2146 results about "Channel data" patented technology

Disclosed is a digital twin-based deduction and optimization method and system for an intelligent reflecting surface (IRS) communication system, including: collecting data from a scenario, including relevant data of an IRS physical model and real channel data; performing real-time data transmission on the collected data; establishing a digital twin three-dimensional (3D) model in a digital twin space based on the data after the real-time data transmission; establishing an IRS reflection mechanism model before fusing with the digital twin 3D model, the generalized Snell Equation being used to simplify a complex system in the IRS reflection mechanism model; deducing and optimizing the IRS communication system to obtain an optimization strategy; and feeding the optimization strategy back to the real world to realize the deduction and optimization of the IRS communication system.

In various examples, multimodal image data may be used to generate a set of top-down tile images, which are applied to a deep neural network generator architecture model to produce lane marking-specific heatmap images corresponding to the set of top-down tile images. The multimodal sensor data may include LIDAR-captured intensity channel data, LIDAR-captured feature height channel data, and optical color image channel data. The set of top-down tile images may be processed by the generator model to automatically detect lane boundaries and navigation boundaries to generate pixel-level heatmap images that may classify lane markings by marking characteristics such as line type and / or color. The generator model may comprise an encoder-decoder architecture, with multiscale feature extraction and / or context extraction functional layers intervening between the encoder model and the decoder model.

A system for the temporal ordering of multi-channel data streams, comprising the following: a multi-channel data reception module configured to receive real-time data streams from multiple sources, extract temporal metadata, and detect discrepancies in timestamps; a time synchronization module configured to align data packets using timestamp normalization, adaptive buffering, and AI-based predictive modeling; an AI-powered predictive modeling module trained to estimate missing or delayed timestamps and to reorder out-of-order data packets based on learned patterns; an adaptive buffer module configured to dynamically adjust buffer sizes based on real-time data arrival rates and network conditions; an ordering and output processing module configured to reconstruct the correct order of the data packets and transmit them to a processing or analysis system; and a module for error detection and correction that is configured to detect inconsistencies in timestamps, transmission delays and missing data points, thus ensuring increased reliability of the ordered data sequence.

The invention relates to the technical field of graphics card power consumption adjustment, and discloses a method, a device and equipment for dynamically adjusting the power consumption of a graphics card component. The method comprises the following steps: carrying out multi-channel data acquisition and working load characteristic analysis on operation parameters of a graphics card GPU core, a video memory and a power management unit to obtain a component temperature state evaluation matrix; executing component-level power consumption distribution based on multi-objective optimization to obtain a power consumption distribution scheme of each component; inputting the power consumption distribution scheme of each component into a power consumption-frequency-voltage mapping model, and calculating an optimal frequency voltage configuration parameter through a binary search algorithm to obtain a frequency voltage control instruction; performing multi-step prediction to obtain a module power consumption demand prediction value; according to the method, the frequency voltage combination with the highest performance efficiency can be found under the condition that the power consumption constraint is met, and accurate power consumption control is achieved.

The invention discloses a charging robot fault diagnosis method and system based on deep learning, and particularly relates to the technical field of robot fault diagnosis, and the method comprises the following steps: collecting multi-type sensing channel data in an operation process, and constructing a structured input sequence; calculating a local disturbance amplitude and trend difference to generate a weak anomaly confidence value, and judging whether subsequent analysis is triggered or not; if so, extracting a modal drift residual index and a micro-abnormal evolution trend index, and splicing to form a fusion feature vector; outputting a numerical coefficient of each channel through the trained deep learning model; adjusting the sampling frequency according to the numerical coefficient and the characteristic; according to the invention, through unifying a multi-channel data processing flow, constructing a trend-driven anomaly identification mechanism, and combining with deep learning model output, dynamic closed-loop regulation and control of sampling frequency are realized, data consistency, identification sensitivity and resource utilization efficiency of the charging robot fault diagnosis system are improved, and fault diagnosis accuracy is improved. And refined and adaptive monitoring of multi-channel states is realized.

The invention discloses a system and method for classifying and recognizing epilepsy in electroencephalogram signals based on a time sequence. The system comprises a cross-domain mixed self-supervised learning module, a multi-scale electroencephalogram feature learning module, an epileptic seizure guided self-attention learning module and a classifier. The cross-domain hybrid self-supervised learning module comprises a time domain context prediction task unit, a channel information reconstruction task unit and a frequency spectrum masking identification task unit; the cross-domain hybrid self-supervised learning module reconstructs an input electroencephalogram signal according to a time domain, a space domain and a frequency domain to obtain an electroencephalogram representation model; the time domain context prediction task unit is used for capturing dynamic change characteristics before and after epileptic seizure by learning a time domain evolution rule of electroencephalogram signals; the channel information reconstruction task unit is used for reconstructing missing electroencephalogram signal channel data and enhancing spatial information representation of an electroencephalogram representation model; the frequency spectrum masking identification task unit trains the electroencephalogram characterization model to characterize epilepsy nerve oscillation through a random masking part electroencephalogram signal frequency component in a frequency domain; the multi-scale electroencephalogram feature learning module captures local and global level epilepsy features of an electroencephalogram representation model through different resolutions to obtain a first epilepsy electroencephalogram feature model; the epileptic seizure guided self-attention learning module optimizes the first epileptic electroencephalogram feature model by fusing time sequence and spatial features to obtain a second epileptic electroencephalogram feature model; and the classifier classifies and outputs the electroencephalogram features of the second epileptic electroencephalogram feature model based on the long short-term memory network to determine whether the epileptic seizure occurs. The invention provides an efficient, accurate and robust solution for automatic detection and clinical auxiliary diagnosis of the epileptic seizure.

The invention discloses an intelligent control type expansion coating preparation reaction kettle device which comprises a reaction kettle body, the interior of the reaction kettle body is divided into a plurality of independent stirring cabins through partition plates, each stirring cabin is internally provided with an independent stirring assembly, a feeding assembly and an observation assembly, and each stirring assembly comprises a stirring shaft, stirring blades and a driver; the stirring blade is provided with a semiconductor chilling plate and a heating element, and the feeding assembly comprises a plurality of feeding channels connected with the stirring cabin, is provided with a flow meter, an electromagnetic valve and a dynamic adjusting module, and is used for independently controlling the feeding amount and the feeding speed of each cabin; the control system comprises a multi-channel data acquisition module, a learning module and a coordination control module, the multi-channel data acquisition module is provided with a sensor network for monitoring the temperature, the pressure and the material state of each stirring cabin in real time for each stirring cabin, and the control system performs coordination control on the stirring cabins according to preset parameters or autonomous learning results. And the stirring speed, temperature and feeding strategy of each cabin are dynamically adjusted.

The invention discloses a credit marketing intelligent recommendation system. According to the system, for the existing credit marketing problem, a customer portrait is constructed through multi-channel data acquisition, cleaning and preprocessing, and personalized credit product recommendation is carried out based on content and collaborative filtering. And training a recommendation model by using a neural network model and a reinforcement learning algorithm, updating recommendation in real time or at regular intervals according to real-time conditions and feedback of customers, establishing a system evaluation effect including recommendation accuracy, customer satisfaction and business indexes, and optimizing model parameters, improving the algorithm and updating data according to evaluation. The method can improve the customer satisfaction and the business conversion rate, reduces the risk, adapts to the market change, and effectively solves the defects of the existing credit marketing channel and strategy.

The invention provides a customer group dynamic division method and system, and relates to the technical field of customer group management.The method comprises the steps that firstly, commodity related data are collected in real time through a multi-channel data collection module, commodity behavior analysis is conducted in combination with service requirements, features are extracted according to analysis results, and commodities are divided into multiple groups; and then, dynamically dividing customers by combining service requirements, generating initial customer group information, and optimizing and adjusting the customer group information according to the updated user portrait information. And finally, performing preference calculation according to the optimized customer group information, and generating a personalized push strategy. According to the method, through dynamic division and optimization of customer group information, unique requirements and behavior characteristics of each customer can be better met, and personalized services are provided.

The invention belongs to the field of power equipment monitoring, and discloses an electrical signal acquisition system based on intelligent dynamic sampling, which comprises a signal acquisition module, a signal conditioning module, an abrupt change detection module, an impedance calculation module and an upper computer, and is characterized in that the signal acquisition module comprises a first ADC module, a second ADC module, a first MCU module and a second MCU module. Short-circuit impedance real-time calculation and waveform abrupt change detection are adopted for cooperative judgment, and when equipment operates stably, the sampling rate of the system is kept low; when impedance changes or waveform changes suddenly, the sampling rate is improved automatically, important signal changes are recorded accurately, and waveform abrupt change detection is carried out by adopting an edge detection algorithm improved based on an FIR (Finite Impulse Response) filter. The system ensures the time sequence consistency of data under different sampling frequencies through multi-channel data synchronization and timestamp management, and meanwhile, smooth transition is performed by applying an interpolation algorithm during sampling switching, so that the continuity of a data sequence is ensured. The data redundancy is reduced; and the integrity of key information is ensured.

The invention relates to the technical field of big data, in particular to an e-commerce marketing propaganda system based on behavior analysis, which comprises a global user portrait module, an intelligent recommendation engine, a marketing fatigue management module, a supply chain collaboration module, a lightweight terminal module and a budget and value management module. In the prior art, user portraits are constructed only depending on single channel data such as online clicking or purchase records, so that user interest modeling is incomplete and lagged; according to the method, all-channel behavior data such as APP, Web, offline POS and social media are integrated, a user-commodity-scene heterogeneous graph is constructed by using a graph neural network, and an interest attenuation period (for example, the weight is reduced by 50% after the interest of mother and infant users lasts for 18 months) is dynamically captured through an LSTM model; for example, after a user tries on a certain style of clothes offline, the system associates online behaviors in real time and recommends commodities of the same style, the cross-scene conversion rate is improved by 32%, and the user portrait coverage degree is improved by 60%.

The invention discloses a micro-seismic multichannel data processing method and system based on a DBSCAN algorithm, and belongs to the technical field of micro-seismic monitoring. According to the invention, through feature vector construction and optimization distance calculation, the clustering reliability is enhanced; a multi-dimensional feature vector Xi containing P-wave refinement arrival time ti, an amplitude Ai and space coordinates (xi, yi, zi) is constructed, distance calculation is performed by combining a normalized cosine distance with wave velocity constraint, a micro-seismic wave propagation rule is fully utilized, the discrimination capability of DBSCAN clustering on physical consistency is enhanced, and the robustness of a screening result is improved, so that noise interference can be effectively processed in a complex environment, and the accuracy and the robustness of the screening result are improved. And the stability and the reliability of a micro-seismic positioning result under different geological conditions are ensured.

The invention discloses a resource allocation method and system for 5G communication in an underground coal mine, and relates to the technical field of communication information processing, and the method comprises the steps: obtaining data information of an underground coal mine roadway, building a digital twin communication model through combining a synchronous positioning and map building algorithm and a wireless fingerprint matching algorithm, generating a channel profile map according to real-time data, and transmitting the channel profile map to a server; extracting channel data of each space node; constructing a computing resource pool for the 5G base station and the edge computing node, and packaging different service types into a plurality of network slice containers; according to the channel data, carrying out initial allocation on each container through a resource composer, and carrying out dynamic task migration among different MEC nodes to obtain a resource allocation state; dividing scheduling time according to a channel profile and a resource allocation state, and dynamically adjusting a duration proportion and bandwidth occupation of each window according to a task migration state; according to the invention, through digital twinning and dynamic scheduling, the problems of 5G communication coverage and interference in the underground coal mine are solved.

The invention discloses a multi-protocol channel data aggregation method based on 5G communication, and relates to the technical field of heterogeneous network data processing under the fusion of 5G communication and edge computing, and the method comprises the steps: constructing a protocol adaptive conversion engine, recognizing an input protocol type through learning, and automatically selecting a conversion strategy and a data packaging format. An edge-cloud collaborative intelligent scheduling architecture is introduced, a data aggregation task is dynamically determined, a dynamic channel aggregation algorithm of a 5G network state and protocol characteristics is designed, and a transmission path and an aggregation strategy are intelligently selected. By constructing a protocol adaptive conversion engine, an edge-cloud collaborative intelligent scheduling architecture and a dynamic channel aggregation algorithm fusing 5G network state and protocol characteristics, automatic identification, efficient aggregation and optimized transmission of multi-protocol data are realized, dynamic scheduling of tasks is realized in combination with an SDN / NFV technology, and the dynamic scheduling efficiency of the multi-protocol data is improved. The channel selection mechanism based on the utility function comprehensively considers the network state and protocol characteristics, and guarantees the transmission quality of high-priority and time delay sensitive data.

The invention relates to the technical field of image recognition, in particular to a bearing surface defect automatic detection system based on image recognition, and the system comprises an image collection and preprocessing module which obtains a bearing surface image, and carries out the cutting and filtering of the image, so as to remove the noise and adjust the contrast, and obtain a processed image; and carrying out gray level conversion on the processed image to generate a preprocessing result. According to the method, environmental noise interference is reduced through image cutting and filtering, the contrast ratio is adjusted, the image quality is optimized, and the distinction degree of the defect area and the background is improved. Image channel information is standardized through gray level conversion, so that the calculation precision of feature extraction is kept consistent, and the analysis result is prevented from being interfered by multi-channel data. According to the method, image hierarchies with different scales are constructed by an image pyramid method, so that the adaptability to fine cracks and large-area spalling defects is improved in the local contrast and texture feature extraction process, and the stable recognition capability to defects with different sizes is enhanced.

The invention discloses a long-term vibration monitoring method and system based on multi-channel data acquisition, and the method comprises the following steps: installing a sensor node integrated with a multi-mode energy collection module on a monitored object, and providing continuous power supply for a multi-channel data acquisition and edge processing unit; according to the invention, through a dynamic adaptive sampling mode and edge end extraction key features, the data load in a distributed monitoring scene is significantly reduced; the multi-mode energy collection module is integrated to collect vibration and environmental mechanical energy, and the energy management unit is combined to dynamically distribute stored energy and supply power, so that long-term autonomous operation of the sensor node is realized; the abnormal state is locally inferred and identified through the edge processing unit, and only key feature data instead of full data is uploaded, so that the real-time response capability to the equipment abnormality is improved; multi-dimensional correlation features are extracted by collecting multi-channel sensing data and combining a deep learning model and a time sequence-space feature fusion algorithm, and the health state of the equipment is comprehensively reflected.

The invention relates to the technical field of CPU card chips, and discloses a CPU card chip test method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection of a CPU card chip, and obtaining an initial test data matrix; constructing a test parameter association diagram of the initial test data matrix and predicting missing data to obtain a complete test data matrix; performing principal component analysis on the complete test data matrix to obtain an effective test data set; constructing a multi-level fuzzy set according to the effective test data set to obtain fusion test data; and inputting the fusion test data into a variational auto-encoder for self-supervised pre-training, executing conditional interpolation by using a diffusion state space model to obtain a defect detection result and an uncertainty estimation value, and realizing optimal fusion of electrical characteristics and functional test data by using a multi-stage fuzzy set and an adaptive weight adjustment mechanism. Complementary advantages of dual-channel data are fully utilized, and a more comprehensive and accurate test result is provided.

The invention relates to the technical field of Bluetooth communication, and discloses an optimization method for secure communication of Bluetooth chip equipment. The method comprises the following steps: identifying a device type identifier and a protocol version feature, extracting a hardware capability parameter and generating an initial security policy set; collecting channel data, and calculating a stability index and a dynamic noise baseline; then, screening and sorting security policies to generate an execution queue, adjusting an encryption strength threshold according to a noise baseline to perform iterative optimization, and outputting final policy configuration; and when the target Bluetooth chip establishes the communication connection, loading the final security policy configuration and monitoring the channel state in real time, and triggering a policy dynamic switching mechanism when the channel state change exceeds the preset tolerance so as to optimize the security communication effect of the Bluetooth chip equipment. According to the method, the security policy can be accurately matched with the device and the channel state, the policy is dynamically adjusted, and the security and the stability of communication of the Bluetooth chip device are effectively improved.

The invention provides a satellite communication method and device supporting fire rescue, and the method comprises the steps: carrying out the time alignment processing of real-time multi-source data collected from a fire rescue site and a pre-stored multi-source scene database, forming a time synchronization data set, and generating a terminal state data set and a scene feature data set; performing spatial geometric analysis based on the stored satellite orbit data and terminal position data, and generating available channel data in combination with meteorological monitoring data; task priority data and resource allocation demand data are generated through service feature analysis and task feature calculation; generating network configuration data and terminal access data based on slice configuration and resource allocation; and generating real-time resource data and terminal parameter data according to load statistical analysis and service quality calculation. According to the invention, through multi-source data fusion and dynamic resource adjustment, the reliability and service quality of satellite communication in a fire rescue scene are improved.

The invention discloses a statistical analysis-based engineering cost dynamic adjustment method and system, and aims to solve the problems of many errors and poor accuracy and reliability when the engineering cost is changed due to factors such as material price fluctuation, the method comprises the steps of obtaining a price change message and extracting related data, matching an engineering material data table by using a preset template, and obtaining an engineering material data table; the system queries and processes price data according to a change material identifier, carries out source diversification processing on the change price data, and also relates to initial construction and dynamic updating of an engineering cost template, the system is composed of a price change module, an engineering material data table module, a to-be-changed module, a to-be-replaced module and other modules, and all the modules work cooperatively to realize corresponding functions. The method has the following advantages that on the aspect of data processing, multi-channel data are automatically integrated, prices are fused, and the operation risk is reduced; in the aspect of template construction and updating, historical data and algorithms are utilized, and the material matching accuracy is improved; during price adjustment, the confidence interval is calculated based on statistical analysis, and the accuracy and reliability of the project cost are effectively guaranteed.

The invention relates to the technical field of bearing fault diagnosis, in particular to a multi-bearing fault positioning and diagnosing method, which comprises the following steps of: acquiring bearing vibration signals of a plurality of sensing points, extracting peak time information to complete synchronous calibration, extracting high-frequency band energy and constructing a feature vector, screening deviation features and clustering and grouping, and identifying an energy abnormal channel, positioning a bearing position, extracting time domain and frequency domain features, comparing fault modes, and outputting a fault type diagnosis result. According to the method, the consistency of multi-channel data synchronization is realized by extracting the peak point of the vibration signal and calibrating the time offset, the high-frequency energy change rate is utilized to match the weight coefficient, the detection sensitivity of tiny fault features is enhanced, and the separability of a composite fault state is improved by combining feature relative deviation rate screening and density trend clustering. A frequency section energy proportion change recognition channel is adopted, accurate positioning of a fault bearing is achieved, and accurate judgment of a fault type is achieved in combination with a multi-feature comparison mode.

Systems and methods for monitoring and enforcing a service level agreement for an entity are provided. A system includes a scanning engine, an assessment engine, a terms repository, an agreements repository, an assessments repository, and a processing circuit. The processing circuit is configured to perform operations including: receive device connectivity data; determine an entity; identify a data set of computing resources associated with the entity; reference a service level agreement associated with the entity; parse at least one term from the service level agreement; retrieve an assessment map for the at least one parsed term, wherein the assessment map comprises a key performance indicator, a data set of actual service level values, and a response protocol; score the identified data set of computing resources associated with the entity; determine an assessed service level value; store the assessed service level value; and monitor the identified data set of computing resources.

The invention relates to a bridge modal parameter automatic identification method and system considering multi-channel information, and the method comprises the following steps: S1, directly analyzing a multi-channel monitoring signal through COV-SSI, generating a stability diagram, and automatically extracting a stability axis in combination with a DBSCAN clustering algorithm, thereby achieving the automatic identification of a modal frequency; s2, performing signal decomposition on the multi-channel monitoring data by adopting MvFIF to generate an intrinsic mode function (IMF) group with a mode alignment characteristic; s3, the instantaneous frequency and bandwidth of the IMF in the step S2 are calculated through HHT, IMF components containing target modal frequency are screened out, and linear reconstruction is carried out; s4, taking the multi-channel monitoring data reconstructed in the step S3 as system input of a COV-SSI algorithm, calculating a system matrix and an output matrix, and calculating a modal damping ratio by utilizing eigenvalue decomposition; according to the method, the spatial-temporal correlation among multi-channel data can be considered, and synchronous decomposition of multi-channel monitoring data is realized; and automatic and accurate identification of the structural modal parameters under different noise levels can be realized.

The invention discloses a 16-channel high-precision synchronous data acquisition system based on a multi-parameter model, which belongs to the technical field of data acquisition and comprises a multi-parameter model driving module, a self-adaptive channel scheduling module, a closed-loop calibration compensation module and a collaborative synchronous optimization module. The multi-parameter model driving module establishes a multi-dimensional optimization model fusing the sampling rate, the channel load and the error trend to output optimization parameters; the adaptive channel scheduling module performs real-time quality evaluation and dynamic resource allocation according to the optimization parameters to generate a scheduling instruction; the closed-loop calibration compensation module monitors a synchronization error in real time and calculates a compensation parameter; the collaborative synchronous optimization module realizes inter-channel parameter sharing and collaborative control to generate a synchronous control signal, and feeds back an optimization result to the multi-parameter model driving module to form a closed loop, and through deep coupling and closed loop feedback of the four modules, high-precision synchronization of multi-channel data acquisition is realized, and the synchronization precision reaches + / -35ns.

The invention relates to the technical field of storage systems, in particular to an electrolytic cell test data cloud storage method, which comprises the following steps: performing timestamp marking and partition size calculation on electrolytic cell test data, aggregating the data into partitions with fixed sizes according to timestamps, calculating a data density value and a write-in rate value of each partition, obtaining a partition data set, and storing the partition data set into a database; and comparing the write-in rate value in the partition data set with a preset partition threshold value. According to the method, through a dynamic partition management and multi-path data flow processing mechanism, in the storage process of the test data of the electrolytic cell, the data is subjected to partition aggregation through timestamp marking, a data density value and a write-in rate value reflect the dynamic change of the partition, and the size of the partition is adjusted in real time to cope with the fluctuation of a data write-in load. Multi-channel data stream distribution and merge sorting improve concurrent processing performance and eliminate single-channel bottleneck restriction. A double-buffer-area framework is matched with data access frequency analysis to support intelligent migration of cold and hot data, and IO resource configuration is optimized.

The invention provides a two-phase flow measurement method based on differential pressure capacitance sensing and data knowledge driving. The two-phase flow measurement method comprises the steps that throat extension design is conducted on a standard Venturi tube; a multi-layer capacitive sensor array is embedded into a long-throat-neck Venturi tube, various transmitters are installed on the tube, and differential pressure, temperature and pressure data of the various transmitters are synchronously acquired by using a multi-channel data acquisition module; a configured capacitive sensor array excitation-measurement module is utilized to synchronously acquire capacitor array data; developing a gas-liquid two-phase flow calculation model based on data knowledge driving, and packaging the gas-liquid two-phase flow calculation model in a two-phase flow calculation module; capacitance, differential pressure, temperature and pressure data are input into a two-phase flow calculation module, and field learning is carried out by utilizing actually measured data, so that gas-liquid two-phase flow on-line real-time calculation is realized. The problem that time and space of data measured by traditional tandem type multiple sensors are inconsistent is solved, and the problem that when a traditional venturi tube is used for measuring two-phase flow, the phase volume fraction is lost is solved.

The invention discloses an intelligent customer acquisition and user behavior analysis system based on AI full ecology, and relates to the technical field of artificial intelligence and big data marketing, and the system comprises a multi-source data collection module which collects internal and external multi-channel data of an enterprise; the data cleaning module processes abnormal and missing values and unifies data formats; the user portrait construction module extracts multi-dimensional labels to describe user features; the intelligent customer obtaining module uses transfer learning to screen high-potential customers, and the user behavior analysis module mines behavior logic by means of Transform; the intelligent recommendation engine is fused with multiple algorithms to realize personalized recommendation, and the real-time decision module is combined with rules and reinforcement learning to optimize marketing. According to the method, multi-source data integration and deep analysis are realized, clients are accurately identified, behavior requirements are mined, the client obtaining efficiency and the conversion rate are improved, the marketing effect is optimized through personalized recommendation and intelligent decision, and a whole-process intelligent marketing solution from data acquisition to decision execution is provided for enterprises.

The invention discloses a CPE router, a data transmission method and device, a medium and a product, and relates to the technical field of network communication and data transmission optimizing.According to the scheme, the method comprises the steps of collecting and preprocessing inlet flow and wireless channel data of the CPE router, extracting transmission urgency and a channel quality index, constructing a transmission decision model and outputting service priority classification; and meanwhile, a dynamic transmission strategy and an early warning mechanism are formulated in combination with a channel prediction model result, and network resource allocation is optimized. According to the scheme, accurate matching and intelligent scheduling of network resources are achieved by dynamically sensing the channel state and the service characteristics, key service transmission delay is effectively reduced, the throughput of high-priority data is improved, meanwhile, transmission stability in a complex environment is enhanced, data retransmission caused by channel fluctuation is reduced, and the transmission efficiency is improved. Smoother multi-service concurrent experience is provided for users, potential transmission risks are warned in advance, and network service quality and equipment operation reliability are guaranteed.

The invention discloses an aircraft environment monitoring method and system integrated with a semiconductor sensor. The method comprises the steps that S10, a semiconductor sensor array deployed on the surface of an aircraft is used for collecting temperature, humidity, air pressure, gas concentration and surface wind speed data in real time; s20, performing format standardization and error correction on the collected original environment data through a multi-channel data fusion engine, and constructing a unified input vector; s30, calculating a multi-dimensional fluctuation amplitude value of the input vector in the flight mission time window, and judging whether to trigger a high-frequency monitoring mode or not based on the value; s40, performing convolution fitting on the fluctuation amplitude value and a historical trend curve, and calculating a real-time environment disturbance index for updating a steady-state control parameter of the aircraft; s50, dynamically adjusting a control surface response strategy of the aircraft according to the disturbance index and synchronously generating a safety prompt level; and S60, uploading whole-process monitoring and response data to a ground monitoring center for intelligent optimization of model retraining and subsequent task planning.

A novel scatterer density-based predictive channel modeling method includes: obtaining channel data with different scenarios scatterer densities through a channel measurement or a simulation; obtaining corresponding channel statistical characteristic parameters through a data preprocessing based on the channel data; constructing a graph dataset by taking scatterer density in different scenarios as main characteristics to enhance a space-time correlation of data; dividing the graph dataset according to a certain proportion, and then using a graph attention network and a gated recurrent unit network to extract correlated channel space-time characteristics and implementing a cross scenario channel prediction. The method can capture channel variations in different scenarios, and obtain channel characteristics under different scatterer densities through high space-time correlated channel characteristics, and has good performance in channel prediction based on scenario.