311 results about "Adaptive learning rate" patented technology

The invention provides a crop yield prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining continuous historical meteorological data, soil data, crop planting data and remote sensing image data of a designated region for many years, and carrying out the alignment according to a time sequence, and constructing a multi-dimensional spatio-temporal data cube; a fusion prediction model is trained, the fusion prediction model is used for extracting growth feature vectors in the crop growth and development process and carrying out yield prediction, and model parameters of the fusion prediction model are dynamically adjusted through an adaptive learning rate optimization algorithm; the method comprises the following steps: acquiring real-time meteorological data, soil data and remote sensing image data of a specified area, inputting the data into a pre-constructed fusion prediction model, introducing an agricultural knowledge graph into the fusion prediction model to restrain and correct a prediction result of the fusion prediction model, and reasoning to obtain crop growth and estimated yield of each grid of the specified area. And performing spatial interpolation on the estimated yield of each grid to generate a yield distribution diagram of the specified area.

The invention discloses a quality safety optimization method and system based on a multi-modal vertical large model technology, and relates to the technical field of quality safety optimization systems.The method comprises the steps that quality safety related data are collected from a multi-modal data source and preprocessed; fusing data through features and decision levels; a vertical large model of an improved Transform architecture is constructed by using fusion data, a quality feature extraction layer and a safety rule embedding layer are provided, and adaptive learning rate and gradient clipping training are used; inputting the new data into the large model to analyze the quality safety condition, including defect prediction, risk assessment and problem early warning; and according to an analysis result, generating an optimization suggestion through rule reasoning and reinforcement learning. According to the invention, multi-modal quality safety data can be comprehensively collected and preprocessed. The vertical large model is high in pertinence, quality defects, safety risks and potential problems are accurately analyzed, the optimization suggestion module is combined with rules and reinforcement learning, suggestions with expected effects and priorities are generated, and the quality safety level and the resource utilization efficiency are improved.

The invention discloses a carbon emission prediction and optimization method, and the method comprises the steps: obtaining multi-source heterogeneous data including historical carbon emission data, meteorological data, economic indexes, energy consumption data, and Internet of Things sensor data, and constructing a three-dimensional feature matrix through employing an improved spatial-temporal feature extraction algorithm; based on a mixed architecture of a graph neural network GNN and a long and short term memory network LSTM, a prediction model is established in combination with an attention mechanism, and training is performed through an adaptive learning rate optimization algorithm; a prediction result is input into an improved NSGA-III algorithm, and three targets of total carbon emission, economic cost and social benefits are optimized at the same time; and establishing a feedback closed loop through reinforcement learning RL, and updating the model and the strategy on line according to real-time monitoring data. According to the method, multiple advanced technologies such as accurate data processing, dynamic prediction, multi-objective optimization and cross-domain collaboration are integrated, and a comprehensive and effective solution is provided for carbon emission management.

The invention relates to a supporting structure stress state monitoring method based on artificial intelligence, and belongs to the technical field of artificial intelligence and data processing. The method comprises the following steps: acquiring and marking strain data of a supporting structure; after abnormal values are removed, normalizing the multi-sensor data to generate a normalized strain sequence; a state monitoring model is constructed, a deep time sequence neural network architecture is adopted, and the state monitoring model comprises an input layer, a self-adaptive wavelet attention feature mapping layer, a time domain gating convolution module, a global maximum pooling layer, a dynamic feature importance reweighting layer and a full-connection classification layer; inputting a normalized data training model; optimizing a loss function through a quantile interval adaptive learning rate and a momentum updating strategy; after real-time monitoring data is processed, inputting the data into the training model according to time window slices, outputting four types of probabilities, and taking the maximum value as a prediction state; and if a plurality of continuous windows are early-warning and dangerous, triggering the terminal to give an alarm. The accuracy of monitoring the stress state of the supporting structure can be improved.

The invention relates to the technical field of ship energy management, particularly discloses an energy collaborative management system and method based on an energy storage battery, and is used for solving the problems that when an existing ship energy management system integrates energy storage battery information, it is difficult to accurately evaluate actual energy consumption requirements, and consequently energy waste and equipment overload risks are caused. The system comprises an energy storage system unit, a system monitoring unit, a data processing unit and an energy management system unit, and is characterized in that the system operation state is determined according to real-time data monitored by the system monitoring unit; by introducing an adaptive learning rate adjustment formula, dynamically adjusting a weight factor, optimizing an energy efficiency function, adjusting output power of an energy storage battery and a generator in real time, optimizing energy distribution, ensuring stable power supply, reducing overload of the generator and energy waste, improving stability and reliability of a ship electric power system especially in a complex navigation environment, and reducing energy consumption. The service life of equipment is prolonged and the overall operation cost of ships is reduced.

The invention discloses an atmospheric particulate source analysis method based on deep learning, and relates to the technical field of atmospheric particulate analysis, and the method comprises the steps: constructing a large-scale preprocessing data set which comprises atmospheric particulate concentration data from different regions, different seasons and meteorological conditions, and collecting corresponding potential original data; original data are integrated to form a multi-dimensional preprocessing data set; according to the atmospheric particulate source analysis method based on deep learning, through an adaptive learning rate optimization algorithm, the model can more quickly find an optimal solution in a complex and dynamic data environment, and is prevented from falling into local optimum, so that the accuracy of atmospheric particulate source analysis and the application range of the model are improved; through accurate analysis of the source of atmospheric particulates, formulation of urban atmospheric pollution control measures is effectively guided, the risk that residents are exposed to high-concentration particulates is reduced, and scientificity and effectiveness of air quality management are improved.

The invention provides a zero-set differential privacy self-adaptive federated learning system oriented to Non-IID data. The system comprises a central server and a plurality of clients, the central server maintains a global contribution value list in the federation training process and sends contribution values back to the corresponding clients; according to the contribution values, clients meeting conditions are screened out through a client filtering mechanism and are used for carrying out gradient weighting and generating global model updating; in local model training, each client uses a self-adaptive sliding window mechanism to divide a data set sampling and training range, selects an optimal learning rate through a self-adaptive learning rate selection algorithm, selects a gradient cutting threshold value of each round by using a self-adaptive gradient cutting threshold value mechanism, and performs local model training. And the privacy budget allocated to each round of gradient updating is adaptively adjusted through an adaptive differential privacy federated learning algorithm according to the received contribution value. According to the method, the problem of how to consider both data privacy protection and model precision optimization in a non-independent identically distributed (Non-IID) data environment is effectively solved.

The invention provides a machine language large model construction method and system, and the method comprises the steps: receiving user input data, defining an intelligent semantic association network, and extracting the semantic association between a text and visual or auditory data; a cross-modal attention mechanism and an adversarial generative network are utilized to enhance the multi-modal data relationship in the association, and a multi-modal perception mechanism is formed; based on the mechanism, a multi-level feedback system is constructed, feedback signals from different levels are processed, and an optimized prediction path is generated; key parameters are dynamically adjusted in combination with a task adaptive module and the optimized prediction path, and an adaptive learning rate adjustment strategy is generated; based on an incremental context extension module, an intelligent semantic association network, a multi-modal sensing mechanism, a multi-level feedback system and a task adaptive module, a machine language large model is constructed. According to the method, the understanding and processing capability of the model on complex multi-modal data is improved.

The invention relates to the technical field of intelligent data processing, in particular to a model training and content auditing management system based on intelligent data acquisition, which comprises an acquisition processing unit, a model training unit and a content auditing unit. The model training unit constructs a multi-modal fusion model, a joint training mechanism of the model learns modal association, adaptive learning rate strategy optimization training and early stop technology anti-overfitting, the trained model is used for content auditing, the content auditing unit receives multimedia content, extracts key frames, audio voice content and subtitle text, and performs feature representation and fusion to obtain a subtitle text. Whether violation occurs or not is judged through the trained model, all the units cooperate with one another, the collecting and processing unit provides a data basis for model training, the model training unit provides a precise model for content auditing, effective auditing of multimedia content is jointly achieved, and legitimacy and health of the content are guaranteed.

The invention discloses a complex terrain self-adaptive motion control method and system for a double-wheel-foot robot, and relates to the technical field of robot motion control. The method comprises the following steps: constructing a complex terrain model comprising a robot model, obstacles and environmental constraints; a motion decision controller is constructed, a reinforcement learning model is utilized to perform multi-target collaborative optimization training on the motion decision controller by adopting an asymmetric training strategy for different complex terrain models, and risk constraints are introduced during updating of the asymmetric training strategy to constrain behaviors of the strategy; strategy gradient back propagation is carried out according to a training result, and an asymmetric training strategy is optimized by using a self-adaptive learning rate adjustment method based on performance feedback. According to the method, an asymmetric training strategy and a segmented training mechanism are designed in the reinforcement learning process, and the efficient, robust and self-adaptive motion control problem of the double-wheel-foot robot in various complex terrain environments is solved.

The invention discloses an artificial intelligence meteorological data processing system and method, and aims to improve the accuracy and real-time performance of meteorological prediction. The system comprises a data acquisition module, a data preprocessing module, a data storage module, an artificial intelligence analysis module, a prediction output module, a feedback learning module and an anomaly detection module. Through real-time acquisition and fusion of multi-source data, the system performs cleaning, noise reduction, interpolation and format conversion on meteorological data, and performs modeling and prediction by using a long-short-term memory network. The system adopts a Bayesian optimization and adaptive learning rate adjustment mechanism to ensure the precision and stability of the model. And the anomaly detection module is used for identifying extreme weather events and continuously optimizing model parameters through a feedback learning mechanism. The prediction result is visualized in a graphical manner, and an alarm is generated when an abnormality is detected. The method is suitable for the fields of weather forecast, agriculture, traffic and the like, and has wide application value.

The invention relates to the technical field of sales market prediction, and discloses a sales prediction method and system based on deep learning. The method comprises the steps of collecting historical sales data and external environment data to construct a training data set, and performing multi-dimensional feature extraction to generate a time sequence and a context feature set; constructing a prediction model fusing the deep residual network and the long-short-term memory network, wherein the prediction model comprises a convolution module and a circulation module; the model is trained in stages, initial parameters are optimized through unsupervised pre-training, and then supervised learning is combined with self-adaptive learning rate fine tuning; and predicting real-time input data by using the trained model, and if the confidence coefficient of a prediction result is low, triggering an incremental learning mechanism to update the model. The system comprises a data acquisition module, a feature extraction module, a model construction module and the like. Through multiple technical means, the accuracy, adaptability and reliability of sales prediction are improved, and powerful support is provided for enterprise sales decision.

The invention relates to the technical field of power system monitoring, in particular to an electric energy meter and concentrator data fusion monitoring system and method. Comprising the steps that data of an electric energy meter are periodically collected, integrity verification is conducted on the data, noise interference in the data is removed, then the data are fused to construct a power network diagram structure, and the power network diagram structure comprises node information and edge weight information; a graph convolutional network is used for modeling, a power network graph structure is used as the input of a graph neural network model, an adaptive learning rate adjustment algorithm is combined with an early stop method to prevent overfitting and deep training of the model, and space-time association between nodes is captured through multi-layer space-time graph convolution operation. According to the method, the power network graph structure is constructed by fusing the data of the electric energy meter through the concentrator, nodes and edges are analyzed by using the graph neural network, key nodes and potential fault points are identified, and the network health state is evaluated in real time, so that comprehensive monitoring and optimal management of the power system are realized, and the overall performance and reliability of the power system are improved.

The invention discloses an energy storage system health state assessment method and system, mainly relates to the technical field of health state assessment, and is used for solving the problems that in the prior art, key degradation signals on a frequency domain cannot be identified, an entropy change rule of non-stable change cannot be reflected, and a deep neural network is insufficient in perception of a degradation region. Comprising the following steps: acquiring original monitoring data of the energy storage system in real time, calculating to obtain normalized data after time domain and frequency domain processing, and calculating characteristics of the enhanced monitoring data; constructing a residual attention deep neural network, and autonomously adjusting the current adaptive learning rate; dynamic focus loss is obtained through calculation, and a total loss function is calculated; obtaining a trained residual attention deep neural network; and acquiring current monitoring data, calculating features of the enhanced current monitoring data, and further performing energy storage system health state assessment by using the trained residual attention deep neural network.

The invention belongs to the technical field of asynchronous federated learning, and discloses a data isomerism-oriented knowledge alignment asynchronous federated learning method. According to the method, a data quality perception aggregation strategy is introduced, and a knowledge distillation mechanism based on the old degree is combined, so that a global model is subjected to balanced training on heterogeneous data of different devices, and the generalization ability of the model is improved. Meanwhile, a self-adaptive learning rate adjustment mechanism based on aggregation frequency and weight is designed, and it is ensured that contribution of different devices to the global model is more fair. According to the method, the training deviation in asynchronous federated learning is effectively relieved, the accuracy and stability of a global model are improved, and the method has a considerable application value for a real federated environment.

The invention provides a network protocol fuzz testing method based on strategy gradient reinforcement learning, and relates to the technical field of fuzz testing, and the method comprises the steps: building a test corpus through obtaining a to-be-tested protocol data packet format; establishing an action space containing a mutation operation set and a state space of a benchmark test data packet feature vector; determining a reward value based on the test result; calculating a strategy gradient by using a reward value, and updating strategy network parameters in combination with an adaptive learning rate; and performing fuzzy testing by using the trained model. According to the invention, the test efficiency is improved, the variation strategy selection is optimized, and the effectiveness of the network protocol security test is enhanced.

The invention discloses a packaging printed matter printing quality detection method. The method comprises the following steps: S1, image acquisition; s2, image preprocessing; s3, feature extraction; s4, defect identification and classification; and S5, quality evaluation and report generation. According to the method, single detection limitation is broken through, spectrum, audio, touch and image multi-modal data are synchronously acquired, deep fusion is performed by means of a multi-modal fusion algorithm, feature extraction is optimized for different package types, detection comprehensiveness and accuracy are greatly improved, and model construction is more accurate. Structures and training strategies are customized for conventional paper cigarette cases and special-shaped paper cigarette cases, the adaptive learning rate and data enhancement are adopted, the ability of model generalization and complex feature learning is enhanced, in the defect recognition link, the process is optimized, and through multi-mode cooperation, various defects can be accurately positioned, new and special defect types can be recognized, and the defect recognition efficiency is improved. And the defect classification dimension is enriched.

The invention provides an electromechanical equipment fault prediction method and system based on multi-source information fusion, and the method comprises the steps: collecting operation data, including vibration data, temperature data and current data, during the operation of electromechanical equipment; performing feature extraction on the operation data based on a principal component analysis algorithm to obtain a fusion feature vector; inputting the fusion feature vector into a fault prediction model based on a deep belief network, and outputting a prediction result; wherein the deep belief network adopts a small-batch stochastic gradient descent algorithm combined with an adaptive learning rate adjustment strategy during training; and judging whether the electromechanical equipment has a fault hidden danger or not according to the prediction result. According to the method, the relevance between different types of data is mined, and the defect of low prediction precision is overcome.

The invention discloses a test risk identification method and system based on artificial intelligence, and particularly relates to the technical field of data processing. By introducing an adaptive learning rate and a feedback mechanism, the improved test risk identification method is provided for the problem of data imbalance existing in the test process of an existing machine learning model, and the test risk identification method is provided by comprehensively collecting test data including test case execution results and test environment states. Extracting a dependency relationship abnormal value and a network delay high-frequency value between code modules, constructing a data prediction model, and comprehensively calculating a deviation of a prediction result based on the data; by dynamically adjusting the deviation of the model to the successful use case, the identification capability of the failed use case and the high-risk area is enhanced, and the high-risk area is effectively prevented from being underestimated or the normal use case is misjudged to be high in risk, so that the prediction precision is improved, the configuration of test resources is optimized, the invalid test reexamination is reduced, the test efficiency is remarkably improved, and the cost and the period are reduced.

The invention discloses an electric heat storage multi-energy cooperative consistent energy flow intelligent control method and system, and relates to the technical field of electric power system intelligent control, and the method comprises the steps: carrying out the multi-energy system state modeling of an electric system, a heat energy system and an energy storage system; an adaptive deep Q network architecture is introduced, an evolutionary strategy is optimized, and local optimal traps are reduced; designing a dynamic weight adjustment mechanism, and dynamically adjusting each index in the reward function according to different energy flow cooperative control states; and dynamically correcting control parameters through a closed-loop correction mechanism fed back in real time. According to the invention, rapid convergence is realized under the condition of system load fluctuation, and peak-valley difference adjustment and voltage adjustment can be completed within 200 seconds. Through an energy flow consistency optimization model, the consumption rate of distributed energy such as photovoltaic energy is improved. Through an adaptive learning rate and a dynamic weight adjustment mechanism, the response speed of the system is improved, and power fluctuation is remarkably reduced. The dynamic adjustment of the control strategy enhances the stability and power supply quality of the power system.

The invention discloses a coal production operation scene video AI algorithm training reasoning platform and method, and relates to the technical field of industrial safety monitoring, and the method comprises the steps: deploying a plurality of types of sensors, collecting environment data and video streams in real time, carrying out the denoising and timestamp calibration processing of the data, transmitting the processed data to a data processing center, and carrying out the video AI algorithm training reasoning of the coal production operation scene. Outputting an environment parameter data set; the comprehensive model is deployed on an edge computing device, real-time monitoring is performed on a coal production operation site, a video stream is subjected to model reasoning and is combined with environmental data to identify potential safety hazards, and a response is made in time through a deep reinforcement learning strategy. According to the method, the monitoring accuracy is improved through multi-sensor deployment and data preprocessing, the robustness and precision of the AI model are improved by utilizing enhancement processing and high-dimensional mapping, and the system can make a preventive decision by combining the adaptive learning rate and deep reinforcement learning, so that the safety and efficiency of coal production are effectively improved, and intelligent safety production is realized.

The invention provides a joint point detection dynamic model algorithm for racing boat movement, and relates to the technical field of computer vision and movement posture detection, and the algorithm selects a YoloBoat model to carry out preprocessing on racing boat movement image data, and comprises normalized and diversified data enhancement operations. A backbone network of the model adopts CSPDarknet53 to extract features, a feature fusion layer adopts PANet to optimize the features, and an output layer generates a detection result. The key point track is smoothed through spline interpolation, and optimization and improvement such as confidence coefficient filtering are carried out. A data set is constructed by using about 1000 own pictures, key points are manually marked, and the data set is divided into a training set, a test set and a verification set in proportion. In training, an adaptive learning rate and a plurality of data enhancement methods are adopted. And through comparison with OpenPose and other models, the performance of the model is evaluated through multiple indexes. The method is high in detection precision, high in reasoning speed and high in robustness in close-range and long-range scenes, and can cope with various complex scenes. Accurate and real-time joint point detection data are provided for boat racing training and competition.

The invention provides a flexible power distribution network intelligent management and control method based on cloud side-end cooperation, and belongs to the technical field of intelligent power grids. An intelligent decision-making model is constructed and issued to an edge server through the cloud, the power grid state is monitored, and a management and control strategy is formulated. The edge servers exchange strategy information and work cooperatively. The sending end edge server computes policy utility based on multi-edge collaboration. And if the strategy is rejected, the sending end edge server updates the intelligent regulation and control judgment and decision model parameters based on the abnormal operation index probability. And if the strategy is accepted and the strategy utility exceeds the utility threshold, the edge server updates the model according to the operation condition of each regional power grid. If the strategy is rejected for multiple times or does not meet the multi-threshold stability requirement, the cloud side uses a ternary loss function containing multiple deviation factors to update the model, and the self-adaptive learning rate and the ternary loss function are introduced by judging the multi-domain stability requirement. According to the invention, the real-time performance and accuracy of decision making are improved.

The invention discloses a method and device for achieving accurate lifting of a steel plate through automatic magnetism adjustment, and belongs to the technical field of steel plate lifting control. The method comprises the steps that core parameters of an electromagnet of an electromagnet lifting appliance and observation data in the historical lifting process are collected; the observation data in the historical hoisting process are preprocessed; constructing a reinforcement learning model, and training the constructed reinforcement learning model by using the preprocessed observation data in the historical hoisting process; wherein the reinforcement learning model adopts a self-adaptive learning rate and updating frequency; and the specification and number of the current to-be-hoisted steel plates are obtained, the optimal current of the electromagnet is obtained through the trained reinforcement learning model, and steel plate hoisting is achieved. According to the technical scheme, the electromagnetic attraction can be intelligently and accurately controlled, and the hoisting efficiency and precision are improved.

The invention discloses an industrial chain situation prediction analysis system and method based on AIGC, and belongs to the field of data processing systems or methods specially suitable for the purposes of commerce, finance, management, supervision or prediction. An AIGC model based on the combination of a generative adversarial network (GAN) and a variational auto-encoder (VAE) is constructed; through adversarial training, KL divergence optimization and adaptive learning rate adjustment, a data rule is accurately captured, a moving average method, an autoregressive integral moving average model (ARIMA) and a random forest algorithm in situation prediction are cooperated, prediction stability and accuracy are enhanced, traditional defects are improved, in the aspect of analysis and management, polynomial fitting interpretation trend is deeper, and the prediction efficiency is improved. Risks and opportunity points are accurately identified through a comprehensive index system and a scientific method, decision making is assisted, on the whole, the industrial chain situation prediction analysis process is comprehensively innovated, scientific industrial development decision making is powerfully promoted, and enterprise competitiveness and industrial resource allocation efficiency are improved.

A computing system and method can be used to implement a version of federated learning (FL) that incorporates adaptivity (e.g., leverages an adaptive learning rate). In particular, the present disclosure provides a general optimization framework in which (1) clients perform multiple epochs of training using a client optimizer to minimize loss on their local data and (2) a server system updates its global model by applying a gradient-based server optimizer to the average of the clients' model updates. This framework can seamlessly incorporate adaptivity by using adaptive optimizers as client and / or server optimizers. Building upon this general framework, the present disclosure also provides example specific adaptive optimization techniques for FL which use per-coordinate methods as server optimizers. By focusing on adaptive server optimization, the use of adaptive learning rates is enabled without increase in client storage or communication costs and compatibility with cross-device FL can be ensured.

The invention discloses a case data analysis system based on a large model, and the system comprises a data collection module, a preprocessing module, a large model training and optimization module, an analysis module, a visualization and interaction module, a security and privacy protection module, a data preprocessing module with an innovative algorithm, a large model training module with an adaptive learning rate formula, and a disease risk prediction module with an evaluation formula. System functions and performance indexes are clear, equipment and medium requirements are clear, the steps of the method are complete, and high efficiency, accuracy and safety of case data analysis are guaranteed; the case data processing efficiency and accuracy are improved, and powerful support is provided for medical decision making. A large model is accurately analyzed, visual and convenient to check, and a security module guarantees data privacy. The medical quality can be improved, the treatment scheme can be optimized, and the method has important application value.

The invention discloses a meta cosmic user behavior and data analysis method and system based on digital twinning, and relates to the technical field of data processing, and the method comprises the steps: obtaining to-be-analyzed target meta cosmic space activity data; preprocessing the acquired activity data; the preprocessed activity data are classified for feature extraction, and data features of different sources are fused; and based on the analysis of the fusion features, adjusting a target user activity big data analysis algorithm in real time. Data are collected and encrypted in real time through an API interface, data safety and user privacy are ensured, data quality and consistency are improved through a fine preprocessing step, multi-dimensional behavior features of a user are comprehensively captured through feature extraction and fusion of multi-modal data, and user experience is improved. Algorithm parameters are adjusted in real time through an adaptive learning rate method, and the adaptability and accuracy of the algorithm are improved.

The invention discloses a generative adversarial network architecture search method and system and an image generation method, and belongs to the technical field of network architecture search. The searching method comprises the following steps: performing single-path sampling on a pre-constructed generator super network according to a parameter quantity constraint range to obtain an effective subnetwork; training the generator super-net by adopting a complexity adaptive learning rate optimization strategy to obtain a pre-trained generator super-net; adversarial training is carried out on the generator hypernet and the discriminator to obtain a pre-trained discriminator; generating a generator super-network candidate architecture through a genetic algorithm in the early stage of the evolution stage and through a covariance matrix self-adaptive evolution strategy in the later stage of the evolution stage; and performing multi-target non-dominated sorting on the generator super-network candidate architecture, updating an effective sub-network and keeping a Pareto optimal solution to obtain a searched optimal generator architecture and further obtain a searched optimal generative adversarial network architecture. The method not only ensures the search quality, but also improves the calculation efficiency.

The invention relates to the technical field of radar weak target detection, in particular to a radar weak target detection method and device based on deep learning. According to the technical scheme, the method comprises the following steps: data preprocessing and enhancement: receiving an original echo signal of a radar, generating three-dimensional time frequency-wavelet coefficient spatial data by combining time frequency analysis with wavelet transform, and expanding a data set in multiple modes; and feature extraction network construction: constructing a 3D-CNN and LSTM fused network, wherein the 3D-CNN has a convolution operation with a variable convolution kernel size. According to the method, the data are richer and more comprehensive through time-frequency analysis in combination with wavelet transform and data enhancement, and multi-dimensional features can be accurately captured through 3D-CNN and LSTM fusion and an adaptive convolution kernel; and the training effect is ensured through the adaptive learning rate and the early stop method, and the accuracy and adaptability of weak target detection are jointly improved.