191 results about "Iterative search" patented technology

The invention discloses a free-form surface three-axis ball-end cutter equal approximation error finish machining tool path generation method which comprises the following steps: firstly, importing a free-form surface model to be machined, and setting data such as the radius, the line spacing, the approximation error maximum allowable value and the precision of a ball-end cutter; secondly, a group of section planes are planned according to the row spacing to intersect with the curved surface, the intersecting line serves as a cutter contact curve, and an equal-bow-height-error cutter contact iterative search method for driving cutter contact adjustment through the geometric distance is provided for the cutter contact curve on the section planes; the maximum distance between a cutter cutting envelope surface and a cutter contact track line is used as an approximation error, and an adaptive discrete method is adopted to carry out approximation error calculation; and finally, the cutter location points of the equal-bow-height-error cutter contact points serve as initial values, an equal-error cutter location point calculation method of step length self-adaptive adjustment iteration is provided, approximation errors between the cutter location points are all within an allowable range, and therefore the free-form surface three-axis ball-end cutter equal approximation error finish machining cutter path is obtained.

The invention discloses a cable cabling control method based on process big data, and belongs to the technical field of cable cabling control. Comprising the following steps that S1, parameters are collected in real time and preprocessed, historical batch records are called, the tension instability and the arrangement offset trend rate are calculated, simulation coupling factors are constructed, and an optimized coefficient combination is obtained; s2, the current tension instability and the arrangement offset change rate are calculated respectively, and the adjusted take-up speed and the adjusted twisting torque are obtained; according to the method, historical batch records of cables of the same specification are called, the tension instability and the arrangement offset trend rate are calculated, a numerical optimization algorithm is adopted to iteratively search an amplification coefficient and a sensitivity coefficient, a process-oriented optimization objective function is minimized, an optimized coefficient combination is obtained, and the stability of the cable tension is improved. Therefore, self-adaptive parameter optimization based on historical data is realized, the high adaptability of a control strategy to different batches of process fluctuations is improved, and the variability of quality indexes is remarkably reduced.

The invention relates to the technical field of low-altitude safety, and provides an aircraft optimal trajectory screening method and system based on adjustable phase quantum search. Candidate trajectories are generated according to flight mission constraints, cost values are calculated, and Boolean markers are distributed based on cost threshold values to identify high-quality solutions; performing iterative search and measurement on the balanced quantum superposition state of the coding track index by utilizing a Grover quantum search algorithm based on the adjusted search phase to obtain an output index; and mapping the index into a track, obtaining an optimized track through classic local optimization, adaptively adjusting the phase parameter and the cost threshold value based on performance evaluation, and repeatedly executing until an end condition is met, thereby determining an optimal track. On-line reconfiguration of a strategy is realized by introducing an adjustable phase mechanism, a feedback closed loop is established to realize adaptive optimization of parameters, and the real-time requirements of scenes such as urban air traffic control and unmanned aerial vehicle clusters are met.

The invention discloses a natural gas hydrate reservoir layered balanced mining regulation and control method based on simulation optimization. The method comprises the steps that S1, a three-dimensional geologic model is constructed according to logging, earthquake and core data of a target area; s2, establishing a heat-flow-force-chemical multi-field coupling numerical model, and performing initialization; s3, defining a multi-dimensional engineering decision parameter space; s4, establishing a comprehensive objective function; s5, an optimal engineering decision parameter combination Xoptimal is obtained through iterative search; and S6, well drilling and well completion operation is carried out according to the Xoptimal, and dynamic verification and feedback adjustment are carried out on the model and the mining system based on real-time monitoring data in the production process. A heat-flow-force-chemical multi-field coupling numerical model is established based on a three-dimensional geologic model, an agent model and multi-objective optimization are introduced, and dynamic verification and feedback adjustment are performed in combination with monitoring data during production, so that interlayer contradictions are effectively inhibited, balanced utilization of reservoir energy is realized, and the production efficiency is improved. And the overall recovery efficiency and the development economy of the multilayer hydrate reservoir are improved.

A heterogeneous neural processing system includes a first processor configured to perform encoding and decoding operations of an autoencoder, and a second processor configured to perform neural network operations of a particular task, the operations having iterative processing. The processors perform compute tasks through synchronous data exchange to implement a generative AI model. The first processor processes a feature map divided into data rows, caches the data into active memory using row-based depth-first scheduling, and selects deeper operations in the network hierarchy when processing branch inputs, outputs, and residual connections. And H reuses boundary pixels between the cache storage space segments, so that convolution and element-level operation can be executed simultaneously. A neural network adjusts the device analysis model to identify layer dependencies, applies search space constraints, performs an iterative search to generate a fusion plan, and selects an optimal plan based on external memory access and execution delays.

The invention belongs to the technical field of resource scheduling, and discloses an intelligent emergency resource scheduling system for quick response. Comprising the following steps: acquiring state information of a terminal and a network element, analyzing alarms and service changes, and generating an affected area; generating a topological graph model, and constructing an initial configuration vector for each topological node in the topological graph model; rasterizing the affected area, and determining serviceable nodes of the grid area; performing iterative search on the initial configuration vector, and determining an emergency configuration vector of each grid region; determining candidate interconnected node pairs by analyzing signal quality among different serviceable nodes in the affected area; and according to constraints of different edge elements in the topological graph model, constructing an optimal forwarding path in the affected area, and carrying out emergency resource scheduling. According to the invention, the affected range can be accurately determined, so that the configuration adjustment better fits the joint change condition of alarm and service, and the response efficiency and the resource matching degree of emergency resource scheduling are improved.

The invention relates to the technical field of microbial fermentation, in particular to a biological fermentation monitoring method and system based on multi-source data fusion. The method comprises the following steps: acquiring multi-source monitoring data in a fermentation tank and constructing a multi-dimensional state vector; on the basis of data of the multi-dimensional state vector in a preset time window, constructing a metabolic entropy of the current fermentation process; a simulated annealing algorithm is adopted to optimize control parameters, the temperature of the simulated annealing algorithm is positively correlated with the metabolic entropy, and minimization of a second energy function is taken as a target; a catastrophe reignition mechanism is set in the optimization process, fermentation abnormity is diagnosed based on triggering of the catastrophe reignition mechanism, and abnormal state characteristics are matched with a preset abnormity database through iterative search of an optimal weight distribution scheme so as to determine the abnormity type with the highest matching degree. By introducing the metabolic entropy and the catastrophe reignition mechanism, the sensitivity, the accuracy and the robustness of monitoring the biological fermentation process are remarkably improved.

The invention provides a neural network multi-objective optimization and FPGA hardware acceleration collaborative design method, and belongs to the field of deep learning model compression and hardware collaborative design. The method comprises the following steps: constructing a joint optimization space containing a neural network compression parameter and an FPGA hardware design parameter; a multi-target Bayesian optimization search strategy is adopted, iterative search is carried out in the joint optimization space, model precision, FPGA resource occupation and reasoning delay are synchronously optimized, and optimal candidate configuration is obtained; matching the compressed network structure with the FPGA parallel architecture by using a hardware-perceived pruning and quantification strategy; a multi-task performance prediction model is adopted to quickly predict the precision, resource occupation and delay of the optimal candidate configuration so as to accelerate the search process; according to the optimal configuration, a hardware accelerator code facing the target FPGA is automatically generated, and integration and implementation are completed. According to the method, collaborative optimization of neural network compression and hardware design is achieved, FPGA resource occupation can be remarkably reduced, the reasoning speed can be increased, and meanwhile the model precision is kept.

The invention relates to the technical field of industrial process automatic control, and discloses an automatic control method for a water sample analysis process, which comprises the following steps: establishing a unit step response reference model for representing a normalized standard track of a controlled object; iteratively searching an optimal amplitude scaling and time elastic factor in the two-parameter search space to minimize the fitting residual error of the reference model and the real-time data; the optimal amplitude scaling factor is utilized to determine a projection steady-state value to drive an execution unit, and the sampling interval is adaptively adjusted according to the optimal time elastic factor, the time elastic factor is introduced to realize closed-loop compensation of system kinetic parameter drift, a steady-state target can be accurately locked in an unbalanced state, and the stability of the system is improved. And the problem of control model mismatch caused by device aging or environment fluctuation is solved.

The invention discloses an IOS image and CBCT image registration method based on particle swarm and single-tooth optimization, and belongs to the field of image data processing, and the method comprises the following steps: respectively obtaining tooth semantic segmentation results and point cloud data of an IOS image and a CBCT image through preprocessing and segmentation steps; introducing a particle swarm optimization algorithm, and performing iterative search and optimization on six-degree-of-freedom rigid transformation parameters between the IOS image data and the CBCT image data so as to complete global initial registration of the dental arch scale; and on the basis of a result after global registration, a registration object is divided into a plurality of local units based on a single tooth, and local iteration alignment is performed on each unit, so that tiny dislocation on the single tooth is eliminated, and the overall registration effect is optimized. According to the method, on the basis of processing cross-modal difference and realizing global registration, the registration precision of a single tooth level is further ensured through a local optimization mechanism, and reliable automatic registration support is provided for clinical scenes such as orthodontics and implantation.

A reinforcement learning-based energy efficiency optimization method for UAV-assisted MEC networks, belonging to the field of mobile communication, is proposed. First, the system is modeled and state information is collected. The state information is input into the policy model to generate flight and energy transfer actions, enabling UAV movement and service device selection. Next, an energy transfer and task offloading model is defined, and a gradient-based device offloading time allocation algorithm is initiated. Then, a binary search for the equilibrium marginal utility gradient is initialized, followed by a single iterative search, updating the search interval, and completing the equilibrium marginal utility gradient search. The final time allocation scheme is then determined, energy transfer and task offloading are executed, performance data is collected, the reward function is calculated, and experience is stored. Finally, the Critic network, Actor network, and target network are updated, and the optimal policy is obtained through iterative optimization. This invention significantly improves the overall energy efficiency and task processing throughput of the system.

This invention relates to the field of robot motion planning technology, and proposes a method and system for obstacle avoidance path planning for a multi-degree-of-freedom robotic arm. The method includes: constructing a resultant force guiding field composed of an attractive potential field and a repulsive potential field; iteratively searching from the starting point to the target point along the gradient direction of the resultant force guiding field to generate a pre-path point sequence; mapping the generated pre-path point sequence to the joint space through inverse kinematics to obtain a candidate configuration sequence; sequentially detecting whether the local path segments between adjacent configurations in the candidate configuration sequence satisfy collision constraints, and determining that adjacent configurations are gaps to be repaired if they do not satisfy the constraints; establishing a bidirectional random expansion tree and adjusting the expansion step size in real time according to the potential energy intensity of the repulsive potential field in the current region; repairing the gaps to be repaired through the interconnection of the bidirectional random expansion tree, and splicing the repaired path segments with the path segments that satisfy the collision constraints to generate the final obstacle avoidance motion path.

The application discloses a micro-nano grating and photocathode collaborative design method. The method breaks the traditional isolated design, and considers the micro-nano grating and the photocathode as an organic whole, and takes the spectral response or integral quantum efficiency of the system as a whole as a target function. Specifically, the method comprises the following steps: establishing a system structure model and clearly defining an optimization target and process constraints; establishing a complex refractive index-wavelength correlation model of the photocathode to accurately obtain a complex refractive index distribution; determining a set of parameters to be optimized and setting initial values; calculating the system electric field distribution under the current parameters based on an electromagnetic simulation model; combining the electric field distribution and the photoelectric emission mechanism to calculate the spectral response of the system in the target waveband; and using a parameter optimization algorithm to iteratively search the parameter space, and outputting an optimal parameter combination. The application fully considers the electromagnetic and photoelectric coupling between the two and the actual process constraints, has the advantages of high design accuracy, strong pertinence, good implementability and the like, and can significantly improve the detection sensitivity of a weak light detection device.

The invention discloses a multi-target test selection method and system under a test uncertainty condition based on E domination guidance. The method comprises the following steps of: firstly, explicitly modeling uncertainty in a test process by using a detection probability matrix and a false alarm probability matrix; secondly, establishing a multi-objective optimization model which takes the minimum test cost, the minimum missing report rate and the minimum false alarm rate as objectives and takes the fault detection rate and the fault isolation rate as constraints; introducing an E domination relation fused with the preference of a decision maker to improve a traditional Pareto domination criterion; on the basis, designing an E-GPSO algorithm fusing a genetic algorithm and particle swarm optimization to perform iterative search, and dynamically maintaining a non-inferior solution set by adopting an external file based on an E domination criterion; and finally, outputting an optimal test scheme set meeting the constraint and the preference. According to the method, the test cost and the diagnosis reliability can be effectively coordinated and optimized, the decision space is remarkably reduced, and the efficiency and the quality of testability design are improved.

The invention belongs to the technical field of integrated photoelectronics, and particularly relates to an optimization design method of a compact low-loss linear heat insulation optical waveguide coupler. The method comprises the following steps: step 1, setting a performance constraint target: according to specific application requirements, firstly, definitely defining a plurality of optical performance indexes which the coupler must reach as constraint conditions; step 2, establishing a parameterized linear model: constructing a geometric model of the coupler, the width w (z) of the geometric model following a linear function w (z) = w1 + (w2-w1) * z / L, the input / output widths w1 and w2 being known, and the length L being a core variable to be optimized; 3, an iterative search and judgment algorithm is executed, wherein a systematic iterative search algorithm is adopted, and the minimum length Lmin meeting all performance constraints in the step 1 is searched; and 4, outputting a final design: taking the minimum length Lmin obtained in the step 3 as a final design parameter, and generating a final layout of the coupler.

The application relates to the field of low-altitude safety technology and provides an aircraft optimal trajectory screening method and system based on an adjustable phase quantum search. A candidate trajectory is generated according to flight task constraints, and a generation cost is calculated, a high-quality solution is identified based on a cost threshold value, an adjustment search phase is based on, a balanced quantum superposition state of an encoded trajectory index is iteratively searched and measured by using a Grover quantum search algorithm to obtain an output index, the index is mapped to a trajectory, and an optimized trajectory is obtained through classical local optimization, a phase parameter and a cost threshold value are adaptively adjusted based on performance evaluation and repeated execution until an optimal trajectory is determined by meeting an end condition. The adjustable phase mechanism is introduced to realize online reconfiguration of the strategy, a feedback closed loop is established to realize adaptive optimization of the parameters, and the real-time requirements of urban air traffic control, unmanned aerial vehicle clusters and other scenes are met.

This invention provides a method and system for elastic scheduling of cloud-edge resources in intelligent converged terminals for low-latency scenarios, relating to the field of resource scheduling technology. The method includes: acquiring and decomposing the end-to-end latency constraints of a target low-latency service request, and setting N initial candidate execution paths; mapping the N initial candidate execution paths to path state vectors and performing multi-dimensional evaluation analysis; performing path iterative search of the N initial candidate execution paths based on the comprehensive evaluation vector; configuring an elastic scheduling strategy using the path iterative search results; and performing intelligent scheduling management of the target low-latency service request. This invention solves the technical problems of existing cloud-edge-device scheduling methods that rely on static rules or single-node load, leading to unstable latency and low resource scheduling efficiency for low-latency services. It achieves the technical effect of improving the latency stability and resource scheduling efficiency of low-latency service requests through multi-dimensional constraint decomposition and path iterative optimization to realize cloud-edge-device collaborative scheduling.

The invention discloses a searching method suitable for a cascading failure chain containing a direct current system, and belongs to the technical field of power systems, and the searching method comprises the following steps: S1, early-stage data preparation and format conversion: collecting full-amount basic data of a power grid and uniformly converting multi-format data; s2, constructing an accident chain model containing non-direct current characteristics; s3, setting a fault search constraint and termination criterion, including limiting a search range and defining an accident chain termination criterion; s4, dynamically iteratively searching a cascading failure chain; s5, screening and verifying a high-risk fault chain; and S6, outputting a search result. According to the searching method suitable for the cascading failure chain containing the direct-current system, the risk value is calculated, the high-risk chain is screened, and simulation verification is carried out; and finally, outputting a fault chain evolution diagram and a risk parameter table, thereby accurately positioning a high-risk cascading fault chain in the AC / DC hybrid power grid, providing a clear fault evolution path and risk parameters for power grid dispatching personnel, assisting in formulating a scientific prevention and control strategy, and effectively reducing the occurrence probability of blackout accidents.

The invention discloses a discrete space global optimization method based on neighborhood mapping and multi-path parallel search. The method comprises the following steps: acquiring a discrete state variable to be optimized as an initial search starting point; generating an initial candidate set by applying a neighborhood mapping rule, and executing structure pre-screening; constructing a multi-path parallel computing architecture, and distributing the effective candidate states to a plurality of independent computing threads by taking the effective candidate states as local initial starting points of the independent search paths; executing deep iterative search on each search path, wherein each round of iterative search is screened according to an evaluation index system comprising a first evaluation index for macroscopic evaluation and a second evaluation index for microscopic difference distinguishing; and periodically synchronizing local optimal solutions output by all parallel search paths, and screening to obtain a global optimal solution. According to the method, the problem of search stagnation during construction of the high-performance password component is solved, and the high-security password component with both high nonlinearity and low difference uniformity can be efficiently and stably generated.

This invention discloses a multi-step load forecasting method for integrated energy systems based on Bayesian optimization and anti-overfitting, comprising the following steps: acquiring and preprocessing historical time-series data of the integrated energy system; constructing an initial feature set, using a random forest model to calculate the importance of each feature in the initial feature set, and selecting a core feature subset from the initial feature set according to a preset cumulative importance threshold; defining the hyperparameter search space of the hybrid model; using a Bayesian optimization framework, aiming to minimize the comprehensive loss on the validation set, and determining an optimal hyperparameter combination through iterative search; configuring and constructing the hybrid model according to the optimal hyperparameter combination; independently training the hybrid model for electricity load, cooling load, and heating load respectively, inputting the current time-series features into the prediction model, and outputting multi-step predicted values ​​of each type of load within a specified future time period. This effectively improves the overall accuracy and generalization ability of multi-step load forecasting for the integrated energy system over the next 24 hours.

The invention discloses an unmanned aerial vehicle and target simultaneous positioning estimation method based on a TOA sensor. The method comprises the following steps: acquiring arrival time measurement information, wherein the arrival time measurement information comprises first measurement time of a sensor relative to a target, second measurement information of the sensor relative to an anchor point and third measurement information of mutual observation between the sensors; constructing a maximum likelihood estimation problem for the sensor and the target based on the arrival time measurement information; and for the maximum likelihood estimation problem, positioning a sensor position and a target position by using an iterative search algorithm. According to the method, the reference marker with absolute position information is introduced, simultaneous positioning of the sensor and the target is expanded to arrival time measurement under the three-dimensional condition, and the positioning estimation precision of the unmanned aerial vehicle and the target is improved.

This invention relates to the field of flexible DC transmission technology for offshore wind power, and discloses a method and system for collaborative optimization of control parameters of converter stations and wind turbine converters in offshore wind power flexible DC systems. The method includes: constructing an electromagnetic transient simulation model comprising a sending-end converter station, a receiving-end converter station, a DC transmission line, and a wind farm; introducing multiple disturbance conditions and combining variations in control parameters, recording dynamic response process data; establishing a collaborative optimization criterion composed of multiple performance indicators directly mapped from the dynamic response process data; and employing a multi-objective evolutionary optimization mechanism with control parameters as the variables to be optimized and performance indicators as the optimization guide for iterative search. This invention achieves global collaborative configuration of control parameters for converter stations and wind turbine converters, solving the problem of traditional step-by-step tuning methods neglecting dynamic interaction effects, and significantly improving the overall operational robustness of offshore wind power flexible DC systems under multiple disturbance conditions.

The application relates to the technical field of trajectory planning, and proposes a fast evaluation search method, a system and a terminal device of an optimal trajectory. The fast evaluation search method comprises the following steps: sampling a plurality of state points in a pre-planning space, performing pre-collision detection on the state points to remove points in conflict with obstacles, and obtaining effective sampling points; performing cost evaluation on the effective sampling points, wherein the cost evaluation comprises heuristic cost based on historical planning trajectory information and static cost based on motion characteristics; based on the result of the cost evaluation, iteratively searching from an initial state point: calculating the trajectory cost of a neighborhood state point and updating a search path in a gradient direction until an optimal feasible trajectory is output. The application improves the search efficiency of the trajectory by pre-collision detection to screen out invalid sampling points and a gradient iterative search mechanism.

The invention provides a calculation compensation method for an isochronous magnetic field of a cyclotron, and relates to the technical field of data processing, and the method comprises the steps: carrying out the particle tracking through a numerical integration method, and adjusting the initial parameters of particles through iterative search, when the deviation between the position and the momentum of the particle after the particle moves for one circle and the initial value is within a preset error range, obtaining a closed orbit of the charged particle; on the basis of a closed orbit, radial and axial tiny disturbance is applied to the initial state of the particles, multi-circle movement of the particles is tracked, oscillation data is recorded, fast Fourier transform analysis is conducted on the oscillation data, and radial oscillation frequency and axial oscillation frequency of the particles are extracted; and calculating the cyclotron frequency of the particles according to the closed orbit, and generating parameters for evaluating and compensating the isochronous magnetic field of the cyclotron in combination with the radial oscillation frequency and the axial oscillation frequency. The operation stability and the particle acceleration efficiency of the accelerator can be effectively improved.

This invention discloses an adaptive adjustment method for dialysis solution mixing parameters based on MPSO (Particle Swarm Optimization), relating to the field of dialysate preparation technology. The method includes: initializing a digital twin model and a causal inference network; constructing a virtual mapping of the physical structure of the solution mixing device and calibrating parameters to form a calibrated virtual-real mapping decision support environment; preprocessing multi-dimensional process data from historical solution mixing batches based on the virtual-real mapping decision support environment; updating the causal inference network using a causal discovery algorithm; learning the causal relationship between dialysis solution mixing parameters and solution quality; and establishing a dynamically updated causal relationship network. Based on the dynamically updated causal relationship network, iteratively searching for the optimal solution of the fitness function through particle swarm optimization generates a pre-optimized parameter set for the virtual environment. This invention, through an MPSO-based particle swarm optimization mechanism in a dynamic virtual environment, enables adaptive searching and recalibration of key parameters such as pump power, turbulence angle, and mixing time under multiple operating conditions.

The invention relates to the technical field of additive and subtractive manufacturing, in particular to an additive and subtractive robot trajectory planning method based on an improved multi-target sea gull algorithm, and the method takes joint space motion of a six-degree-of-freedom additive and subtractive robot as an object, constructs a trajectory model through a high-order spline curve, and uniformly describes joint displacement, speed, acceleration and jump; a multi-objective optimization model containing motion time, joint load level and trajectory smoothness is constructed, a kinematics constraint screening mechanism is integrated, an improved multi-objective sea gull algorithm is adopted to iteratively search a non-dominated solution set with balanced distribution, and finally a joint trajectory meeting the process requirement is generated; the method effectively reduces motion impact and vibration, improves track precision and constraint feasibility, improves multi-objective optimization efficiency, enhances manufacturing quality and system operation reliability, and is suitable for complex additive and subtractive manufacturing scenes.

The application provides a robot joint space trajectory planning obstacle avoidance point optimization method and system based on operation space dynamics, and belongs to the technical field of industrial robot motion planning and optimization. The method comprises the following steps: constructing a dynamics model of a mechanical arm and initializing a task environment to obtain a joint space dynamics equation, a starting point, a target point and obstacle information; deducing and constructing a Cartesian operation space dynamics evaluation model based on the dynamics model of the mechanical arm; iteratively searching for an intermediate obstacle avoidance point in an obstacle avoidance space based on a particle swarm optimization algorithm and taking the starting point, the target point and the obstacle information as constraints; using the Cartesian operation space dynamics evaluation model, combining joint space displacement, and calculating the comprehensive generation value of a collision-free path from the starting point to the target point via each candidate point; and outputting the candidate point with the minimum comprehensive generation value to generate a final PTP motion trajectory of the mechanical arm. The application integrates dynamics evaluation, optimizes the obstacle avoidance point, and improves the efficiency of the PTP trajectory.

The application provides an unmanned ship path planning method based on information entropy field guidance, and relates to the technical field of unmanned ship navigation. The method first acquires a target region image, labels obstacles, and calibrates the starting point and the ending point of the unmanned ship path; then designs an information entropy value function and gives each position in the target region an initial information entropy value to form an initial information entropy field; iteratively searches the unmanned ship path by using an intelligent sampling strategy combined with an RRT algorithm, and completes initial unmanned ship path planning; optimizes the initial unmanned ship path in four steps to obtain an optimized path, combines a hybrid memory module to accumulate historical path experience, then updates the information entropy value by local enhancement and global attenuation, and outputs the historical optimal unmanned ship path after repeated iteration. The application can comprehensively improve the efficiency, accuracy and practicality of the static obstacle path planning of the unmanned ship in a complex environment.

The application provides a multi-satellite multi-stage observation task planning method and system based on genetic iteration search. A set of to-be-observed tasks and a set of observation time windows of satellites are acquired, and a set of cost coefficients is constructed. The set of cost coefficients includes a plurality of cost coefficient combinations, and each cost coefficient combination includes cost coefficients of all to-be-observed tasks. Based on the set of to-be-observed tasks and the set of observation time windows, an initial to-be-observed task planning scheme corresponding to each cost coefficient combination in the set of cost coefficients is acquired, and a scheme profit of the initial to-be-observed task planning scheme is calculated. The set of cost coefficients is updated based on the scheme profit, and a to-be-observed task planning scheme is acquired based on the updated set of cost coefficients. The effect and reliability of the planning scheme are improved.

The application provides a competition program generation method based on blackboard-driven search and agent cooperation, and belongs to the technical field of software engineering and intelligent code generation. The method comprises the following steps: S1, obtaining a competition topic and completing problem modeling, initializing a shared blackboard and a search tree root node, and writing in constraint and boundary points; S2, generating multiple competition strategies and scoring to form strategy priori, and writing back to the blackboard for guiding subsequent search; S3, taking a candidate code + blackboard context as a state, taking generation / evaluation / repair as an action, and using the iterative search of the Monte Carlo tree search to score back; S4, dynamically cooperating to execute generation, testing and repair according to blackboard information, continuously writing back evidence and outputting the optimal candidate result. The method can realize multi-agent cooperation and knowledge reuse under global search constraints, thereby improving the correctness and stability of the competition program generation.