892 results about "Data dimension" patented technology

The invention relates to a spare part service life prediction method, device and equipment and a computer readable medium. The method comprises the steps of collecting multi-mode state data of a target spare part; verifying the historical consistency of the multi-modal state data; and under the condition that the historical consistency verification of the multi-modal state data is passed, inputting the multi-modal state data into a target residual life prediction model so as to predict a degradation track of the target spare part based on the multi-modal state data by using the target residual life prediction model, the target residual life prediction model is a neural network model obtained by training by taking a physics degradation mechanism of the spare part as priori knowledge; and determining the predicted remaining life of the target spare part based on the degradation trajectory. According to the method, the evaluation one-sidedness caused by insufficient single data dimension is avoided, the prediction credibility is improved through a data verification and physical mechanism constraint model, and the technical problem of low life prediction accuracy caused by spare part life counterfeiting is effectively solved.

The invention discloses a listed company operation risk early warning method based on multi-source auditing and text semantic fusion, and relates to the technical field of auditing, and the method comprises the steps: S1, crawling and converging multi-source heterogeneous data of listed company financial newspapers, auditing suggestions, supervision announcements, inquiry letters, news public opinions and market transactions; according to the method, unstructured texts are subjected to cleaning, blocking and semantic vectorization processing, each text segment is embedded into a high-dimensional semantic space, a vector index is established, a bottom-layer knowledge base of an RAG framework is formed, in the stage, it is ensured that the data structure is uniform, the source is traceable, standardized input is provided for subsequent semantic retrieval and modeling, and the reliability of the system is improved. S2, a query expression is constructed based on a target company, a time window and a risk topic, dense semantic retrieval and sparse BM25 retrieval methods are comprehensively used, a time decay and source credibility weighting mechanism is introduced, and the problems that a traditional method is single in data dimension and information is split are solved.

The invention belongs to the technical field of machine room safety monitoring, and discloses an image review method fusing semantic understanding and visual recognition, which comprises the following steps: calculating visual / semantic feature dynamic credibility in real time through an exponential weighted moving average algorithm in combination with environment interference and equipment state parameters; resNet50 is adopted to extract visual features in global and local branches, and a BERT model is adopted to encode semantic features; correcting the feature correlation degree according to the scene, calculating a dynamic weight, carrying out heterogeneous calibration through an attention mechanism, and calling priority rules such as'physical security features are higher than behavior features' for arbitration during conflicts; the method is advantaged in that low-credibility feature interference fusion is avoided, abnormity identification accuracy in a complex scene of a machine room is improved, multi-modal data cooperation demands are adapted, scene labels are marked based on time, work orders and historical data dimensions, an exclusive sub-model is constructed for a high-density scene through DBSCAN density clustering, and low-frequency scene parameters are migrated to a similar model.

The invention discloses a power grid dynamic scheduling decision-making method and device based on multi-modal prediction, electronic equipment and a storage medium, and belongs to the field of power system regulation and control operation, and the method comprises the steps: obtaining internal state data and external working condition data of each target power grid device, and a future load change curve of a related power transmission and distribution line, and an equipment feature matrix is constructed through space-time alignment. And inputting the feature matrix into a multi-modal neural network, and outputting the health index, the remaining service life and the fault probability. When the equipment health index is lower than a threshold value, a multi-objective optimization model is constructed, a preventive scheduling strategy is generated, and scheduling is executed; and when the equipment fault probability exceeds a set threshold value, updating the power grid line weight based on load prediction, generating a topology reconstruction scheme of the minimum power failure range, and scheduling according to the topology reconstruction scheme. By implementing the method and the device, the problem that the long-term degradation trend and the short-term sudden risk of the equipment cannot be accurately predicted due to single data dimension in the prior art can be solved.

The invention relates to the technical field of intelligent assessment, and discloses a dam risk assessment method, device and equipment based on multi-source data fusion, and the method comprises the steps: determining a monitoring project type of dam multi-source monitoring data, building a risk assessment index system through selecting a key index of the monitoring project type, and obtaining a risk assessment result; the method comprises the steps of dividing a dam region by combining measuring point spatial distribution to obtain a dam partitioning result, processing single measuring point actual measurement data of the same region in the dam partitioning result through a dam risk assessment model to obtain a region basic probability distribution value, and further obtaining a risk assessment result. According to the method, the limitation of single data dimension is broken through by constructing a risk assessment index system, accurate mastering of risk heterogeneity of different regions is realized through region division, a dam risk assessment model combining a cloud model and an evidence theory is adopted, the problem of dimension unification of multi-source data is solved, the risk assessment comprehensiveness is ensured, and the risk assessment efficiency is improved. And the accuracy of risk assessment is also improved.

The invention discloses a large-amount consumption scene intelligent customer obtaining method, system and device based on AI multi-modal data and a medium, relates to the technical field of multi-modal data processing, and solves the technical problems of low customer obtaining efficiency, poor customer conversion quality and lagging credit risk management and control in a large-amount consumption scene. According to the technical scheme, the method is characterized in that a trans-modal feature fusion model based on a Transform model and a'large-amount consumption demand prediction-credit adaptation degree evaluation 'dual-prediction model are constructed by fusing four types of multi-modal data of client texts, images, behaviors and time sequences; the technical problems of single data dimension, disjunction of demand and qualification pre-judgment and insufficient strategy dynamics in traditional customer acquisition are solved, accurate identification of potential customers and pre-management and control of credit risks are realized, and customer acquisition efficiency, customer conversion quality and risk management and control capability of a large-amount consumption scene are improved.

The invention relates to the technical field of artificial intelligence, and provides a marketing data generation method and device based on portrait data, equipment and a medium, marketing association data can be collected and purified based on a three-level data gateway, feature fusion is carried out by using a star-shaped collaborative network constructed based on a dynamic weight mechanism and a federated learning mechanism, and the marketing data generation efficiency is improved. The problems of data dimension limitation and data island are solved; scene recognition is performed based on a marketing data graph constructed by a secondary scene classification tree including a gift scene, and the problems of low utilization efficiency of unstructured data and insufficient crowd portrait granularity are solved; the marketing strategy is generated by using the target engine matched with the scene, so that the problems of scene engine deficiency and gift scene adaptation imbalance are solved; and generating the target marketing data according to the target marketing strategy and the marketing data graph. The problems of low operation efficiency and insufficient content accuracy are solved.

The invention provides an industrial production Internet of Things data anomaly detection method, medium and system, and belongs to the technical field of industrial production Internet of Things. Industrial equipment sensor data is collected and preprocessed to establish a multi-dimensional data set, and principal component analysis and a mutual information algorithm are used to construct a dimension reduction feature data set; a virtual sensor algorithm is utilized to make up for data missing to form an extended data set, a simulation statistical mechanical anomaly analysis model is established based on a statistical mechanical law to convert the microscopic state of massive high-dimensional sensor data into a macro thermodynamic parameter, and a statistical mechanical feature vector is calculated through a virtual particle ensemble simulation equipment operation state. A state evaluation model and a dynamic threshold function are adopted to identify an abnormal mode and perform grading marking, a feedback optimization mechanism is constructed to continuously improve the system performance, and the technical problem that a traditional algorithm has a curse of dimensionality and cannot perform effective anomaly detection due to extremely high data dimensionality of an industrial equipment sensor is solved.

The invention belongs to the technical field of industrial flue gas purification, and discloses an ammonia desulfurization optimization control system based on a machine learning algorithm, which comprises a feature extraction module, a working condition clustering module, a dynamic optimization module and a self-adaptive feedback module, the feature extraction module is used for collecting multi-dimensional operation parameters in a coal burning process, performing data dimension reduction through a PCA algorithm, and extracting key working condition features; the working condition clustering module identifies different operation working condition modes; the dynamic optimization module can construct a multi-modal optimization neural network based on different operation condition modes, and generates optimal control parameters in real time. Through PCA dimension reduction processing of the feature extraction module and in combination with a working condition clustering algorithm, automatic mode recognition and strategy switching under complex working conditions are achieved, fluctuation of desulfurization efficiency is reduced, and meanwhile the problems that the ammonia water adding amount depends on experience setting, raw material waste and secondary pollution are likely to be caused, and the operation and maintenance cost is large are solved.

The invention discloses a general electromyographic signal processing method and system based on a large self-supervised model. The general electromyographic signal processing method comprises the following steps: step 1, acquiring a multi-source original multi-electrode channel EMG signal X from an electromyographic acquisition device; and finally, performing data unification processing, and finally converting into a space-time activity diagram with a fixed size of 224 * 224. On the basis of the space-time activity diagram and the fatigue state mark, constructing an AEMG for training according to heterogeneous unlabeled EMG data collected by a collection device; performing light-weight Adapter layer fine adjustment on the pre-trained large myoelectricity model to adapt to gesture recognition muscle force regression gait analysis or rehabilitation evaluation downstream tasks; aiming at the problem that the dimension and the structure of myoelectricity data are not matched due to different acquisition devices, acquisition parts and acquisition tasks, original signals are converted into space-time activity diagrams in a unified format through data unification processing, device differences are represented by combining a sensor embedding module, effective alignment of cross-source data is achieved, and the accuracy of the data is improved. And a basis is provided for large-scale data utilization.

The invention relates to a real-time monitoring method for soil remediation by utilizing multi-spectrum of an unmanned aerial vehicle and sensing coupling of the Internet of Things, and belongs to the technical field of soil environment monitoring and remediation. The method comprises the following steps: constructing a space grid model of a monitoring area; a space-air-ground integrated monitoring network is arranged, image data are obtained through multi-spectral remote sensing of an unmanned aerial vehicle, and soil environment parameters are collected through a ground Internet of Things sensor; preprocessing and fusing the multi-source spatio-temporal data, and establishing a spatio-temporal matching model; constructing an inversion model of the soil heavy metal content, the organic matter content and the pollutant degradation degree based on the fusion data; and the repair efficiency is dynamically calculated and visualized, and real-time evaluation and early warning of the repair process are realized. According to the invention, space-air-ground data collaboration is realized, the real-time performance, accuracy and space coverage of monitoring are remarkably improved, the defects of high cost, low efficiency and limited data dimension of a traditional method are overcome, and whole-process and intelligent decision support is provided for soil remediation.

The invention discloses a grain crop grain classification detection method and system based on small samples, and relates to the technical field of grain quality detection.The method comprises the steps that hyperspectral images of grain crop grains are obtained, quality categories are marked, and then multi-modal features are extracted to construct a classification data set; the data set is used for training a multi-modal fusion classification model, a feature fusion network carries out dynamic weighted fusion on multi-modal features by means of an attention mechanism, a high-dimensional fusion feature vector is generated, and a classifier detects a quality category according to the high-dimensional fusion feature vector. During detection, the multi-modal features of the to-be-detected grains are input into the trained model, and then the quality category can be obtained. The method effectively solves the problems that a single modal method is difficult to deal with complex differences among grain crop grain types, high in hyperspectral data dimension, few in samples, easy to over-fit and the like under the condition of small samples, dynamic focusing of key information is realized by introducing an attention mechanism to optimize modal fusion weight distribution, and the method is suitable for popularization and application. And the classification accuracy and the model robustness are improved.

The invention discloses a multi-dimensional credit asset traceability system and method. The system comprises a data acquisition and encryption module (10) which is used for performing encryption and digital signature on credit asset data based on a trusted execution environment (TEE) and a hardware security module (HSM) at a data generation end; the parallel Hash processing module (20) is used for performing parallel Hash operation on the encrypted data and generating leaf node Hash values based on GPU acceleration and a multi-thread technology; the nested hash and zero-knowledge proof module (30) constructs leaf node hash values into a compression type Merkle tree and generates a zero-knowledge proof (ZKP), so that a third party can verify the authenticity and consistency of data without accessing full data; the on-chain evidence storage module (40) is used for recording root Hash (RootHash) and zero knowledge proof of the compression type Merkle tree to the block chain; the dynamic authorization and secure multi-party computing module (50) performs access clipping on the credit asset data based on the access intention and realizes verification of the minimum data dimension through secure multi-party computing (SMPC). Through combination of technical means such as trusted hardware acquisition, parallel hash calculation and privacy protection verification, rapid verification is realized on the premise of not exposing original data, and verification performance, data security and privacy protection capability are remarkably improved.

The invention relates to the technical field of trajectory accompanying, in particular to a schizophrenia prediction model construction method based on multi-modal subject data and artificial intelligence, which comprises the following steps: standardizing a prospective queue design and a multi-modal data acquisition protocol; carrying out synchronous acquisition and quality control on multi-modal data; carrying out dimension reduction and cross-modal alignment fusion processing on the data; and combining an improved loss function and a self-adaptive optimization algorithm to train the model, and performing verification and iterative optimization. According to the schizophrenia prediction model construction method based on the multi-modal subject data and the artificial intelligence, multi-modal standardized acquisition is taken as a core, multi-dimensional pathological support is provided for prediction through multi-modal data deep coverage and standardized acquisition, and refined preprocessing is designed for different modals, so that the schizophrenia prediction model construction efficiency is improved. In combination with modal specific feature extraction and cross-modal alignment fusion, a dynamic weighted multi-modal network is matched with multi-task learning and improved AdamW optimization, and in combination with comparison pre-training, characterization capability and training efficiency are improved.

The invention discloses a cardiovascular lesion classification method and system based on data analysis, relates to the technical field of cardiovascular lesion classification, and aims to solve the problem of poor accuracy when cardiovascular lesions of patients are classified. According to the method, multi-dimensional features are extracted for images, time sequence signals and structured data, and fusion is realized through data layer clinical association screening, feature layer composite vector recombination and decision layer weight summation. The method breaks through the limitation of a single data dimension, enables a classification result to be more fit with a pathological mechanism, remarkably improves the recognition precision of complex lesions and complications, guarantees the reliability of hardware through precise configuration of equipment and multi-dimensional detection, customizes a preprocessing process according to the data type, and associates multi-source data through a patient ID and a timestamp, thereby improving the recognition precision of the complex lesions and complications. The problems that equipment is disordered and data formats are different in a traditional process are solved.

The invention discloses a power distribution network power prediction method, system and device based on multi-source heterogeneous data fusion, and a storage medium, and relates to the technical field of power distribution network data processing and prediction, and the method comprises the steps: constructing a data autocorrelation matrix based on the comprehensive operation and maintenance data of a power distribution network; performing eigenvalue decomposition on the data autocorrelation matrix, and constructing a principal component matrix; performing dimension reduction processing on zero-mean data obtained in the process of constructing the data autocorrelation matrix by using the principal component matrix to obtain operation and maintenance feature fusion associated data of the power distribution network; an echo state network model is established and trained, the operation and maintenance feature fusion associated data of the power distribution network is used as the input of the echo state network model, and the output power of the power distribution network is predicted; according to the method, the data dimension is reduced, the calculation complexity is reduced, and the output power of the power distribution network can be predicted more accurately; the operation efficiency and the management level of the power distribution network can be improved, and powerful support is provided for stable supply of a power system.

The invention discloses a transformer fault diagnosis method and system based on a random forest algorithm, and the method comprises the steps: collecting transformer historical fault data, preprocessing the collected transformer historical fault data, and constructing historical sample data; the method comprises the following steps: collecting real-time operation data of a transformer and preprocessing the real-time operation data to obtain a multi-dimensional feature vector; constructing a training sample set by using the historical sample data, and training a random forest model containing T decision trees by using the training sample set; and inputting the multi-dimensional feature vector into the trained random forest model for diagnosis, outputting a fault diagnosis type, dynamically optimizing the random forest model according to the change condition of the real-time operation data, adjusting the branch decision tree, and restarting branch decision tree training. According to the method, high-precision fault diagnosis and quick response can be realized, the decision reliability is greatly improved, the maintenance cost is reduced, the feature data dimension is reduced, and the sensitivity of fault detection and analysis is improved.

The invention discloses a postoperative sub-delirium syndrome risk prediction system for a cardiac surgery patient, and relates to the technical field of medical treatment, and the system comprises a clinical data collection module which is used for collecting multi-dimensional clinical data of the cardiac surgery patient in a perioperative period under extracorporeal circulation, the data comprises patient baseline information, preoperative neurocognitive function evaluation indexes, intraoperative physiological parameters and postoperative early-stage vital signs; and the risk identification module is used for utilizing a single-factor statistical analysis mechanism. According to the postoperative sub-delirium syndrome risk prediction system for the cardiac surgery patient, accurate prediction of the sub-delirium syndrome risk is achieved by collecting multi-dimensional clinical data of the patient in the perioperative period, such as baseline information, preoperative neurocognitive function, intraoperative physiological parameters and postoperative early-stage vital signs; the independent risk factors are scientifically screened and quantified by using single factor analysis and a multivariate Logistic regression model, and the problem of incomplete data dimensions in traditional evaluation is effectively solved.

The invention discloses an electric energy quality data monitoring method, and relates to the technical field of electric power system monitoring and analysis, and the method comprises the following steps: S1, obtaining an original voltage and current waveform set; s2, performing time-frequency domain decomposition by using the waveform set; s3, performing feature fusion by using the feature component set; s4, performing time serialization by using the electric energy quality index set; and S5, performing multi-dimensional structured integration by using the time sequence index data. By setting a voltage and current characteristic component coupling mechanism, unified expression of voltage and current characteristics can be ensured on an original signal level, and fine-grained description of electric energy quality characteristics is realized in an index calculation link, so that the problems of data dimension separation and insufficient index precision in the prior art are effectively solved. The method not only improves the comprehensiveness and reliability of the electric energy quality monitoring result, but also lays a solid data foundation for subsequent time serialization and structuralization integration.

The invention discloses a tractor transportation operation working condition construction method based on improved particle swarm optimization and KMeans fusion, and relates to the technical field of agricultural machinery working condition analysis. The method comprises the following steps: acquiring original data of tractor transportation operation through a plurality of data acquisition modes, and carrying out preprocessing and three-stage screening to obtain an effective kinematics fragment; selecting multi-dimensional characteristic parameters to construct a characteristic matrix, and performing data dimension reduction by adopting principal component analysis; optimizing a KMeans clustering initial center by using an improved particle swarm optimization (IPSO) algorithm which introduces a dynamic inertia weight and a Gaussian mutation strategy, and performing clustering analysis on the feature space after dimension reduction; and selecting representative fragments based on feature similarity, and synthesizing a standardized working condition curve by taking the sum of average relative errors of all feature dimensions as a target function. According to the method, the problems that a traditional clustering algorithm is prone to falling into local optimum and the working condition construction accuracy is insufficient are solved, the constructed working condition can truly and comprehensively reflect the actual transportation operation characteristics of the tractor, and a reliable basis is provided for tractor power system optimization, operation efficiency improvement and energy consumption reduction.

The invention relates to the technical field of well site fracturing, and discloses an intelligent fracturing method and system for precise fracture forming, and the method comprises the steps: collecting underground multi-dimensional monitoring data in real time; dynamically constructing and correcting a three-dimensional crack expansion model by using the monitoring data, and outputting a prediction path containing crack geometric parameters; based on the prediction path and the real-time downhole pressure deviation, a control instruction is generated through an optimization algorithm; and the control instruction is executed, the sliding sleeve valve element is driven to move, the flow of fracturing fluid is adjusted, and accurate formation of cracks is controlled. According to the method, by fusing underground multi-dimensional monitoring data and dynamically correcting the three-dimensional crack propagation model, prediction deviation caused by single data dimension and static model solidification in a traditional method is overcome, prediction errors of the crack length and the steering angle are reduced, accurate prediction and control of the crack propagation path are achieved, and the method is suitable for large-scale popularization and application. And the geological targeting property of fracturing construction is obviously improved.

The invention discloses a cross-modal neural signal dimension reduction method and system, and the method comprises the steps: synchronously collecting neural signal data of at least two different modalities, at least comprising an electrocorticogram signal in the first modality and a neural signal in the second modality of other types, and carrying out the time alignment; performing preprocessing and feature extraction on the original neural signal data of each modal to obtain feature representation representing information of each modal; aligning feature representations of different modes according to timestamps, and constructing a paired multi-mode feature set; constructing and training a dimension reduction model, and performing joint dimension reduction mapping on the paired multi-modal feature set; inputting new to-be-analyzed multi-modal neural signal data into the dimension reduction model, and mapping to obtain corresponding low-dimensional feature representation; and performing neural information analysis or application on the low-dimensional feature representation. According to the method, association information between different neural signal modes can be effectively reserved, the data dimension and noise interference are reduced, and the accuracy of subsequent analysis is improved.

The invention discloses a cross-industry data sharing method and system using a trusted data space, and relates to the technical field of data sharing, and the method comprises the steps: obtaining a cross-industry data dimension relation; obtaining a sharing request, and carrying out dimension migration analysis on the sharing request to obtain a dimension migration path set; according to the dimension migration path set, adaptive reorganization operation is executed on the to-be-shared data, and reorganized shared data is obtained; according to the cross-industry data dimension relationship and the dimension migration path set, performing cross-industry confidence evaluation on the recombined shared data to generate a shared decision result; and performing data sharing on the recombined shared data through a secure channel of the trusted data space. The technical problems that existing cross-industry data sharing is difficult, and data resources are wasted are solved.

The invention provides a radar signal classification method and system based on multi-modal feature adaptive fusion. The method comprises the following steps: performing compressed sensing processing on a time-frequency image by using a pre-constructed sparse sampling matrix to generate observation data; inputting the observation data into a multi-branch feature extraction network, and extracting local texture features and global semantic features through the multi-branch feature extraction network; calculating a global information theory feature tensor based on the time-frequency image, and generating a gating weight matrix based on the global information theory feature tensor; and performing adaptive fusion on the local texture features and the global semantic features by using the gating weight matrix to generate adaptive fusion features, and generating a classification result of the radar signals according to the adaptive fusion features. According to the technical scheme provided by the invention, the data dimension is reduced through compressed sensing, the complementary features are extracted by using the multi-branch network, and the precision and robustness of radar signal classification are effectively improved in combination with an adaptive fusion mechanism guided by an information theory.

The invention relates to the technical field of hydraulic support intelligent supporting scheme design, in particular to a hydraulic support intelligent supporting method and system and electronic equipment. The method comprises the following steps: collecting roof pressure, bracket attitude and geological structure parameters in real time through a multi-modal sensor array; performing spatio-temporal feature fusion by utilizing the edge computing nodes to generate a three-dimensional support state map; constructing a dynamic weight adjustment strategy based on a reinforcement learning algorithm, and generating multi-objective optimization support parameters; a control instruction is issued through a dual-mode communication network to realize self-adaptive adjustment of the supporting strength; and a closed-loop feedback verification mechanism is established in combination with the laser radar scanning roof sinkage. And a digital twin model and an energy consumption optimization function are integrated. The problems that a traditional method is single in data dimension, rigid in decision and insufficient in reliability are solved, dynamic optimization of support parameters, active prevention and control of risks and collaborative improvement of energy efficiency are achieved, and the support safety and the intelligent level under complex working conditions are remarkably improved.

The invention discloses a heavy rainfall short and temporary trigger forecasting method based on cooperation of radar echo extrapolation and machine learning, and relates to the technical field of weather forecast, and the method comprises the steps: S1, multi-source data collection: collecting three types of basic data, namely conventional radar echo data, dual-polarization radar data and automatic weather station real-time observation data; according to the method, the dual-polarization radar data and the real-time observation data of the automatic weather station are fused, comprehensive capture of key features formed by heavy rainfall is achieved, the problems that in the prior art, the data dimension is single, and the forecasting basis is insufficient are solved, and the real-time forecasting of heavy rainfall is achieved through the steps of splitting data collection, preprocessing, fusion, extrapolation forecasting and collaborative forecasting. A radar echo extrapolation and machine learning cooperation mechanism is constructed, double improvement of precision and timeliness of heavy rainfall short and temporary trigger forecasting is realized, the problems of large forecasting deviation and poor timeliness caused by single use of a single algorithm or model are solved, and heavy rainfall disaster loss is effectively reduced.

The invention provides a liver and gall disease prediction model construction method, system and device combined with multi-modal data and a medium, and belongs to the technical field of data prediction model construction. Medical data of a patient is collected; extracting the medical data in the medical data set, and evaluating the feature weight of each piece of medical data by using a regression analysis statistical algorithm; a comprehensive feature vector set is generated through fusion, and a preliminary liver and gall disease prediction model is constructed; and deploying the liver and gall disease prediction model to a medical terminal, and performing periodic updating through a cloud. A regression analysis statistical algorithm is utilized to evaluate a feature weight, features, such as key features such as glutamic-pyruvic transaminase and liver ultrasound image texture features, which have important influences on liver and gall disease prediction can be screened out, the data dimension is reduced, the model complexity is reduced, the key feature effect is highlighted, and the interpretability and prediction accuracy of the model are improved.

The invention provides a marine acoustic environment monitoring system based on a noise map. The marine acoustic environment monitoring system comprises a buoy network module, a data fusion processing module, a noise map generation module, a noise source identification module and an edge response module, the buoy network module serves as a sensing tail end, multi-source information is gathered to the data fusion processing module through the first data bus for standardization processing, and then the noise map generation module is driven through the second data bus to construct a three-dimensional sound field model. The output of the model is transmitted to a noise source identification module through a third data bus for intelligent analysis and traceability; the alarm decision of the noise source identification module is output to the edge response module, and the edge response module dynamically regulates and controls the communication and acquisition strategy of each buoy node through a distributed control circuit to form a complete closed loop. According to the invention, high-precision and automatic monitoring of the marine acoustic environment is realized, the problems of limited monitoring range and single data dimension of the traditional means are solved, and the reliability and emergency capability of the system are improved.

The invention belongs to the technical field of information fusion, and discloses a satellite and low-altitude facility data fusion method based on a space-time self-calibration module, a hierarchical feature compression module and a dynamic weight feedback module. GNSS original acquisition data and low-altitude facility data are sent into the space-time self-calibration module, and the space-time self-calibration module automatically constructs a cross-source space-time reference based on feature matching; the layered feature compression module performs bidirectional feature distillation on a feature layer, and compresses data dimensions while reserving complementary information; the dynamic weight feedback module constructs a double-loop feedback mechanism, evaluates data quality in real time and adjusts a fusion strategy, the double-loop feedback mechanism of the dynamic weight feedback module comprises feature weight feedback and confidence feedback, the feature weight feedback dynamically adjusts fusion weight, and the confidence feedback evaluates low-altitude facility data quality; and outputting a fusion result.

The invention relates to the technical field of aviation safety and data processing, in particular to a flight data anomaly detection method based on DEWMA-IForest, and the method comprises the following steps: carrying out the preprocessing of original flight data; initializing a smoothing coefficient and a sliding window size parameter; calculating an arithmetic mean value and a standard deviation by taking data in the sliding window as a statistical sample, and respectively taking the arithmetic mean value and the standard deviation as a DEWMA statistic initial value and a control limit calculation reference value; setting an initial control limit range; calculating the DEWMA statistical magnitude and the standard deviation of the data to be measured; dynamically updating the control limit range; and DEWMA fitting reconstruction data and a control limit are calculated. According to the method, the dynamic control limit is adapted to the time-varying property of the QAR data, so that the sensitivity of small-amplitude trend anomaly is improved; high-dimensional data dimension disasters are avoided through multi-algorithm fusion; and the abnormal criterion can be explained, so that a scientific basis is provided for aircraft operation monitoring and intelligent maintenance decision making.