784 results about "Dynamic planning" patented technology

The embodiment of the invention relates to the technical field of ship scheduling, in particular to a multi-source data-based intelligent auxiliary decision-making method for ship entering and leaving a port and berthing and leaving the port, and the real-time performance and accuracy of port scheduling decision-making can be improved by fusing multi-source heterogeneous data to construct a ship navigation situation awareness system. The space-time alignment technology effectively eliminates space-time dislocation of environment interference factors and enhances the characterization capability of the ship motion state and environment coupling relation. Quantitative evaluation of multi-dimensional navigation risks is realized based on a ship behavior prediction model, and potential risks such as channel deviation and berthing conflicts can be identified. A collaborative decision-making scheme considering safety and efficiency is generated through a collaborative optimization mechanism of the dynamic scheduling rule base and the risk indexes, and ship trajectory control precision is optimized through dynamic planning of navigational speed adjustment and steering time sequence. Closed-loop linkage of port resource allocation and ship navigation control is achieved through automatic instruction generation and execution, and the reliability and the response speed of the port operation process can be ensured.

The invention relates to a heterogeneous multi-unmanned aerial vehicle cooperative path planning method based on multi-agent deep reinforcement learning, and solves the problem that a traditional path planning algorithm is difficult to deal with a dynamic planning problem and an agent cooperation problem compared with the prior art. And the existing multi-unmanned aerial vehicle cooperative path planning algorithm has the defects of insufficient feature extraction capability, low experience learning efficiency and poor cooperative strategy flexibility in a dynamic uncertain environment. The method comprises the following steps: analyzing a multi-unmanned aerial vehicle cooperative path planning task; building an airspace reinforcement learning environment; introducing an unmanned aerial vehicle kinetic equation; modeling a multi-unmanned aerial vehicle decision model in multi-unmanned aerial vehicle cooperative path planning as a POMDP model; designing an MASAC-SEPR algorithm model to carry out path planning on multiple unmanned aerial vehicles, and generating a multi-agent collaborative path optimization network model; and training the multi-agent collaborative path optimization network model. According to the method, the efficiency and the accuracy of multi-unmanned-aerial-vehicle cooperative path planning are remarkably improved, and a reliable solution is provided for heterogeneous multi-unmanned-aerial-vehicle cooperative operation in a dynamic uncertain environment.

The invention relates to an unmanned aerial vehicle intelligent dynamic path guidance method and system based on deep reinforcement learning. The method comprises the steps of obtaining three-dimensional terrain point cloud and obstacle information of a flight area, constructing an environment topological structure diagram, extracting features through a three-layer diagram convolutional network, and generating high-dimensional environment feature representation. And a deep reinforcement learning state space is constructed, and a multi-target reward function is designed. And initializing the deep reinforcement learning network by using [-0.1, 0.1] uniformly distributed random parameters to generate an initial path scheme. Optimizing through a dynamic planning algorithm, adjusting a node sequence according to a path length and a task dynamic threshold value, and calculating a target function value. And judging whether the current path scheme reaches the optimal balance point, if not, adjusting the weight of the reward function by using a gradient descent method, and regenerating the path scheme until the path scheme is optimal. And finally, converting the scheme into a waypoint coordinate and speed instruction, performing real-time monitoring during flight, and triggering re-planning when an obstacle exceeds a safety threshold, so as to achieve the purposes of safe flight of the unmanned aerial vehicle in a complex environment and the like.

The invention discloses a multi-unmanned aerial vehicle track dynamic planning and collaborative obstacle avoidance method based on reinforcement learning, and the method comprises the steps: 1, initial environment construction: constructing an urban low-altitude environment three-dimensional grid map, and planning an initial four-dimensional track for each unmanned aerial vehicle; 2, conflict perception: in the operation process of the unmanned aerial vehicle, calculating conflict information between the unmanned aerial vehicle and surrounding obstacles and other unmanned aerial vehicles in real time; 3, conflict resolution: training an intelligent agent to learn an optimal avoidance strategy by using a time sequence neural network model and a reinforcement learning algorithm, and dynamically adjusting a track; and 4, track adjustment: after successful conflict resolution, backtracking an unmanned aerial vehicle action sequence, recording the position of the unmanned aerial vehicle in each time step, and obtaining a new unmanned aerial vehicle four-dimensional track. And finally, a flight route set meeting the unmanned aerial vehicle task requirement and the safety interval meeting the requirement is obtained. According to the invention, the operation efficiency of the unmanned aerial vehicle is improved, and a more intelligent, efficient, understandable and assessed cooperative obstacle avoidance strategy is realized.

The invention provides an industrial robot distributed collaborative debugging method and system, and relates to the technical field of distributed control, and the method comprises the steps: building a directed acyclic graph model of an industrial robot collaborative debugging task, analyzing the directed acyclic graph model through a critical path method, and determining a critical path of the collaborative debugging task; reliability modeling is carried out on each industrial robot node in the collaborative debugging task, and the failure probability of each collaborative debugging sub-task under the current scheduling scheme is calculated in combination with the time redundancy characteristics of each collaborative debugging sub-task; according to the obtained directed acyclic graph model of the industrial robot collaborative debugging task, the key path of the collaborative debugging task and the failure probability of each collaborative debugging sub-task under the current scheduling scheme, defining a state space of a task scheduling scheme, searching the state space through a dynamic planning algorithm, and scheduling the task scheduling scheme according to the searched state space; and obtaining a scheduling scheme with the minimum global failure probability as an optimal fault-tolerant scheduling strategy at the current moment.

A source-grid-load-hydrogen storage multi-stage planning method and system considering flexible resources, relating to the technical field of source-grid-load-hydrogen storage system planning. The method comprises: collecting source-grid-load-hydrogen storage data for data preprocessing; constructing a flexibility supply and demand characteristic model and a source-grid-load-hydrogen storage multi-stage dynamic planning model; calling a solver to solve the source-grid-load-hydrogen storage multi-stage dynamic planning model to obtain an optimal solution; and outputting a multi-stage source-grid-load-hydrogen storage investment result, a multi-stage source-grid-load-hydrogen storage operation policy, and a multi-stage flexibility supply evaluation result within a planning period. Using the minimization of investment costs, operation costs, and insufficient flexibility penalty costs within the whole planning period as target functions, various constraints such as a new energy permeability constraint and a load loss rate constraint are introduced, a dynamic planning method is proposed, and a source-grid-load-hydrogen storage multi-stage planning solution that has sufficiently economical planning operation and is sufficiently flexible is obtained. According to a processing method based on piecewise linearization, the model is simplified, and the computation speed is increased.

The invention provides an SLA demand-oriented computing network multi-element fusion arrangement method, which comprises the following steps of: matching a demand timeliness priority queue with a directed acyclic graph of container arrangement, and searching an arrangement scheme with optimal resource utilization efficiency and most balanced load by applying a dynamic planning method to form an initial arrangement decision; in combination with a preset calculation network element arrangement strategy template, dynamic selection and adjustment are carried out according to actual conditions, including selection of an arrangement strategy with a preferential resource utilization rate in a service peak period and selection of an arrangement strategy with a preferential cost in a service valley period; and recording and analyzing various calculation network elements generated in a container arrangement process, including resource occupation, starting time, a dependency relationship and an arrangement decision, and optimizing a resource prediction and scheduling algorithm according to historical data.

The invention belongs to the technical field of agricultural automation, and provides a closed park-oriented tractor multi-machine collaborative operation scheduling system and a control method, and the method comprises the steps: employing a multi-source heterogeneous sensor network to construct an environment perception layer, generating an environment state tensor and a dynamic environment model through space-time registration and data fusion, and obtaining a dynamic environment model; a complex task is decoupled into an atomic operation unit, a single tractor optimal task sequence is generated by utilizing dynamic planning, a genetic algorithm is improved to realize global operation distribution, and an optimal path is planned for tractors in a closed park through an ant colony algorithm in combination with a dynamic environment model to control the tractors to perform collaborative operation. Deploying an edge computing node to receive the tractor pose data in real time, judging an actual conflict risk by calculating space-time conflict density, fitting a prediction trajectory function to detect a prediction conflict risk, and if the actual conflict risk or the prediction conflict risk exists, triggering conflict coordination; and selecting a hierarchical coordination strategy for the tractor according to the conflict risk type to realize obstacle avoidance control.

The invention discloses a polling path dynamic planning method, medium and equipment, and the method comprises the steps: obtaining environment information in real time, dynamically switching an RTK-GPS mode, a visual inertial navigation mode and a laser SLAM positioning mode, generating the current positioning information of a robot in combination with a global map, and constructing a local environment map; and planning an adaptive second preset inspection path based on the first preset inspection path. Environmental feature information is actively collected in an outdoor environment, a real-time obstacle avoidance path is generated by dynamically updating a local environment map, and the real-time obstacle avoidance path is fused to a second preset inspection path, so that smooth switching and dynamic obstacle avoidance cooperation of a positioning mode in an indoor and outdoor scene are realized; the problem of planning failure caused by multiple positioning source conflicts, local path oscillation and environment sudden change in a complex scene is solved, and the continuity of the inspection process and the path planning robustness are guaranteed.

The invention relates to a dynamic planning method, device and equipment for a low-altitude route of an unmanned aerial vehicle and a medium. The method comprises the following steps: acquiring real-time dynamic environment change data and obstacle distribution information of a low-altitude flight area, constructing a preliminary data set, and generating an initial flight path; according to the initial flight path, predicting energy consumption data, and if the energy consumption data exceeds a threshold value, performing optimization to obtain an optimized path; performing risk assessment quantification, combining a safety margin adjustment mechanism to obtain a safety index, and if the safety index is lower than a threshold value, performing safety distance adjustment to generate a safety path; monitoring flight conditions, generating a condition monitoring update report, acquiring new obstacle position information, and generating a dynamic obstacle avoidance path; and generating an execution instruction set of the unmanned aerial vehicle according to the dynamic obstacle avoidance path. By adopting the method, the dynamic environment real-time adaptability of the unmanned aerial vehicle can be improved, and the route meeting the task requirement can be planned in time when the unmanned aerial vehicle faces sudden obstacles or environment changes.

The invention provides an electric power system natural disaster early warning and response method based on real-time monitoring, and relates to the technical field of electric power early warning response, and the method comprises the following steps: data analysis and processing: carrying out the analysis and processing of real-time monitoring data in an electric power system through combining a long short-term memory network with a Bayesian network; generating a scheduling scheme: updating the state of the power system in real time and generating an optimal scheduling scheme based on a multi-source data fusion and optimization method combining a Kalman filter and a genetic algorithm; generating an emergency response strategy: simulating the influence of natural disasters on the power system in real time in the virtual model through a digital twin technology and a self-adaptive dynamic planning method, and providing the emergency response strategy; and anomaly detection and self-healing: carrying out anomaly detection and self-healing on the operation state of the power system by combining big data analysis with edge calculation.

The invention discloses a hydrological monitoring point dynamic planning method and system, and relates to the technical field of hydrological monitoring, and the method comprises the steps: collecting hydrological monitoring point parameters of a target region, and making an initial point planning scheme; and performing hydrological dynamic prediction on the region based on prediction of a future time zone to obtain a hydrological prediction result. And according to the result, performing redundancy adjustment on the initial point location scheme to generate a first adjustment scheme. And based on the monitoring demand and the prediction result, correcting the point location blind area to form a second adjustment scheme. And in combination with the geographic feature data and the damage risk threshold, compensation adjustment of the damaged point location is carried out, and a third adjustment scheme is generated. And integrating the above adjustment schemes, carrying out global optimization, and generating an optimized hydrological monitoring point layout. The technical problems of poor planning flexibility and low precision of the hydrological monitoring point positions in the prior art are solved, and the technical effects of improving the planning flexibility and precision of the hydrological monitoring point positions and improving the hydrological monitoring quality are achieved.

The invention discloses a live-line work safety monitoring system based on artificial intelligence, and relates to the technical field of intelligent electric power safety, and the system comprises a data collection module which is used for collecting and preprocessing work data in a live-line work area in real time, and extracting live-line body point cloud data based on the work data; the finite element grid unit is used for dividing all charged bodies into finite element grid units and judging high-temperature charged bodies; and the violation judgment module is used for calculating the electric field intensity value of the position where the operator is located in real time. By combining an electric field, a temperature field and three-dimensional point cloud multi-modal data, comprehensive dynamic perception of an operation area is realized, finite element modeling and dynamic non-uniform heat source distribution analysis are adopted, a high-temperature charged body is accurately identified, the equipment fault early warning capability is improved, action characteristics of an operator are captured through a long-short-term memory network, and the working efficiency is improved. And the illegal behaviors are accurately judged by dynamically combining environmental data, and an optimal operation path is generated in real time by utilizing a three-dimensional grid division and dynamic planning algorithm.

The invention discloses a dynamic planning method for an air route of an urban low-altitude unmanned aerial vehicle. The method comprises the following steps: firstly, constructing an urban low-altitude environment model based on an airspace rasterization technology, fusing multi-dimensional constraint factors such as geographic data, meteorological conditions and airspace control information through multi-source environment information, and accurately calibrating the position of a take-off and landing point; secondly, a dynamic grid availability evaluation model is established in combination with environmental constraints and a real-time airspace state, and the navigation feasibility of each grid unit is quantitatively analyzed; then, an improved A * algorithm is combined with a grid availability evaluation result to generate a global optimal initial route; finally, a rolling time domain optimization strategy is introduced, and uncertain factors such as sudden obstacles and airspace dynamic limitation are responded in real time through periodic non-flight route re-planning after the unmanned aerial vehicle takes off. Through collaborative fusion of static environment modeling and a dynamic optimization mechanism, the problem of real-time planning of the air route of the unmanned aerial vehicle in the urban low-altitude complex environment is effectively solved, and the environmental adaptability and task reliability of the system are remarkably improved.

The invention discloses an ice storage air-conditioning system optimization control method based on a dynamic programming algorithm, which comprises the following steps: acquiring operation data of an ice storage air-conditioning system in real time, and constructing a directed graph model, directed graph nodes and interconnection weights among the nodes; transferring and converting a cold storage and release operation mode of the ice storage air-conditioning system in a preset optimization time domain into a shortest path for solving the directed graph model; based on a directed graph model, solving a shortest path by using a dynamic programming algorithm; and on the basis of the shortest path, an ice storage air conditioning system control strategy is generated and issued to a system execution layer, and the control strategy comprises but is not limited to a switching decision of a cold storage and release operation mode in each time period of the optimization time domain and an equipment start-stop state. The cold storage and release operation mode is controlled through the dynamic planning method, efficient matching of cold storage air conditioner loads is achieved, the system energy efficiency is improved, the operation cost is reduced, and operation and maintenance operation is simplified.

The invention belongs to the technical field of integrated energy system planning optimization, and provides an integrated energy system planning optimization method and system based on deep reinforcement learning in order to solve the problems of difficult solution, long time consumption and the like in integrated energy system planning. Determining an objective function and constraint conditions of the integrated energy system, and forming an initial dynamic programming optimization problem; converting an initial dynamic programming optimization problem into a Markov decision process; performing off-line learning on historical operation data of the integrated energy system by using a DDPG algorithm and a prediction network; and applying the optimization parameters of offline learning to an Online-DDPG algorithm, and solving an initial dynamic planning optimization problem to obtain an optimal planning scheme of the integrated energy system. The problem of solving the model caused by curse of dimensionality and information loss is effectively solved, and meanwhile, the anti-interference capability to load and renewable energy fluctuation is improved.

The invention discloses a simulation optimization method for a digital twin factory. The simulation optimization method comprises the steps of obtaining workshop equipment data and performing classified storage; generating a task priority and resource configuration rule based on the basic operation data of the equipment; constructing a three-dimensional space model by using a 3D modeling tool, and creating a digital twinborn simulation factory model in combination with real-time production data; constructing a dynamic planning state space and a decision variable, and determining the resource use time, the resource allocation position and the specific quantity of each task; according to the resource allocation scheme and the task chain dependency relationship, evaluating a risk value of a task node in combination with historical equipment fault data; and based on the influence range report of the production fault chain, evaluating a task execution sequence and adjusting resource allocation and task time arrangement. According to the method, the influence of faults on a production plan and resource waste are reduced, the stability and adaptability of a factory system are remarkably enhanced, and the reliability and fine management level of overall production are improved.

The invention discloses a ship path dynamic planning method based on a collision risk field, and the method comprises the following steps: carrying out the grid division of a map of a navigation water area of a ship, and obtaining a grid map of the navigation of the ship; performing path search on the grid map by adopting a path planning algorithm to obtain an initial route of the ship; establishing a collision risk field model of the target ship according to real-time navigation parameters of the target ship, and substituting the coordinates of the central point of each grid in the grid map into the collision risk field model to obtain a collision risk value of each grid in the grid map; when the ship sails to the grid with the collision risk value larger than a set threshold value, the grid which is closest to the terminal point and has the collision risk value smaller than the set threshold value serves as a local optimal point, and the ship route of the ship is updated; and repeating the steps until the ship arrives at the destination.

The invention discloses a thermal power plant intelligent inspection path dynamic planning and anomaly recognition method, and particularly relates to the technical field of thermal power plants, and the method comprises the following steps: S1, constructing a dynamic three-dimensional risk thermodynamic diagram and an equipment health gene code; s2, multi-target dynamic path planning; s3, multi-modal data collaborative acquisition and anomaly detection are carried out; s4, performing abnormal root cause association and risk level judgment; s5, re-planning a closed-loop path of abnormal driving; and S6, executing and dynamically updating. According to the invention, through a core technology architecture of dynamic risk thermodynamic diagram-equipment health gene code-multi-modal anomaly detection-closed-loop path planning, full-process optimization of intelligent inspection of the thermal power plant from risk perception, path decision-making to anomaly handling is realized, and the inspection efficiency, the equipment reliability and the emergency response capability are significantly improved; meanwhile, risks of man-made misjudgment and system failure are reduced, and core technical support is provided for intelligent transformation of the thermal power plant.

The invention relates to the technical field of asphalt pavement paving, and discloses an asphalt pavement 3D intelligent paving cooperative control system and method based on BIM + GIS, and the system comprises a data fusion layer, a real-time control layer, a multi-machine cooperation and task distribution layer, and a quality closed-loop layer. The real-time control layer comprises multi-source data acquisition (positioning / temperature / compaction degree and the like) and three types of core decisions carried out by the cloud center, and the three types of core decisions carried out by the cloud center comprise feeding demand prediction (based on paving speed + thickness) of a dumper, thickness / speed adaptive adjustment of a paver and dynamic planning of a rolling path of a road roller. According to the method, real-time correction of transverse deviation and elevation deviation is achieved through combination of PID control and fuzzy control, the expansion amount of a hydraulic cylinder of the ironing plate is dynamically adjusted by associating the paving speed, the multi-source sensing technology of Beidou, IMU, laser ranging and infrared imaging is adopted, time sequence consistency data synchronization is achieved through 5G communication, and the real-time correction of the transverse deviation and the elevation deviation is achieved. And the precision and the closed-loop optimization effect can be controlled in real time.

The invention relates to an irrigation amount control method and system for prune cultivation. The method comprises the steps of performing timestamp calibration and dynamic weight distribution processing according to soil data, meteorological data and transpiration data to obtain a multi-modal data set; based on the multi-modal data set, microclimate abnormal event detection and soil humidity correction processing are carried out, and anti-interference soil water content data are obtained; according to the anti-interference soil moisture content data and preset prune growth stage parameters, carrying out root system layered water absorption dynamic modeling treatment to obtain a deep root system moisture demand prediction value; according to the deep root system moisture demand prediction value and the transpiration data, irrigation amount dynamic planning processing is carried out, and the target irrigation amount is obtained. According to the method, through multi-source data fusion, root system water absorption modeling processing and dynamic irrigation planning, the target irrigation amount of prune cultivation can be accurately determined, the accuracy and scientificity of irrigation management are remarkably improved, and powerful guarantee is provided for high-quality growth of prunes and efficient utilization of water resources.

The invention discloses an intelligent three-dimensional warehouse storage management method and system, and relates to the technical field of warehouse management, and the method comprises the steps: receiving a task instruction from an upper system through a communication network, and distributing specific execution equipment according to an equipment operation state and real-time inventory data; calculating a logistics path based on the equipment position information, the reservoir area environment data and the task demand, and generating a control instruction to drive the execution equipment to execute operation according to the path; according to the goods attributes and the storage rules, goods allocation distribution is optimized through a dynamic programming algorithm, and goods storage and taking operation is completed; the operation state of execution equipment is collected in real time through a sensor, abnormity is recognized based on collected data, an alarm signal is triggered through an upper system, and meanwhile a task execution strategy is adjusted. According to the intelligent three-dimensional warehouse storage management method provided by the invention, comprehensive monitoring of the equipment health state is realized, abnormity can be found in time, an alarm mechanism can be triggered, and the equipment fault risk is remarkably reduced.

The invention provides a carbon neutralization path dynamic planning method and system based on carbon asset matching, and the method comprises the following steps: obtaining multi-source carbon asset data in an enterprise, a park or a region, and carrying out the preprocessing of the multi-source carbon asset data; the carbon asset value is dynamically evaluated by constructing the time-varying value evaluation model and comprehensively considering the change of factors such as policies, markets and technologies along with time, and meanwhile, various carbon assets are managed in a classified manner by constructing the four-dimensional asset matrix, so that the carbon asset management efficiency and benefit are remarkably improved; through supply side geographical matching and demand side time sequence matching, carbon asset supply and demand are accurately connected from space and time dimensions, cost and loss are reduced, an improved Bellman equation is applied, decision weight is dynamically adjusted under triple constraint conditions, carbon asset transaction and emission reduction measures are scientifically coordinated, and the utilization efficiency of carbon assets is improved; by constructing a reinforcement learning model, path planning is continuously optimized, and the scheme is ensured to be able to land and adapt to changes.

The invention discloses a complex water area garbage intelligent detection and collection method based on multi-modal perception, and the method comprises the steps: obtaining image perception data and semantic environment data of a complex water area; the method comprises the following steps: performing preprocessing, performing feature extraction and semantic coding, and then performing multi-modal feature alignment fusion, feature extraction and multi-task detection to obtain a garbage detection result, thereby effectively realizing deep fusion of semantic priori and visual features, and improving the accuracy and robustness of garbage recognition in a complex water area environment; then task importance evaluation is carried out to construct a garbage disposal priority queue, a path dynamic planning strategy oriented to energy consumption constraint and task priority collaborative optimization is constructed to carry out path generation, navigation energy consumption is reduced on the premise that task benefits are considered, and garbage detection and collection tasks in a complex water area are achieved; the system adapts to various intelligent devices such as unmanned ships, unmanned aerial vehicles and shore-based platforms, and has high autonomy, environmental adaptability and engineering deployability.

The invention provides a multi-unmanned aerial vehicle task allocation method, device and equipment and a storage medium, and belongs to the technical field of unmanned aerial vehicle task allocation. The method comprises the following steps of: initializing task target data and unmanned aerial vehicle cluster data, and calculating cost information of executing each task target by each unmanned aerial vehicle in an unmanned aerial vehicle cluster; based on the cost information of each task target, a dynamic planning strategy is adopted to select an optimal task target set needing to be executed; and adopting a greedy strategy to sequentially select the unmanned aerial vehicle with the minimum cost for executing each optimal task target from the unmanned aerial vehicle cluster to perform task allocation until task allocation of the optimal task target set is completed. According to the method, the task value can be maximized, the execution cost of the unmanned aerial vehicle is reduced from local optimization, and the execution success rate of the current task is guaranteed to the maximum extent.

The invention relates to the technical field of charging management, and discloses an intelligent charging management method, device and equipment for an electric scooter and a storage medium, and the method comprises the steps: carrying out the parameter collection and initialization setting of a single-phase bidirectional converter and a permanent magnet synchronous motor in the electric scooter, obtaining system basic parameters and communication data, and obtaining a network authentication identifier and a charging station operation parameter. And dynamic planning of a charging strategy is executed according to the parameters to generate a segmented charging control curve. And the data are input into the self-adaptive prediction controller to realize real-time adjustment in the charging process, and charging state data and power compensation parameters are obtained. And adopting a double-neural network model structure to obtain a battery safety feature matrix and a fault probability vector. And constructing a three-dimensional charging state space model to carry out optimization calculation on the power compensation parameters, inputting a deep reinforcement learning network to carry out iterative optimization, and generating charging control parameters of the next period, thereby improving the charging efficiency and the electric energy quality of the electric scooter.

The invention relates to a simulation-based satellite scheduling task dynamic planning method, device and equipment, and the method comprises the steps: carrying out the modeling according to various time-varying environment dynamic parameters in an environment where a satellite is located, and obtaining a dynamic environment model which comprises an environment factor model and an emergency triggering mechanism; the method comprises the following steps: constructing a satellite resource dynamic model according to various resources of satellites, resource consumption during task execution and resource supply, constructing a multi-satellite collaborative model according to an interaction relationship among a plurality of satellites and related information of each satellite, and constructing a multi-satellite collaborative model under a dynamic environment model, the satellite resource dynamic model and the multi-satellite collaborative model. According to the method, tasks in a to-be-executed task data set containing single-satellite and cooperative tasks are dynamically scheduled by adopting a task planning algorithm, a task execution planning table of satellites is obtained, satellite task planning can be carried out aiming at emergencies and multi-satellite cooperative tasks, and the planning is closer to the actual situation.

The invention discloses a system combat ability dynamic game comprehensive evaluation method based on deep learning, and relates to the technical field of combat ability evaluation.According to the method, key features of time and space in a battlefield environment are dynamically extracted by combining a multi-sensor fusion technology with a time sequence convolutional network and a graph neural network, a situation model is updated in real time, and the dynamic game of the system combat ability is comprehensively evaluated. The optimization process of unmanned cluster marshalling and task allocation is accelerated by utilizing a near-end strategy optimization PPO and transfer learning technology, and adaptive adjustment of a marshalling scheme on complex task requirements is realized by defining a multi-task collaborative efficiency index; in the aspect of strategy optimization, a multi-agent game model driven by deep reinforcement learning is constructed, friend or foe resource allocation, dynamic environment change and uncertainty factors are integrated, an optimal strategy is effectively generated, and low-efficiency schemes are eliminated through combat scene simulation; and meanwhile, through a real-time monitoring and dynamic planning mechanism, emergencies such as target change and emergency task insertion are dealt with, and the task execution continuity and cooperative efficiency are improved.

The invention discloses an aircraft real-time resource allocation method and system, relates to the technical field of aircraft control, and is used for solving the technical problem of conflict between resource limitation and demand caused by aircraft resource allocation, and the aircraft real-time resource allocation method comprises the following steps: constructing a task priority queue and a resource state matrix of an aircraft, defining a state space; defining an action space, and defining a reward function according to the dynamic resource allocation target; building a real-time control software environment of the aircraft; preparing training data of a double-depth Q network, and constructing a double-depth Q network structure; constructing an experience playback buffer area, carrying out dual-depth Q network training, and updating parameters of a main network; obtaining a value function between the state and the action of the aircraft, and enabling the aircraft to make an optimal resource allocation decision through the value function; dynamic planning is used to refine and allocate resources in real time, maximization of task income under the condition of meeting resource constraints is achieved, and real-time resource allocation of the aircraft is completed.

The invention discloses a multi-angle video automatic editing method based on content defect detection, and relates to the technical field of video processing. According to the method, multi-track video materials are accurately synchronized to the same time axis through cross-correlation calculation, and flaws in video content are analyzed frame by frame and classified and identified by applying fuzzy detection of a dynamic threshold and combining jitter detection and shielding detection of rotation and displacement estimation. And according to a detection result, an editing scheme is intelligently generated through a dynamic programming algorithm of state transition optimization, high-quality fragments are selected, defective parts are automatically removed, the continuity and multi-view diversity of video contents are reserved, and finally, a high-quality recorded video is generated through integration. The video editing efficiency is greatly improved, the manual intervention requirement is reduced, meanwhile, it is ensured that the quality of the output video meets the professional application requirement, and the method is suitable for multiple scenes such as conference recording, educational training and multi-angle video production and has high practical value and popularization prospects.