162 results about "Constrained optimization problem" patented technology

The invention provides a pulse passing rate improving method based on accumulation signal demodulation, and belongs to the technical field of nuclear electronics digital pulse processing. Comprising the following steps: performing baseline deduction and monopulse screening on a radiation field pulse signal; constructing a nuclear pulse signal model, fitting to obtain model parameters, and constructing a system response matrix; modeling a nuclear detection physical process into a mathematical model; solving a constraint optimization problem by using a non-negative least square method; and demodulating and calculating all the pulse signals including the stacked pulses which are discarded by stacking, so as to obtain delta pulses containing energy information. According to the invention, the accumulation signal which is abandoned by a traditional accumulation abandoning algorithm is demodulated, so that the technical problem of increasing the pulse passing rate is solved, and meanwhile, heavy peaks and high-energy trailing in an energy spectrum are inhibited. Compared with an existing accumulation discarding method, the pulse passing rate effect is remarkably improved on the premise that the energy spectrum accuracy is guaranteed.

The invention relates to the technical field of wireless communication, and discloses an unloading method in an ultra-dense millimeter wave MEC network. Aiming at the problems of insufficient terminal capability, high energy consumption of an ultra-dense base station, millimeter wave coverage limitation, communication security risk and the like under the condition of sharp increase of a calculation-intensive task, the method comprises the following steps: firstly, obtaining network basic information, constructing a network architecture containing communication, calculation unloading and a security model, and establishing a multi-constraint optimization problem; based on the optimization problem, initializing a multi-strategy black-wing plinuary optimization algorithm population through logic mapping and elite selection; updating individual positions through global and local search in algorithm iteration, and screening historical optimal individuals; and finally, configuring and unloading resources according to the configuration. According to the method, the NOMA technology, a multi-step unloading framework and a hybrid communication mode are combined, the optimization efficiency is improved through a multi-strategy black-wing optimization algorithm, the local energy consumption is reduced, the communication rate is improved, the safety is guaranteed, the optimal scheme meeting time delay and safety constraints is rapidly converged, and the user experience is improved.

A vehicle is controlled for travel on a road having a geometric design defined by one or a combination of a docking line, a cross-section, and a cross-section of the road such that different values of parameters of the geometric design of the road, traffic on the road, traffic rules for traffic flow on the road define different traffic scenarios. By relaxing configuration parameters of a real-world scene and tightening corresponding limiting parameters, a mixed integer non-convex constraint optimization problem for a current real-world traffic scene is converted into a mixed integer convex optimization problem for approximate representation of the real-world traffic scene, so that a carrier is controlled. A mixed integer convex optimization problem for the converted approximate representation of the real-world traffic scene is solved to generate current control commands for controlling one or more actuators of the vehicle.

The invention discloses an electric vehicle lifting torque control method and system. The method comprises the steps that driving operation, vehicle state and environment data are collected in real time and preprocessed into multi-dimensional real-time feature vectors; based on the vector, extracting and updating a driving style feature vector by using an incremental clustering and lightweight time sequence convolutional network, and further mapping to generate a personalized cost function weight coefficient in model prediction control; synchronously operating the vehicle dynamics and thermodynamics simplified model, predicting a dynamic physical constraint boundary of a driving system in a future time domain, and constructing a dynamic feasible domain curve; taking a driver instant demand as a tracking target, fusing a personalized weight and a dynamic feasible region curve, constructing and solving a finite time domain constraint optimization problem, and generating a current period target torque instruction; and filter parameters are adaptively adjusted through inverse dynamic model feedforward compensation in combination with a dynamic feasible region, a final torque control instruction is generated, and unification of safety, smoothness and individuation of torque control is achieved.

The invention provides a delay compensation control method for a double-vibrator wave energy conversion device, which comprises the following steps of: performing numerical modeling and hydrodynamic analysis on the double-vibrator wave energy conversion device, establishing a time domain motion equation of the double-vibrator wave energy conversion device, and introducing a time delay function into the time domain motion equation to simulate control signal transmission delay; simulating the execution delay of the brake by using a partial differential equation; establishing a state-space equation of the device, replacing a convolution term of the time-domain motion equation with the state-space equation, and calculating to obtain a motion state of the device; a Hamiltonian function is defined to convert a constrained optimization problem into an unconstrained optimization problem, and the Hamiltonian function is solved to obtain an optimal control criterion considering control delay so as to realize maximization of energy capture under the optimal control criterion. According to the method, the operation characteristics of a physical system are truly reflected by introducing control delay, so that the locking control method is effectively implemented in an actual physical device, and the energy capture efficiency of a wave energy conversion device and the reliability of system operation are effectively improved.

The invention discloses a quadruped robot landing planning method based on scene decoupling and risk avoidance. The quadruped robot landing planning method aims at solving the problems that in the prior art, perception is not precise in a complex environment, and the obstacle avoidance capacity is insufficient. The method comprises the following steps: acquiring and fusing multi-source sensor data, and segmenting an original point cloud; generating a scene decoupling elevation map of a multi-layer structure by using the segmentation point cloud; constructing a multi-objective optimization problem taking risk avoidance as a core based on the elevation map, wherein the multi-objective optimization problem comprises a comprehensive cost function and a strict obstacle avoidance constraint; utilizing a reaction formula adjusting module of a capturable region theory to cope with a dynamic instability risk; and solving the multi-target and multi-constraint optimization problem in real time by adopting a hierarchical solving strategy, and finally generating an optimal foot end drop point considering safety and stability. According to the method, the terrain adaptability, the motion stability and the decision intelligence of the quadruped robot in an unstructured environment are remarkably improved through fine decoupling of a scene and quantitative avoidance of multi-source risks.

The invention relates to the technical field of enterprise management, in particular to an enterprise process custom configuration method and system based on a code-free technology, and the method comprises the steps: constructing a multi-layer directed hypergraph containing data, control and condition dependence, calculating an edge weight, and converting the dependence into a first-order logic constraint set; performing incremental satisfiability detection, and outputting a conflict core set containing conflict types and severity in combination with minimum unsatisfiability subset mining and strong connected component analysis; mapping the conflicts into modifiable elements, and generating parameterized candidate repair schemes based on a repair operation library and historical cases; converting the candidate scheme into a weighted constraint optimization problem, and solving an optimal scheme by adopting a branch and bound algorithm; influence propagation verification is completed in the copy, a differential patch and a rollback identifier are generated, transactional submission is carried out after passing, and the weight and the case library are updated. According to the method, automatic diagnosis and low-risk repair of implicit dependency conflicts are realized.

The application discloses a target direction finding method based on a short baseline unified model, which is realized by a computer and comprises the following steps: firstly, an improved polar coordinate representation (MPR) unified model is constructed; secondly, a closed-form initial solution of direction finding under the MPR model is obtained; then, an improved successive unconstrained minimization method based on the MPR is used to solve a quadratic constraint optimization problem to obtain the angle of the target source; thirdly, a direction finding initial solution is used to guide source deployment; fourthly, time-space reference system error calculation is performed; finally, target source TDOA measurement and time-space reference system error elimination and final solution solving are performed; the application effectively avoids the threshold effect of the short baseline positioning system based on the far-field direction finding unified model of the modified polar coordinates, so that the far-field target direction finding method of the unmanned aerial vehicle group based on the time difference of arrival measurement becomes possible; the calibration source and the target source are jointly monitored, the time-space reference system error is eliminated through the time difference measurement of the calibration source, and thus the direction finding precision is improved.

The embodiment of the invention relates to the technical field of radars, and discloses an MIMO radar emission sequence set design optimization method, which comprises the following steps: constructing a signal model designed based on an AF emission sequence set for an MIMO radar system, selecting ISL of minimizing local AAF and CAF as an optimization criterion, selecting an energy constraint and a dynamic range constraint as constraint conditions, and selecting an energy constraint and a dynamic range constraint as constraint conditions; establishing a four-order non-convex constraint optimization problem; decomposing the fourth-order non-convex constraint optimization problem into a plurality of sub-problems which can be iteratively solved by utilizing an MBI algorithm; sequentially solving the sub-problems by means of CVX to obtain a preliminary solution; and continuously adjusting a search direction and a step length by using a BLS algorithm, and carrying out iterative optimization on the approximate solution until an Armijo condition is met, thereby obtaining a global optimal solution meeting a design requirement, and further designing a radar emission sequence set with good related characteristics, thereby improving the target detection capability, the anti-interference capability and the resolution of the MIMO radar system.

The invention discloses a zero-order primal dual method and device for a black-box constraint optimization problem, and the method comprises the steps: constructing a corresponding black-box constraint optimization problem based on a preset constraint optimization scene, and determining a tightly convex feasible region containing an original variable; converting the black box constraint optimization problem into an unconstrained optimization sub-problem which can be solved iteratively; on the basis of a function value query result of the black box constraint optimization problem, gradient correlation information of the unconstrained optimization sub-problem about an original variable is estimated, iterative solution is carried out on the unconstrained optimization sub-problem on the basis of the gradient correlation information, and the original variable after iterative updating is limited in the tightly convex feasible region; and when a preset convergence condition is met, obtaining a final original variable as an optimization result. A black box constraint optimization problem is converted into an unconstrained sub-problem which can be solved iteratively by introducing a near-end Lagrangian fusion optimization framework, so that efficient optimization is realized under the black box condition that only function values can be obtained.

The invention relates to the field of intelligent scheduling allocation, in particular to an edge cloud computing resource allocation optimization method based on deep learning, which adopts a space-time prediction algorithm based on multi-head attention and gating mechanism optimization to design time coding and space coding. The spatial relationship and interaction between time sequence characteristics of the computing power load and edge server nodes are captured, and meanwhile, a multi-head attention mechanism and expansion causal convolution are combined, so that instantaneous computing power load fluctuation can be captured, and the long-term trend of the computing power load can be mined; therefore, a reliable basis is provided for subsequent computing power scheduling by predicting an accurate computing power load. The invention designs an alternating direction multiplier method based on genetic algorithm optimization, which is not only suitable for a nonlinear and multi-constraint optimization problem, but also can be expanded to a larger-scale distributed edge node cloud computing system, and meanwhile, a global optimal solution is quickly approached through the genetic algorithm, so that the quality of an initial solution is improved, and model convergence is accelerated; and the distributed collaborative allocation scheduling efficiency is improved.

A vehicle includes an electric powered propulsion system. An electric energy storage system is electrically connected to the electric propulsion system and is configured to have an electrical energy storage component and a controller. A charging port is connected to the electric energy storage system and configured to connect to an external power source. The controller includes a memory and a processor. The memory stores instructions for causing the processor to optimize a charging profile based on a plurality of received parameters using a multi-objective constrained optimization problem. The received parameters include a power type of a connected external power source, and at least one of a requested ready to depart time, a targeted state of charge, and an effective range.

The invention provides a new energy power system optimization regulation and control system and method based on a large language model, and belongs to the technical field of power system operation control. In the system, a new energy power system scheduling model knowledge base stores an objective function and a constraint condition of a new energy power system scheduling problem model; the information extraction agent generates a structured scheduling demand according to an input scheduling instruction based on a natural language and sends the structured scheduling demand to the problem modeling agent; the problem modeling agent calls an objective function and a constraint condition corresponding to the scheduling demand from the new energy power system scheduling model knowledge base to construct a corresponding constraint optimization problem; and the code writing agent converts the constraint optimization problem into an executable code. According to the method, the scheduling instruction based on the natural language can be automatically converted into the optimization code to be output, the optimization result can be obtained after the code is operated, automatic modeling and solving of the new energy power system scheduling problem are achieved, and therefore the threshold of new energy power system scheduling is greatly reduced.

The invention provides an active power distribution network fault self-healing cooperative control method, and belongs to the technical field of power grids, and the method comprises the steps: collecting the multi-source operation data of a power distribution network in real time, and enabling a model to output a prediction sequence of a power grid state in a period of time in the future through the fusion of a power grid topological relation and spatial-temporal dynamic characteristics; inputting the prediction sequence into a collaborative optimization controller; the collaborative optimization controller takes the space-time diagram convolutional network model as an internal dynamic prediction model, and generates an optimal switch action control sequence by solving a constrained optimization problem; and executing a first-step control instruction in the control sequence. The method has the advantages that the space-time correlation between the power grid topology and the dynamic operation data is captured by using the graph structure model, the accuracy of system state prediction under the new energy output fluctuation condition is improved, and more reliable look-ahead information is provided for subsequent optimization decision making.

The invention discloses a path planning method based on a quadratic unconstrained binary optimization model, which belongs to the technical field of path planning, is used for unmanned system navigation, and comprises the following steps: rasterizing an environment area, determining a starting point, an ending point and the positions of barrier grids, determining a set of the grids where barriers are located, and defining grid binary variables; defining a target function, and establishing an optimization problem of the target function under the conditions of adjacent grid constraints, in-out constraints, obstacle avoidance constraints and starting point and terminal point position constraints; converting the constrained optimization problem into a quadratic unconstrained binary optimization model, solving the quadratic unconstrained binary optimization model, determining the value of each grid variable according to the value of the independent variable when the target function takes the minimum value, and finally obtaining a planned path. According to the method, multiple complex constraint conditions are directly embedded into the target function through the penalty term, the complex constraint conditions do not need to be independently processed, and the logic of the solving process is simplified.

The invention relates to the technical field of water treatment, and discloses an electrochemical descaling automatic control system based on autonomous prediction of a pole reversal time sequence and an operation mode, the system comprises a sensing layer used for collecting raw water quality parameters and system state parameters in real time, and the system state parameters comprise real-time scale thickness and real-time scale thickness; the prediction layer is connected with the sensing layer, the prediction layer is connected with the sensing layer, the acquired data are utilized, the future scale thickness and the descaling efficiency under different control strategies are predicted through an AI prediction model, and the decision-making layer is connected with the prediction layer and used for solving an optimization problem with constraints according to a prediction result, and an optimal reverse-pole power supply control strategy is generated. The system can sense the dynamic change of the water quality of industrial raw water and the equipment state in real time, dynamically predict the scaling trend through the AI model and generate an optimally matched inverse pole strategy, so that the system can automatically adjust the equipment descaling state to the optimal working point according to the water quality fluctuation, efficient descaling is realized, and the effect is ensured to be continuous and stable.

The invention relates to an inertial parameter error-oriented optimal balancing mass gradient prediction method, which comprises the following steps of: based on balancing mass minimization, establishing and solving a constraint optimization problem of optimal balancing of dynamic and static imbalance of a satellite, and obtaining optimal balancing mass and position meeting an existing counterweight mounting position set of the satellite; analyzing a gradient matrix of the optimal balancing quality relative to the dynamic and static unbalance of the satellite; establishing a transformation matrix from the local coordinate system of each part of the satellite to the overall coordinate system of the satellite, and further calculating the gradient value of the dynamic and static unbalance of the satellite relative to the inertial parameters of each equipment or part; and in combination with the balancing quality optimal solution, the gradient matrix, the transformation matrix and the gradient value, analyzing the influence of the inertial parameter error of each device or part on the optimal balancing quality.

The application discloses a backdoor attack method based on color frequency injection and adaptive local enhancement, and relates to the technical field of machine learning and artificial intelligence security. The method comprises the following steps: introducing low-frequency color offset and weak high-frequency signal into an image in a CIELAB color space to perform global color-frequency injection; using a pre-trained proxy model to locate a high-sensitive perception domain of the model through mixed evaluation of gradients and class activation maps, and generating a binary mask; in an HSV color space, respectively applying nonlinear stretching factors to saturation and brightness of the sensitive domain based on the mask to perform adaptive local enhancement; using Gaussian smoothing, adaptive noise and histogram matching to eliminate edges and statistical abnormalities caused by local enhancement, completing compensation color enhancement to generate a poisoned image; and modeling a trigger core parameter as a constrained optimization problem, and using a particle swarm optimization algorithm to jointly dynamically update the trigger core parameter to obtain an optimal strategy. The application anchors the trigger feature depth in the core semantic area of the model and lurks in the normal data manifold, guarantees a high attack success rate, realizes extreme visual and feature concealment, and has strong anti-defense robustness.

Methods and systems are disclosed for automatically integrating subsurface structural maps with strike and dip information measured in subsurface wells. The method includes obtaining a seismic image volume for a subsurface region of interest and a well log for each of a plurality of wellbores penetrating the subsurface region of interest. Further, the method includes determining a seismic map of a geological surface from the seismic image volume, wherein the seismic map comprises an estimated depth and an estimated vector normal to the seismic map at a plurality of horizontal locations and determining an intersection point for each of the plurality of wellbores with the geological surface. Additionally, the method includes forming a cost function based, at least in part, on the seismic map and the intersection points of the plurality of wellbores and constructing a subsurface map by solving a constrained optimization problem based on the cost function.

Traditional algorithms for solving constrained optimization problems are complicated to implement, difficult to interpret, and require significant computational resources. Disclosed embodiments convert constrained optimization problems into parametric optimization problems, in which at least a subset of the constraints are converted into parametric quadratic penalty (PQP) terms that each depends on a translational parameter. The parametric optimization problem may be used for optimization in a power system (e.g., for optimal power flow, economic dispatch, etc.). When solving the parametric optimization problem, the translational parameters are updated to ensure convergence. The parametric optimization problem can be solved with reduced computational expense, using only a linear equation solver to solve a sequence of primal variables only, thereby reducing computational complexity and expense. In addition, the disclosed embodiments provide a means to incorporate constraints into machine-learning algorithms. The disclosed algorithmic framework also provides interpretability and insights for analysis.

This application relates to the field of rail transit technology and provides a method and system for deploying metamaterial tags in a railway scenario. The method includes: constructing an observation equation with the Euclidean distance between the train and the metamaterial tag as the observation distance, and constructing observation vectors for N metamaterial tags based on the observation equation; constructing a Fisher information matrix about the motion state vector based on the observation vectors and the train's motion state vector, and using the trace of the inverse of the Fisher information matrix as the Cramer-Rhodes lower bound; using the spatial coordinates of the N metamaterial tags as optimization variables, minimizing the Cramer-Rhodes lower bound as the objective, and introducing dynamic environmental constraints to form a constrained optimization problem; solving the optimization problem using a particle swarm optimization algorithm to obtain a set of target tag position coordinates; and deploying metamaterial tags at corresponding positions along the track based on the target tag position coordinates. This improves motion compensation accuracy.

The application provides a hybrid control method for a six-bar tensegrity robot for Mars surface exploration, which firstly models the six-bar tensegrity robot used, defines the coordinates and adjacency relationship of each face, and pre-establishes an energy landscape graph. Then in the process of controlling the six-bar tensegrity robot, the environment data is synchronously collected, and the rolling or jumping mode is switched according to the robot state and the environment data. In the rolling mode, the A algorithm is used to search for an energy-optimal face conversion sequence, and based on the adaptive thrust of the quadratic programming, the optimal motor thrust distribution is obtained by solving a constrained optimization problem for each face conversion in the face conversion sequence. In the jumping mode, a safe margin height is reserved, the size and angle of the motor thrust are adjusted to overcome the Mars gravity, and the terrain adaptability of the six-bar tensegrity robot is improved. The combination of the two solves the problems of low marching efficiency, high energy loss and weak maneuverability of the prior art on the complex surface of Mars.

The application discloses a preset time control multi-electron gun cooperative scanning optimization method and system, relates to the technical field of industrial process control and optimization, and comprises the following steps: S1, a dynamic coupling model is constructed, and multi-electron gun system parameters and a communication topology are initialized; S2, a leadership comprehensive score is dynamically calculated, and a main leader and an auxiliary leader are elected; S3, the main leader solves a local constraint optimization problem, and a global energy reference trajectory is generated; S4, each electron gun independently generates a control instruction through a respective preset time distributed optimal controller based on the global energy reference trajectory, and converges a tracking error; S5, the control instruction of each electron gun is subjected to constraint processing, and is executed; and S6, system performance is monitored, and adaptive adjustment is performed; the preset time control multi-electron gun cooperative scanning optimization method and system provided by the application solve the problems of serious interference between electron guns and poor anti-disturbance robustness in the prior art.

The invention discloses a low-altitude intelligent network-oriented multi-stage security task unloading optimization method, and belongs to the technical field of low-altitude intelligent network edge computing. The method aims at solving the problems that exploration and development of a traditional task unloading method in a low-altitude dynamic heterogeneous network environment are unbalanced, optimization precision is insufficient, and safety guarantee is insufficient. The method comprises the following steps: constructing a network architecture containing communication, calculation and security models based on basic information of low-altitude intelligent network equipment; configuring a multi-constraint optimization problem; after the initial population is generated, searching a globally optimal solution by adopting an improved escape optimization algorithm; and executing calculation energy consumption optimization configuration according to the optimal solution. According to the method, algorithm innovation and security constraint design are combined, task unloading efficiency, resource allocation rationality and data transmission security are cooperatively improved, and dynamic heterogeneous features of the low-altitude intelligent network are adapted.

The invention discloses a multi-target execution plan optimization method, system and product oriented to intelligent application, and belongs to the technical field of intelligent application. A logic application diagram and context information are obtained, and the context information comprises a service level target and a load feature; the method comprises the steps that a logic application diagram, a service level target, load characteristics and a pre-constructed operator performance model are integrated to obtain a constraint optimization problem, a multi-target constraint optimization solver is used for solving the constraint optimization problem, an optimal physical execution plan is generated, and an execution engine is used for executing the optimal physical execution plan. According to the method, the multi-dimensional service target can be understood and adjusted in real time by obtaining the service level target set by the user, the hysteresis is avoided by generating the optimal physical execution plan before plan execution, the self-adaptive capability and the quality of the generated result are improved by using the pre-constructed operator performance model and the multi-target constraint optimization solver, and the method has the advantages of being high in practicability and the like. And the scientificity, the globality and the reproducibility of a scheduling decision are ensured.

The application discloses a signal strength and sidelobe suppression combined optimization elastic Doppler complementary waveform design method, first proposes a Pareto effective framework for Golay complementary waveform design, and the framework jointly optimizes a Doppler elastic transceiving sequence pair. The framework considers a constrained optimization problem in terms of SMR and SNR, constructs a loss function of a Pareto multi-objective optimization problem by using an epsilon-constraint method, and obtains all possible Pareto optimal solutions. Secondly, in order to solve the optimization problem, a model-driven machine learning algorithm is innovatively designed to process the multi-objective optimization problem. The application effectively reduces the interference of sidelobes on weak target detection, improves the accuracy and reliability of target detection of a radar system, can more clearly detect and distinguish multiple targets, and enhances the overall performance of the radar system.

The invention provides an optimal rate segmentation method for improving reliability for short packet communication, which comprises the following steps of: S1, establishing a communication model based on rate segmentation multiple access, comprising a plurality of rate segmentation strategies, and representing the error probability of decoding a single stream by a user by utilizing a finite code length domain theory; s2, based on the decoding error probability of the user side, constructing an optimization problem of minimizing a maximum user weighted decoding error rate by taking power distribution and code length distribution as optimization variables; s3, introducing slack variables, code length integer field slack and Taylor approximation methods, converting non-convex constraints in the optimization problem into convex constraints, and constructing a convex constraint optimization problem; and S4, solving the convex constraint optimization problem by adopting an iterative algorithm based on continuous convex approximation to obtain an optimal power allocation scheme, a code length allocation scheme and a rate segmentation strategy. The method aims at minimizing the maximum decoding error probability of the user side, so that the reliability of a multi-user short packet communication scene is improved.

The application provides a delay compensation control method for a double pendulum wave energy conversion device, which comprises the following steps: numerical modeling and hydrodynamic analysis are performed on the double pendulum wave energy conversion device, time domain motion equations are established, a time delay function is introduced into the time domain motion equations to simulate control signal transmission delay, and partial differential equations are used to simulate the execution delay of the brake; state space equations of the device are established, the convolution items in the time domain motion equations are replaced by the state space equations, and the motion state of the device is calculated; a Hamilton function is defined to convert a constrained optimization problem into an unconstrained optimization problem, the optimal control criterion considering the control delay is obtained by solving the Hamilton function, and the maximization of energy capture under the optimal control criterion is realized. The application truly reflects the operation characteristics of the physical system by introducing the control delay, so that the closed-loop control method is effectively implemented in the actual physical device, and the energy capture efficiency of the wave energy conversion device and the reliability of the system operation are effectively improved.

The application belongs to the field of compound robot motion planning and control, and discloses a compound robot dynamic obstacle avoidance control method based on partition adaptive potential field and acceleration MPC fusion. According to the minimum clearance between the robot and the obstacle, the collision, obstacle avoidance and safety area are divided, a continuous partition scheduling function is designed to construct a weighted artificial potential field, a potential field hybrid force is outputted and an MPC initial value sequence is constructed. A discrete prediction model is established by taking acceleration as the control input, a constraint optimization problem in a prediction time domain N=3 is solved online, input constraints, change rate constraints and obstacle avoidance soft constraints with a relaxation variable are applied, the potential field initial value is used for guiding the solution and triggering the initial value updating, and PID weighted terminal guidance and potential field hybrid force fusion are introduced in the target near field. The application considers the safety of dynamic obstacle avoidance, motion stability and target arrival accuracy in a narrow environment.

The embodiment of the invention provides a 5G-Advanced sensing communication collaborative resource scheduling method and system for a smart substation, and belongs to the technical field of power system operation and maintenance. The method comprises the steps that a 5G-Advanced cellular-free unmanned aerial vehicle communication and sensing integrated network architecture is constructed, the network architecture comprises a passive sensing layer, an air-ground cooperative access layer and a centralized processing layer, and deep integration of sensing, energy supply and communication functions is achieved; establishing a system model based on the network architecture, wherein the system model comprises a network topology model, a communication and inductance integrated transmission model and a constraint optimization problem model; a cross-CPU dynamic resource scheduling algorithm based on MADDPG is designed, and joint optimization of user association, power distribution and load balancing is achieved through a centralized training-distributed execution mechanism; based on a scheduling algorithm and a system model, substation global real-time sensing, data transmission and operation and maintenance performance collaborative guarantee are achieved. The sensing fusion and coverage capability is greatly improved, the dynamic scene adaptability is better, and the sensing-operation and maintenance collaboration value is higher.