236 results about "Ant colony optimization algorithms" patented technology

The invention relates to the field of computer vision, in particular to a crop disease and insect pest real-time identification, prevention and control decision-making method and system based on multi-modal edge calculation. Comprising the following steps: acquiring an unmanned aerial vehicle multispectral image, an infrared thermal image and an NDVI index, performing preprocessing in combination with field Internet of Things sensing data, and generating a multi-modal feature input sequence; constructing a spectrum-environment fusion feature matrix on the basis of a Transform attention mechanism, and performing disease and pest detection by using a YOLOv7-Spectral model to generate a disease and pest distribution heat map; calculating a disease risk index (BRI) and a pesticide application priority map based on historical data; planning a pesticide application path of the unmanned aerial vehicle by adopting an ant colony optimization algorithm, and optimizing a pesticide application scheme in combination with wind speed and humidity parameters; the unmanned aerial vehicle performs precise pesticide application according to the optimized path and monitors the disease change trend; adjusting the model through pesticide application feedback data, and optimizing a disease and pest prevention and control strategy by adopting federal learning. According to the invention, the identification precision is improved, the pesticide use is reduced, and accurate, efficient and intelligent prevention and control are realized.

The invention discloses a self-adaptive load balancing ring main unit, relates to the technical field of power distribution network protection, and solves the problems that the load balancing strategy of the existing ring main unit is not flexible enough and the monitoring and early warning capabilities are insufficient. The system comprises a data acquisition module, a data processing and analysis module, a load balance control module, a monitoring and early warning module and an expansion and compatibility module. Load distribution is intelligently optimized through real-time data and edge calculation; pID control and fuzzy logic are integrated, parameters are dynamically adjusted, and efficient operation of a power system is ensured; a non-dominated sorting genetic algorithm and an ant colony optimization algorithm are applied to balance energy consumption and stability on the basis of meeting load balance, and an optimal load scheme is generated; safety is improved through closed-loop control and an early warning system, network resource management is enhanced through the software defined network technology, and it is ensured that the system is flexible and extensible; according to the invention, the intelligent load balancing capability of the existing ring main unit is greatly improved, and the operation efficiency and stability are enhanced.

The invention provides a large model scientific and technological problem generation method fusing external knowledge, which comprises the following steps: dynamically solving an optimal knowledge fusion strength parameter in a current environment by adopting a gradient descent algorithm through designing and optimizing an objective function according to association representation and difference representation between knowledge units, the knowledge unit balancing module is used for balancing correlation and complementarity among knowledge units; on the basis of real-time updating of knowledge fusion strength parameters, heuristic search is carried out in the knowledge unit association map by utilizing an ant colony optimization algorithm, and candidate fusion knowledge units which achieve target correlation and target complementarity with the current knowledge unit are selected by combining semantic similarity, association degree and difference among the knowledge units; a knowledge mapping ontology is used for guiding fusion of cross-domain knowledge, an ontology inference engine based on description logic is adopted to infer a semantic consistency relationship between fused knowledge units, and potential semantic conflicts are identified and eliminated through a consistency check algorithm.

The invention discloses a power plant equipment fault prediction method based on a time sequence large model, and the method comprises the following steps: S1, collecting and preprocessing the time sequence data of a multi-source sensor of a power plant, and generating a standardized time sequence data set; s2, constructing a time sequence large model, inputting standardized data, and outputting a future operation state predicted value; s3, comparing the running state prediction value with an actual measurement value to generate a prediction residual sequence; s4, constructing a Bayesian neural network model, inputting a prediction residual sequence, and outputting error probability distribution; s5, optimizing a Bayesian neural network structure and hyper-parameters by adopting an ant colony optimization algorithm; s6, confidence interval estimation is executed, and whether the state is a high-risk state or not is judged; and S7, outputting a running state label, and dynamically acquiring a data closed-loop updating model. According to the invention, high-precision prediction and uncertainty evaluation of the operation state of the power plant equipment are realized, so that the accuracy and response time efficiency of fault early warning are improved.

The invention relates to the technical field of AUVs (Autonomous Underwater Vehicles), in particular to a real-time communication and positioning data fusion processing method for AUV (Autonomous Underwater Vehicle) collaborative operation, which comprises the following steps of: deploying a multi-modal sensor, acquiring communication data, kinematics data, acoustic positioning data and visual data required by an AUV, and preprocessing the data; the method comprises the following steps: fusing multi-modal data by using an extended Kalman filtering algorithm, calculating three-dimensional position and attitude information of the AUV in real time, carrying out dynamic path planning by using an ant colony optimization algorithm based on fused positioning data, optimizing a collaborative operation path between the AUVs, and sharing the positioning data and a path planning result in real time through an underwater communication network. And dynamically adjusting a task allocation strategy by using an improved task allocation algorithm, optimizing the cooperation efficiency, and finally, storing all data, path planning records and task allocation schemes in a database. According to the invention, the accuracy and stability of AUV collaborative operation are improved.

The invention belongs to the technical field of power grid power transmission, and discloses a power grid intelligent power transmission line fault positioning system, which comprises the steps of collecting power operation data, spatial data and environmental data; correcting the power operation data based on the constructed power correction model to obtain corrected power data; performing topology establishment on the corrected power data and the spatial data by adopting a neural network algorithm to obtain a power grid topological graph, and performing graph decomposition on the power grid topological graph by adopting an ant colony algorithm to obtain B topological sub-graphs; constructing a fault prediction model for predicting a fault type; performing fault prediction on the B topology sub-graphs through a fault prediction model, marking the topology sub-graphs with predicted fault types as fault sub-graphs, positioning fault points in the fault sub-graphs through a particle swarm algorithm, and determining final fault point positions; and the positioning speed of the fault point location is greatly improved.

The invention discloses an unmanned aerial vehicle multi-mode automatic obstacle avoidance method based on a Beidou system, and relates to the navigation and obstacle avoidance field in the robot field. Comprising unmanned aerial vehicle starting and navigation, image processing and feature extraction, environment mapping and path planning, obstacle recognition and avoidance, multi-modal fusion dynamic obstacle avoidance, flight control and information transmission and an emergency obstacle avoidance mechanism in an emergency. According to the invention, a Beidou system, machine vision, a deep learning algorithm and multi-sensor fusion are combined, high-precision positioning, real-time path optimization and dynamic obstacle avoidance are realized, and safe and efficient flight of the unmanned aerial vehicle in a complex environment is ensured. Through the three-dimensional mapping and ant colony optimization algorithm, the path planning efficiency is improved, the method is adaptive to the environmental change, and the autonomy, flexibility and task execution capability rate of the unmanned aerial vehicle are significantly enhanced.

The invention relates to the technical field of educational resource recommendation, and discloses an educational resource intelligent recommendation method and system based on big data driving, and the system comprises a data fusion processing module, a portrait modeling module, a resource feature engine module, an intelligent recommendation core module and a closed-loop feedback module. By fusing knowledge state, cognitive ability and interest preference three-dimensional portraits, capturing user learning ability evolution in real time, dynamically updating knowledge mastery by adopting a knowledge tracking model, and perceiving interest migration in combination with an attention mechanism, the problem of learning cold start in a new field is solved, recommendation coverage range and accuracy are improved, and user experience is improved. The cognitive load sensitive ant colony optimization algorithm is designed, the learning path continuity is guaranteed through a heuristic function, the learning path structure reasonability is optimized, the cognitive burden of a user is reduced, a personalized knowledge attenuation model is constructed by fusing an Ebbinghaus forgetting curve, the knowledge long-term retention rate is increased, and the review resource release accuracy is enhanced.

The invention relates to the technical field of data processing, in particular to a confidential propaganda and education system based on multi-modal interaction, which comprises a data acquisition module, a data processing module, a training module and an optimization module. The data collection module collects user eye movement tracks, voice instructions, operation behaviors and physiological signal data, noise is eliminated through time synchronization and self-adaptive filtering, and then a three-dimensional data cube is constructed through a tensor decomposition algorithm. And reversely adjusting knowledge graph nodes and scene parameters by using a genetic algorithm to form a closed-loop feedback link. And the cross-module cooperation unit aggregates a global cognitive feature model through a federated learning framework, and realizes privacy protection and resource dynamic allocation in combination with differential privacy protection and an ant colony optimization algorithm. Through multi-modal data fusion, dynamic strategy generation and closed-loop evaluation optimization, the problem of unidirectional interaction and feedback lag of a traditional system is solved, and user confidential knowledge internalization efficiency and complex scene coping capacity are improved.

A method for carbon tracking and analysis in a city is provided. Basic information of a target city is acquired. A three-dimensional visual city model is constructed based on the basic information. The target city is divided into a plurality of sub-regions. A carbon emission monitoring plan is formulated based on regional properties of each sub-region. According to the acquired carbon emission monitoring data, a carbon emission change of each sub-region within the current preset period is analyzed by means of linear regression to obtain carbon emission change trend data of each sub-region. Carbon emission tracking is performed based on the carbon emission change trend data. A current carbon tracking route and a carbon prediction route are generated by means of a preset ant colony optimization algorithm. A system non-transitory computer-readable storage medium for implementing carbon tracking and analysis in a city and are also provided.

The invention relates to a landslide grading early warning method based on multi-modal data change characteristics. The method comprises the following steps: establishing a mountain digital twinborn body; the method comprises the following steps: deploying a multi-node sensor network in a target area, configuring an edge computing unit, collecting geological data in real time, and screening the geological data based on mountain digital twin to form effective local data; constructing a lightweight multi-modal neural network model at each node, dynamically searching hyper-parameters by using an ant colony optimization algorithm, and generating an encryption model weight update quantity packet; the central server dynamically calculates node weights according to disaster feature vectors output by the digital twins, generates a global model through weighted aggregation, and directionally distributes and updates the global model; real-time monitoring data and a model prediction result are fused, millimeter-level disaster evolution simulation is executed through a variable step size physical engine, an advanced early warning signal is triggered when a deduced prediction risk exceeds a threshold value, and the edge model adaptability, federal aggregation precision and early warning advancement are remarkably improved.

The invention discloses a transmission path planning method and system for a box-type logistics system, and the method comprises the steps: constructing a four-dimensional space-time transmission network model which comprises the three-dimensional space coordinates of the box-type logistics system and department operation time window constraints, and quantifying the node traffic capacity; a hierarchical planning architecture is adopted, and a dynamic Bayesian network is introduced to predict the node congestion probability; a multi-agent cooperation mechanism is established, an online layer adjusts a path weight in real time by improving an ant colony optimization algorithm, and an offline layer optimizes global strategy parameters by using deep reinforcement learning and constructs a knowledge distillation channel to realize cross-period experience migration; and a multi-objective evaluation function is applied, transmission time cost, system energy consumption indexes and path load balance degree are synchronously optimized, a conflict resolution mechanism based on the game theory is designed, and transmission path planning of each box body is realized. According to the invention, through dynamic path planning and real-time flow monitoring, materials can be rapidly and accurately delivered to a destination, and the overall efficiency of a logistics system is improved.

The invention provides an inspection robot path planning method based on a transformer substation environment, and belongs to the technical field of transformer substation inspection, and the method comprises the steps: constructing a transformer substation weighted map, obtaining a plurality of optimal planning paths through employing an iteration method, and selecting the optimal planning path with the highest pheromone concentration as the optimal inspection path of an inspection robot. Comprising the following steps: calculating heuristic estimation cost of each node to obtain an initial planning path, introducing an inspection importance weight factor to calculate heuristic information between the nodes, and evaluating path fitness to obtain an optimal initial planning path in a current iteration process; calculating a pheromone increment between the nodes to update a pheromone concentration between the nodes; and calculating a selection probability based on an ant colony algorithm, and selecting a next node according to the selection probability until a planned path is constructed as an optimal planned path of the iteration. The problem that an existing automatic inspection system is insufficient in path planning in a complex environment is solved.

The invention relates to the technical field of data processing of organic displays, in particular to an interaction control method of a double-screen organic display system, which acquires touch control, deformation and ambient light data in real time through a multi-mode sensor array, and realizes cross-screen heterogeneous data space-time alignment by adopting an improved clock synchronization protocol and a sliding window mechanism. And generating a time sequence feature vector set with weight. And based on an LSTM-Transform hybrid model, fusing historical communication delay data, analyzing space-time relevance by using a multi-head attention mechanism, and outputting a cross-screen communication delay probability distribution diagram. And generating a multi-target task allocation strategy in combination with a deep reinforcement learning model, and iteratively calculating a shortest path allocation scheme of a rendering task through an improved ant colony optimization algorithm. The space-time synchronization precision of double-screen interaction is effectively improved, signal misalignment caused by communication delay is reduced, the distribution efficiency of distributed rendering resources is optimized, and the environmental adaptability and operation robustness of a flexible display system are enhanced.

The invention belongs to the technical field of navigation, and relates to an unmanned aerial vehicle multi-target image acquisition path planning method, equipment and a storage medium. The method comprises the following steps: acquiring a city live-action three-dimensional model; rasterizing the building model to create a three-dimensional grid map; acquiring distribution information of obstacles through the live-action three-dimensional model, judging whether the corresponding grid units are occupied by the obstacles or not, and generating an obstacle map according to a judgment result; dividing and storing space nodes in the grid map based on the obstacle map; performing initial path search by calculating a cost function of an initial node; and taking the obtained initial path as the basis of initial pheromone distribution, and obtaining an optimized path by adopting an ant colony optimization algorithm. According to the method, the influence of building shielding on image quality is avoided, time and energy waste of the unmanned aerial vehicle on an unnecessary path is reduced, the target image is obtained in the shortest time and the optimal path, and a flight task is completed.

The invention discloses a thermal power plant inspection robot path planning method, device and equipment and a storage medium, and the method comprises the steps: carrying out the semantic analysis and information extraction of an inspection task instruction, an equipment document and a historical inspection log through a large language model; obtaining a semantic importance score, a perception modal demand, recommended residence time and a shooting angle of each inspection target; establishing a multi-target path optimization model based on an improved ant colony optimization algorithm, and inputting the semantic importance score, the perception modal demand, the recommended residence time and the shooting angle into the multi-target path optimization model to obtain a joint scheduling sequence; the thermal power plant inspection robot is controlled to execute the joint scheduling sequence to perform autonomous navigation and perception tasks, the current path planning is adjusted according to the task completion condition, the intelligence, autonomy and adaptability level of the inspection robot can be remarkably improved, and the speed and efficiency of path planning of the thermal power plant inspection robot are improved.

The invention discloses a computer wireless network operation supervision system and method, and relates to the technical field of network security, and the method comprises the steps: collecting network state data; constructing a QUBO model, and solving and generating a network spectrum allocation strategy matrix through a quantum annealing machine; analyzing the network spectrum allocation strategy matrix, generating a network base station radio frequency parameter instruction set and a network terminal access strategy table, and adjusting a network flow table rule through an SDN controller; comparing the expected configuration network parameters with the new network state data, and positioning the root cause of the network abnormal condition; and redundant links in the network are screened out, an ant colony optimization algorithm is adopted to optimize a topological structure, a spectrum allocation strategy matrix is updated, fault nodes are isolated, and topological connection is reconstructed. According to the method, computer wireless network operation supervision is improved through the quantum annealing machine, and in the aspect of spectrum resource allocation, the quantum annealing machine solves by constructing a QUBO model, so that optimization of frequency allocation and transmitting power is realized.

The invention relates to the technical field of industrial control, in particular to a TSN-based industrial equipment cooperative control method and system and a readable storage medium. The method comprises the following steps that: a switch acquires the flow of a TSN network from a preset detection starting moment t1 to a current moment tK; constructing an optimization objective function of the static scheduling strategy; obtaining a flow priority, a distribution time slot and a bandwidth resource which enable the optimization objective function to reach the minimum; and constructing a dynamic scheduling model based on an intelligent scheduling algorithm of an ant colony optimization algorithm so as to obtain adjusted flow priority, allocation time slot and bandwidth resources. According to the invention, a static traffic scheduling strategy is adopted to ensure that key control traffic can be transmitted preferentially; the network state is analyzed in real time by adopting an intelligent flow dynamic scheduling algorithm in combination with an ant colony optimization algorithm, and network resource allocation is optimized; the problem that traditional flow scheduling cannot adapt to network changes in real time is effectively solved.

The invention discloses a Web end real-time path planning method and system, a storage medium and equipment, and relates to the technical field of path planning, and the method comprises the steps: collecting traffic data and environment change information in a Web end application scene in real time in the driving process of a vehicle, and converting the traffic data and environment change information into a standard data format for algorithm processing; performing path initial planning by adopting a hybrid algorithm to obtain an initial path; acquiring actual path driving information fed back by the environment sensing device and the user, comparing the actual path driving information with the initial path, and judging whether an actual path in the actual path driving information is different from the initial path or not; if yes, revising the recommended path in the navigation system in real time according to a preset proofreading rule to obtain a target navigation path shared to other vehicles; wherein the hybrid algorithm is a combination algorithm of a genetic algorithm and an ant colony algorithm. According to the invention, real-time optimization of the distribution path is realized, various road condition changes are efficiently coped with, and individual requirements of clients are met.

The invention relates to the technical field of robots, and discloses a robot laser welding work center material distribution regulation and control method and related devices.The method comprises the steps that a welding production plan and a real-time material inventory condition of a robot laser welding work center are obtained; performing feature extraction on the welding production plan and the material inventory condition to obtain target features of the welding production plan and the material inventory condition; the target features of the two are input into a biologically heuristic material delivery intelligent regulation and control model based on fusion of a particle swarm optimization algorithm and an ant colony optimization algorithm, and a material delivery regulation and control strategy of the robot laser welding work center is obtained; and carrying out corresponding material distribution regulation and control on the robot laser welding work center according to the material distribution regulation and control strategy. According to the invention, timely supply of the materials can be guaranteed, and meanwhile, reduction of material distribution cost and reduction of stock overstock can be effectively realized.

The invention relates to the technical field of intelligent welding systems, in particular to a laser cutting method, system and device suitable for steel structure machining, and the method comprises the steps that a grid map used for planning a cutting path of a to-be-cut steel plate is obtained; acquiring the temperature rise amount of each edge generated by laser cutting; marking the cut edges before each edge is cut as each cut edge, and obtaining the residual heat coefficient and the heat accumulation coefficient of each edge; obtaining the angle influence degree of each edge by comparing the cutting duration of each edge with all previous cut edges and combining the included angle between each edge and the previous cut edge adjacent to each edge with the difference between the included angle and a preset angle threshold value; and heuristic information of each edge of the ant colony optimization algorithm during path planning is obtained, and then the optimal laser cutting path is obtained. According to the method, the cutting path of the steel plate can be optimized by reconstructing the heuristic information in the ant colony optimization algorithm, and then the intelligent welding effect of a subsequent steel structure is improved.

The invention discloses a photovoltaic power generation system maximum power tracking control method, and relates to the technical field of power system operation control, and the method comprises the steps: collecting operation data of a photovoltaic power generation system, and carrying out the preprocessing of the operation data, and obtaining feature data, so as to construct a maximum power point prediction model; training a maximum power point prediction model based on the obtained historical operation data sample, and predicting a target working point by using the trained maximum power point prediction model; and in combination with the target working point and an ant colony optimization algorithm, determining an optimal working point, and obtaining control parameters of maximum power tracking by constructing an adaptive voltage controller. According to the MPPT dynamic prediction method based on deep learning, historical operation data and environmental parameters are fused through the LSTM neural network, tracking lag under the conditions of illumination abrupt change and local shadow is avoided, and the prediction precision and response speed of the maximum power point are improved.

The invention relates to the field of power transmission line planning and design, and discloses a power transmission line optimization method which comprises the following steps: performing ground feature recognition and constructing a multi-rule constraint model by obtaining a digital elevation model and remote sensing image data so as to optimize line path planning; calculating and comparing the comprehensive cost of different line schemes by using an ant colony optimization algorithm under the constraint condition of considering a plurality of factors such as a voltage grade, ground feature crossing, a tower type, construction cost and the like; and finally, realizing visual display of the optimal power transmission line path in combination with a GIS tool, and providing visual line planning and decision support. According to the method, an economical and efficient route planning scheme can be generated in a complex ground feature environment.

The invention provides an indoor space environment control method based on an optimization algorithm and related equipment. The method comprises the following steps: constructing an indoor comprehensive environment model in combination with historical equipment control parameters and historical space environment data; constructing a comprehensive objective function; creating a state space of the ant colony optimization algorithm, and generating constraint conditions of the ant colony optimization algorithm; taking the comprehensive target function as an algorithm optimization target, and generating an initial equipment control strategy of the environment control equipment by adopting an ant colony optimization algorithm based on the indoor comprehensive environment model; taking the optimal parameter interval as a strategy initial solution of a model prediction control strategy, and generating a strategy prediction result through the model prediction control strategy based on the indoor comprehensive environment model; and updating algorithm parameters of the ant colony optimization algorithm according to the strategy prediction result, and generating an optimal equipment control strategy of the environment control equipment. The method has the effect of meeting the environmental experience comfort level of the user on the basis of the lowest energy consumption.

The invention discloses a residual capacity balance control method and system for an aged lithium battery, and relates to the technical field of new energy materials and devices, and the method comprises the steps: inputting quantum magnetic field response data and infrasonic wave signals into a single-chip microcomputer, carrying out the wavelet noise reduction and histogram equalization processing based on an STM32 single-chip microcomputer, and generating an acoustic-magnetic coupling feature matrix; inputting a dynamic pheromone equalization model based on the acoustic-magnetic coupling characteristic matrix, calculating a capacity difference coupling coefficient between the battery packs through an ant colony optimization algorithm, and generating a capacity redistribution path topological graph; according to the capacity redistribution path topological graph, the energy transfer direction is controlled through a bidirectional Buck-Boost circuit, and an equalizing current threshold value is dynamically adjusted based on the quantum spin resonance magnetic field gradient; according to the invention, the quantum magnetic field response data of the aged lithium battery pack and the infrasonic wave signal generated by electrolyte decomposition are collected, so that the physical and chemical states in the battery are accurately monitored.

The invention discloses an improved integrated learning rock debris element content analysis system and method based on laser spectrum information. The method comprises the following steps: step 1, collecting a rock debris element LIBS spectrum data set; 2, dividing the rock debris element LIBS spectral data set into a training set and a test set, and carrying out noise reduction processing on original spectral data; 3, selecting characteristic spectral lines of the rock debris analysis elements according to the correlation coefficients, then selecting correlation interference spectral lines of the analysis elements, respectively combining the characteristic spectral lines with different correlation interference spectral lines to obtain more than one spectral line combination, and taking the spectral line combination as input of an integrated learning device; 4, constructing an ant colony optimization algorithm to optimize and improve the least square support vector machine ensemble learning base learner as an ensemble learner; and 5, designing a weighted distribution integration strategy of the base learners, and completing the optimal combination of the base learners. According to the method provided by the invention, a strong learner is constructed by combining a plurality of base learners, so that the accuracy and robustness of prediction are improved.

The invention discloses a decentralized federated learning communication topological structure optimization method and device and a medium, and relates to the field of distributed machine learning and optimization algorithms, and the method comprises the steps: optimizing a communication topological structure of a terminal node through employing an ant colony optimization algorithm when a decentralized federated learning system is subjected to iterative training; a fitness function of the ant colony optimization algorithm takes minimum communication load and communication delay as a target function; summarizing and averaging the model parameters of each terminal node model in the optimal communication topological structure of the terminal nodes to obtain an average model parameter; updating the model parameters of the terminal node model of each terminal node in the optimal communication topological structure by adopting the average model parameters to obtain the updated terminal node model of each terminal node; and when the number of iterative training times reaches the maximum number of training times, completing training of the decentered federated learning system, and outputting an optimization result. According to the invention, the communication efficiency in the decentralized federated learning system is improved.

The invention discloses a real-time dynamic ship route induction method for a ship in an inland river route network. The method comprises the following steps: collecting information; calculating the real-time dynamic speed of the ship and the interpenetration clearance of the ship; establishing a ship route network weighting topological graph; establishing a generalized path resistance function; establishing an induction path optimization model and solving an optimal route; and outputting a real-time dynamic ship induction route. The real-time dynamic induction route of the ship is calculated by establishing the ship route induction model on the basis of the real-time dynamic ship traffic flow prediction result in combination with the ship route network topological structure in the inland river route network, so that the method has the characteristics of high universality and wide coverage; the LSTM network is adopted to predict the ship navigational speed, the prediction precision is high, convergence is fast, and the workload is small; according to the invention, accurate real-time dynamic induction route information of the ship can be provided, and the reliability is good; due to the fact that the ant colony optimization algorithm of operational research is used for achieving real-time dynamic route planning of the ships in the inland river route network, economical efficiency is good.

The present invention relates to the field of computer vision, specifically a method and system for real-time identification and control of crop pests and diseases based on multimodal edge computing. The system comprises: acquiring drone multispectral imagery, infrared thermal imaging, and the NDVI index, preprocessing them in combination with field IoT sensor data to generate a multimodal feature input sequence; constructing a spectral-environmental fusion feature matrix based on the Transformer attention mechanism, and utilizing the YOLOv7-Spectral model for pest and disease detection to generate a pest and disease distribution heat map; calculating the pest risk index (BRI) and pesticide application priority map based on historical data; employing an ant colony optimization algorithm to plan drone application paths and optimizing the application plan based on wind speed and humidity parameters; accurately applying pesticides along the optimized paths by the drones and monitoring disease trends; adjusting the model based on application feedback data, and optimizing pest and disease control strategies using federated learning. The present invention improves recognition accuracy, reduces pesticide use, and achieves precise, efficient, and intelligent control.

The invention relates to the technical field of clean energy utilization, and discloses a clean energy comprehensive utilization energy storage method and system, and the method comprises the steps: building a three-level hybrid energy storage model; optimizing model parameters by adopting an improved ant colony optimization algorithm; setting an operation constraint condition; and an energy flow path in the energy storage system is optimized. In the prior art, a single energy storage mode cannot adapt to wind and light fluctuations of different time scales, and especially under the conditions that wind and light output fluctuates violently and power grid load changes are complex, a traditional method is difficult to realize high-efficiency and low-loss energy storage coordination control. Through a multi-level hybrid energy storage architecture and an intelligent optimization scheduling mode, collaborative optimization and efficient energy scheduling of different energy storage units are realized, so that the problem that the energy storage life is shortened due to slow response time and deep charging and discharging of a traditional energy storage system is avoided; and the stable output and energy storage efficiency of the wind-solar complementary energy in the power grid are effectively improved.