1252 results about "Dynamic programming" patented technology

The invention relates to a hydrological flow prediction method and system based on multi-site time-space association. The prediction method comprises the following steps: carrying out dimension reduction and feature reconstruction on original multi-site hydrological data through an auto-encoder; space-time correlation modeling and adjacency matrix dynamic construction are carried out, a multi-dimensional Euclidean distance matrix between stations is calculated based on a multivariable dynamic time warping (MDTW) algorithm, a similarity matrix is generated in combination with dynamic programming, and a dynamic adjacency matrix is constructed by fusing a geographic space adjacency relation; extracting spatial features of a GCN (Graphics Convolutional Network); carrying out adaptive time sequence decomposition and trend-period modeling; and carrying out multi-stage fusion prediction and result output, and generating a final prediction result through a decoder in combination with the decomposed trend item and periodic item. According to the method, accurate extraction and dynamic correlation modeling of spatial-temporal characteristics of multi-site hydrological data are realized, the accuracy and robustness of single-site flow prediction are improved, and the problems that multi-site spatial-temporal correlation modeling is insufficient, non-linear time sequence alignment is difficult, and single-site prediction precision is limited are solved.

The invention relates to the technical field of smart energy management, and discloses an energy consumption optimization method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting energy consumption data, environment data and equipment operation state data of a target region in real time, carrying out the data preprocessing, and generating a sample data set; based on space-time correlation analysis, integrating the sample data set into an energy consumption feature matrix by adopting a multi-dimensional data fusion strategy combining feature-level fusion and decision-level fusion; inputting into an energy consumption prediction model constructed based on a deep learning algorithm, and outputting an energy consumption prediction result of the target area in a future time period; and according to an energy consumption prediction result, generating an optimal energy consumption control strategy through a dynamic programming algorithm. The dynamic optimal energy consumption control strategy is generated through linkage of the deep learning algorithm and the dynamic programming algorithm, and the problems that in the prior art, energy consumption optimization precision is insufficient, and adaptability is poor are solved.

The invention relates to a remote-control system of a stratospheric airship and a control method thereof. The control system is divided to four levels: a decision-making level for executing the selection and switch of a remote-control flight mode according to the actual working state data, and sending a corresponding mode command; a planning level for dynamically planning and generating an airship motor drive command according to the mode command sent by the decision-making level; an executing level for realizing the drive control to an airship motor according to the motor drive command of the planning level; and a perceiving level for finishing the real-time monitoring and data collection of the airship working state, and providing the original data of the airship working state to the decision-making level. The four-level control system architecture design of the control system is capable of simplifying the control device, improving the control efficiency, and fundamentally solving the problems of the function, efficiency and reliability of the near space airship ground and airborne control system.

The invention discloses an industrial land intelligent site selection method based on spatial big data and an artificial intelligence big model. The method comprises the following steps: 1) obtaining a feature matrix containing spatial topology constraints and economic association rules; 2) extracting key analysis elements in user site selection requirements; 3) inputting the spatial key analysis elements into CNN convolutional neural network branches to obtain spatial features; the policy text type key analysis elements are input into a Transform branch, and policy features are obtained; inputting the industrial chain type key analysis elements into GNN graph neural network branches to obtain industrial chain features; 4) generating an industrial land analysis conclusion; and 5) generating a comparison report, a site selection proposal, a risk avoidance strategy and an emergency plan of each feasible scheme. According to the invention, through fusion of multi-source spatial data (industrial layout, traffic network and regional assessment) and an artificial intelligence large model, dynamic planning constraint matching, full-life-cycle risk assessment and multi-objective optimization site selection are realized.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an intelligent charging and endurance optimization method for an unmanned aerial vehicle nest, and the method comprises the steps: collecting flight, battery and environment data through an unmanned aerial vehicle, transmitting the data to a nest, and precisely evaluating the remaining endurance of the unmanned aerial vehicle through the nest in combination with historical tasks and geographic information; performing dynamic task planning based on reinforcement learning, and generating an optimal charging strategy by using a dynamic planning algorithm; by means of a multi-mode sensor and a self-adaptive charging control model, intelligent regulation and control of the charging process are achieved; and through solar auxiliary power supply and multi-machine cooperative charging, the endurance is enhanced. The problems of low charging efficiency, poor endurance management, insufficient system reliability and the like in a traditional method are effectively solved, the utilization rate of nest charging resources is remarkably improved, the service life of a battery is prolonged, the endurance of the unmanned aerial vehicle and the system stability are enhanced, and the method has important significance in promoting efficient application of the unmanned aerial vehicle in multiple fields.

The invention discloses a cross-domain collaborative interface dynamic configuration method and device and a computer program product. According to the cross-domain collaborative interface dynamic configuration method, service flow priorities are determined by analyzing service layer data, and an initial interface configuration scheme is generated in combination with network state information. And when the initial scheme does not meet the transmission requirements, optimizing the interface parameters by adopting a dynamic programming algorithm, generating a new configuration scheme, and issuing the new configuration scheme to the target network management system. In the service switching process, network performance is monitored in real time, interface parameters are dynamically optimized and updated to a network management system, and intelligent management of service flow and efficient utilization of network resources are achieved. The method can adapt to different manufacturer devices and network levels, guarantees the service quality of cross-domain services, and improves the operation efficiency and reliability of an electric power communication network.

The invention provides a lottery store violation detection method and system based on multi-modal data fusion, and relates to the technical field of intelligent monitoring, and the method comprises the steps: detecting a current abnormal event, carrying out time sequence perception target detection on monitoring video data, and recognizing violation electric equipment in a video image frame based on adaptive feature enhancement and multi-target tracking. The method comprises the following steps: acquiring time sequence data of current abnormity and illegal electric equipment detection, constructing a time sequence incidence relation of current abnormity, equipment detection and an electric state based on a dynamic causal network, calculating a time-varying weight and instantaneous causal intensity by utilizing conditional entropy increment, acquiring optimal time lag in combination with eigenvector conversion and a dynamic programming algorithm, and determining the current abnormity and illegal electric equipment detection according to the optimal time lag. And weighting the integral of the instantaneous causal intensity and the exponential function of the optimal time delay to obtain an event matching score, and distinguishing a temporary power utilization event and a continuous illegal power utilization event based on the event matching score.

The invention relates to the technical field of agricultural planting, and discloses a crop planting suggestion generation method adapting to different climate conditions. Real-time climate data are collected through a multi-source climate sensor, climate dynamic feature data are generated through a spatial-temporal feature fusion algorithm, and planting strategy parameters are obtained by inputting the climate dynamic feature data into a pre-trained mixed decision model. And constructing a multi-objective optimization model taking the crop adaptability matching degree and the resource utilization efficiency as objectives, and globally optimizing a planting scheme by adopting a hierarchical dynamic programming algorithm integrating fuzzy logic constraint and an adaptive decomposition strategy. A multi-level regulation and control framework comprising a decision-making layer, an adaptation layer and an execution layer is constructed, the decision-making layer generates a global planting sequence, the adaptation layer uses a sliding window optimization algorithm to adjust local parameters, the execution layer regulates and controls soil humidity and illumination intensity based on a robust feedback control algorithm, and finally a planting control instruction is output. And accurate and efficient climate adaptability planting decision is realized.

The invention provides a hybrid vehicle energy management method, device and equipment and a medium, and the method comprises the steps: obtaining the real-time traffic information and configuration information of a vehicle, and calculating the demand power and demand torque of the vehicle according to the real-time traffic information and configuration information; calculating energy consumption of the battery according to the battery remaining capacity, the capacity information and the temperature information to obtain energy consumption information of the battery; power parameters of the vehicle are obtained, and the actual power and the actual torque of the vehicle are calculated according to the energy consumption information and the power parameters; actual energy consumption of the vehicle is calculated according to the actual power and the actual torque, and an energy distribution strategy is made according to the actual energy consumption; and constructing a dynamic programming algorithm to perform power distribution on an engine and a motor of the vehicle according to the energy distribution strategy. According to the technical scheme of the embodiment, through real-time interaction of the Internet of Vehicles, the vehicle can complete efficient energy distribution according to the processing information, and energy consumption and environmental pollution of the hybrid power vehicle are reduced.

The invention provides an energy storage power station and power grid collaborative complementary regulation method and system, and relates to the technical field of smart power grids, comprising the steps of obtaining disturbance components by performing multi-scale decomposition on power grid frequency and voltage data, and constructing a distributed model prediction control framework to optimize energy storage resources in groups; and designing a self-adaptive dynamic programming controller to evaluate the system state and output the optimal charging and discharging power, and responding to the topological change of the power grid in real time. According to the invention, collaborative optimization configuration of energy storage resources can be realized, the power grid stability is improved, the service life of energy storage equipment is prolonged, and the system operation cost is reduced.

In a general aspect, a quantum program can be automatically synthesized or compiled. In some implementations, a discretized state space is obtained. The discretized state space includes a plurality of states for one or more qubits of a quantum processor. A discrete action space is obtained. The discrete action space includes a plurality of unitary operations for the one or more qubits of the quantum processor. A policy that uses the discretized state space and the discrete action space to generate quantum programs for quantum state preparation is defined. A dynamic programming process is used to improve the policy. An initial state and a target state of the one or more qubits is identified. The policy is used to generate a quantum program to produce the target state from the initial state. The quantum program include a subset of the unitary operations in the discrete action space.

The invention provides an optimization method of a heat supply system, which comprises the following steps: acquiring a real-time heat source operation state, a power grid load, a user demand and an environment constraint condition of the heat supply system, extracting characteristic information of sudden cooling and equipment fault information, and determining a heat source priority and a response speed initial value; acquiring parameters required by a cost function corresponding to the adjusted heat source priority sequence, determining cost weights of different heat sources after adjustment through a linear weighting method, and outputting a cost optimization sequence; identifying a power grid load according to the cost optimization sorting and the environment constraint condition, and if the power grid load is lower than a preset threshold value, calculating a heat source output distribution initial scheme through a dynamic programming algorithm to obtain a preliminary output matrix; and according to the optimized output distribution scheme, summarizing the total heat source emission amount, and if the total heat source emission amount exceeds a preset threshold value, reducing the high-emission heat source output proportion through an iterative adjustment algorithm, and outputting a target output distribution matrix.

The invention discloses an inspection method, system and device of an intelligent inspection robot and a medium, and belongs to the technical field of intelligent inspection, and the method comprises the steps that the intelligent inspection robot receives an inspection task instruction, and analyzes and obtains an inspection site, inspection time and inspection content; providing a navigation map; planning a plurality of polling paths to be selected, and calculating an optimal path through a dynamic planning algorithm; the intelligent inspection robot performs autonomous movement according to the optimal path, and performs data acquisition on an inspection object and an environment where an inspection point is located at a set point location; the intelligent inspection robot processes and analyzes the collected data, preliminarily recognizes potential abnormities, and transmits all the data to the cloud; generating alarm information and an inspection report; and the intelligent inspection robot executes automatic nest-returning charging operation according to the inspection period and the residual electric quantity state to guarantee continuous completion of the task. The task adaptability, the risk response efficiency and the continuous operation capability of the intelligent inspection robot in a complex environment are improved.

The invention relates to the technical field of underground pipe gallery distributed intelligent control, and discloses an underground pipe gallery intelligent illumination and energy consumption optimization system, which comprises a dynamic credibility evaluation module for analyzing multi-modal environment sensing data in real time and generating a dynamic credibility score; the edge-cloud collaborative optimization module is used for realizing distributed collaborative learning through local model fine tuning and global knowledge distillation; and the event-driven resource allocation module dynamically optimizes a lighting strategy and a communication priority based on credibility scores and emergency information, and through multi-modal data cross validation and adaptive weight adjustment, the robustness of anomaly detection is significantly improved, and misjudgment caused by sensor failure or environmental interference is effectively suppressed; the dynamic planning algorithm is used for synchronously optimizing energy consumption and equipment service life, efficient balance of safety and energy efficiency is achieved, the system is particularly suitable for complex underground environments and can automatically adapt to sudden anomalies, and the intelligent level and the energy utilization efficiency of pipe gallery illumination are remarkably improved.

The invention discloses an electric vehicle V2G micro-grid energy storage capacity optimization method based on dynamic planning, particularly relates to the technical field of micro-grid energy storage optimization, and aims to solve the problem of energy storage capacity optimization defects caused by spatial dynamic characteristics caused by mobility of an existing electric vehicle. Performing geographic grid division on the node position, generating a space-time matrix by combining with a timestamp, and identifying a high-frequency access node; constructing a state transition equation based on the inter-node impedance matrix, and setting a node-level safety boundary by taking a line capacity change rate as a dynamic constraint; a staged reverse dynamic programming algorithm is adopted, spatial dimension optimization is preferentially executed on the high-frequency access nodes, and charging and discharging constraints fusing voltage deviation and capacity overrun penalty terms are generated; carrying out global optimization by taking the minimization of the total operation cost as a target after the constraints are integrated, and outputting an energy storage capacity configuration strategy of each time period; and finally generating an energy storage deployment scheme.

The invention discloses an Android system user interface interaction method and device based on a large language model, and relates to the technical field of intelligent man-machine interaction, and the method comprises the steps: receiving a natural language instruction of a user, and synchronously capturing a real-time interface state of a current Android screen to generate a structured context containing semantic information of interactive elements; inputting the instruction and the context into a large language model, and generating a hierarchical task plan formed by a plurality of atomic operations with a logic dependency relationship; based on a dynamic adaptation mechanism, converting the atomic operation sequence into an operation instruction which can be executed by the Android system; receiving interface change information after instruction execution, and feeding back the interface change information as a new round of context to the large language model to determine a task execution state; and finally generating natural language feedback information to the user according to the execution state. By means of the mode, complex cross-application tasks can be understood and executed, and interaction intelligence of the Android system is remarkably improved through real-time interface perception and dynamic planning adjustment.

The invention relates to the technical field of aerospace, in particular to low-altitude traffic flow airspace-oriented real-time planning, and provides a centimeter-level precision detection network covering a low-altitude airspace by integrating multi-dimensional data sources such as radar electromagnetic feature recognition, ADS-B (Automatic Dependent Surveillance-Broadcast) automatic monitoring, Beidou or GPS (Global Positioning System) space-time reference positioning and the like. The three-dimensional trajectory and motion situation of the aircraft are solved in real time, intelligent reconstruction of an airspace sector and adaptive optimization of a flight corridor are realized by adopting a dynamic programming algorithm driven by reinforcement learning based on the real-time pose data of the aircraft, and a flight conflict prediction model constructed by combining a space-time convolutional neural network is used for predicting the flight conflict. According to the method, potential risks can be pre-judged, an optimal avoidance path can be generated, full-process digital management and control from identity verification to airspace authorization can be realized by constructing an aircraft digital identity authentication system and a dynamic access control mechanism, and modules such as a three-dimensional navigation information service module, a low-altitude digital communication private network module and an intelligent early warning and warning system module are integrated. And the guarantee of full-life-cycle service is provided for the low-altitude aircraft.

The invention provides a fused salt heat storage coupled compressed air energy storage cooperative power generation system and a control method thereof.The fused salt heat storage coupled compressed air energy storage cooperative power generation system comprises a data acquisition module, a multi-target optimization decision algorithm based on dynamic programming, an energy distribution module, a dynamic adjustment module and a safety protection module; a multi-objective optimization decision algorithm based on dynamic programming automatically switches system working modes, an energy distribution module optimizes fused salt and air flow of a high-temperature fused salt air heat exchanger, and a dynamic adjusting module adjusts fused salt pump flow, compressor rotating speed and valve opening in real time. And the safety protection module is used for monitoring and preventing over-temperature, fused salt solidification and pipeline corrosion. The problems that in the prior art, a fused salt heat storage type photo-thermal power generation system is insufficient in heat storage capacity under the extreme weather condition, a compressed air energy storage system depends on fossil fuel, the afterburning efficiency is low, and the energy loss is caused due to the low system coupling degree can be effectively solved.

The invention relates to the technical field of three-dimensional modeling, in particular to a three-dimensional modeling system and method based on a laser radar, and the system comprises a point cloud boundary extraction module, a boundary error analysis module, a self-adaptive cutting repair module and a stitching structure optimization module. According to the method, three-dimensional coordinates, normal vectors, reflection intensity, distances and indexes of boundary points are called layer by layer, a Sobel operator and a Canny algorithm are combined to cooperatively recognize a boundary and an error region, then local region repair is completed based on least square plane fitting and spatial residual analysis, a tension vector is analyzed through dynamic planning, and stitching path adjustment is executed. On the cooperative basis of multi-type point cloud features, a spatial topological structure and boundary continuity dynamic modeling, the problems of local damage, connection errors and instable stitching paths of point cloud boundaries in complex scenes are solved, and the integrity of boundary structures, the coherence of stitching transition and the rationality of spatial geometric expression are remarkably improved.

The invention discloses an unmanned aerial vehicle inspection system and method for a super-large-scale photovoltaic power station, and relates to the technical field of substation inspection, and the system comprises a multi-rotor unmanned aerial vehicle group deployment module, a honeycomb scanning network construction and three-dimensional radiation thermograph generation module, an inspection path dynamic planning module, and a photovoltaic panel inclination angle optimization path optimization module. The fault probability analysis module is used for collecting data through electroluminescence and infrared thermal imaging and generating a fault distribution diagram by fusing a historical defect feature library, the hidden fault recognition module is used for dynamically recognizing fault types in combination with multi-source data and flight instructions, and the intelligent charging docking module is used for carrying out intelligent charging. And adjusting a return flight threshold according to the fault level and battery attenuation and triggering a charging mechanism. The technical problems that in the prior art, a large-scale photovoltaic power station is low in inspection efficiency, insufficient in fault detection precision and poor in environmental adaptability are solved, and the technical effects of improving the inspection efficiency, the fault detection precision and the environmental adaptability through cooperative inspection of the unmanned aerial vehicle group are achieved.

The invention belongs to the technical field of trajectory planning, and discloses an urban road trajectory planning and control method and system, and the method comprises the steps: generating an obstacle set and a Frenet projection of a road physical boundary through employing a spatial clustering algorithm; utilizing an extreme value method and a self-adaptive smoothing algorithm to dynamically construct a feasible trajectory region trajectory, and synchronously performing interval constraint expansion on a future prediction trajectory of the dynamic obstacle; constructing a path optimization objective function with interval constraints, and solving an optimal path variable in real time by using a numerical optimization method; an expected speed and acceleration sequence of each sampling point is generated based on a space-time domain dynamic planning method, and a longitudinal motion curve is optimized in real time by using an objective function; and inputting the space path and the longitudinal speed sequence into an MPC system, and calculating an optimal front wheel steering angle and acceleration control instruction to realize vehicle trajectory tracking. The method can sense errors and environment changes in a self-adaptive mode, the response speed and robustness of dynamic traffic flow are improved, and multi-target collaborative optimization is achieved.

The invention relates to an intelligent management and control system for bottom mud elution operation. Comprising the steps that a polluted sediment depth feature semantic construction and evolvable model is adopted; a comprehensive sensor collects multi-dimensional information, and GNN is used for constructing a semantic map of bottom mud pollution factors; understanding the bottom mud as an evolvable pollution body to establish a response model; the bottom mud / pollution response expression model is used for follow-up control prediction; adopting elution path self-adaptive planning; taking the pollution semantic model as input, and identifying an excitation point block sensitive to disturbance of the polluted bottom mud; constructing a path diagram by using a dynamic programming algorithm; updating the response model of the polluted bottom mud, and performing path reconstruction and compression to form a workflow; a working environment perception and pollution migration prediction technology is adopted; a dynamic water body sensing system is deployed, and flow velocity, water temperature and water level changes are collected in real time; constructing a pollution migration and propagation prediction model to visualize the evolution of the pollution thermodynamic diagram along with time as a control system feedforward factor; and a multi-stage cooperative control mechanism of interference and response linkage is adopted.

The invention provides a multi-agent cooperative task inference and robot scheduling system, which comprises an interaction unit used for acquiring a natural language instruction issued by a user, sending the natural language instruction to an intelligent processing unit and interacting with the intelligent processing unit, receiving an execution result of a task and body state information of a robot, and sending the received execution result to the intelligent processing unit; sending a task missing information supplementing request when the task information is missing; the intelligent processing unit is used for dynamically sensing an environment and a task state according to a user instruction, a body state and environment information fed back by the robot and stored memory, and performing dynamic planning and decision making based on a sensing result and an inverse result of a task execution result to realize task reasoning and robot scheduling; and the robot cluster executes tasks according to the action sequence and feeds back own body state information and environment information. According to the invention, through a multi-agent cooperation framework based on a large model, the robot is endowed with dynamic reasoning and active interaction capabilities, and the requirements of efficient task reasoning and cooperative execution in a dynamic environment are met.

The invention provides a computing power center resource centralized self-adaptive control scheduling system and method, and the method comprises the steps: S1, a user submits a computing power training task through an API, and sends the task to a DAG task composer, and forms a task list to be scheduled; s2, the intelligent monitoring analysis module is used for continuously collecting node data and calculating real-time CS and HS scores of each node; s3, analyzing the dependency relationship type data by a task composer, and calling a dynamic programming algorithm in an adaptive scheduler to make a decision; s4, the adaptive scheduler performs dynamic task distribution according to the task list of the DAG task orchestrator and the intelligent monitoring analysis module, and S5, the distributed runtime and environment management module is used for uploading the prepared Docker mirror image to the distributed storage; and S6, through the unified communication adaptation layer, the mirror image is pulled from the distributed storage on the target node and the container is started, the user does not need to concern the difference of the operating system in the whole process, and efficient and intelligent adaptive scheduling is realized.

The invention discloses a generative dialogue optimization method and system based on multi-step intensified simulation, and the method comprises the steps: generating a plurality of candidate replies based on the current dialogue history through a basic dialogue model; using the user simulator to generate a dialogue chain for each candidate reply; calculating a reward for each dialogue chain, selecting K dialogue chains with the highest reward value as high-quality samples, and taking the dialogue chain with the lowest reward value as a negative sample; updating parameters of the basic dialogue model by using the high-quality sample and the negative sample through a contrast learning and reinforcement learning method to obtain a trained basic dialogue model as a customer service model; and the customer service model generates a reply according to a statement input by the user. According to the conversation generation method, multi-round conversation dynamic planning, medical scene adaptability optimization and efficient strategy exploration can be fused, and double optimization of user trust establishment and business goal achievement is achieved while compliance is ensured.

The invention provides a process optimization analysis system for vacuum concentration, which comprises a multi-source sensing module for acquiring temperature gradient distribution, vacuum degree dynamic waveform, material rheological characteristics and phase change latent heat parameters of an evaporation chamber in real time by adopting a distributed sensor array; the digital twin modeling module is used for constructing a hidden Markov chain model of process parameters and concentration kinetics based on transfer learning, and generating an adaptive covariance matrix by fusing historical batch data; the multi-objective optimization module is used for solving a Pareto frontier solution set in a non-convex solution space by adopting a mixed integer dynamic programming algorithm, and synchronously optimizing an energy efficiency ratio, a product yield and an equipment life index; and the adaptive execution module predicts and controls the hysteresis effect of the real-time compensation system through a nonlinear model, and establishes a double-closed-loop anti-interference regulation mechanism. Accurate sensing, dynamic modeling, multi-objective optimization and self-adaptive execution of the concentration process can be realized, the concentration efficiency, the product quality and the equipment operation stability are improved, and the process intelligence level is improved.

The invention discloses an automatic driving bus collaborative formation dynamic scheduling method, which comprises the following steps of: after introducing a vehicle formation operation, constructing a vehicle energy consumption model based on vehicle specific power, and measuring the vehicle operation energy consumption of an automatic driving bus formation in any formation mode; in an intelligent network connection environment, based on a rolling time domain optimization framework, a Markov decision process model is constructed, and an automatic driving bus collaborative formation dynamic scheduling problem is explained; defining a state variable and a decision variable of the Markov decision process model, and determining constraint conditions of related variables and a target function of the Markov decision process model; the decision space of the Markov decision process model is reduced, and an approximate dynamic programming algorithm is used to solve the automatic driving bus collaborative formation scheduling problem; a multi-step look-ahead strategy based on dynamic planning is provided, the convergence efficiency of an approximate dynamic planning algorithm is improved, and an automatic driving bus collaborative formation optimization scheduling scheme is obtained. And the automatic driving bus capacity utilization rate is improved.

The invention provides an unmanned aerial vehicle flight path dynamic planning method and system, and relates to the technical field of flight path planning, and the method comprises the steps: obtaining the starting and ending points of the flight of an unmanned aerial vehicle, and planning an initial unmanned aerial vehicle flight path based on the starting and ending points of the flight of the unmanned aerial vehicle and a preset constraint; constructing an unmanned aerial vehicle flight environment model based on a preset flight area map, the elevation information and the obstacle information; route optimization is carried out based on the initial unmanned aerial vehicle flight route and a flight environment model, and an unmanned aerial vehicle initial obstacle avoidance route is generated; the unmanned aerial vehicle initial obstacle avoidance route is optimized based on a preset iterative optimization model, and an optimized unmanned aerial vehicle obstacle avoidance route is obtained; and updating the optimized unmanned aerial vehicle obstacle avoidance route to the initial unmanned aerial vehicle flight route to generate a final unmanned aerial vehicle flight route. According to the invention, the flight time and cost are saved, the adaptability to the environment, especially the urban complex environment, is enhanced, and the safety is guaranteed.

The invention discloses an intelligent scheduling method based on task optimization. According to the method, firstly, the task load of each time period is predicted through historical data, and then an M / M / c queuing model is established to quantify a task queuing duration expected value as an optimization target. A two-stage optimization strategy is adopted: in the first stage, the optimal number of service desks in each time period is determined by applying dynamic planning, and accurate distribution of human resources is realized; in the second stage, specific employees are distributed to shifts through constraint planning, and meanwhile, complex constraints such as total working time fixing, continuous working duration, shift intervals and skill matching are met. According to the system, a queuing theory, constraint optimization and a prediction model can be combined, and on the premise of ensuring the feasibility of a scheduling scheme, the task processing waiting time is effectively shortened, and the service quality and the human resource utilization rate are improved.

The invention discloses a video anti-shake method and system based on multi-scale fusion and adaptive smoothing, relates to the technical field of video image processing, and aims to effectively solve the image quality problem caused by shake in a video shooting process. Gradient histograms and wavelet energy distribution characteristics of video frames are extracted through graying and normalization processing, and the gradient histograms and the wavelet energy distribution characteristics are input into a jitter type recognition network to recognize translation, rotation and Z-axis jitter probabilities. And further extracting motion, frequency domain and edge features, and generating multi-modal coupling features through combination of a dynamic feature interaction network and a dot product attention mechanism. And constructing a motion trajectory by using the features, optimizing the trajectory by using a texture perception double-layer smoothing strategy, introducing an adaptive penalty term into a dynamic planning cost function, and outputting a smooth motion compensation parameter. And finally, processing the boundary region through motion compensation and image extrapolation to generate an anti-shake video frame. Through multi-scale feature fusion and a self-adaptive smoothing strategy, the video anti-shake effect is effectively improved, and the method is suitable for complex scenes.