3148 results about "Stream" patented technology

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention discloses a teaching data management method and system based on artificial intelligence, and the method comprises the steps: obtaining a standardized time series data stream according to a heterogeneous data stream generated by a multi-source teaching platform in real time; based on the standardized time sequence data stream, performing classified encryption on the teaching data through a dynamic hierarchical storage strategy based on attribute-based encryption to obtain a security hierarchical storage topological structure; according to a user query request and a teaching scene label, extracting a target data set from the security hierarchical storage topological structure to obtain an enhanced multi-modal teaching data set; based on the enhanced multi-modal teaching data set, generating an interpretable teaching mode graph through a dynamic sub-graph evolution algorithm; and according to the teaching mode map and the real-time teaching feedback data, generating a personalized teaching recommendation strategy through a course-learner dual-channel adaptive recommendation model. According to the embodiment of the invention, the utilization efficiency of teaching resources can be improved, and personalized and intelligent teaching recommendation and decision can be realized.

The invention relates to a cloud edge cooperative computing framework and processing method for multi-modal data stream fusion processing, and the method comprises the following steps: S1, carrying out the noise suppression based on an original data stream collected by an edge computing node through employing an improved Wiener filtering algorithm, achieving the signal denoising through the adaptive threshold wavelet transformation, and obtaining a cloud edge data stream; and a timestamp alignment technology is utilized to solve the problem of time delay difference of multi-modal data, and a space-time alignment purified data stream is generated. Through combination of the improved Wiener filtering algorithm and the adaptive threshold wavelet transform, the noise suppression efficiency of the original data stream is significantly improved, the timestamp alignment technology effectively solves the time delay difference of the multi-modal data, the generation of the space-time alignment purified data stream ensures that the subsequent processing has a unified time sequence benchmark, and the efficiency of noise suppression of the original data stream is improved. The space-time attention fusion network adopts a collaborative architecture effect of a bidirectional gating circulation unit and a lightweight 3D convolutional network.

The invention relates to the technical field of intelligent fusion terminal communication, and discloses an intelligent fusion terminal multi-protocol communication method and system based on edge computing. According to the method, a protocol adaptive engine is deployed at an edge node, an original data stream of a communication link is collected and analyzed in real time, and a current protocol type is dynamically identified in a fuzzy matching mode. And based on an identification result, the system dynamically loads a corresponding protocol analysis module, generates an adaptive instruction set, and realizes standardized data frame encapsulation through a protocol conversion intermediate layer. And meanwhile, the system monitors the link state, triggers incremental updating of the protocol feature library, and realizes seamless protocol switching. According to the invention, the communication compatibility and reliability are improved, and the requirements of high-reliability scenes such as the industrial Internet of Things are met.

The invention discloses a video stream processing method for dynamic Gaussian compression and adaptive code rate regulation, which is suitable for scenes such as virtual reality, augmented reality and three-dimensional video, and comprises the following steps: S1, Gaussian attribute modeling and initialization; s2, constructing a binary hash grid; s3, constructing a deformation prediction network; s4, designing a mask pruning mechanism; s5, entropy modeling and arithmetic coding and decoding module design; s6, model training; and S7, video stream transmission under multiple code rates. According to the method, a unified scheme combining Gaussian volume cloud coding and adaptive video transmission is proposed for the first time, the video data storage and transmission cost is remarkably reduced, and the comprehensive performance superior to that of an existing method is obtained on multiple real and synthetic data sets.

The invention discloses an Internet of Things operation management scheduling system based on cloud edge collaboration, relates to the technical field of task scheduling optimization, and aims to solve the problem of adaptability of edge computing and cloud edge collaboration architecture, the Internet of Things perception access technology is adapted and fused with heterogeneous equipment through an architecture adaptation module, various IOT data are stably acquired, and the cloud edge collaboration is realized. The task execution position is dynamically adjusted in combination with the task distribution unit; real-time stream calculation and an ETL processing mechanism are combined to fit a comprehensive scheduling index Zdzh, the utilization rate of calculation resources is increased, and task scheduling is optimized; internet of Things equipment data, internet data and government affair data are analyzed based on the data lake and warehouse architecture, a data access strategy is optimized, and the data fusion efficiency is improved; and calculating and evaluating a sudden anomaly prediction coefficient Tycs, triggering an anomaly early warning mechanism or a sudden anomaly emergency mechanism, optimizing task migration, calculating node load balancing and data traceability analysis, so as to improve the stability and intelligent level of an Internet of Things operation management scheduling system.

The invention relates to the technical field of cluster video stream processing, and discloses a video stream real-time coding and decoding transmission method under a cluster. The method comprises the following steps: firstly, acquiring video stream coding parameter text data, link state time sequence data and equipment performance index data of multiple nodes of a target cluster to form a transmission link data set; semantic analysis is carried out on the coding parameter text data to obtain a coding semantic feature vector, dynamic fluctuation features are extracted from the link state time sequence data to obtain a link fluctuation feature vector, and cross-modal fusion is carried out to generate a fusion transmission feature set; generating an abnormal association degree score set by using a pre-trained multi-layer sensing network model, and obtaining an abnormal source node and an equipment defect type by combining root cause tracing according to the abnormal association degree score set; and finally, generating a dynamic optimization strategy and feeding back to the transmission control system to trigger parameter calibration. According to the method, the abnormal root cause can be accurately traced, the transmission parameters are optimized, and the cluster video stream transmission quality is improved.

A system for detecting service interruptions in a cable broadband network using telemetry-based anomaly detection, wherein the system comprises the following: a telemetry acquisition unit configured to acquire multi-parameter telemetry data from heterogeneous broadband infrastructure elements, including cable modems, amplifiers, optical nodes and cable modem termination systems (CMTS), wherein the telemetry data includes the signal-to-noise ratio, modulation error ratio, forward error correction counter, power levels and latency statistics; a preprocessing and harmonization module that is operationally coupled with the telemetry acquisition unit, wherein the module is configured to normalize heterogeneous telemetry streams by adjusting sampling rates, synchronizing timestamps, interpolating missing data, and filtering out false outliers; an anomaly detection unit that is communicatively connected to the preprocessing and harmonization module, wherein the unit comprises a hybrid detection framework with statistical prediction models and machine learning models, wherein the statistical prediction models include ARIMA or Holt-Winters models to predict the expected telemetry behavior and the machine learning models include recurrent neural networks and autoencoders trained on historical telemetry; an ensemble evaluation subsystem within the anomaly detection unit, configured to combine the outputs of the statistical prediction models and the machine learning models to generate anomaly probability evaluations with adaptive confidence intervals; an interruption classification module configured to receive anomaly probability values ​​and correlate anomalies across multiple devices, geographic clusters, and time windows, wherein the interruption classification module differentiates between transient anomalies and service-impairing interruptions based on a multidimensional correlation; and an alerting interface configured to transmit outage alerts with severity, root cause metadata, and geolocation to a network management system so that the operator can intervene.

The invention is suitable for the technical field of data asset management, and provides a data asset management system based on a block chain and big data analysis, and the system comprises a first terminal which carries out the storage of the ownership information of data assets through a block chain network, and generates a storage data package; generating an evaluation result based on a preset technical index analysis model; writing the hash value of the evaluation result into transaction data of the main chain of the block chain, and generating an evidence storage voucher matched with the block chain transaction; an interaction data sequence is packaged for identity signature, encryption and compression to generate a target code stream, and the target code stream is sent to the second terminal; the second terminal decrypts the encrypted data in the target code stream by using a private key, decompresses the data by using a compression algorithm matched with the first terminal, and restores the data into an interactive data sequence; verifying the access authority and the transaction condition of the evidence storage data packet; checking the anchoring state of the evidence storage voucher on the main chain of the block chain; and the data transaction is completed, the data asset state report is generated, and the data element marketization configuration efficiency is improved.

The invention belongs to the technical field of multi-source heterogeneous data processing and intelligent strategy self-adaption, and discloses a real-time data stream classification and security strategy self-adaption system based on an AI model, which comprises the following steps: acquiring a multi-source heterogeneous data stream, injecting five-dimensional semantic tags, complementing an implicit relationship among the tags through association reasoning, and generating a standardized enhanced data stream; the optimal AI model is matched to execute real-time data flow classification, credibility grading is carried out, and a candidate strategy set is generated through matching; matching an association rule through a rule relation graph, and combining system real-time state mirror image execution strategy influence chain rehearsal to generate a rehearsal verification strategy set; cross-domain transactions are packaged according to cross-domain transaction description specifications, and transaction execution states and physical feedback data are collected in real time through four-stage submission protocol execution; life cycle management is implemented through rule efficiency evaluation, and a rule evolution instruction and sample enhancement data are obtained by combining full-link auditing and are fed back to a preorder link to form a closed loop.

The invention discloses an Internet of Things protocol analysis method and device based on multi-modal AI and a medium, and relates to the field of industrial Internet of Things, and the method comprises the steps: collecting a protocol byte stream of industrial equipment and associated multi-modal physical data; the protocol byte stream and the multi-modal data are analyzed through the multi-modal AI, and multi-modal features are extracted and fused; inputting the data into a pre-trained protocol grammar generation model, outputting a protocol grammar tree of a communication protocol corresponding to the industrial equipment, and performing semantic decoding on the protocol grammar tree to obtain structured protocol data; obtaining a multi-modal physical measured value of the industrial equipment, verifying the structured protocol data based on the multi-modal physical measured value, and calculating a comprehensive confidence coefficient; and calibrating the structured protocol data based on the comprehensive confidence coefficient. Various industrial protocol messages are automatically identified and analyzed through the multi-mode AI, protocol parameters do not need to be manually configured, personal errors are reduced, and the data acquisition speed and efficiency are improved.

The invention provides an intelligent laboratory full-process collaborative management system and method based on multi-dimensional data fusion, and the method comprises the steps: obtaining an original data stream from laboratory instrument equipment, carrying out the format recognition and conversion of heterogeneous instrument data through a standard protocol adapter, and generating a first data set in a unified format; aiming at the first data set, executing automatic analysis by adopting data middleware, and integrating into a structured second data set based on a metadata rule of field mapping and unit conversion; according to the second data set, a multi-source data fusion model is constructed, data are classified according to instrument types through batch processing and aggregated according to timestamps, and a fused third data set is generated; aiming at the initial process configuration, adjusting a node sequence and a parameter threshold value by using a visual design tool, and generating an optimized process configuration adaptive to the diversified scene; according to the optimized process configuration, process scheduling is executed through a dynamic process engine, execution time and resource occupation are monitored in real time, and a process execution log is generated.

The invention relates to the technical field of intelligent manufacturing decisions, in particular to a digital twinning-based manufacturing system decision analysis method and system, and the method comprises the steps: deploying a plurality of Internet of Things sensors on a physical manufacturing system, and collecting a physical real-time data stream of equipment in real time; the method comprises the following steps: establishing a virtual data acquisition channel aligned with a physical manufacturing system clock, injecting a physical real-time data stream into a digital twinning creation model, and performing data preprocessing based on distributed edge calculation to delay and compress original data acquisition of the physical real-time data stream to 10ms level, the time sequence database and the NTP / GPS clock are synchronized to ensure the state alignment error lt of the physical-virtual system; compared with the prior art, the deep space-time prediction network is fused with a CNN-LSTM-attention mechanism, the accuracy of multivariable coupled KPI prediction is improved, in addition, model failure is recognized in real time through Page-Hinkley inspection, and a prediction error reaches a relatively stable state through an adaptive retraining mechanism.

The invention relates to the technical field of computer video processing, and discloses a low-delay video stream real-time processing method and device, and the method comprises the steps: obtaining original video stream data, and processing the original video stream data through employing a lightweight motion prediction method; processing the macro block data set and the predicted coding configuration parameter by adopting multi-thread assembly line coding to obtain a coded data block; establishing a data transmission mechanism to perform data flow control on the unified memory access interface; a heterogeneous task scheduling strategy is adopted to distribute task division results; a lightweight neural network is adopted to carry out parameter adaptive adjustment, and an optimized video stream processing result is obtained; according to the method, a zero-copy data transmission technology is adopted, and optimal configuration and efficient utilization of computing resources are achieved.

The invention relates to the technical field of geographic space intelligence, and discloses a territorial space purpose control intelligent analysis system, which comprises the following modules: a multi-source data acquisition module, which is based on satellite remote sensing and an IoT sensor, plans vector data, adopts a spatio-temporal data fusion algorithm, and integrates territorial, ecological and economic field heterogeneous data through a distributed crawler technology; generating a territorial space total element data set; the multi-source data acquisition module comprises a remote sensing acquisition sub-module, an Internet of Things access sub-module and a planning data analysis sub-module. Through a distributed data crawling and real-time stream fusion technology and spatio-temporal data modeling, rapid integration of multi-source heterogeneous information is realized, low-efficiency delay of traditional manual acquisition is eliminated, high-precision deformation monitoring and land use change identification are synchronously completed, the violation behavior discovery timeliness is remarkably improved, and the method is suitable for large-scale popularization and application. The three-dimensional space analysis algorithm accurately quantifies the above-ground and underground space element interaction relation, and the engineering conflict risk is effectively avoided.

The invention relates to a high-precision data acquisition method and system for an MEMS sensor, and the method comprises the following steps: carrying out the multi-channel parallel sampling of an initial electric signal outputted by the MEMS sensor, and obtaining an original data flow matrix; performing adaptive denoising processing on the original data stream matrix to obtain a denoised data set; performing signal phase reconstruction on the noise reduction data set through a phase unwrapping technology to obtain a continuous phase feature sequence; performing dynamic drift compensation on the continuous phase feature sequence to obtain a calibration signal vector; performing transient characteristic extraction on the calibration signal vector to obtain a sensor response characteristic curve; and performing high-precision data resampling on the sensor response characteristic curve based on an adaptive quantization coding technology to obtain a high-precision data acquisition result, thereby solving the technical problem of how to effectively remove noise interference generated in a multi-channel parallel sampling process.

The invention discloses a multidirectional frame audio stream transmission method, device, equipment and medium, and the method is realized through cooperation of a transmitting end and a receiving end: the transmitting end cuts an original audio stream into independent audio frames, gives priority identifiers to the independent audio frames, and determines redundant coding parameters and transmission paths for different priority frames in combination with a predefined static strategy; generating a data packet containing an original data block and a redundant data block, and sending the data packet through at least one network path; and a receiving end caches the multi-path data packet, recovers lost data by using redundant data blocks to recombine a complete audio frame, and executes error concealment processing on the frame which cannot be recombined to generate a replacement frame. According to the method, based on a multi-path parallel transmission, forward error correction (FEC) redundancy mechanism and a cost-aware static scheduling strategy, lossless forwarding and instantaneous recovery of audio streams are realized on the premise of not waiting for network feedback and avoiding inter-frame dependence, and high-quality real-time audio transmission service can still be provided in a complex network environment.

The invention provides a mass data association fusion method and device and a medium, and the method comprises the steps: firstly receiving mass data from a plurality of heterogeneous data sources, and carrying out the preprocessing of the data; then, the data stream is integrated into an annotated data stream; wherein the labeled data stream comprises a two-dimensional semantic vector and a dynamic quality label; and finally, inputting the data into an AI association fusion model to obtain a fusion result. Wherein the association fusion model is provided with an association fusion and semantic mapping layer, and is used for carrying out association fusion according to the layered dynamic knowledge graph and a preset fusion rule, and carrying out semantic mapping according to the twin-tower sub-model and a consistency judgment rule. According to the method, real-time updating of the incidence relation is achieved through the layered dynamic knowledge graph, and the real-time scene requirement is met; the association complexity of mass data is reduced by using a double-tower model, and the processing speed is increased; and in combination with the judgment rule, the conflict resolution precision is improved. Finally, efficient, accurate and real-time fusion of mass data is realized, and a reliable basis is provided for analysis and decision of various data.

The invention relates to the technical field of intelligent monitoring, in particular to a multi-thread low-power-consumption intelligent monitoring system based on an AI processor. The method has the advantages that aiming at the problems of unbalanced computing power and power consumption, high multi-task processing delay and strong hardware dependence in the prior art, the NPU module of the RK3588 processor is combined with the INT8 quantitative model, so that the power consumption is lower than 10W under the 6TOPS computing power; a dynamic multi-thread scheduling mechanism is designed, parallel processing of more than eight paths of video streams is supported through binding of a priority queue and an NPU core, and end-to-end delay is compressed to be within 200 ms; a zero-copy video stream architecture is constructed, data transfer is eliminated through memory mapping, and preprocessing time consumption is reduced by 90%; an energy efficiency control module is integrated, the NPU voltage frequency is dynamically adjusted according to the load, and the energy efficiency ratio reaches 0.83 TOPS / W; space-time alignment of multi-model reasoning results is realized by adopting a frame ID synchronization technology, and the mismatching rate is lower than 0.1%.

The invention relates to the technical field of biomedical engineering and computer vision, in particular to a non-contact physiological signal extraction method and system based on frequency self-adaption and illumination noise perception.The method comprises the following steps of multi-mode video stream collection and spatio-temporal data preprocessing, illumination-noise perception mask generation and feature filtering, multi-mode video stream collection and spatio-temporal data preprocessing, illumination-noise perception mask generation and feature filtering, and non-contact physiological signal extraction. Frequency adaptive gating and frequency domain feature enhancement, depth time attention feature re-calibration, physiological signal regression and closed loop optimization; the method has the beneficial effects that a lightweight end-to-end deep learning network architecture is constructed by systematically fusing three core modules of illumination-noise perception mask, frequency adaptive gating and depth time attention, and the defects that a traditional physical model depends on artificial prior and is poor in anti-interference performance and high in reliability are overcome. And the one-sidedness caused by high calculation complexity and difficulty in distinguishing the signal and noise of the existing deep learning model is avoided, and the weak physiological signal can be recovered from the face video more accurately and robustly.

The invention belongs to the technical field of computers, particularly relates to a digital twin power plant infrastructure multi-source heterogeneous data real-time fusion method, and aims to solve the problems of high data fusion delay, semantic segmentation and poor system adaptability in the prior art. The method comprises the following steps: constructing a unified space-time reference frame to realize nanosecond-level time synchronization and space coordinate normalization; the method comprises the following steps: accessing and preprocessing multi-source data such as a building information model, an Internet of Things sensor, a construction log and a video stream, and generating a standardization unit with space-time metadata; performing semantic analysis and cross-modal feature alignment based on the power plant infrastructure ontology knowledge base; and millisecond-level dynamic fusion is realized by adopting an event-triggered streaming engine. According to the scheme, real-time fusion within 100 milliseconds is realized, the semantic alignment precision is 98% or above, the state confidence is 90% or above, the system throughput is improved by three times by relying on a cloud edge collaborative architecture, and precise twin mapping and intelligent decision making of the whole process of power plant infrastructure construction are comprehensively supported.

The invention discloses a data storage method for an artificial intelligence learning mode, and relates to the technical field of computer data storage, and the method comprises the steps: 1, merging a multi-source perception stream into blocks in real time at the edge through monotone serial number writing, so as to provide a replayable time sequence; 2, asynchronous erasure coding is executed on the blocks, Merkel roots are calculated and written into a local cache, and dual guarantee of loss tolerance and integrity is achieved; 3, pushing slices and roots to object storage in sequence according to a network, and calling a time travel interface to solidify an incremental snapshot; 4, the cloud end monitors a snapshot hash event, a serial number chain is written through differential scanning, a gap is reconstructed through slices, an index is refreshed, and continuous consistency is kept; 5, the training process generates a Merkel proof online verification sample, and damaged data are immediately interpolated and repaired and an audit chain is recorded; and step 6, after training is finished, generating a leatherwise list and a frozen root, asynchronously cleaning redundant slices, updating a version table, and finally forming single-fingerprint traceable cost archiving.

The invention relates to the technical field of data processing, in particular to a multi-source heterogeneous data flow real-time fusion analysis system which comprises a data source interface unit, a streaming semantic alignment unit, a multi-layer fusion calculation unit, a lightweight knowledge evolution unit and a dynamic resource scheduling unit. The data source interface unit generates stream feature fingerprints and analysis rules through a protocol semantic self-recognition engine, non-preset interface data is analyzed in a zero configuration mode, the stream semantic alignment unit constructs three-dimensional semantic anchor points, multi-modal data soft synchronization is achieved by means of a dynamic alignment matrix, and multi-modal data soft synchronization is achieved. The multi-layer fusion calculation unit projects heterogeneous features and arbitrates conflict data through a domain-hierarchical architecture, the lightweight knowledge evolution unit incrementally updates a knowledge graph and performs closed-loop feedback, the dynamic resource scheduling unit guarantees real-time performance, data is transmitted through cross-domain channel encryption, and the real-time performance and accuracy of multi-source heterogeneous data stream fusion analysis are improved.

A system and method for adaptive data processing combining compression and encryption. The system analyzes input data characteristics, compares probability distributions, and creates a transformation matrix to convert data into a dyadic distribution. It generates a main data stream of transformed data and a secondary stream of transformation information. The system dynamically selects and applies processing techniques, including transformation, encoding, compression, and encryption algorithms, based on analyzed characteristics and real-time performance metrics. It compresses the main data stream using Huffman coding and implements security measures to protect the output. A feedback loop monitors technique effectiveness, updates a knowledge base, and influences future selections. The system can operate in lossless, lossy, or modified lossless modes, adapting to different application requirements. This approach offers an efficient solution for scenarios where both data reduction and security are critical concerns.

The invention discloses an access control equipment data management method and system based on multi-source fusion. The method comprises the following steps: collecting a multi-source access control data stream in real time; based on a preset feature extraction rule set, extracting a multi-modal biological feature vector, a voucher legality identifier, an abnormal behavior probability value and an equipment health degree index, and inputting the multi-modal biological feature vector, the voucher legality identifier, the abnormal behavior probability value and the equipment health degree index into a dynamic security assessment matrix generation model to generate a real-time security assessment matrix; matching the dimension safety score of the real-time safety evaluation matrix with a preset threshold strategy library, and dynamically generating an access control strategy instruction set; and issuing the access control strategy instruction set to the target access control equipment execution terminal. The method has the following advantages and effects: the fault tolerance bottleneck of a single-dimensional decision chain is broken through, and the system misjudgment rate is reduced by at least one order of magnitude on the premise of ensuring the security by establishing a dynamic coupling mechanism of the multi-source data stream.

The invention discloses an offshore oil and gas resource efficient evaluation method based on a well-free / few-well condition, and the method comprises the steps: S10, carrying out the collection of seabed multi-field coupling data, and obtaining an original data flow; s20, performing multi-source data intelligent fusion processing: performing data space-time alignment, performing electromagnetic seismic joint inversion by using a joint objective function, performing leakage path analysis, and extracting reservoir physical property parameters and a leakage network; s30, dynamic geological knowledge graph construction: constructing a graph neural network and establishing a cross-regional reservoir parameter prediction model through transfer learning to obtain a dynamic geological model and transfer learning parameters; s40, virtual well intelligent generation: guiding virtual well generation to obtain a virtual well data set; and S50, reservoir modeling evaluation: training reservoir modeling together with the virtual well data and the real seismic attributes to obtain resource probability distribution and sweet spot division. According to the method, the dependence on dense drilling is reduced, the defect of multiplicity of solutions of geophysical data is overcome, and the technical bottleneck of modeling under the condition of less wells / no wells is broken through.

The invention provides an enterprise security management method and system based on AI, and relates to the technical field of security management, and the method comprises the steps: configuring a storage path and authority of a data storage unit, and initializing operation parameters of a calculation processing unit; acquiring images and / or video streams of the enterprise security control key area in real time; the real-time data is preprocessed; analyzing the preprocessed image and / or video stream by using the trained AI model, and identifying a preset target and action features thereof in enterprise production operation; comparing the recognized preset target and the action characteristics thereof with a preset early warning rule, and when the deviation exceeds a dynamic deviation threshold value, judging that the action is a wrong action, generating an early warning event and triggering an alarm mechanism; according to the method, data related to an early warning event is stored and examined, an optimization model and rules are fed back through an examination result, a security risk trend is analyzed, improvement suggestions are generated, and intelligentization, precision and high efficiency of enterprise security management are realized.

The invention provides a data security risk early warning method and system based on big data analysis, and the method comprises the steps: firstly constructing a data security risk feature map, collecting a heterogeneous data set in a mobile communication network in real time through a multi-source data access interface, generating a multi-source heterogeneous data fusion stream through distributed cleaning and standardization processing, and carrying out the data security risk early warning. Then inputting the data security risk feature into a preset distributed risk feature learning network, performing feature mapping and association enhancement processing based on a data security risk feature map to obtain a real-time risk feature vector, performing big data association analysis on the real-time risk feature vector, mining a risk feature conduction dependency relationship, generating a risk propagation path weight set, and performing big data association analysis on the real-time risk feature vector; and finally, determining a risk diffusion level and a key influence node, generating a security risk early warning instruction containing a risk diffusion path identifier, and pushing the security risk early warning instruction to a mobile communication security management platform, thereby realizing accurate early warning and quick response of the data security risk, and ensuring safe and stable operation of a mobile communication network.

The invention relates to the field of intelligent transportation, and discloses a handling equipment management method for intelligent transportation, which comprises the following steps: acquiring an original data stream of handling equipment according to an Internet of Things sensor; performing space-time alignment calibration on the original data stream to obtain a structured operation state data set with equipment space-time relevance; inputting the structured operation state data set into a space-time convolutional network for feature learning; according to the method, the original data stream of the carrying equipment is collected through the Internet of Things sensor, the state data of the equipment can be obtained in real time in combination with the space-time alignment calibration technology, and the space-time relevance of the data is ensured. The accurate real-time monitoring provides a reliable data basis for subsequent decision making, the scheduling problem caused by data delay or inconsistency in a traditional method is avoided, the structural data are input into the space-time convolutional network for feature learning, the operation features of the equipment can be effectively extracted, and the method is suitable for large-scale popularization and application. And accurate input is provided for subsequent dynamic path planning.

The invention provides a marine target real-time positioning and tracking system and method based on multi-source data fusion, and belongs to the field of target positioning and tracking. The sensor portion collects raw data. The data acquisition and time synchronization module is responsible for receiving and aligning data of each sensor; the multi-stage coordinate system registration and transformation module unifies all observation data to a world coordinate system by using IMU data and preset calibration parameters; the AI visual target detection module processes the video stream to identify a target; the adaptive federated Kalman filtering fusion module receives each path of processed data and outputs optimal target state estimation; and the dynamic task and resource scheduling module performs optimal allocation on system resources according to the current tracking state to form closed-loop control. Through deep fusion of high-precision RTK positioning data, laser ranging data, photoelectric pod attitude information and an AI vision algorithm, the positioning precision, tracking robustness and system real-time performance of a sea target under a complex dynamic sea condition are improved.