4248 results about "Global optimal" patented technology

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to an unmanned aerial vehicle real-time path planning system and method based on dynamic weight distribution and multi-source data fusion. The system comprises a multi-source data fusion module, an integrated laser radar, a millimeter wave radar, a visual sensor and a Beidou positioning unit. The dynamic weight distribution module dynamically adjusts the weight coefficient of each sensor according to the environmental complexity, the threat level and the state of the unmanned aerial vehicle by adopting a mixed decision-making mechanism combining fuzzy logic and reinforcement learning; an improved RRT * algorithm and a Markov decision process are built in the real-time path planning module, and a global optimal path and a local obstacle avoidance track are generated by adopting a layered planning architecture; the unmanned aerial vehicle cooperative control module comprises a dual-redundancy flight control system and a dynamic obstacle avoidance unit; and the communication relay module supports 5G and low-orbit satellite dual-mode communication, updates an environment cognitive model of each unmanned aerial vehicle through federated learning, and realizes multi-source fusion real-time path planning based on dynamic weight distribution and the unmanned aerial vehicles.

The invention discloses an environment emergency aid decision-making method and system based on artificial intelligence, and the method comprises the steps: obtaining a plurality of historical environment disaster event instances, and constructing the multi-modal event features of each historical environment disaster event; analyzing an event triggering rule of each historical environment disaster event and a corresponding disposal scheme triggering rule based on the multi-modal event features, and constructing a disaster environment knowledge graph; environmental condition sensing information is obtained, and environmental emergency event prediction is carried out in combination with the disaster environment knowledge graph; constructing an event situation prediction model, and predicting the situation change of the current environment emergency event; a disaster environment knowledge graph is utilized to obtain candidate disposal schemes of real-time environment emergency events in a target area, and a multi-target grey wolf optimization algorithm is introduced to optimize the disposal schemes to generate an optimal disposal scheme for environment emergency decision assistance, so that the real-time performance, reliability and global optimality of the environment emergency decision are improved; and rapid and accurate disposal of environmental emergency events is realized.

The invention, which relates to the technical field of battery allocation, discloses a deep-learning-based battery allocation method for a battery switching cabinet, comprising the steps of constructing a data set by combining battery inventory, user behaviors and traffic flow, and predicting battery demand distribution in a specific time period by using a deep-learning LSTM-GNN model fused with spatial-temporal characteristics; according to the model, a topological structure of a power conversion cabinet network is constructed, a spatial dependency relationship is captured, and time dynamic modeling is realized in combination with historical power conversion records, so that the prediction accuracy is improved; the real-time health states of the batteries in the battery replacement cabinet are analyzed, the availability of the current inventory is evaluated in combination with a demand prediction result, the number of the batteries meeting the demand is calculated, and the priority of battery allocation is determined according to the demand urgency degree and the health states; in the aspect of allocation strategy generation, a battery allocation problem is modeled as a Markov decision process, an optimization model is constructed through a reinforcement learning algorithm PPO, and a global optimal allocation strategy is generated by integrating allocation paths, quantity and cost.

The invention discloses an industrial automation control method and system, and relates to the technical field of data processing, and the method comprises the steps: collecting the operation data of production equipment, constructing a digital twin model, and forming a virtual production network; obtaining resource state data of each factory in the virtual production network in real time, dynamically generating an optimal cross-factory scheduling scheme, and simulating the scheduling scheme in the virtual production network for simulation verification and scheme adjustment; and deploying the scheduling scheme to a physical factory, controlling equipment to execute tasks through an automatic control system, and summarizing production data and simulation results for storage and backup. According to the method, the accuracy and efficiency of industrial automation control are remarkably improved, equipment operation data can be processed more efficiently, equipment dynamic behaviors are accurately simulated, a cross-factory optimal scheduling scheme is generated in real time, a closed-loop optimization mechanism is formed through simulation verification and feedback adjustment, the dynamic adaptability and global optimality of the scheduling scheme are achieved, and the scheduling efficiency is improved. Therefore, the resource utilization efficiency is greatly improved.

The invention relates to the technical field of data processing, in particular to a cloud backup platform data deduplication method based on distributed storage. Comprising the following steps of: performing self-adaptive blocking on data by utilizing a dynamic blocking algorithm, generating a unique identification code and extracting multi-dimensional feature information; establishing a global index through a distributed hash table, mapping a data position and maintaining index consistency; performing redundancy analysis and similarity matching based on a deep learning algorithm, and executing a duplicate removal strategy; according to the access frequency and the load condition, distributed storage of data blocks is realized by adopting a hierarchical graph division algorithm; and a partitioning strategy, index frequency and storage layout are dynamically optimized by using a reinforcement learning algorithm, and global optimization of performance and resources is realized. According to the invention, high efficiency, accuracy and dynamic optimization of data deduplication are realized, and the data storage and management efficiency of the cloud backup platform is improved.

The invention discloses a low-airspace aircraft flight control system and method based on an ai algorithm, and relates to the technical field of ai algorithms, and the system comprises a data processing module which collects the environment data of an aircraft flight airspace, and carries out the denoising of the collected data; and the airspace unit capacity dynamic adjustment module dynamically adjusts the airspace unit capacity based on the flight state, the wind speed and the communication precision of the aircraft, and predicts the unit capacity demand in the future time period through an LSTM model. According to the method, a path with the optimal total cost is generated in a complex environment through an ai algorithm based on a heuristic search strategy, population diversity of a genetic algorithm is further matched, accumulated cost and prediction cost are combined through heuristic search, the generated path has global optimality, and the path has the optimal total cost. A path generation strategy can be flexibly adjusted in a scene with sparse or dense obstacles, and the method adapts to the complexity of low-airspace flight.

The invention discloses a cloud computing resource optimization method based on intelligent scheduling, and belongs to the technical field of cloud computing resource processing. The method comprises the steps of obtaining real-time operation data of target data in a data optimization detection range, collecting historical resource scheduling records and task execution logs, and constructing a multi-dimensional resource state data set; according to the method, multi-objective optimization, simulation verification and reinforcement learning feedback in the step S5 are carried out, a perception-prediction-scheduling-monitoring-optimization closed-loop mechanism is constructed, the resource utilization rate, the response time and the energy consumption cost of a multi-objective optimization function are balanced, and a particle swarm optimization algorithm is combined with simulation verification to generate a global optimal strategy; and reinforcement learning dynamically adjusts model parameters by taking the execution deviation as a reward signal, continuously updates a resource perception dimension and a prediction model, realizes continuous iterative upgrade of a resource optimization effect, and performs optimization processing on cloud computing resource optimization based on intelligent scheduling.

The invention discloses a semantic alignment method for infrared image and microwave non-image information fusion. The method comprises the steps that infrared image and microwave non-image information fragments in the same scene are collected; carrying out preprocessing operation on the acquired infrared image and microwave non-image information; extracting spatial and semantic features of the infrared image, and extracting frequency domain features of microwave non-image information; embedding the infrared image features and the microwave non-image features into the same high-dimensional semantic space; calculating the similarity in semantic representation; and carrying out global optimal matching by adopting a Hungary algorithm. Respectively performing wavelet decomposition on the infrared image and the microwave features by adopting a wavelet transform fusion algorithm, and extracting low-frequency and high-frequency coefficients under different resolutions; by constructing a unified semantic representation space and an efficient feature fusion mechanism, the difference between the infrared image and the microwave data in the feature expression form and the semantic level is effectively solved, complementary information of two modes is fully mined, and the target recognition and environment perception ability in a complex environment is improved.

The invention provides a computer room energy consumption and computing power balance optimization method and system based on swarm intelligence, and relates to the technical field of data center management, and the method comprises the steps: collecting server node real-time operation data, constructing a multi-objective optimization function, predicting an energy consumption and computing power change curve through a recurrent neural network, and generating an initial task distribution scheme; and a global optimal allocation scheme is searched by adopting a parallel ant colony algorithm, the running state of the server is monitored, and task migration and energy consumption adjustment are executed, so that collaborative optimization of energy consumption reduction of the machine room and balanced allocation of computing power is realized, and the service quality and the resource utilization efficiency are improved.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle flight path positioning method and system based on multi-source information fusion. The method comprises the following steps: acquiring an original observation data stream of an unmanned aerial vehicle in real time through a sensor array, and preprocessing to output a multi-source data sample set; calculating relative motion increment between adjacent key frames through an IMU pre-integration model, and extracting local pose estimation through a visual geometric calculation method; evaluating the confidence of each sensor in real time to dynamically generate an adaptive weight coefficient; and constructing a tight coupling fusion filter taking position, speed and attitude errors as core state quantities, and outputting a global optimal trajectory of the unmanned aerial vehicle. According to the invention, the weight is dynamically distributed through the real-time confidence of the sensor, the multi-source data association is fully mined, and the flight path positioning precision and environmental adaptability of the unmanned aerial vehicle are improved.

The invention relates to a network flow anomaly detection strategy generation method based on machine learning, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, collecting network traffic data in a preset time window, and extracting feature vectors containing traffic, a time sequence and a protocol type; and inputting the feature vector into a long short-term memory auto-encoder model, and calculating a reconstruction error to judge whether the network flow is abnormal or not. Aiming at the abnormal feature vector, adopting a multi-agent depth deterministic strategy gradient algorithm to construct a plurality of cooperative agents, and independently generating a candidate abnormal detection strategy by each agent; through a cross-agent strategy evaluation mechanism, the difference between a joint strategy and a single-agent strategy in the aspect of anomaly detection accuracy is compared, cooperation gain is calculated, strategy exploration parameters of all agents are adjusted according to the cooperation gain, and a global optimal anomaly detection strategy is optimized and determined in real time. According to the method, high-precision and low-missing-report network traffic anomaly detection can be realized, and the method has good self-adaptability and real-time performance.

The invention discloses an intersection entrance lane mixed vehicle team cooperative guidance control method, which overcomes the problems of large calculation amount and difficulty in achieving a global optimal state in the prior art, and comprises the following steps: 1) obtaining road information; 2) dividing intelligent agents; and 3) establishing a multi-agent ecological cooperative control model of the intersection entrance lane mixed vehicle fleet: firstly, establishing a single-agent speed trajectory planning model, calculating an optimal passing trajectory of a single agent, and then establishing a multi-agent cooperative control model by taking the overall operation efficiency of the vehicle fleet as an optimization target on the basis of the optimal passing trajectory of the single agent, so as to realize the multi-agent ecological cooperative control of the intersection entrance lane mixed vehicle fleet. Cooperative control among a plurality of agents is realized, and a model is trained through a reinforcement learning method after model construction is completed; and 4) the controllable agents execute the optimized trajectory: issuing the established and trained model to each controllable agent, adopting a rolling time domain updating strategy, calculating the speed trajectory of each agent passing through the intersection by using road traffic information, and inputting the speed trajectory to a CAV controller for execution.

The invention relates to the technical field of supply chain management, in particular to a supply chain middle station collaborative management method and system based on an AI intelligent decision engine. Comprising the steps of accessing multi-source data of each participant of a supply chain; utilizing a deep learning model to predict market demands and dynamically adjust the market demands; based on the prediction result, using an optimization algorithm to realize global optimization configuration and scheduling of resources; a machine learning risk assessment model is constructed, various risks are monitored in real time, and early warning is performed in time; a collaborative decision-making platform is established, online communication, negotiation and decision-making of participants are supported, and real-time data sharing is achieved; defining a performance indicator system, evaluating the performance of the supply chain in real time, and automatically adjusting a strategy for continuous improvement. The method can improve the demand prediction accuracy, optimize the resource configuration, enhance the risk response capability, improve the collaborative decision-making efficiency, realize the continuous optimization of the supply chain, and effectively solve the problems of unsmooth data circulation, low collaborative efficiency and the like in the traditional supply chain management.

The invention belongs to the technical field of motor control, and discloses a control method of a motor for an inverter compressor, which comprises the following steps: embedding a distributed thermocouple array in a motor stator slot to acquire temperature data of the motor, synchronously acquiring operation parameters of the motor, generating a dynamic thermal field characteristic spectrum through a data fusion algorithm, coordinates of the hot spot areas with the temperature abnormally increased are marked, a dynamic correlation model is established, and heat conduction characteristic parameters in the dynamic correlation model are updated through a sliding window algorithm; outputting a predicted temperature rise curve through the dynamic correlation model, adjusting a PWM control strategy of the inverter, inputting the PWM control strategy to a motor driving unit to execute preliminary regulation and control, collecting feedback data to generate a multi-dimensional feedback feature vector, iteratively optimizing a combined weight of a switch interval compensation amount and a harmonic injection strategy through a gradient descent algorithm, and performing harmonic injection control. And a global optimal strategy considering the heat dissipation efficiency and the operation stability is formed, and the operation stability and the energy efficiency level of the inverter compressor under various working conditions are remarkably improved.

The invention discloses a road bridge design method and system based on a BIM real scene model, and relates to the technical field of constructional engineering information, and the method comprises the steps: a BIM model carries out multi-objective optimization through a quantum annealing algorithm in combination with a bridge-crossing topology library, synchronously optimizes topological connectivity, structural stress and energy consumption indexes, generates a parameterized component, and feeds back the parameterized component to the BIM model; the model of the BIM model is loaded and optimized based on augmented reality equipment, the model is superposed to a reality scene through space anchoring, and the BIM model is corrected in combination with a conflict monitoring engine and a knowledge graph; the corrected BIM model is deployed to edge computer equipment, a dynamic deviation thermodynamic diagram is generated by scanning a construction surface, a lofting robot is driven to execute coordinate dotting, and meanwhile construction error data is transmitted back; according to the method, the problem that a global optimal solution is difficult to find when a traditional optimization algorithm is used for processing a complex multi-target problem is solved by utilizing an advanced quantum computing technology, and the economical efficiency and the structural safety of bridge design are greatly improved.

The invention relates to the technical field of energy scheduling, and discloses an intelligent optimization scheduling method of an informatization energy management system, which comprises the following steps of: data acquisition and fusion: acquiring real-time data of each node in an energy system through distributed Internet of Things equipment and a multi-mode sensor network, and fusing and processing the acquired data to obtain a data fusion result; a high-quality data set in a unified format is generated; and prediction modeling and demand response: based on user behavior analysis and a machine learning algorithm, establishing a demand response model, and constructing a multi-stage energy demand and supply prediction model including short-term, medium-term and long-term time dimensions. Through combination of a collaborative optimization algorithm and a distributed decision-making mechanism, a global optimal energy scheduling scheme is realized on the basis of local optimization of each distributed energy unit, high collaboration and optimal resource allocation among the energy units in a complex energy network are ensured, and the energy scheduling efficiency is improved. Therefore, the energy utilization efficiency and the operation stability of the whole system are improved.

The invention provides an emergency command converged communication system resource dynamic scheduling method based on artificial intelligence. The method comprises the following steps: S1, acquiring a plurality of sensors and log data of an emergency command system; constructing a dynamic graph neural network optimization communication topology, and selecting an optimal communication path; combining resource prediction and the optimized communication topology to formulate a global optimal resource scheduling strategy, and generating an optimal resource allocation scheme for each task; the resource pool comprises available computing resources and communication bandwidth; dynamically adjusting resource allocation and a communication path in a task execution process, and generating task execution feedback of a corresponding task; the system state is monitored in real time, and resource prediction, topology adjustment and scheduling strategies are optimized based on execution error feedback of task execution feedback. According to the method, efficient and intelligent dynamic resource scheduling is realized in an emergency command converged communication system through resource prediction of self-supervised learning, self-adaptive topological optimization of a variable topological graph neural network and global scheduling of reinforcement learning driving.

The invention provides an additive manufacturing residual stress optimization method and system, and the method comprises the steps: searching a global optimal solution enabling the residual stress of an additive manufacturing part to be minimum from a technological parameter space through the combination of multi-physics field numerical simulation and an intelligent optimization algorithm; the multi-physics field numerical simulation comprises the steps of sampling from a process parameter space by using an optimization algorithm to obtain process parameters, inputting the sampled process parameters into a finite element model, performing thermal analysis on the process parameters based on the finite element model, and obtaining transient thermal distribution through thermal analysis. And performing structural analysis by using transient heat distribution to obtain residual stress under the current process parameters, and transmitting the obtained residual stress to an intelligent optimization algorithm for realizing process parameter optimization. The method has the advantages of high precision, low cost and the like, and high-precision collaborative optimization of multiple types of process parameters can be realized.

The invention provides an automatic heuristic algorithm planning method based on a large language model, and the method comprises the following steps: carrying out the initialization and problem modeling, starting from a basic heuristic mode through guiding the large language model, generating a candidate algorithm set in combination with a plurality of cognitive perspectives, and providing diversified starting points for a search space; configuring core parameters of Monte Carlo tree search; in each iteration process, planning is started in a heuristic space by utilizing Monte Carlo tree search, and the process is composed of five core stages of selection, reflection, expansion, simulation and back propagation; and after all iterations are completed, the path with the highest average reward and the corresponding optimal heuristic algorithm are returned, and the global optimality of the final solution is ensured. According to the method, the effective experience can be automatically extracted from the heuristic strategy generated historically, and real-time feedback adjustment and strategy induction optimization of the heuristic structure are realized, so that the knowledge migration and generalization ability in the search process is remarkably enhanced.

The invention provides a dynamic scheduling management method and system for spaceflight measurement and control task resources, and relates to the technical field of spaceflight measurement and control resource scheduling. Defining a task feature vector according to the time sensitivity, the resource demand intensity and the coupling degree between the tasks of the tasks; satellite health parameters, equipment working conditions and environment data are collected in real time through a satellite-ground Internet of Things terminal, and digital twin bodies are constructed; performing priority ranking, resource matching and conflict resolution mechanism on the tasks according to the task feature vectors, performing global optimization scheduling on the spaceflight measurement and control tasks, and executing cross-domain two-stage scheduling during global optimization scheduling; based on bitmap decision matrix calculation, weighting calculation is carried out in combination with task matching values and conflict depths, the priority of conflict tasks is dynamically adjusted, conflicts generated in the scheduling process are dynamically eliminated, and the method can be applied to resource scheduling and optimization of low-orbit satellites and ground stations.

The invention discloses a multimodal transport cargo receiving, unloading and transferring multi-machine cooperation method and system. The multimodal transport cargo receiving, unloading and transferring multi-machine cooperation method comprises the steps of obtaining freight basic elements, constraint conditions and real-time environment information, and assigning a matched number of unmanned transfer vehicles and unmanned aerial vehicle sets; generating an initial optimal path; and the local path of the initial optimal path is adjusted in real time, the unmanned transfer vehicle and the unmanned aerial vehicle set advance to the unloading destination according to the global optimal path, and multi-vehicle cooperative cargo unloading is completed according to the conflict-free motion trail. A multi-machine cooperation technology is adopted, advanced technologies such as unmanned driving, the Internet of Things and an artificial intelligence algorithm are fused, the unmanned transfer trolley, the unmanned aerial vehicle and the intelligent gantry crane can work efficiently and cooperatively, various operation scenes such as indoor and outdoor switching, place layout difference and extreme weather can be dealt with, cargoes can be rapidly and accurately received, unloaded and transferred, and the working efficiency is improved. And the efficiency of multimodal transport is greatly improved.

The invention discloses a group aircraft collaborative perception decision control method and system in a complex high-dynamic environment, and relates to the technical field of cluster aircrafts, and the method comprises the steps: obtaining the performance parameters of a target group aircraft, building a task scene model in combination with the geographic information of a target task area, and building a target optimization function. Iteratively optimizing an aircraft and task allocation scheme through a graph neural network algorithm, outputting a task allocation result of each aircraft, and then according to a flight path constraint condition, analyzing a matching degree between a flight cost generated in a flight path and a corresponding task demand; and generating a globally optimal task allocation and flight path cooperation scheme. Task distribution and path planning are dynamically adjusted by sensing task requirements and environment changes in real time and combining performance parameters of all aircrafts, and compared with a traditional static method, the method can quickly respond to dynamic changes of task scenes, and task distribution reasonability and path planning effectiveness are ensured.

The invention discloses a distributed energy collaborative scheduling optimization method based on edge computing. According to the method, a plurality of edge computing nodes are deployed in a distributed energy system, a multi-protocol compatible OPC UA communication channel is constructed through protocol conversion middleware to collect data, and after the edge computing nodes clean and normalize the data, a preliminary scheduling scheme is generated through an improved genetic algorithm; the improved genetic algorithm is optimized through cooperation of a deep reinforcement learning model and an adaptive attenuation mechanism. And uploading the preliminary scheduling scheme to a cloud end, and obtaining a global optimal scheduling strategy through a multi-target particle swarm optimization algorithm. And the cloud carries out credible evidence storage on the global optimal scheduling strategy abstract value through an alliance chain smart contract, and establishes a PoA consensus mechanism. And when the communication is interrupted, the edge computing node starts the local emergency scheduling module, and incremental data synchronization is performed after the communication is recovered. The distributed energy scheduling optimization problem is effectively solved, the energy utilization efficiency is improved, and the system stability and reliability are enhanced.

The invention relates to the technical field of display screen encryption, and discloses an encryption method and system for an LED conference display screen, and the method comprises the steps: collecting and analyzing conference scene data, and obtaining a conference feature vector, a content feature vector and user role information; performing multi-layer neural network processing to obtain a dynamic encryption demand score and a plurality of candidate encryption strategies; constructing a double-layer model based on the dynamic encryption demand score and a plurality of candidate encryption strategies; solving according to the double-layer model to obtain a global optimal encryption strategy; lED display content is scanned and graded in real time, and a multi-element content set is obtained; according to the method, the multi-level encrypted display data stream is generated based on the dynamic sensitivity level of the multi-element content set, decryption control is carried out, the decrypted display content which can be accessed by the authorized user is obtained, the conference scene, the content characteristics and the user behaviors can be analyzed in real time, the encryption strategy is adjusted in a self-adaptive mode, and multi-level and differentiated content protection is achieved.

The invention discloses a pipe network on-line monitoring system, which relates to the field of urban capital construction and comprises a sensing deployment module, a signal acquisition module, a signal processing module, a pipe network modeling module, a leakage positioning module, an edge calculation module, an energy scheduling module, an anomaly prediction module, a deposition evaluation module, an early warning visual module and a center management module. According to the method, the recognition capability of the system on a hidden and complex pipeline structure is improved, accurate and efficient three-dimensional modeling can be carried out, the path deviation resistance capability is improved, and the more comprehensive and robust leakage sensing capability is realized; the method is advantaged in that communication burden and response time delay are substantially reduced, sensitivity and foresight of abnormal evolution trend identification are improved, overall identification accuracy of the system is improved, a global optimal risk solution can be efficiently output, a high-risk area is visually displayed, and rapid pre-judgment and decision making of operation and maintenance personnel are facilitated.

The invention discloses an intelligent storage robot path planning method, and belongs to the technical field of intelligent robots, and the method comprises the steps: improving a bidirectional A-Star algorithm, and combining a Time Elasticic-Band (TEB) obstacle avoidance algorithm, and providing a new intelligent storage path planning method. The improved A-Star algorithm adopts bidirectional search to replace unidirectional search of a traditional A-Star algorithm; 5-neighborhood search is adopted to replace 8-neighborhood search; introducing a self-adaptive dynamic search factor to improve a heuristic function; and smoothing the path by adopting a redundant node removal and linear interpolation method. The improved bidirectional A-Star algorithm searches to obtain a global optimal path, and the TEB algorithm optimizes and adjusts a local path according to the global path to complete dynamic obstacle avoidance. According to the method, the speed and precision of path planning are effectively improved, the local path can be adjusted in real time to avoid dynamic obstacles, and the requirement for path planning of the intelligent storage robot is met.

The invention relates to the technical field of unmanned aerial vehicle cluster control, and particularly discloses an intelligent cooperative flight path planning method and system for an unmanned aerial vehicle cluster system, and the method comprises the steps: firstly constructing a dynamic environment perception model, collecting data through all unmanned aerial vehicle sensors, and carrying out the preprocessing, attention feature extraction and federal learning fusion, and generating a global environment situation map; planning and screening a candidate track set by adopting a particle swarm-genetic hybrid optimization algorithm for adaptive weight adjustment on the basis of the image; a global optimal track consensus is achieved through an improved consensus algorithm and conflict resolution through a distributed collaborative negotiation mechanism and no human-computer interaction evaluation indexes; and finally, monitoring the environment in real time during execution, triggering dynamic re-planning when the environment is abnormal, and ensuring track adaptation through multi-level threshold and incremental planning. According to the method, the unmanned aerial vehicle cluster can quickly respond to the environment change and adjust the flight path, so that the task execution efficiency and success rate of the unmanned aerial vehicle cluster in the complex dynamic environment are improved.

The invention discloses an electric unmanned motorcade multi-task joint scheduling method based on multi-agent reinforcement learning. The method comprises the following steps: predicting a travel demand; constructing a state; task candidates are generated, wherein charging candidates, order sending candidates and scheduling candidates are generated; the reward function design comprises local instant reward design and global reward construction; generating a value function; strategy improvement and Actor updating are carried out; and performing environment execution and iterative training. According to the invention, three tasks of electricity supplement, order scheduling and relocation are regarded as a joint optimization problem. A task value learning mechanism based on an Actor-Critic architecture in reinforcement learning is designed, and optimal allocation of tasks and vehicles is realized in combination with a KM algorithm. The algorithm can continuously optimize the value evaluation of various tasks in different states, and the KM algorithm ensures the global optimality of task matching. Through the combination, the system not only has the learning ability, but also can make an optimal task allocation decision in real time.

The invention provides a page structure optimization method and system for a presentation file. The method comprises the following steps: identifying visual elements in a presentation page, and carrying out logical relationship analysis on the visual elements to obtain logical relationship information of the presentation page; mapping the visual structure information into a page three-dimensional implicit field to obtain three-dimensional voxels corresponding to the visual elements; calculating a visual focus thermodynamic diagram in the page of the presentation file, and distributing a three-dimensional voxel corresponding to the target visual element to a visual sensitive area; on the basis of the position relation of the visual elements and the first typesetting information, combining an information density energy function to predict a global optimal layout, and optimizing a narrative path in the presentation file according to logic relation information and user narrative preference to obtain third typesetting information, so that the spatial arrangement relation of the visual elements conforms to the narrative logic of the user preference; and executing secondary typesetting on the PowerPoint page based on the third typesetting information to optimize the page structure of the PowerPoint page and improve the page typesetting efficiency.