4122 results about "Particle swarm optimization" patented technology

The invention relates to a multi-objective optimization scheduling method and system for a multi-AGV flexible job shop. The method comprises the steps of collecting job shop layout data, AGV operation parameters and production task data, and constructing a scheduling system basic model; performing recurrent neural network training and physical constraint model integration to obtain a hybrid scheduling prediction model; subtask decomposition and path search processing are carried out, and a dual-objective optimization model is obtained through constraint linearization conversion; performing particle swarm optimization solution on the dual-objective optimization model, and obtaining a non-dominated solution set through two-stage iterative calculation of an adaptive weight strategy and constraint conversion; performing multi-dimensional evaluation calculation on the non-dominated solution set to obtain an optimal scheduling execution scheme; and performing model feedback updating on the hybrid scheduling prediction model to obtain a target scheduling prediction model. According to the invention, multi-target balance of task completion time and energy consumption is realized, and the real-time response capability and operation stability of the system are improved.

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.

According to the optical storage direct flexible system scheduling method based on multi-objective optimization and an adaptive scheduling strategy, monitoring devices are installed on a photovoltaic array, an energy storage unit and a load side, a data sensing network covering the whole link of'source-storage-load-network 'is constructed, and key data are collected in real time. A multi-objective optimization model with maximization of economical efficiency, reliability and clean energy consumption rate as objectives is established, and a hybrid optimization mechanism of a genetic algorithm and particle swarm optimization is adopted to generate a day-ahead scheduling reference scheme. And designing an adaptive scheduling algorithm and a dynamic parameter adjustment mechanism based on fuzzy logic, and combining a rolling optimization window to realize real-time optical storage coordination control and power real-time balance. According to the invention, the operation efficiency, stability and flexibility of the optical storage direct-flexible system are effectively improved, the capability of coping with emergencies is enhanced, and the optimization of the overall performance of the system is realized.

The invention relates to the technical field of power battery packs, and discloses a power battery pack state evaluation method and system, and the method comprises the steps: collecting an operation characteristic data set of a power battery pack under various working conditions, and constructing a hybrid evaluation model; performing parameter configuration on the double-extended Kalman filter to obtain an improved filter; acquiring real-time voltage, current and temperature data, and inputting the real-time voltage, current and temperature data into the improved filter for state calculation to obtain a charge state value and an internal parameter value; inputting the charge state value and the internal parameter value to perform multi-branch adversarial neural network analysis to obtain a fault mode type and a fault severity; according to the method, the multi-factor health index is calculated, particle swarm optimization fitting is carried out on the degradation curve of the power battery pack, the residual service life prediction value is obtained, more accurate residual service life prediction is further achieved, the precision in a wide temperature range is remarkably improved, and the difference between monomers in the power battery pack can be effectively processed.

The invention provides a power grid fault waveform identification and intelligent relay protection rapid control method and system, and relates to the technical field of electric power, and the method comprises the steps: collecting the voltage and current waveform data of a power grid, carrying out the digital filtering and noise elimination processing, carrying out the Fourier transform and wavelet transform, extracting the time domain and frequency domain features, and constructing a combined feature vector. And a deep learning model including a convolutional neural network and a long-short-term memory network is used to identify and evaluate the type, position and amplitude of the power grid fault based on the feature vector database. And then, a multi-target particle swarm optimization algorithm for constructing a target weight matrix based on historical fault data is adopted to optimize fault isolation time, a power supply recovery rate and system stability to obtain a control strategy, and a relay protection device setting value is adjusted according to the control strategy to execute fault isolation and power grid topology reconstruction.

The invention discloses a partial discharge detection method and device, and the method comprises the steps: outputting a multi-mode signal matrix after noise reduction through employing a self-adaptive noise reduction algorithm according to a multi-mode sensing signal during the operation of a generator; based on the multi-modal signal matrix, a space-time convolutional neural network is adopted to extract discharge features, meanwhile, a topological relation between signal time-frequency features and sensor space is captured, and a multi-modal feature fusion tensor is output; according to the multi-modal feature fusion tensor, a model is generated through a dynamic map, manifold learning and a particle swarm optimization algorithm are combined, and a dynamic fault map containing discharge intensity, phase and frequency point distribution characteristics is output; and based on the dynamic fault map, calculating a real-time discharge danger coefficient by using a risk prediction model, and outputting a discharge grading early warning instruction and a maintenance priority sequence. According to the embodiment of the invention, high-precision and traceable discharge detection and graded early warning can be realized.

The invention relates to the technical field of ecological environment protection, in particular to an ecological environment damage intelligent identification management method based on multi-source data fusion, which comprises the steps of data acquisition and preprocessing, data fusion and feature extraction, ecological environment damage evaluation model construction, damage dynamic analysis and trend prediction, restoration suggestion generation and visual display. The method comprises the following steps: collecting water quality, atmosphere, biological and social economic data in real time through a sensor network deployed in an ecological sensitive area, preprocessing the data, extracting key features by adopting a multi-modal data fusion technology and a deep learning model, and constructing an ecological damage assessment model; the change rule of ecological damage and the potential high-risk area are predicted based on a time sequence analysis method, and an optimal ecological restoration scheme is generated in combination with a particle swarm optimization algorithm. According to the method, the evaluation result and the restoration suggestion are displayed through the visual interface, a scientific basis is provided for decision makers, and the efficiency and the effect of ecological environment restoration work can be improved.

The invention discloses an electromechanical construction method and system based on a BIM technology, and relates to the field of electromechanical construction, and the method comprises the steps: collecting a three-dimensional point cloud of a construction site; a construction site buult BIM model is constructed; constructing an electromechanical BIM model of the construction site; obtaining a logic dependency relationship between electromechanical tasks in the electromechanical BIM model by utilizing a judgment matrix method DSM, and extracting attribute parameters of task nodes; constructing a directed acyclic graph (DAG) for task collaborative optimization according to the logic dependency relationship between the electromechanical tasks and the time sequence and resource parameters of the nodes; obtaining task nodes on the critical path according to a directed acyclic graph (DAG) of task collaborative optimization; establishing a multi-objective optimization model according to task nodes on the critical path, and solving through a multi-objective particle swarm optimization algorithm MOPSO by taking the shortest construction period and the highest resource utilization rate as optimization objectives and taking process logic and resource occupation as constraint conditions to obtain a resource optimization configuration set; and the collaboration of electromechanical construction tasks is improved.

The invention discloses a power industry robot collaborative inspection and fault self-diagnosis system and method, and belongs to the technical field of power inspection, and the system comprises a management module which receives an inspection task instruction and obtains inspection task information according to the inspection task instruction; the environment identification module is used for acquiring inspection environment data and identifying obstacles; the path planning module is used for generating an inspection path set by adopting a multi-target particle swarm optimization algorithm according to the obstacle and inspection task information; the scheduling module is used for acquiring the state data of each robot and distributing the routing inspection paths in the routing inspection path set to each robot; the fault feature extraction module is used for acquiring multi-sensor data acquired by the robot and generating a fault feature vector; and the fault diagnosis module performs fault analysis on the fault feature vector to obtain a fault risk analysis report. The obstacle is recognized by acquiring the environment data, the inspection path set is generated by combining the inspection task information and adopting the multi-target particle swarm optimization algorithm, and the method can adapt to the complex inspection environment.

The invention discloses a sewage plant total nitrogen concentration real-time prediction and process regulation and control method based on an optimization integration algorithm, and belongs to the technical field of environment monitoring and treatment. According to the method, sewage plant data are monitored and collected, a sliding window and a time sequence are combined to analyze and clean the data and reconstruct features, total nitrogen concentration strong correlation variables are screened, data quality is standardized and optimized, a plurality of machine learning algorithms are adopted to construct a prediction model, and an optimal model is optimized through cross validation and performance evaluation. The robustness is improved by global parameter adjustment in combination with optimization algorithms such as a particle swarm, process schemes such as aeration intensity and carbon source adding are generated through multi-objective optimization after containerization deployment, and a whole-process intelligent management and control system is constructed by integrating virtual verification, graded early warning and a self-adaptive feedback mechanism. According to the method, the problems of detection lag, insufficient model generalization ability, regulation response delay and the like of a traditional method are solved, and the operation energy consumption and the medicament cost are remarkably reduced while it is guaranteed that the effluent quality stably reaches the standard.

The present invention provides a flight attitude control method based on inertial navigation technology, which relates to the field of aircraft control technology and includes an attitude control system. The attitude control system includes a quantum inertial navigation fusion module, a distributed multi-source collaborative inertial navigation module, a real-time error compensation module based on deep learning, an energy collection and self-powered inertial system module, a distributed attitude data management module based on blockchain, a multimodal fusion and virtual twin collaborative control module, and a terahertz communication and inertial navigation fusion anti-interference module. The present invention uses a quantum annealing algorithm combined with a compressed sensing algorithm to optimize quantum sensor data, and then combines the D-S evidence theory and the particle swarm optimization algorithm in the distributed multi-source collaborative inertial navigation module to fuse multi-source data, which can deeply mine the deep connection and complementary information between different sensor data, break the simple data level superposition limitation, and greatly improve the accuracy of the attitude information after fusion.

The invention discloses a slope instability sliding real-time early warning method based on an improved machine learning algorithm. The method comprises the following steps: S1, outputting a consistent slope monitoring data set; s2, constructing a slope monitoring map structure based on the consistent slope monitoring data set and the spatial position information of each sensor; s3, outputting a slope state feature vector; s4, performing online optimization on key parameters of the dynamic graph attention residual image convolutional neural network model by using an adaptive particle swarm optimization algorithm, and outputting an optimized dynamic graph attention residual image convolutional neural network model; and S5, re-mapping the consistent side slope monitoring data set to generate a new side slope state feature vector, judging an instability sliding risk in the side slope state according to a comparison result between the side slope state feature vector and an early warning threshold value, and generating side slope instability real-time early warning information. According to the method, the risk that too many invalid connections are established in a state stable region is effectively avoided, and the physical rationality and the anomaly capture capability of the slope map in the actual instability trend are enhanced.

The invention relates to the technical field of bridge engineering, in particular to a cable structure bridge design method adopting a BIM (Building Information Modeling) model, which comprises the steps of three-dimensional bridge foundation model construction, cable structure parameterization configuration, nonlinear mechanical simulation, construction error modeling, feedback optimization and the like. Through introduction of structured geological data and NURBS curved surface reconstruction, a geological-structure integrated model is realized. Adopting a genetic algorithm and a particle swarm optimization method to intelligently configure pile foundation and cable parameters; a construction error closed-loop adjustment mechanism is constructed through dynamic simulation and real-time tension feedback control; and finally, parameter correction write-back and BIM delivery model integration is realized. According to the invention, the design precision, the construction stage adaptability and the digital delivery integrity of the cable structure can be improved, and the method is suitable for cable structure bridge engineering with complex geology and high-precision control requirements.

The invention provides a thermal power plant boiler combustion visual monitoring and early warning method and system, a storage medium and electronic equipment. According to the method, obtained multi-measuring-point temperature data are input into a pre-constructed temperature field reconstruction model, the model comprehensively considers the geometric structure and combustion thermodynamic characteristic factors of a hearth, model parameters are dynamically adjusted through a particle swarm optimization algorithm, the temperature field reconstruction precision is continuously improved, and a three-dimensional real-time temperature distribution diagram of the hearth is generated; fusing and presenting the diagnosed combustion abnormal information and the visual image of the hearth temperature field, generating a visual in-furnace combustion state monitoring interface, and meanwhile, pushing the abnormal information to a mobile terminal to realize remote monitoring and early warning; the combustion state data in the long-period operation process of the boiler are mined and analyzed, typical combustion modes are extracted, a combustion mode library is continuously enriched and optimized, an online learning algorithm is used for adaptively updating a combustion abnormity diagnosis model, and the intelligent level of combustion state sensing and abnormity early warning is continuously improved.

The invention belongs to the technical field of power distribution network voltage regulation and control, and discloses a distributed photovoltaic complex power prediction-cluster-cooperation-based power distribution network voltage regulation and control method, which integrates photovoltaic historical data, inputs an improved back propagation neural network model and outputs predicted photovoltaic active power output. Estimating the reactive capacity boundary of each node in real time based on the running state of the network-following inverter; dividing a distributed photovoltaic cluster by establishing a two-dimensional modularity function of a net load index and an equivalent electrical distance; a multi-device differential cooperative control strategy is provided for the voltage out-of-limit risk in the cluster; and constructing an optimization function with minimum network loss and voltage offset as a target, and optimizing and solving the function by using an improved multi-organization particle swarm optimization algorithm to obtain a multi-device adjustment sequence and a device action amount. According to the method, the renewable energy consumption capacity is improved and the network loss is reduced while the voltage stability of the power distribution network is ensured, and the comprehensive adjustment cost is optimized.

The invention discloses a low-altitude aircraft take-off and landing platform site selection optimization method. The method comprises the steps that real-time dynamic data and static GIS data including urban traffic flow data, meteorological data, landform data, environment data and POI data are acquired; constructing an urban three-dimensional digital model according to the static GIS data, and constructing a digital twin model according to the three-dimensional digital model and the real-time dynamic data; constructing environment constraint conditions and safety constraint conditions of candidate take-off and landing platform positions in the digital twin model; performing multi-objective optimization on the digital twin model through a particle swarm optimization algorithm, and generating an optimal candidate take-off and landing platform site selection scheme set meeting environment constraint conditions and safety constraint conditions; and dynamically updating the digital twin model according to real-time data feedback, and dynamically adjusting the site selection scheme of the take-off and landing platform. According to the method, the virtual city model is constructed through the digital twin, an accurate simulation environment and real-time feedback are provided for site selection optimization, and the accuracy and feasibility of a site selection scheme are improved.

The invention relates to the technical field of power system automation, in particular to a real-time monitoring and fault positioning system for a vehicle-mounted mobile substation. The system comprises a signal acquisition and processing unit which monitors and acquires line parameters and environmental parameters of a transformer substation and traveling wave signals generated when a fault occurs in real time; the fault type identification unit calculates the confidence coefficient of each fault type based on the traveling wave signal so as to judge the fault type generated by the traveling wave signal, and marks the arrival time of the traveling wave head; an algorithm fusion positioning unit preliminarily positions a fault point according to the fault type and the arrival time of a traveling wave head, and then constructs a fitness function through a particle swarm optimization algorithm in combination with line topology and environmental parameters to correct a preliminary positioning error; according to the system, high-precision fault positioning and rapid isolation recovery are realized, the accuracy of fault section division in the complex power distribution network is ensured by modeling switch state and branch change, and misjudgment caused by topological change or equipment overload is effectively avoided.

The invention provides a brushless motor attitude detection and balance control method and system based on a gyroscope, and relates to the technical field of control, and the method comprises the steps: collecting angular velocity and acceleration data through a six-axis gyroscope, carrying out the noise reduction through wavelet transform, and carrying out the attitude calculation through the combination of an extended Kalman filter and a quaternion algorithm. A rotor position signal is obtained through a magnetic encoder, nonlinear compensation is carried out, and rotating speed data are calculated. A motor state is modeled by adopting a long-short-term memory network, a double-layer adaptive fuzzy neural network controller is constructed, and attitude error compensation and rotation speed fluctuation suppression are realized. A controller model is optimized through particle swarm optimization and a genetic algorithm, a compensation current vector is corrected in real time, and the control precision and stability of the brushless motor are improved. According to the method, the operation efficiency and the dynamic response capability of the brushless motor are effectively improved.

The invention provides a crop accurate management method and system based on a crop growth model. The method comprises the following steps: collecting environment data of a crop growth period by integrating a remote sensing technology, a field intelligent sensor and an unmanned aerial vehicle, and carrying out space-time alignment to form a comprehensive environment data set; and dynamically adjusting parameters of the crop growth model by using a Bayesian rule, evaluating the influence of environmental uncertainty on crops in combination with Monte Carlo simulation, and providing yield prediction with a confidence interval. Based on the prediction, a particle swarm optimization algorithm is applied to formulate management schemes such as irrigation, fertilization, pest control and harvesting opportunities, and meanwhile, a multi-objective optimization strategy is adopted to balance economic benefits and environmental protection. After the scheme is implemented, the crop growth response is monitored in real time so as to reinforce learning and automatically adjust management measures, and precise agricultural management is realized. According to the technical scheme provided by the embodiment of the invention, the crop management precision and efficiency are improved.

The invention provides a dynamic graph neural network modeling method for space-time big data, and relates to the technical field of data processing, and the method comprises the steps: mapping a network function entity into a topology vertex and mapping a topology correlation characteristic into a weighted transmission link, and triggering a sequence through a signaling event to drive topology reconstruction, and generating a communication network topology model; inputting the communication network topology model into a dynamic graph neural network, executing state feature space aggregation of a topological vertex neighborhood through a spatial-temporal feature extraction layer, and fusing time evolution dependency of a historical topological sequence to generate a network node spatial-temporal state tensor; and based on the network node space-time state tensor, a particle swarm optimization algorithm is adopted to calculate a whole network risk level quantitative topology feature, and network resource strategy optimization is dynamically executed to suppress end-to-end risk conduction. The adaptive capacity of the network to the dynamic scene is improved.

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 relates to the technical field of cloud computing, and discloses a resource scheduling method, device and equipment, and the method comprises the steps: generating a multi-dimensional data topological graph of cloud environment monitoring data, decomposing a to-be-scheduled job set into quantum computing unit sets which can be processed in parallel, and adding priority labels for the quantum computing unit sets through a quantum heuristic algorithm, according to priority labels and a multi-dimensional data topological graph, physical resources of a cloud environment are divided into non-uniform resource fragments matched with task requirements, based on the non-uniform resource fragments, resource scheduling is carried out through an improved hybrid particle swarm optimization algorithm, and weighting and fitness evaluation and resource constraint correction are introduced. According to the resource scheduling method and device, the multi-dimensional data topological graph is generated to comprehensively present the resource state and association, then the to-be-scheduled job set is decomposed into the quantum computing unit sets, the priority labels are added, and then the non-uniform resource fragments are divided for resource scheduling, so that the resource adaptation accuracy is improved, and the scheduling efficiency and the resource utilization rate are improved.

The invention relates to the technical field of industrial automatic control, and discloses an electric control cabinet internal intelligent layout optimization and thermal management system, which comprises an intelligent layout optimization module, which adopts a particle swarm optimization algorithm to comprehensively analyze the multi-dimensional parameter size, the weight, the heat dissipation requirement and the like of internal equipment of an electric control cabinet, realizes layout optimization, and provides a thermal management module for the internal equipment of the electric control cabinet; the method comprises the following steps: receiving equipment parameters input by a user or automatically extracted from an equipment database, including but not limited to physical sizes, power consumption, thermal loads and the like of the equipment, and optimizing targets, including maximizing a space utilization rate, minimizing heat dissipation obstruction, optimizing a maintenance path and ensuring a safe distance between the equipment. According to the internal intelligent layout optimization and heat management system of the electric control cabinet, by comprehensively applying artificial intelligence, three-dimensional modeling, a multi-element heat dissipation technology and an Internet of Things technology, optimal layout and efficient heat management of internal equipment of the electric control cabinet are achieved, and the operation efficiency, safety and maintainability of the electric control cabinet are remarkably improved.

The invention discloses a multi-unmanned aerial vehicle maritime search and rescue path planning method based on multi-target particle swarm optimization. The method comprises the steps that S1, an area to be searched and rescued is converted into a two-dimensional search and rescue area; s2, performing grid decomposition on the two-dimensional search and rescue area; s3, obtaining a probability map through a Gaussian mixture model; s4, establishing an unmanned aerial vehicle search and rescue multi-target collaborative optimization function; s5, solving the function based on a hybrid particle swarm optimization algorithm to obtain an optimal solution; and generating a specific search and rescue path of the unmanned aerial vehicle according to the solved optimal solution. According to the method, an unmanned aerial vehicle search and rescue multi-target collaborative optimization function is established, the unmanned aerial vehicle is guided to execute the search and rescue task by taking resource balanced allocation, maximum path safety, preferential coverage of a high-risk area and minimum energy consumption as targets, task allocation, path planning and energy management are improved, multi-target optimization of multi-unmanned aerial vehicle search and rescue path planning is realized, and the search and rescue efficiency is improved. An accurate flight path is formulated for each unmanned aerial vehicle, comprehensive coverage of search and rescue tasks is ensured, and the search and rescue efficiency and success rate are remarkably improved.

The invention relates to the technical field of electricity meter metering, and discloses an electricity meter adaptive error compensation method and system, and the method comprises the steps: calibrating an electricity meter, collecting baseline data under different environment conditions, and recording a measurement error; environment data and ammeter data are collected in real time through an ammeter, and key features are extracted; constructing an error model, and predicting the error of the current ammeter data; and performing real-time correction and parameter adjustment according to the prediction error, and outputting the corrected measurement data. By introducing a real-time error prediction and dynamic compensation mechanism based on environmental data and combining a particle swarm optimization algorithm to optimize regularization parameters in a ridge regression model, the measurement precision of the electric meter under a complex environmental condition is effectively improved. The method can adaptively adjust the measurement data of the electric meter, reduces errors caused by environmental factors, improves the accuracy and stability of electric power measurement, and provides more reliable data support for an electric power management and monitoring system.

A 100-meter gridded spatialization method for carbon emissions of different land use types based on multi-source heterogeneous data is provided, including the following steps: S1, fitting Luojia-1A Satellite nighttime light data year by year with defense meteorological satellite program-operational linescan system (DMSP-OLS) and national polar-orbiting partnership-visible infrared imaging radiometer (NPP-VIIRS) fused 1-kilometer gridded nighttime light data, performing additive fusion on previous light data and current light data, introducing a time inertia weight factor to improve particle swarm optimization-back propagation (BP) neural network algorithm, and forming 100-meter nighttime light data correctable on a long time series; and S2, simulating a complex nonlinear dynamic changing relationship between multilevel 100-meter gridded data of different land use types in different industries and energy carbon emissions in different industries based on the 100-meter nighttime light data, and establishing a 100-meter spatialization inversion model for energy carbon emissions of different land use types in different industries.

The invention relates to a charging pile intelligent operation management method and system based on the Internet of Things. The method comprises the following steps: performing Internet of Things topology construction on charging pile nodes to obtain a node communication distance matrix and a network connectivity matrix; performing deep learning dimension reduction and node screening based on the network connectivity matrix to obtain a fault feature vector; the fault feature vector and the charging pile operation state data are input into a hybrid neural network for risk calculation, a fault risk assessment value is obtained, and the hybrid neural network comprises a space perception sub-network and a time sequence analysis sub-network; performing normalization and dimension reduction mapping on the power response data of the charging pile to obtain a time domain mapping matrix; and performing particle swarm optimization calculation based on the time domain mapping matrix and the fault risk assessment value to obtain a charging power distribution scheme, and generating a control instruction set according to the charging power distribution scheme. According to the invention, the dynamic optimization of the charging power distribution scheme is realized, and the charging efficiency and the load balance are effectively balanced.

A path planning system for an unmanned aerial vehicle in a network of unmanned aerial vehicles is disclosed. The system includes the unmanned aerial vehicles (UAVs). The system further includes a first processing circuitry configured with a particle swarm optimization component to offline generate paths for each of the UAVs by PSO to minimize path length and avoid static obstacles. The system further includes a second processing circuitry configured with a deep reinforcement learning (RL)-based planner component for each UAV, to perform real-time path planning to navigate the UAV through dynamic environmental conditions using a particular path generated by the PSO for the UAV as a consistent reference for the UAV. The system further includes a reward component to calculate a reward as part of the path planning by the deep RL-based planner component to determine potential paths and converge to an optimal path for the UAV.

The invention provides an unmanned aerial vehicle infrared target positioning system fusing multi-model Kalman filtering and deep learning, and relates to the technical field of unmanned aerial vehicle navigation and positioning. The main body system comprises a data acquisition module, a multi-source data fusion module, a feature extraction enhancement module, a dynamic model optimization module, a target state estimation module, a positioning calculation module and a flight decision planning module, and the data acquisition module is provided with a high-resolution infrared sensor and accurately captures target infrared radiation information; a high-definition vision camera and a high-precision laser radar are integrated at the same time, environment data are collected in an omnibearing mode, and the data collection module can collect the environment data according to the environment complexity and the real-time change of the target motion state by adopting a self-adaptive sampling strategy based on fuzzy logic, particle swarm optimization and a self-adaptive threshold value. And the sampling frequency and the resolution of the sensor are dynamically and intelligently adjusted.

The invention relates to the technical field of agricultural production data prediction, and discloses a tobacco plant number prediction method based on deep learning, and the method comprises the steps: collecting tobacco field environment data through a soil humidity sensor, a weather sensor, a laser radar point cloud sensor, a multispectral sensor and other multi-source agricultural sensors, carrying out the multi-source feature fusion through a dynamic feature selection network, and generating an environment feature vector. And inputting the model into a multi-modal time sequence prediction model adopting a space-time attention mechanism, and modeling a tobacco plant growth trend. And constructing an adaptive weighted regression model according to a prediction result, and dynamically adjusting the weight by using an improved particle swarm optimization algorithm. And constructing a hierarchical correction model to optimize prediction data. A real-time prediction updating method is provided, and newly-added data can be processed. The multispectral sensor data is subjected to dimension reduction, correlation modeling and interpolation fusion processing. According to the method, multi-source data is fully utilized, the accuracy, real-time performance and stability of tobacco plant number prediction are improved, and powerful support is provided for tobacco planting management.