163 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 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 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 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 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 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 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.

The invention relates to an ocean platform pipeline wiring method and system based on an ant colony optimization algorithm, and a medium, and belongs to the technical field of intelligent optimization algorithms. Comprising the following steps: S11, environment modeling: discretizing a three-dimensional space of an ocean platform into a grid model, marking an obstacle region and a feasible region, and calculating a grid node energy value; s12, parameter initialization: constructing a heuristic information matrix based on pipeline inlet and outlet coordinates and equipment parameters, and determining a node transition probability by the matrix through a direction probability allocation strategy and a distance sensitive function; s13, path optimization: generating candidate paths through dynamic smooth search, local pheromone adaptive adjustment and collaborative ecological niche search, and updating pheromones; and S14, constraint verification and result output: verifying whether the path meets the engineering constraint or not, and outputting the three-dimensional coordinate, length and bend number of the optimal pipeline path after iterative optimization. According to the method, efficient, safe and economical wiring of the offshore platform pipeline is achieved.

The invention relates to the technical field of offshore wind turbine generator operation and maintenance, and discloses a failure evaluation system based on an offshore wind turbine generator. Comprising a sensor data acquisition module, a data preprocessing module, a failure feature extraction unit, a failure probability calculation module, a maintenance decision module and the like. The system obtains parameters such as vibration and temperature of the wind turbine generator in real time, after preprocessing, failure features are extracted through a deep convolutional neural network, and a dynamic Bayesian network model is constructed to calculate the component failure probability. An optimal maintenance path is generated by using an improved ant colony optimization algorithm, and meanwhile, a dynamic risk assessment module, an abnormal signal detection module and a maintenance strategy optimization module are arranged. The method can accurately evaluate the unit failure risk, timely discover abnormity, optimize the maintenance decision, improve the operation reliability of the offshore wind turbine generator, reduce the operation and maintenance cost, and guarantee the stability and high efficiency of offshore wind power generation.

The invention provides a foundation pit soil body reinforcement method based on electrical parameter monitoring, and relates to the technical field of geotechnical engineering monitoring and automatic reinforcement, and the method comprises the following steps: burying an electrode array in a foundation pit soil body to collect electrical parameters such as resistivity and capacitance value, carrying out normalization, anomaly detection and space-time alignment processing, and determining the electrical parameters in the foundation pit soil body; generating soil state data through an adaptive weighted fusion algorithm; constructing a stability model based on the fused data to calculate a stability index, smoothing fluctuation by adopting moving average filtering, predicting future change through a GRU model, and triggering early warning when the future change is lower than a safety baseline; dividing risk levels according to the stability index and the deformation factor, and generating a reinforcement instruction when the risk is high; the comprehensive reinforcement priority is calculated by combining the emergency degree and the regional importance determined by the entropy weight method, and the resource demand is evaluated by applying a multi-objective optimization algorithm; and an ant colony optimization algorithm is used for dynamically optimizing the reinforcement scheme, and automatic grouting reinforcement is achieved. The construction safety and the resource utilization efficiency can be improved.

The invention provides an aquaculture net cage cleaning mechanical arm path planning method which comprises the steps that S11, three-dimensional coordinate data of a to-be-cleaned area are obtained and subjected to two-dimensional mapping, and a two-dimensional to-be-cleaned area is obtained; s12, if the number of the two-dimensional to-be-cleaned areas is smaller than or equal to a preset threshold value, path planning is conducted on the two-dimensional to-be-cleaned areas directly through an ant colony optimization algorithm; if the number of the two-dimensional to-be-cleaned areas is greater than a preset threshold value, performing block processing on the two-dimensional to-be-cleaned areas through a clustering algorithm to obtain a plurality of two-dimensional to-be-cleaned sub-areas, and performing path planning on the plurality of two-dimensional to-be-cleaned sub-areas through an ant colony optimization algorithm; s13, integrating the optimal paths of the two-dimensional to-be-cleaned area / the two-dimensional to-be-cleaned sub-area to generate a global path; and S14, carrying out inverse mapping on the global path to obtain a three-dimensional space path planning graph. According to the method, through a three-level architecture of regional blocking, clustering optimization and global integration, the task balanced distribution problem during collaborative operation of multiple mechanical arms is systematically solved.

The invention provides a workpiece surface target merging and partitioning method, system and device, and relates to the technical field of image processing, and the workpiece surface target merging and partitioning method comprises the steps: obtaining a surface image of a target region of a target maintenance workpiece; performing graph sampling and visual processing on the surface image to generate a binary image of the target area, and analyzing the binary image to obtain a plurality of connected domains of the target area; performing convex hull generation according to the connected domains to obtain a convex hull corresponding to each connected domain; generating a rotation enclosing rectangle set according to the leading edge vector of each convex hull; and through a variant ant colony optimization algorithm, carrying out global optimization on the rotating enclosing rectangle set, and generating a combined partition of the target area. According to the invention, through accurate capturing of the original image and subsequent layer-by-layer optimization, the redundancy of the laser maintenance path is reduced, and the operation cost is reduced; through construction and global optimization of a high-adaptation form carrier, precise coverage of a complex form target is realized, and the maintenance precision is improved.

The invention relates to an MPPT (Maximum Power Point Tracking) method, system and equipment based on distributed photovoltaic and a medium, and relates to the technical field of distributed photovoltaic power generation, and the method comprises the following steps: preprocessing photovoltaic array data, and obtaining irradiance, temperature and MPP voltage data sets; determining the optimal number of hidden layer nodes of the ElmanNN by adopting K-fold cross validation and RMSE evaluation, and dividing a data set into a training set, a validation set and a test set; inputting the training set to firefly algorithm global search and ant colony optimization algorithm local optimization, and defining a stable solution as an FA-EAS optimal parameter after convergence; updating an ElmanNN weight threshold based on the optimal parameter, and deploying a training model to carry out MPP voltage online prediction; and the difference value of the predicted voltage and the reference voltage is input into a PID controller, the control quantity is calculated through incremental PID, a PWM signal is generated to control the duty ratio of the Boost converter, and maximum power point tracking is achieved. According to the invention, the tracking speed, stability and success rate of MPPT can be improved.

The embodiment of the invention discloses a multi-path data transmission method. Comprising the steps that for each data transmission path in a plurality of data transmission paths, network state information representing the data transmission quality of the data transmission path is acquired under the condition that the current moment is a preset moment, and the data transmission path is used for data transmission between a data sending end and a data receiving end; processing the network state information by using an ant colony optimization algorithm to obtain the data transmission quality; determining a preset number of target transmission paths from the plurality of data transmission paths based on the obtained plurality of data transmission qualities by using the ant colony optimization algorithm; and based on all the determined target transmission paths, performing data transmission between the data transmitting end and the data receiving end in a current time period corresponding to the current moment. According to the technical scheme of the embodiment of the invention, the data transmission efficiency can be improved during multi-path data transmission.

A dynamic competition perception workflow scheduling method for a heterogeneous computing system comprises the following steps: constructing a scheduling problem model based on a scheduling object set and bus resources of an obtained system; constructing a dynamic competition perception simulator according to the scheduling problem model, wherein the method comprises the following steps of: decomposing a time-varying behavior of cross-domain bus data transmission into a time-invariant meta-process; each meta-process is mapped into a timeout event carrying a specific timestamp; the completion event indicates that all the timeout events to which the same scheduling object belongs are completed, so that the end of the task or cross-domain bus data transmission is marked; according to an event-driven mechanism, a completion event is arranged on the premise that the preorder constraint of a scheduling object is met, and total completion time estimation is generated while integration of multiple scheduling strategies is supported; according to the total completion time estimation, generating an approximate optimal scheduling solution by using a hybrid earliest completion time genetic algorithm; and optimizing the approximate optimal scheduling solution by adopting a quota weighted ant colony algorithm to generate a final scheduling solution.

The invention discloses a drilling parameter multi-objective optimization method based on an ant colony optimization algorithm-DeepAR model, relates to the technical field of petroleum and natural gas engineering drilling parameter optimization, can predict the mechanical drilling speed in a period of time in the future, and has input and output with adaptive length. Based on a mechanical drilling speed mathematical model, a mechanical specific energy mathematical model and a drill bit wear mathematical model, a multi-objective optimization model is established on the basis of a mechanical drilling speed prediction model, an objective function is optimally solved through a multi-objective optimization algorithm, and three parameters of drilling pressure, rotating speed and displacement are optimized. An intelligent algorithm based on an ant colony optimization algorithm is adopted, and while-drilling multi-target, interactive and dynamic drilling parameter optimization is achieved. The problems that a mechanical drilling speed prediction model is poor in stability, the complex dependency relationship among long-distance drilling data parameters cannot be accurately captured, and a drilling parameter optimization objective function is single and excessively depends on a complex physical drilling speed equation are solved.

The invention discloses a multi-step optimization method for a drifting flight dynamic path of an amphibious unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicles. Riverway environment information is acquired, and a local map containing obstacle information is constructed; setting an operation starting point and a plurality of target points based on the local map, and generating an optimal global route plan by adopting an ant colony optimization algorithm; according to obstacle information in the local map, using a bidirectional A * algorithm to simulate the preliminary path, and generating a flight path feasible in dynamics; predicting the drifting direction of the unmanned aerial vehicle in real time to obtain a drifting value; establishing a YOLO model to obtain the number of obstacles; objective weighting is carried out through an entropy weight method according to the collected data, a comprehensive score is calculated, an intelligent decision-making takeoff strategy is carried out when a takeoff threshold value is exceeded, and segmented motion conversion is completed. According to the invention, hierarchical path optimization is carried out according to different motion modes, the autonomous intelligent operation capability of the amphibious unmanned aerial vehicle is improved, the measurement task is better completed in a complex environment, and a monitoring basis is provided for digital river construction.

The invention discloses a lithium ion battery SOC (state of charge) and SOH (state of health) joint estimation method based on an FOMISUKF-EKF algorithm. The method comprises the following steps: acquiring dynamic response and static characteristic data required by battery modeling; selecting a fractional order equivalent circuit model and establishing a state-space equation; identifying each parameter value of the model by adopting an ant colony algorithm; after the identified initial parameters are injected into the algorithm, SOC estimation under a micro scale is realized based on fractional order unscented Kalman filtering and a multi-innovation sliding mode observer; and when a preset period is met, switching to a macroscale, taking the SOC sequence as input, and performing joint identification on the battery parameters and SOH by adopting an EKF algorithm. According to the method, the multi-information theory and the sliding-mode observation algorithm are introduced on the basis of the traditional UKF algorithm, the sliding-mode observer is used as the state variable of the input end estimation system, the influence of system parameter change and external disturbance can be reduced to a certain extent, and rapid convergence and effective suppression of measurement noise are realized.