes a master resource scheduler: for receiving service requests, determining whether current cluster resources are sufficient and automatically scaling, and for phased distribution of service requests; and slave resource schedulers: for receiving phased distributed service requests, determining whether the resources within the containers providing the service locally are sufficient and automatically scaling, and for feeding back request results and resource usage to the master resource scheduler. This invention utilizes a planning algorithm to predict service deployment methods and a search algorithm to predict whether service deployment methods will lead to excessively long service response times. While ensuring timely service completion, it rationally and efficiently utilizes cloud resources. Combined with deployed services, this invention performs targeted automated deployment of microservices under known microservice processes and estimated resource consumption and service duration, while also considering cloud resource utilization and timely scaling up or down cloud resources.

This invention relates to the fields of digital marketing technology and artificial intelligence optimization technology, and particularly to a method and system for joint budget allocation and ROI optimization of cross-platform advertising expenditures. It addresses the channel contribution assessment bias caused by traditional "last click" attribution through the Shapley value attribution model; it achieves quantitative prediction of inter-channel synergy and crowding-out effects by constructing a joint prediction model using nonlinear saturated response functions and cross-influence coefficients; it overcomes budget inefficiencies caused by independent optimization on a single platform through global optimization solutions using nonlinear programming and sequential quadratic programming algorithms; and it ensures the safe implementation of automated decision-making through safety executors and anomaly circuit breakers. This method elevates advertising budget allocation from a "local, static, and biased" level to a "global, dynamic, and scientific" level, significantly improving the overall advertising ROI by an estimated 15%-30%.

This invention relates to the field of satellite telemetry, tracking, and command (TT&C) technology, and provides a method, system, device, and medium for satellite IoT TT&C mission planning. The method includes: S1: obtaining the set of observable arc segments for each ground station; S2: dividing the mission planning time period into m time segments; S3: first solving the mission planning result for the first time segment; S4: removing observable arc segments in the current time segment that intersect with the previous time segment, completing the mission planning for the current time segment; S5: taking the current time segment as the previous time segment and the next time segment as the current time segment, repeating step S4 until the mission planning for the second to the mth time segments is completed; S6: combining the mission plans of the m time segments to form the satellite IoT TT&C mission plan. This scheme can plan all actions within the planning time period at once, designing a reasonable planning scheme and reducing the complexity of the mission planning algorithm.

The application relates to a factory equipment instrument inspection control method, device and equipment based on a UAV, and a medium. The method comprises the following steps: based on equipment instrument position data and corresponding optimal observation directions, determining inspection necessary position data of the UAV and corresponding flight attitude data in a three-dimensional model corresponding to an equipment instrument area; adopting a preset inspection trajectory planning algorithm to generate an inspection operation flight path of the UAV according to the inspection necessary position data of the UAV and the corresponding flight attitude data; and based on the inspection operation flight path of the UAV, performing safe inspection on the equipment instrument area to obtain image data corresponding to the equipment instrument and devices at the current position, thereby completing the inspection operation of the UAV on the factory equipment instrument. The application can enable the UAV to accurately and safely shuttle in the equipment instrument area, and improve the working efficiency and safety of the system.

The application provides an optimization method and system based on psychological self-adaptation of first-aid personnel. Wherein, dynamic information of a first-aid scene and a sea area along a route corresponding to a first-aid task is collected to obtain a risk assessment result; psychological state monitoring data of the first-aid personnel is acquired, a machine learning algorithm is used to analyze a psychological stress level of the first-aid personnel to obtain a psychological state analysis result; according to the risk assessment result, an intelligent path planning algorithm based on a particle swarm optimization algorithm is applied to generate a specific execution path; based on the specific execution path, an adaptive adjustment mechanism based on Bayesian optimization is introduced to perform real-time re-evaluation processing on the path when unexpected emergency conditions are encountered to generate an alternative route; and a maritime communication system and an intelligent navigation auxiliary device are integrated to obtain an optimized sea first-aid path. The technical scheme provided by the application realizes intelligentization and real-time optimization of path planning.

The application discloses a five-axis mechanical arm obstacle avoidance planning and cross-platform control system based on A* and APF algorithms and relates to the technical field of mechanical arm motion control. In view of the problems that the existing A* algorithm is easy to generate a virtual path exceeding the limit, motion step is stuck, and the traditional potential field method does not cover the anti-collision of the connecting rod and is easy to fall into local optimization, the application builds an improved D-H kinematics model and a standardized simulation environment, proposes an improved A* global planning algorithm with joint physical constraints and quintic polynomial smoothing, and ensures the continuous and smooth trajectory. Meanwhile, the application proposes an improved APF local obstacle avoidance algorithm by introducing a velocity Jacobian matrix to map the repulsive force of the whole arm connecting rod. The system adopts cross-platform communication between the upper computer and the ROS2 bottom layer node, and the smooth trajectory is losslessly issued to the entity mechanical arm for execution. The application takes into account the path feasibility and safety, greatly reduces the hardware impact, and provides a quantitative algorithm comparison and verification mechanism.

A method and system for human-machine interaction safety control of a quadruped robot for emergency inspection of power distribution networks includes: acquiring interactive commands issued by operators to the robot and extracting intent data sets; constructing a spatiotemporal three-dimensional grid map of the environment; based on the intent data sets, performing action path planning through a path planning algorithm to generate an initial execution path sequence; conducting multi-dimensional safety risk quantification assessment of the path points in the initial execution path sequence and calculating a comprehensive risk index; if the comprehensive risk index of all path points is lower than a safety threshold, the initial execution path sequence is converted into commands and executed; otherwise, the cost function of the path planning algorithm is changed, and an alternative path sequence is regenerated; if the comprehensive risk index of all path points in the alternative path sequence is lower than a safety threshold, the alternative execution path sequence is converted into commands and executed; otherwise, an early warning is issued. This invention achieves efficient and safe operation in complex and dangerous environments.

The present application relates to the field of intelligent filling planning, and more particularly to a goaf intelligent filling planning method based on machine vision and autonomous navigation. The content includes: collecting point cloud data of the goaf environment and registering to obtain point cloud data under the global coordinate system; based on the point cloud data under the global coordinate system, introducing a local spectral weighted index algorithm to generate a structure weighted geometric reconstruction index; based on the structure weighted geometric reconstruction index, identifying the filling points and constructing the filling point set; based on the filling point set, combining the structure weighted geometric reconstruction index, introducing a progressive filling planning algorithm to select the next filling point and generate an ordered filling path. The problems of traditional point cloud processing methods that are difficult to automatically identify structural abnormalities or high-risk areas, and filling path planning that fails to make comprehensive decisions combining structural risk and spatial distance, and is difficult to achieve continuous, controllable and risk-prior intelligent filling planning are solved.

The application belongs to the technical field of electric digital data processing, and particularly relates to a digital processing system for adaptive depth of a land leveler. The system acquires a tool spindle torque signal sequence and a rack three-axis acceleration signal sequence in real time, extracts a frequency domain feature vector through fast Fourier transform of a sliding window, and inputs a classifier to output a soil dynamics parameter set. The dynamics parameter set is substituted into a state space equation, a torque fluctuation variance minimization and an effective soil breaking volume maximization are taken as double objective functions, a maximum response rate of a hydraulic system and an allowable maximum structural stress are combined to construct a nonlinear programming model. A sequential quadratic programming algorithm is called to iteratively solve and output an optimal loosening depth setting value, which is converted into a hydraulic proportional valve current control sequence through trajectory interpolation. The application suppresses depth control oscillation during operation of soft and hard alternating soil, reduces transient impact load of a transmission system, and balances loosening operation power consumption and soil breaking quality.

This invention discloses a multi-mode collaborative simulation method and system for unmanned aerial vehicle (UAV) swarms, comprising: building a layered and decoupled integrated simulation framework, including an environment layer, simulation layer, communication layer, control layer, and resource scheduling layer; constructing a standardized communication system to achieve unified encapsulation and transmission of virtual environment, software-in-the-loop, hardware-in-the-loop, virtual-real combination, and real environment data, and adapting to multiple communication links; deploying a simulation fusion planning algorithm in the control layer to automatically match simulation modes and achieve scene mapping and swarm collaboration; connecting each simulation module to the simulation layer for debugging; setting the swarm size and tasks according to requirements, with the resource scheduling layer dynamically allocating resources and starting the simulation; and finally analyzing simulation data and optimizing iteratively. This invention deeply integrates multiple simulation modes, supports large-scale swarm collaboration, and improves simulation efficiency and realism through layered decoupling and dynamic resource scheduling, solving the problems of isolated simulation modes and difficult resource scheduling in existing technologies.

The application discloses a kind of mechanical arm trajectory planning method, system and medium, belong to mechanical arm trajectory planning, including: the image set of different posture injection object is collected, and trajectory data of mechanical arm injection is collected;Label injection area in image set, and train target recognition model based on YOLOv8 using labeled image set;Using target recognition model identifies injection area in current image set, the center point of the injection area is used as injection point;Using the improved dynamic motion primitive DMP algorithm plans the motion trajectory of mechanical arm from current position to injection point to complete injection after returning current position;Improved dynamic motion primitive DMP algorithm is obtained by introducing velocity term scaling factor and superimposing multiple nonlinear Gaussian radial basis functions;For the existing mechanical arm motion trajectory planning algorithm in prior art is easy to fail, the adaptability of algorithm to the same situation of first and last trajectory is improved.

This invention discloses an integrated aerospace service system for planning and processing, belonging to the field of civilian satellite observation and services. The system includes a mission coordination and integrated management subsystem, a space information management subsystem, and an information comprehensive analysis subsystem. The system is designed and implemented using a "system + service" approach, effectively achieving system iteration, stability, and resilience. The system fully utilizes multi-satellite joint mission planning algorithms and remote sensing image intelligent analysis algorithms, enabling integrated planning and processing services. This invention effectively solves the problems of slow response speed, incomplete response chains, and poor service product quality when facing major events.

The application provides a multi-target waypoint planning method and system fusing an improved A* algorithm and an SPFA algorithm, and the method mainly comprises the following steps: a mobile navigator rasterizes the surrounding environment, and calibrates a target point set and a starting point of the navigator to simulate a task scene; a distance value calculation of the starting point to the target point is performed through construction of the improved A* algorithm; a negative edge weight map with point priority, direction and without negative loop is established through fusion of the improved A* algorithm and the SPFA algorithm, and the best waypoint order can be obtained according to the point priority in the negative edge weight map; the navigator moves according to the waypoint order to traverse the task target points, and global path planning and local path planning algorithms are realized. Through the application, a large number of path points and inflection points are reduced, the total path length is shortened, the total path cost is reduced, the overall efficiency is improved, and a reasonable and fast feasible path is planned in a multi-target waypoint task in a complex environment.

This invention discloses an online self-calibration method and system for wake model parameters based on the DE-SQP joint framework. The method includes constructing a wake calculation system comprising a wind turbine model, a wind speed model, and a wake model; acquiring measured power data of the wind turbine generator under a target operating condition, and calculating predicted power data for the corresponding operating condition using the wind turbine model and wind speed model in the wake calculation system; setting an optimization objective function based on the measured and predicted power data; using a differential evolution algorithm to globally search and optimize the multidimensional empirical parameters of the wake model to obtain a globally optimal preliminary parameter vector; using the preliminary parameter vector as the initial point, employing a sequential quadratic programming algorithm for local fine-tuning, utilizing its fast convergence characteristic to obtain the final calibration parameters; and applying the final calibration parameters to the wake model in real time to complete the online self-calibration of the model parameters. This invention can significantly improve the prediction accuracy, robustness, and generalization ability of the model across the entire wake region.

The application relates to the technical field of satellites and provides a constraint design method and device in satellite networking task planning, an Internet of Things system, a storage medium and a computer program product, the method comprising the following steps: acquiring a task planning period required for networking of a satellite and a ground station, and acquiring ephemeris orbit prediction information of the task planning period; designing a task planning algorithm for coverage optimization of the Internet of Things system facing the networking of the satellite and the ground station based on a genetic algorithm according to the ephemeris orbit prediction information; performing constraint design on the designed task planning algorithm to obtain a task planning algorithm after constraint design; and performing coverage optimization on the Internet of Things system for the networking of the satellite and the ground station by using the task planning algorithm after constraint design, so that the time length during which the Internet of Things system in a target area of the ground station can connect the satellite is improved. The scheme can perform coverage optimization on the satellite Internet of Things by using the task planning algorithm after constraint design, and improve the communication service stability of the satellite Internet of Things.