2170 results about "Multiple target" patented technology

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention discloses a multi-target intelligent optimization method and system for blasting parameters of a strip mine in a high-altitude cold region. The method comprises the following steps: carrying out data acquisition to obtain a parameter data set; performing data preprocessing on the parameter data set to obtain a feature sample set; constructing an initial blasting parameter model based on a machine learning algorithm, and performing hyper-parameter optimization on the model to obtain a blasting parameter model; a multi-objective optimization function is constructed: based on the multi-objective optimization function and the blasting parameter model, solving is carried out in combination with environmental condition constraints, and a pareto optimal solution set is obtained; according to the pareto optimal solution set, a representative solution is selected, a visual scheme is generated, and blasting parameter optimization of the strip mine in the high-altitude cold region is completed. According to the method, temperature, oxygen and frozen soil constraint conditions of the high-cold and high-altitude environment are introduced, blasting safety, lumpiness uniformity and the explosive utilization rate are considered at the same time through multi-target collaborative optimization, the method can adapt to the extreme environment, meanwhile, the one-sidedness of single-target optimization is avoided, and the intelligent level of blasting design and implementation is greatly improved.

The invention provides an unmanned aerial vehicle pose visual angle optimization method and system for fracture refined shooting. The method comprises the steps of performing fracture detection and boundary extraction on a coarse inspection image; recovering a camera track and sparse point cloud based on multi-view three-dimensional reconstruction, carrying out back projection and estimating a normal vector of a crack surface; constructing a shooting spherical shell with limited inner and outer radiuses by taking the crack point as a center, and generating a view cone and spherical shell intersection region allowed to be shot as a candidate set in combination with a normal vector; sampling in the candidate area to generate a plurality of candidate shooting points, and synchronously resolving the flight and holder integrated pose of the corresponding unmanned aerial vehicle; constructing a multi-target cost function including path length, attitude, pan-tilt angle and shooting error, and generating an optimal shooting point sequence and an inspection path through an optimization algorithm; the system realizes fine, efficient and automatic shooting of cracks in a complex structure environment through cooperation of multiple modules, and effectively improves the imaging quality and the detection precision.

The invention relates to an unmanned aerial vehicle group-oriented sensing communication integrated network multi-resource joint scheduling method, and belongs to the technical field of wireless communication, and the method comprises the steps: building a system model of multiple UAV-ISAC tasks, and defining a joint optimization problem; extracting spatio-temporal features from the dynamic heterogeneous graph in which the unmanned aerial vehicle, the user and the sensing target are abstracted as nodes and the relationship is abstracted as edges; taking the features as input, and adopting a layered multi-agent reinforcement learning architecture to solve the joint optimization problem on line; in the architecture, resource allocation and trajectory planning actions are generated through cooperation of a central Actor and all unmanned aerial vehicle Actors, and system performance is evaluated by a central Critic; constructing a multi-target weighted reward function, stabilizing a training process by combining experience playback and a Mini-batch sampling mechanism, and updating network parameters in parallel; and obtaining an optimal resource allocation and unmanned aerial vehicle trajectory strategy through training. The sensing performance is improved, the communication quality is guaranteed, and the defects in the aspect of dynamic resource scheduling in the prior art are overcome.

This invention discloses a monocular ranging system and method for underground unmanned vehicles, comprising the following steps: During the vehicle's travel route, images of the preceding vehicle are acquired via sensors, and detection box information and the type information of the preceding vehicle are automatically identified; the vehicle pose of the preceding vehicle is determined based on the type information and detection box information; the pixel coordinates of the closest point on the preceding vehicle to the sensor are obtained in the preceding vehicle image based on the vehicle pose; the measured distance between the closest point and the sensor is obtained, and the measured distance is then predicted and updated to obtain the vehicle distance. By real-time perception of the vehicle body, rear, and wheel hubs, a multi-target information fusion method is proposed based on the detection results, providing a prerequisite for accurate ranging. Then, a two-stage accurate ranging method is adopted, combining a traditional geometric ranging model with a Kalman filter algorithm to further improve the accuracy of the final result, thereby improving the stability and robustness of the ranging of the underground unmanned vehicle.

The invention belongs to the technical field of electric power engineering digitization and intelligent drawing auditing, and particularly relates to a dynamic rule engine and multi-target auditing task decomposition method oriented to electric power drawings. Obtaining a power rule file, analyzing and verifying to generate qualified rules, and screening the qualified rules to obtain an effective rule pool; the method comprises the following steps: reserving candidate areas by preprocessing an electric power engineering drawing, generating structured cells through line segment classification and combination and table detection, extracting and cleaning a text, and affiliating the text to the corresponding cells to form a structured result; screening candidate rules from the effective rule pool, extracting auditing objects from the structured result, generating auditing points, calculating the priority of the auditing points, and packaging the auditing points into an assignment unit set capable of being executed in parallel; on the basis of the dispatching unit set, optimizing task distribution by adopting a dynamic model and a greedy algorithm in an execution environment with the maximum concurrency, and doubly optimizing task execution by quantifying benefits and verifying rules; and performing rule judgment and result measurement on a task execution result.

The embodiment of the invention provides an AI large model training resource dynamic scheduling method and device based on a VGPU, and effective planning of resources is realized through innovatively designing a resource prediction system and through index analysis and demand prediction. A multi-target scheduling mechanism is constructed, and a reliable scheduling strategy is established in combination with competition analysis and scheme screening. Virtualized isolation is introduced, and the training stability is ensured through VGPU and resource control. According to the method, the defects of the traditional technology in the aspects of resource prediction, scheduling optimization, virtualization isolation and the like are effectively overcome, and technical support is provided for large model training.

The invention discloses a target range federated cross-domain threat research and judgment method and system, and aims to solve the problems of difficulty in cross-domain threat association analysis, entity splitting and low research and judgment precision caused by incapability of sharing original security data among multiple target ranges. The method comprises the following steps: each sub-target range locally performs structured processing and high-order semantic coding on original security data to generate a standardized research and judgment intermediate representation; the central coordination node executes cross-domain entity disambiguation based on the intermediate representation, constructs a time sequence event atlas, fuses multi-source evidences through a Bayesian network, and outputs a global threat research and judgment conclusion; and the result is transmitted back to a related target range according to an authority strategy to form closed-loop feedback. The system comprises a research and judgment agent unit and a central coordination node which are deployed in each sub-target range and are used for respectively realizing local feature extraction and collaborative reasoning. According to the method, on the premise of ensuring that the original data is not out of the domain, cross-domain accurate reduction and cooperative defense of complex threats such as an APT attack chain are realized.

The invention provides a robot trajectory tracking and obstacle avoidance control method, system and device and a medium, and belongs to the field of intelligent robot control, and the method comprises the steps: obtaining motion state information, obstacle information and a reference trajectory; calculating a look-ahead distance based on preset prediction time, a safety coefficient and a current maximum speed in the motion state information; selecting a locally planned target point from the reference trajectory according to the look-ahead distance; based on a dynamic window algorithm, generating a plurality of candidate tracks according to the motion state information and the obstacle information; based on the multi-target cost function and the target point, evaluating each candidate track to obtain a comprehensive cost value; and determining the candidate trajectory with the minimum comprehensive cost value as an optimal trajectory, obtaining an expected linear velocity and an expected angular velocity based on the optimal trajectory, and generating a control instruction to drive the robot to move along the optimal trajectory. According to the invention, the motion stability, path continuity and overall robustness of the robot in a continuous multi-section inspection task are improved.

The invention relates to the technical field of artificial intelligence and decision support, and discloses a large model agent-based strategic decision generation method, which comprises the following steps of: performing semantic analysis and multi-target game balance on a strategic instruction by a main control agent, and decomposing to generate subtasks; a corresponding domain agent calls an RAG enhancement module to retrieve related multi-modal data, and a preliminary strategy of embedded compliance verification is generated based on the data; and finally, dynamically arranging and coordinating the preliminary strategy by a hierarchical workflow engine, and generating a comprehensive decision report after receiving strategy correction information of a commander. According to the invention, through multi-agent cooperation and multi-target game balance, the decision-making globality and strategic depth are realized; by fusing dynamic multi-modal data, the adaptive capacity of the decision to the external environment is improved; through workflow arrangement and man-machine cooperation, logic self-consistency, execution feasibility and safety of a final scheme are ensured.

The invention discloses a heterogeneous computing power resource dynamic scheduling system and method based on multi-objective optimization, and the system is deployed in a digital ecological platform complex based on multivariate consensus and embedded intelligent management. Comprising a platform access module, a data acquisition and perception module, a multi-target modeling and optimization module, a dynamic scheduling and execution module and a fault-tolerant mechanism module. The system registers as a computing power scheduling service node through an intelligent contract interface, obtains and verifies the compliance of a computing task, collects heterogeneous computing power resource node state data, and generates a scheduling decision by dynamically adjusting the weight of a target function; evaluating task suitability by using an AMCU suitability scoring model, and executing a scheduling AMCU strategy; according to the method, the technical problems of low resource scheduling efficiency and poor fault-tolerant capability in a heterogeneous computing power environment are solved, and the system throughput and the resource utilization rate are improved.

The invention discloses an unmanned workshop equipment collaborative intelligent optimization method, which comprises the following steps of: acquiring and processing multi-dimensional information such as equipment health degree, load rate and operation state in real time through an equipment state sensing module, and acquiring a high-credibility state feature vector by using a time-frequency analysis and normalization technology; a multi-target dynamic priority evaluation function is constructed in combination with task remaining periods and original priorities, and priority self-adaptive sorting of tasks with resource conflicts is achieved; in the scheduling process, the equipment and task states are continuously monitored, reordering is automatically triggered when abnormal changes are detected, dynamic optimization of the scheduling scheme is achieved, the utilization rate of production equipment and the task completion efficiency can be improved, and the self-adaptability and robustness of an unmanned workshop scheduling system are enhanced.

The invention belongs to the technical field of robot path planning, and provides a path planning method for an electric power inspection mobile robot, which comprises the following steps of: firstly, constructing an environment grid map, establishing a corner directional expansion safety boundary, and performing safety expansion on an outer right angle of an obstacle; secondly, establishing an intelligent neighborhood selection mechanism based on fuzzy control, and performing adaptive adjustment neighborhood search; then, a multi-target heuristic function fusing the local obstacle density, the safety distance and the traffic difficulty is established, path search is carried out, and an initial path is obtained; then, constructing a path optimization strategy, and carrying out redundant point deletion and B spline curve smoothing processing on the initial path until a target executable path is obtained; and finally, optimizing an executable track in the target executable path in a time-space domain through an improved time-domain elastic band algorithm to obtain an optimal path. According to the invention, accurate obstacle avoidance of complex geometric obstacles can be realized.

The invention relates to a side cloud collaborative adaptive workflow scheduling method and system, and belongs to the technical field of distributed computing and artificial intelligence. The method comprises the following steps of: firstly, in a macroscopic candidate screening stage, reducing problem granularity through task clustering, and obtaining balance between utilization and exploration based on a weighted distance probabilistic preferential strategy; then, in a collaborative scheduling decision-making stage, a global network state diagram is constructed through a graph neural network, deep spatial features of nodes and neighborhoods of the nodes are extracted, context-aware state representation is formed, and a reinforcement learning agent makes an optimal collaborative decision in multiple options such as local execution, edge migration or cloud unloading according to the state representation; and finally, in a local adaptive optimization stage, performing fine-grained optimization after the task is issued, dynamically adjusting a scheduling frequency and a multi-target weight through an online learning mechanism, realizing balance between a task deadline and a resource utilization rate, and ensuring efficient and robust execution of a node level.

The invention discloses a multi-satellite layered cooperative task planning method for multi-target situation awareness, and belongs to the field of aerospace. The implementation method comprises the following steps: defining a multi-satellite collaborative planning problem of a multi-target sensing task as a quintuple, and determining satellite resources and time constraint conditions in an inter-satellite collaborative planning problem of the multi-target sensing task; constructing a multi-target sensing task multi-satellite collaborative planning model, describing elements, related constraints and simplified conditions of a multi-target sensing task multi-satellite collaborative planning problem, analyzing and decoupling multiple coupling complex constraints, and designing a layered bidding and tendering collaborative mechanism; on the basis, a multi-target situation awareness hierarchical collaborative task planning method is constructed, a two-dimensional coding structure related to task types is constructed according to multi-dynamic target orbit determination requirements, a mixed profit value function related to a double-satellite orbit determination observation configuration, attitude maneuver cost, double-satellite collaborative rewards and the like is designed, and multi-satellite collaboration is guided. And a multi-satellite collaborative observation sequence is obtained, and multi-satellite layered collaborative task planning is realized.

The invention relates to the technical field of radar data processing, in particular to a method which is suitable for a multi-external radiation source and multi-station system, carries out single-frame and two-frame multi-target measurement value matching under the condition of no bistatic distance information, has good adaptability to the multi-target positioning problem of external radiation source systems of different systems, and is high in positioning accuracy. The bistatic joint multi-target positioning method based on the external radiation source system is beneficial to the processes of multi-target positioning, real-time tracking, track generation and the like, and comprises the following steps: matching multi-source and multi-station angle measurement values to obtain candidate association, and estimating the intersection point position and the speed state of the angle measurement values; approximately constructing a linear equation of bistatic speed and angle measurement value errors, establishing measurement value error estimation of candidate associations and a real target, and removing wrong candidate associations by using an association hypothesis decision; and establishing a cost function according to the measured value error estimation of the candidate association between the two frames and the state mahalanobis distance, and removing the error candidate association between the two frames by using an association hypothesis decision.

The invention relates to the field of artificial intelligence, in particular to a rice growth vigor dynamic monitoring method and system based on modal fusion and deep learning. The method comprises the following steps: for a plurality of target regions in the same administrative division, acquiring multi-dimensional agricultural data related to rice growth vigor in each target region; performing space-time fusion on the multi-dimensional agricultural data of the plurality of target areas to obtain a linkage monitoring map; performing rice growth prediction on each target area in the linkage monitoring map through a DSW Transform model to obtain an independent growth prediction result of each target area; a correlation weight is dynamically calculated through a deep learning model of a cross-modal attention mechanism in combination with geographic data, meteorological data and a linkage monitoring map, and rice growth prediction is performed on a rice block formed by linkage of each target area based on the correlation weight to obtain a comprehensive growth prediction result; and pushing rice growth information in each target area to the user according to the independent growth prediction result and the comprehensive growth prediction result.

The invention discloses a multi-target license plate recognition method based on a visual attention mechanism, and belongs to the technical field of image processing and mode recognition, and the method comprises the steps: obtaining an input image, carrying out the multi-scale feature extraction, and generating a multi-scale feature map; performing spatial saliency calculation and normalization processing on the multi-scale feature map to generate an attention map; carrying out region division on the input image, and adopting differentiated image preprocessing strategies for different regions to generate a preprocessed image; based on the preprocessed image and the attention map, multi-target detection is carried out through a target detection network, candidate area screening is carried out, and a candidate license plate area is generated; and performing binarization processing, character segmentation and feature recognition on the candidate license plate region, and performing verification in combination with context information to generate a license plate recognition result. The attention map is generated by adopting a visual attention mechanism, differentiated image preprocessing and multi-target detection are guided according to the attention map, and multi-target license plate recognition can be completed in a complex scene.

The invention belongs to the field of artificial intelligence, particularly relates to a power plant infrastructure multi-target intelligent scheduling optimization method, and aims to solve the problems of static weight imbalance, disturbance response hysteresis and process coupling effect modeling insufficiency of traditional scheduling. According to the method, a multi-dimensional space-time semantic model is constructed, BIM, sensor and environment data are integrated, construction period, cost, resource and safety four-dimensional target weights are dynamically set, and an improved non-dominated sorting genetic algorithm is adopted to generate an initial Pareto optimal schedule; and then inferring an inter-process nonlinear coupling delay factor through a graph neural network, embedding a disturbance response module, starting local rolling re-optimization when a progress deviation or an external event is detected, limiting an influence subnet and freezing a stable region. According to the scheme, stage self-adaptive target focusing, chain risk pre-buffering and minute-level robust adjustment are achieved, the stability of a critical path is improved by 45%, the secondary optimization frequency is reduced by 60%, the calculation efficiency and the execution toughness are both considered, and efficient and accurate landing of a large power plant infrastructure project is supported.

The invention provides a building engineering project scheduling method based on multi-objective dynamic microphone optimization, and the method comprises the steps: firstly, building an optimization model with the minimization of the construction period, the minimization of the cost and the minimization of the resource fluctuation as targets, and enabling an objective function to cover the total construction period, the total cost and the resource fluctuation variance; secondly, designing an improved multi-target dynamic microphone optimization algorithm which ensures the feasibility and diversity of solutions through a dynamic elite retention mechanism, a mixed position updating rule, a three-layer constraint processing mechanism and a dual-threshold convergence monitoring system, so as to improve the robustness of the dynamic microphone optimization algorithm; and finally, solving the problem of the constructional engineering project by adopting a multi-target dynamic microphone optimization algorithm to obtain a construction project scheduling scheme. According to the method, on the premise of meeting resource limitation and task logic relations, the project construction period, the total cost and the stability of resource use are collaboratively optimized, automatic and intelligent optimization of the project scheduling process is realized, and the project management efficiency and scientificity are improved.

The invention discloses a multi-component catalyst active site prediction system and method fused with quantum embedding, and relates to the technical field of catalysis and material informatics, and the system comprises a structure and site enumeration module which generates candidate sites; the adaptive quantum embedding calculation module obtains key reaction microcosmic parameters; the unified site fingerprint and feature engineering module constructs and fuses standard site fingerprints; the physical consistency machine learning module predicts adsorption energy and other parameters and uncertainty thereof; the active learning and sample selection module selects a high-value sample optimization model; the microdynamics evaluation module calculates index values such as activity; and the multi-objective optimization and sorting module generates an optimization sorting list. According to the method, the unification of calculation precision and efficiency is realized, the problem of non-unification of locus characterization is solved, the model interpretability and extrapolation reliability are improved, and the comprehensive evaluation and optimization sorting of multi-target performance are completed.

The invention relates to the technical field of data processing and optimization decision making, in particular to a dynamic optimization processing system for multi-source heterogeneous data, and aims to solve the problems that the integration efficiency of the multi-source heterogeneous data is low, and the real-time performance of multi-target collaborative optimization is poor. Comprising the steps that a multi-source data acquisition and preprocessing module realizes standardized fusion; constructing a multi-objective function and constraint conditions based on the dynamic optimization model of the spatial-temporal characteristics; an NSGA-II algorithm is improved to generate a Pareto optimal solution set; and closed-loop optimization is formed by real-time monitoring and a feedback adjustment mechanism. Through a data-model-scheme-feedback system, dynamic collaborative optimization of economy, ecology, livelihood and space targets is realized. The method is suitable for complex decision support scenes such as urban and rural planning, resource allocation and environment monitoring, and improves the multi-source heterogeneous data processing efficiency and optimization precision.

The invention discloses a biconical antenna balun equivalent circuit modeling optimization method based on a multi-conductor transmission line, and belongs to the technical field of electromagnetic simulation. The method comprises the following steps: firstly, establishing a three-dimensional electromagnetic model of a biconical antenna balun based on a finite element method, and setting ports and defining boundary conditions in combination with an actual current transmission path; then, distribution parameters among conductors are extracted through a multi-conductor transmission line theory, and a Balun circuit topological structure is constructed; and finally, taking the antenna coefficient as an optimization target, carrying out iterative optimization on equivalent circuit element parameters by adopting a multi-target genetic algorithm, considering a plurality of optimization targets in a broadband range, and finally obtaining a high-precision equivalent circuit model. Through combination of electromagnetic simulation and an intelligent optimization algorithm, high-efficiency extraction of complex Balun structure distribution parameters and adaptive optimization of circuit parameters are realized, and the simulation precision of the biconical antenna is remarkably improved.

The invention provides a BIM-fused port infrastructure digital twinborn operation and maintenance management system and method. The method comprises the steps of collecting a port infrastructure BIM full-life-cycle multi-source heterogeneous data set, constructing a port infrastructure real-time three-dimensional digital twinborn body, constructing an equipment health prediction model and generating a fault diagnosis result; simulating tide influence to form a stress distribution prediction result; a three-level linkage optimization model is constructed, a scheduling scheme is generated, a multi-energy collaborative optimization engine optimization scheme is used, a three-dimensional safety matrix is constructed through unmanned aerial vehicle inspection and visual recognition, risks are monitored in real time, and safety early warning is generated. The prediction model is constructed, faults are accurately diagnosed, the tide influence is quantified, and the prediction precision is improved; multi-target collaborative optimization is realized through a linkage optimization model and an engine, and the problem of single-target optimization is solved; and on the basis of a monitoring and early warning system, rapid identification and hierarchical response are realized, a closed-loop link is formed, and the port operation and maintenance adaptability is improved.

The invention provides a foaming machine group production scheduling method under multi-target constraint. The method comprises the following steps: S1, initializing resource and task data; s2, generating an initial production scheduling scheme; s3, carrying out feasibility check and conflict repair; s4, multi-algorithm fusion optimization is carried out; s5, making a decision and outputting a production scheduling scheme; s6, online rearrangement and exception handling are carried out; and S7, monitoring and data feedback are executed. Through the full-process design of data-initial scheme-feasible scheme-optimization scheme-execution-feedback, the problems of many resource conflicts, difficult target balance, poor dynamic adaptation and the like in traditional production scheduling are solved, and efficient and intelligent production scheduling of the foaming machine group is realized.

The invention discloses a multi-unmanned aerial vehicle cooperative path planning and obstacle avoidance method. The method comprises the following steps: constructing a role perception graph and generating cooperative prior data; generating control decision data; obtaining a final strategy model; and driving the multiple unmanned aerial vehicles to execute cooperative path planning and obstacle avoidance tasks according to the control decision data. According to the method, the role perception graph is constructed, the leader-follower-target interaction relationship is subjected to structured coding, and the task-oriented ternary interpretable priori is generated, so that the problem of perception information flattening in a traditional method is solved, and the effectiveness of collaborative decision making is improved. According to the method, a priori-guided decision-making mechanism is formed, the problem of non-stationarity in multi-agent training is effectively relieved, and the convergence speed of an algorithm and the stability of a strategy are improved. According to the method, a course learning mechanism of hierarchical reward and dynamic weight scheduling is adopted, the problems of reward sparseness and multi-target conflict are effectively solved, and the finally obtained strategy is better balanced in multiple dimensions.

The invention discloses a car following control method and a related product. According to the scheme, multi-source data are acquired, and the multi-source data are fused to obtain a fusion result; based on the fusion result, performing multi-modal trajectory prediction on the driving behavior of the preceding vehicle to obtain a prediction result of the driving behavior of the preceding vehicle; the multi-modal trajectory prediction comprises longitudinal motion prediction and transverse motion prediction; quantifying a car following risk by using a multi-dimensional risk index to obtain a risk level; constructing a multi-target cost function based on the fusion result, the front vehicle driving behavior prediction result and a control vector, and solving the multi-target cost function by using a preset constraint condition to obtain an initial control parameter of the vehicle; and on the basis of the risk level, initial control parameters of the vehicle are adjusted, and target control parameters of the vehicle are obtained. Compared with the problem of response lag in adaptive cruise control in the prior art, the adaptive cruise control method has obvious advantages.

The invention discloses a power distribution network optimization scheduling method and system based on multi-target deep reinforcement learning, and relates to the technical field of power distribution network optimization operation, and the method comprises the steps: building a power distribution network operation model and a probabilistic load flow model considering the photovoltaic output and load demand uncertainty, completing the probabilistic load flow calculation under the uncertainty, and obtaining a power distribution network optimal scheduling model; analyzing to obtain a probability density function of node voltage and line power flow of the power distribution network; introducing a utility function based on preference, establishing voltage out-of-limit and line overload risk indexes considering severity weight, and constructing a risk-economic collaborative power distribution network multi-objective optimization operation problem; the decision process of the problem is modeled into a multi-target Markov decision process, a decomposition-based multi-target deep reinforcement learning algorithm is adopted, learning and training of the decision process are performed on reinforcement learning agents, a Pareto strategy set is obtained, an optimal operation strategy is screened out, and optimization regulation and control are performed on the power distribution network. And collaborative optimization of the operation risk and cost of the power distribution network system is realized.

The invention discloses an automatic extraction method for semiconductor device model parameters, and the method comprises the steps: obtaining a multi-target response through the key parameters of a semiconductor device model, and constructing and training an agent model to predict a target response mean value and uncertainty parameters; constructing a multi-objective optimization collaborative circulation framework assisted by an agent model; generating a candidate parameter population in key parameters according to a position updating mechanism of a multi-objective optimization algorithm, predicting candidate parameters by using a trained proxy model, selecting the candidate parameters according to a prediction result to carry out real simulation evaluation, adding an obtained real sample into a non-dominated file, and updating the file based on a non-dominated relationship. Adding a training set dynamic updating agent model into the real sample, generating a potential search trajectory, selecting a representative representative Pareto approximate solution from the potential search trajectory, and updating the position of the population and the direction of the next-generation search trajectory according to the representative solution selected from the file; and terminating the circulation, outputting the solution set in the file, and checking.

The application discloses an improved radar two-dimensional ambiguity resolution method for multi-targets, comprising the following steps: arranging point trail data after radar signal processing according to pulse repetition period from small to large; constructing a reference lookup table for resolving range ambiguity based on the arranged data; constructing a reference lookup table for resolving velocity ambiguity based on the arranged data; traversing all the frequency combinations of radar transmission, selecting N groups from M groups of frequency for multi-target pairing; obtaining a range pairing table based on the reference lookup table for resolving range ambiguity, calculating the standard deviation between rows of the range pairing table, and solving the real range of targets; obtaining a velocity pairing table based on the reference lookup table for resolving velocity ambiguity, calculating the standard deviation between rows of the velocity pairing table, and solving the real velocity of targets; and merging and processing output target data. The application can solve the problem that the traditional one-dimensional set method cannot meet the real-time requirement when the number of targets is large and the ambiguity is large.