1440results about How to "Accurate capture" patented technology

The invention provides a new material production process parameter optimization method and system based on artificial intelligence, and relates to the technical field of parameter optimization, and the method comprises the steps: constructing a multi-dimensional process parameter feature space, collecting and preprocessing process parameters, extracting correlation features through a deep convolutional neural network, and building a parameter feature space model; generating a technological parameter collaborative optimization model, constructing a multi-objective optimization function, and performing iterative optimization by using reinforcement learning and an improved ant colony algorithm; and performing process parameter adaptive control, constructing a dynamic control interval, collecting parameters in real time and performing adaptive adjustment to realize closed-loop control and model dynamic update.

The invention relates to the field of inductive control of illuminating lamps, in particular to an inductive control system of an inductive LED illuminating lamp. The method comprises the following steps: the lighting scene module is used for identifying all LED lighting lamps in an area; calculating node positions of all the LED lighting lamps in the area one by one, and identifying use scenes in the area to construct a plurality of LED lighting scene sub-areas; the ambient light feature module is used for acquiring ambient light parameters of a scene sub-region; performing light spatial distribution tracking on the ambient light parameters of the scene sub-regions, and performing ambient light state feature mining to generate ambient light comprehensive state features; and the multi-area lighting cooperation module is used for carrying out dynamic light source output parameter adjustment and multi-area lighting cooperation smoothing on the plurality of LED lighting scene sub-areas based on the environment light comprehensive state characteristics so as to generate multi-area lighting cooperation control parameters. According to the invention, rapid and accurate induction control of the LED illuminating lamp is realized.

The invention provides a Python monitoring task resource use method and system, and relates to the technical field of intelligent monitoring and resource optimization, and the method comprises the steps: extracting process call stack information through a multilayer convolutional neural network, constructing a process dependency graph, calculating a process criticality score, and recognizing a core monitoring process. And obtaining resource use data of the core monitoring process through a resource monitoring coroutine, determining an optimal sampling period by adopting a self-adaptive feedback control algorithm, and writing the data into a time sequence database. And performing data aggregation analysis by using a sliding time window algorithm to obtain a performance index, and constructing an anomaly detection model. Dimensionality reduction is carried out based on a principal component analysis method to construct feature vectors, and load types are divided by adopting a clustering algorithm; and generating a differentiated process optimization strategy according to the feature vector and the load type. When performance fluctuation is detected, an optimization strategy is executed, the anomaly detection model and the deep reinforcement learning model are updated according to the performance improvement index, and finally an optimization diagnosis report is generated.

The invention discloses a method for inverting key parameters in an underground water pollutant biodegradation numerical model based on a neural network algorithm. Carrying out preprocessing and refined dimension reduction on the data, and processing high-dimensional multi-scale data; building a multilayer neural network architecture under a physical information neural network framework, embedding a related reaction equation of a pollutant natural attenuation process, and coupling a biochemical reaction mechanism and a data driving model; a neural network model is trained, errors in the training process are calculated, and the accuracy of an inversion result is improved by using a sub-when optimization algorithm; integrating observation data and a model prediction result, determining final parameter setting of a numerical model, and carrying out underground water damage real object quantification; comparative verification of a neural network parameter inversion model is carried out, and feasibility and accuracy of the parameter inversion method are ensured. According to the method, the problem that the key parameters of the biodegradation numerical model are difficult to accurately invert in the real object quantification process of underground water pollution under complex geological conditions is solved.

The invention relates to the technical field of environmental sound event detection, in particular to an environmental sound event detection method based on multi-modal data fusion, which utilizes a microphone array, a camera and a sensor to synchronously acquire audio, video and environmental data to form a multi-modal data source. And the collected multi-modal data is preprocessed, so that the data quality and the accuracy of subsequent analysis are improved. And key features are extracted from the processed data, and the features are fused to form multi-modal features. And a deep learning model is constructed, and multi-modal features are used for training, so that a model capable of identifying an environmental sound event can be obtained. And performing optimization such as pruning, quantification and knowledge distillation on the trained model, and deploying the model on edge equipment to realize real-time preliminary feature extraction and event detection. The problems that an environment sound event detection method in the prior art is poor in robustness, low in real-time performance and limited in generalization ability are solved.

The invention provides an electric power spot market price prediction method and system based on artificial intelligence, and relates to the technical field of electric power price prediction, and the method comprises the steps: calculating an impact factor weight through employing a deep reinforcement learning optimization fuzzy reasoning system based on historical transaction data and external association data; performing wavelet transform decomposition on the data to obtain a feature fluctuation sequence, and performing weighted fusion to generate a market feature fusion sequence; the method comprises the following steps: constructing a bidirectional feedback neural network by using a graph attention network to extract market transaction features and generate feature correction parameters, generating layered early warning signals in combination with a cascade forest algorithm to construct a dynamic market behavior feature map, and inputting an optimized feature map into a prediction model group comprising a plurality of prediction sub-models, and realizing information sharing and complementary learning through a cross-modal feature interaction module, generating an initial prediction result, performing constraint correction according to an intelligent clearing rule, correcting a correction prediction result, and finally outputting a predicted price of the electric power spot market.

The invention discloses an automatic labeling and acquisition standardization method and system based on video content, and the method comprises the steps: decoding a video file into a processable video frame sequence, carrying out the standardization processing, unifying the format and the coding standard, and adjusting the resolution and the frame rate; performing content analysis on the standardized video frame sequence, extracting key frames, identifying objects, scenes and actions, and generating a video analysis result and a preliminary label; according to a video content analysis result, separating an audio stream from the video to carry out noise reduction processing and voice-to-text analysis, and combining the processed text with the video content analysis result to generate multi-modal comprehensive annotation information; based on the comprehensive labeling information, taking feedback of the user in video retrieval, label clicking and watching behaviors as an adjustment basis of label weights, and updating weight distribution of labels by analyzing user behavior data; labeling information and weight distribution are integrated into an index database, and indexes are updated in real time according to user behaviors and content changes.

The invention discloses a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, and relates to the technical field of carbon sink monitoring and optimization. According to the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, space-time estimation is carried out on CCER forestry carbon sink project data through the deep learning model, and the dynamic change of the carbon sink amount is accurately predicted. Spatial features are extracted through a convolutional neural network, time sequence data are analyzed in combination with a long-short-term memory network, and a carbon sink prediction model is constructed. And through statistical analysis and a spatial interpolation algorithm, carbon sink quantity change is monitored in real time, an abnormal trend is identified, and early warning is provided. Based on a mixed integer linear programming algorithm, forest land resource allocation and vegetation types are optimized, a carbon sink optimization scheme is generated, and carbon sink benefit maximization is promoted. And through an intelligent carbon sink management scheme, sustainable ecological development and green and economic popularization are assisted.

The invention discloses a power transmission line inspection image denoising method and system, and the method comprises the following steps: S1, obtaining an original image of an insulator, adding noise to the original image, and constructing an insulator noise image data set; s2, improvement is carried out based on an SADNet denoising network model, a Mix module, a CSM module and a CCAM module are introduced, and an SADNet-S denoising network model is constructed; s3, training the SADNet-S denoising network model by using the insulator noise image data set to obtain an optimal training weight; and S4, loading the optimal training weight into the SADNet-S denoising network model, and inputting the noise image of the insulator to be detected into the SADNet-S denoising network model to obtain a denoised insulator image. The invention provides an improved SADNet-S denoising network model based on multi-scale feature extraction, dynamic adaptive filtering and an attention mechanism, and by adopting the SADNet-S denoising network model, the strong generalization and robustness of the SADNet-S denoising network model are brought into full play, so that efficient image denoising is realized, and the target detection precision is remarkably improved.

The invention discloses a new energy power generation equipment health management platform based on a large model, and relates to the technical field of power generation equipment health management. The system comprises a multivariate data acquisition and region identification module, a data preprocessing and region adaptation unit, a region perception large model analysis module, an intelligent operation and maintenance decision and scheduling module and a spare part inventory optimization sharing module. The multivariate data acquisition and area identification module deploys multiple types of sensors at key equipment parts of photovoltaic power generation stations in different areas, acquires equipment operation parameters and environment state data, endows the areas with unique codes and feature tags, and ensures that the data are traceable and are associated with area characteristics; according to the invention, the health management efficiency and accuracy of the new energy power generation equipment are significantly improved; the system not only realizes real-time monitoring and fault early warning of the equipment state, but also reduces the maintenance cost and improves the system operation efficiency by optimizing the operation and maintenance resource configuration and intelligent spare part inventory management.

The invention relates to the technical field of mineral resource reserve simulation, in particular to a geological mineral resource reserve simulation method. The method comprises the following steps: carrying out multi-source heterogeneous data acquisition on a target geological area, and carrying out comprehensive feature extraction to obtain a comprehensive data set; constructing an initial static geologic model according to the comprehensive data set to obtain the initial static geologic model; building a multi-physics field coupling model according to the initial static geologic model to obtain a multi-physics field coupling model grid; performing metallogenic fluid migration and enrichment simulation according to the multi-physics field coupling model grid to obtain a metallogenic fluid migration simulation result; carrying out geologic body geometrical morphology updating on the initial static geologic model according to a metallogenic fluid migration simulation result to obtain a dynamic geologic model; and performing reserve prediction model training according to the dynamic geologic model to obtain a reserve prediction model. According to the method, the mineral resource reserves are evaluated more accurately and dynamically through a mineral resource reserve simulation technology.

The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method and system based on a pulse neural network. The method comprises the following steps: constructing a fault diagnosis network, wherein the fault diagnosis network comprises an improved gating attention coding module and a pulse residual network; the method comprises the following steps: replacing a common convolution layer of a gating attention coding module with a one-dimensional sparse convolution layer, and replacing a two-dimensional batch normalization layer with a one-dimensional batch normalization layer to obtain an improved gating attention coding module; setting a pulse neuron in the fault diagnosis network as a PSN neuron, and embedding a time step shrinkage layer in the pulse residual network; and inputting the mechanical vibration signal of the current sampling period into a fault diagnosis network, and outputting a predicted fault category label of the current sampling period. According to the invention, high-efficiency and high-precision mechanical fault intelligent diagnosis is realized.

The invention relates to the technical field of program exception positioning, in particular to an AI-based data interface program exception fault pre-positioning algorithm, which comprises the following steps: S1, data stream pre-analysis; s2, capturing an abnormal signal; s3, performing abnormal clustering and feature extraction; s4, interface behavior modeling is carried out; s5, abnormal traceability positioning; s6, generating a self-adaptive repair suggestion; s7, performing feedback circulation and continuous learning; and S8, performing visualization and report generation. Through the steps of data stream classification, key field extraction, behavior modeling and the like, the system can accurately capture abnormal signals in mass data, meanwhile, an abnormal mode is visually displayed through clustering analysis and a dimension reduction technology, a reliable foundation is laid for subsequent positioning and repairing, and in addition, the method has the advantages of being high in practicability and easy to popularize. The abnormal source is quickly positioned by adopting methods such as topological sorting and causal network reasoning, the false alarm rate and the missing report rate in the positioning process are effectively reduced by combining a sliding window and time sequence analysis, and the problem solving efficiency is greatly improved.

The invention provides a wind turbine generator array wake flow management method and system for Doppler laser wind measurement, and relates to the technical field of laser, and the method comprises the steps: collecting the real-time wind speed data, wind direction data and turbulence intensity data of each generator in the front and back direction through deploying a Doppler laser radar in a wind turbine generator array; establishing a wake flow influence model containing a wake flow influence coefficient, an attenuation coefficient and a diffusion coefficient based on the data, and calculating to obtain a wake flow influence range, loss power and a recovery distance; according to the method, the wind turbine generator array is cooperatively controlled, and the power generation efficiency of the wind turbine generator array is optimized by adjusting the yaw angle or the pitch angle of the upstream unit and the rotating speed of each unit. The wind speed data acquisition precision of the wind field is improved, the wake flow influence evaluation accuracy is enhanced, and the overall power generation efficiency of the wind field is remarkably improved.

The invention discloses a high-resolution remote sensing image change detection method based on multi-scale convolution decoding, electronic equipment and a storage medium, and belongs to the technical field of remote sensing image change detection. In order to meet the high-resolution remote sensing image change detection requirement, the method comprises the following steps: constructing a remote sensing image data set; a first stage and a second stage are formed based on a CNN module, a third stage and a fourth stage are formed based on a MambaVision module and a Transform module, and four dual-time-phase multi-scale feature maps are respectively obtained through processing of the four stages; constructing a time feature interaction module TFIM; a multi-scale convolution decoder EMCAD is constructed; and constructing a loss function, carrying out training, testing and evaluation by using the constructed remote sensing image data set, and applying the obtained trained optimal model to high-resolution remote sensing change detection. According to the invention, the detection capability of tiny changes in a high-resolution image is improved.

The invention discloses an image recognition system based on machine vision, which comprises a deep learning model, and is characterized in that an image recognition module comprises an image data sensing module for receiving and processing original image data, capturing an image from a camera or an image file, and performing preliminary format conversion and preprocessing; the image feature extraction module is used for converting the image into a form which can be understood by a machine based on a convolutional neural network; the online learning framework is used for dynamically adjusting model parameters according to data input in real time; the environment adaptability adjusting module is used for dynamically adjusting image processing parameters, changing a feature extraction weight, adjusting a learning rate and adjusting an identification strategy according to environment changes; the recognition and classification module is used for executing object recognition and classification tasks based on the extracted features and the learned model; the parameter mapping unit is used for mapping the environment parameters to specific parameter adjustment of the model; and the environment adaptability development unit is used for extracting useful information from the environment data and converting the useful information into an instruction of an adaptability adjustment strategy.

The invention relates to the technical field of intelligent assistant systems, and discloses an intelligent assistant system and method based on an AI technology, and the system comprises a user interaction module, a knowledge graph module, a natural language processing module, and a reasoning and decision module. The user interaction module realizes input and output interaction with a user; the knowledge graph module constructs a multi-domain knowledge system; the natural language processing module uses a BERT model to understand user intentions; and the reasoning and decision-making module fuses rule reasoning and machine learning prediction to realize accurate response. Particularly, an index construction unit is arranged in the reasoning and decision-making module to improve the retrieval efficiency, a reasoning engine adopts a Markov logic network to carry out logical reasoning, and a decision-making optimization unit continuously optimizes a decision-making strategy through reinforcement learning. The AI technology is comprehensively applied, so that the knowledge coverage, the response accuracy, the logical reasoning ability and the decision intelligent level of the intelligent assistant system are remarkably improved, and more efficient and convenient intelligent assistant services are provided for users.

The invention discloses an AI digital human interaction system based on emotion recognition, relates to the technical field of emotion calculation, and improves the accuracy of emotion recognition through multi-modal emotion data acquisition and processing of an emotion primary recognition module and a secondary recognition module. The system effectively distinguishes complex emotions, such as fatigue and depression, of the trainee in the virtual education task by calculating the first emotion coefficient and the second emotion coefficient; secondary emotion recognition is carried out, so that misjudgment is reduced, and the emotion state recognition accuracy is ensured; through an emotion feedback module, the system dynamically adjusts the expression, intonation and interaction frequency of the AI digital human according to a second emotion coefficient, so that the emotion feedback is more natural, and the emotion recognition sense of the student is enhanced; besides, the culture adaptation analysis module collects cross-culture emotion data based on the culture background of the trainee, calculates and evaluates a culture adaptation coefficient, and automatically adjusts an emotion recognition and feedback strategy, thereby improving the adaptability of the system in a multi-culture environment and the accuracy of emotion expression.

The invention relates to the technical field of power grid monitoring, in particular to a power grid abnormal state monitoring and early warning system which comprises a historical reference establishment module, an operation deviation analysis module, an abnormal threshold adjustment module and an abnormal early warning module. According to the invention, through real-time data acquisition and historical data analysis, the monitoring and early warning efficiency of power grid operation is improved, and through collection of power grid operation records, extraction of data of differential time periods, evaluation and analysis of the average load and volatility of the data, and construction of a historical power grid operation reference, the operation trend and potential abnormity can be captured more accurately. The method reduces the uncertainty of judgment depending on experience, can recognize a deviation mode and an adjusted load detection threshold value by comparing current data with a historical reference, enables the abnormal early warning to be more accurate, avoids excessive false alarms and missed alarms, optimizes the evaluation of early warning levels and the implementation of early warning notification, guarantees the safety and stability of a power grid, and improves the safety and reliability of the power grid. And the economic efficiency and the reliability of power grid operation are also improved.

The invention relates to the technical field of medical data analysis, in particular to a lung cancer dynamic evaluation method and system based on immune monitoring, and the method comprises the following steps: collecting immune index data obtained in the immune monitoring process of a patient, analyzing the mean value change between adjacent segments of an immune index data time sequence, marking an abnormal fluctuation region, and calculating the immune index data; and generating abnormal fluctuation section data. According to the method, the immune monitoring data of the patient are divided into the time sequences, the abnormal fluctuation sections and the abnormal change points are accurately extracted through the mean value change analysis and sliding window method, the remarkable fluctuation of the immune system can be captured more rapidly and accurately, and therefore the timeliness of dynamic monitoring is improved. And further in combination with statistical feature analysis and similarity matrix calculation, differentiated grouping of immune indexes among patients is refined, and personalized evaluation for patient groups with different immune states is facilitated.

The invention relates to the technical field of power system monitoring and fault diagnosis, and discloses a partial discharge detection network system based on an adaptive attention mechanism. Comprising a data acquisition module, a signal preprocessing and time-frequency image generation module, a deep learning network of an adaptive attention mechanism, a fault classification and diagnosis module and a real-time monitoring and alarm module, the data acquisition module acquires a multi-mode partial discharge signal in real time, and preprocessing is performed through the signal preprocessing and time-frequency image generation module. The deep learning network of the adaptive attention mechanism carries out feature extraction and classification on the time-frequency image, finally, the fault classification and diagnosis module outputs a diagnosis result, and the real-time monitoring and alarm module carries out real-time monitoring and automatic alarm according to the diagnosis result. According to the system, multi-mode partial discharge signals are collected in real time, feature extraction and classification are carried out through a deep learning network, the attention degree of different features can be dynamically adjusted through a self-adaptive attention mechanism, and the accuracy and robustness of detection are improved.

The invention discloses a lightweight space-time traffic flow prediction method based on GAT and Mama, and relates to the technical field of intelligent traffic systems. According to the method, the feature expression ability of the model is improved through multi-dimensional feature learning (time, date and space-time) of an embedded layer, rich features of input data can be deeply captured, and the learning ability of the model is enhanced; a graph attention module is used to adaptively pay attention to the relative importance between nodes, so that the graph structure relationship in the data can be more effectively captured and utilized, and the analysis accuracy of the model on the spatial data is improved; the GAT-Mama module optimizes the time sequence analysis and spatial data processing capability, and the model can more accurately capture the dynamic dependency relationship between time points and between spatial positions through a multi-layer graph attention mechanism and residual connection; the GAT-Mama layer structure supports flexible adjustment, a user can adjust the depth of the model according to specific task requirements so as to adapt to spatio-temporal data prediction tasks with different complexities, and good adaptability is achieved.

The invention discloses a deep learning-based deep forged compressed face image identification method, which belongs to the field of artificial intelligence image identification, and comprises the following steps of: 1, carrying out frequency domain modal extraction on a deep forged compressed face image by adopting dynamic adaptive discrete cosine transform; 2, a multi-head self-attention mechanism and a multi-dimensional entropy state load mutual feedback mechanism are adopted to extract frequency domain high-dimensional load features; step 3, extracting spatial domain features based on the pre-trained deep residual network; 4, performing feature fusion on the frequency domain high-dimensional load features and the spatial domain features based on an attention mechanism; and 5, discriminating and classifying the fusion features by using a classifier comprising a full connection layer. According to the method, high-accuracy identification can be carried out on the compressed face image manufactured by adopting a deep counterfeiting means.

The invention discloses a data analysis method and system and a storage medium, and is applied to the field of natural language processing. The method comprises the steps of obtaining a natural language input by a user; constructing a semantic data model for converting the natural language into a semantic query statement; combining the semantic data model with a user-defined rule to construct a semantic parser; inputting the natural language into the semantic parser for parsing to obtain a semantic query statement; inputting the semantic query statement into a semantic corrector for correction to obtain a target semantic query statement; converting the target semantic query statement into a structured query statement through a semantic translator; and inputting the structured query statement into the database for query to obtain a query result.

The invention relates to the technical field of steam extraction check valves, and discloses a design method and device for quickly responding to a steam extraction check valve under a high-parameter working condition, and the method comprises the following steps: constructing a valve working condition database and a valve body database; based on the valve working condition database and the valve body database, a valve performance prediction model is constructed, and a valve state quantity prediction value is obtained through performance influence factors collected in real time; correcting the predicted value to obtain a valve state quantity corrected value; a valve control environment based on reinforcement learning is established, a valve optimization control strategy model is constructed, a real-time optimal control action is generated and executed based on a valve state quantity correction value, and efficient optimization control over the valve is achieved; according to the method, through combination of data-driven performance prediction and reinforcement learning optimization control, rapid response and efficient operation of the steam extraction check valve under the high-parameter working condition are achieved, and the reliability and the energy-saving effect of the system are improved.

The invention provides a range hood control method and system based on the Internet of Things, and relates to the technical field of artificial intelligence and the Internet of Things, and the method comprises the steps: obtaining multi-source layered environment characteristic data of heating power, oil smoke and airflow through constructing a layered data collection network, and carrying out the processing of edge calculation and a cloud server, drawing force required by each area is calculated, a self-adaptive fan control curve is generated, and then an initial control parameter set containing a main fan rotating speed value, an auxiliary fan rotating speed value and an air deflector angle value is generated; the method comprises the following steps: constructing a vortex flow prediction model based on a vortex tensor and a velocity divergence by using a cloud edge collaborative architecture, predicting an airflow movement track, calculating a real-time airflow deviation degree, dynamically adjusting an initial control parameter group to generate a target control parameter group, and finally continuously optimizing the prediction model through a deep reinforcement learning network.

The invention provides a sunlight greenhouse intelligent environment prediction and regulation method comprising the following steps: collecting greenhouse data and meteorological data outside a greenhouse, combining to form a greenhouse environment data set, and carrying out data noise processing, screening, correlation analysis and normalization processing to obtain a prediction data set; constructing and training a greenhouse temperature and humidity prediction model comprising an input layer, a convolutional neural network layer and a gating circulation unit neural network layer; inputting the prediction data set into a greenhouse temperature and humidity prediction model, and training, verifying and adjusting hyper-parameters of the greenhouse temperature and humidity prediction model to obtain a greenhouse temperature and humidity prediction result; and constructing a greenhouse environment control module, and adjusting the greenhouse environment based on the optimal temperature and humidity information of crop growth and the greenhouse temperature and humidity prediction result. According to the intelligent prediction and control method, the greenhouse control precision and management efficiency can be remarkably improved, modernized and intelligent development of agricultural production is achieved, crop growth is promoted, and losses caused by extreme weather are reduced.

The invention discloses a weather detection method and system based on a weather rainfall radar and a storage medium. The system comprises the weather rainfall radar, a weather data centralized control management center and a radar time sequence chart neural network detection algorithm arranged in the weather data centralized control management center. According to the method, different cloud layer characteristic weathers are collected through a meteorological rain detection radar, and obtained electromagnetic wave scattering parameters of the different cloud layer characteristic weathers are input into a radar time sequence chart neural network detection algorithm for analysis and calculation, so that polarization phase differences corresponding to the different cloud layer characteristic weathers are obtained to detect the height and vertical distribution of cloud layers; and when the height and the vertical distribution of the cloud layer exceed boundary threshold values, sending out risk early warning information of different weather types, and automatically detecting spectral widths of different cloud layer characteristic weather and corresponding polarization phase differences. And the weather detection precision and efficiency are improved.

The invention discloses a photovoltaic power generation day-ahead prediction method based on causal inference and multi-scale feature fusion, and belongs to the technical field of photovoltaic power generation prediction. According to the method, historical meteorological characteristic data is divided into a plurality of samples, and generated power of the samples in the future M steps is used as corresponding labels. A PCMCI causal inference method is used to calculate a causal inference weight between a sample and a label, the sample is weighted and then is input into three base learners of BiLSTM, TCN and GAF-SE-ResNet50 in parallel, the trend of an input sequence is recognized from three dimensions of global, local and periodical, and a photovoltaic power generation power day-ahead preliminary prediction value is obtained through corresponding training. Wavelet features of the local weather forecast sequence of the prediction point are extracted, and a final prediction value is obtained through fusion of a cross attention mechanism and the initial prediction value of the base learning device. Accurate and stable photovoltaic power generation prediction in a complex climate change environment is realized.

The invention relates to integrated airborne communication antenna equipment and a communication method. The method comprises the following steps: carrying out CSI data acquisition and space-time focus modulation on a communication channel to obtain CSI space-time characteristic data; performing residual vector quantization operation to generate a quantized data stream, and performing recursive coding processing to obtain a target data stream; mapping the target data stream to a three-dimensional tensor space, constructing a real value tensor model, and performing iterative operation to obtain a CSI feature vector; performing fractional order wavelet transform decomposition on the CSI feature vector to obtain a radio frequency feature set, and inputting the radio frequency feature set into a federal fractional scattering network for feature processing to obtain an identity authentication result; and calculating an amplitude-phase weight coefficient of the antenna array, generating a beam parameter, and performing compensation matrix operation on the beam parameter according to the CSI feature vector to obtain a pre-coded signal. According to the invention, the dynamic adjustment of the antenna beam parameters is realized, and the accuracy and anti-interference capability of beam forming are improved.