2118 results about "Stream" patented technology

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 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 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 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 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.

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.

The invention relates to the technical field of audio and video emergency transmission, and discloses an audio and video low-delay return method and system in an extreme environment. The method comprises the following steps: acquiring original multi-modal data of audio and video acquisition equipment in a target area, and analyzing a data state of the original multi-modal data; and meanwhile, available network transmission links are monitored, and the quality is evaluated. Self-adaptive coding parameters are generated in combination with the link quality and the data state, dynamic coding is executed on original data, and a coding stream suitable for redundant transmission is formed. And carrying out cooperative distribution transmission based on the network link set and the coded stream to generate return data. According to the generation process, a device and energy management instruction is formed and issued to the acquisition and relay device. According to the method, the network state and the content characteristics are optimized in a dynamic coding link in a collaborative manner, and the audio and video quality, the real-time performance and the system energy efficiency which are transmitted back in an extreme environment are improved through a feedback closed loop for transmitting a result to equipment management.

The invention relates to the technical field of enterprise data management, in particular to a lightweight-based enterprise data dynamic integrated management system, which is characterized in that an acquisition module is used for deploying edge computing nodes, receiving multi-source heterogeneous information streams from manufacturing execution systems and equipment logs, dynamically analyzing and standardizing the information streams, and adding metadata tags; uploading is carried out in a batch processing mode; the map construction module is used for constructing a semiconductor blood relationship map by taking the standardized key information as a blood relationship clue; a graph database is used for efficient storage, and a RESTful API interface is configured to support batch import, so that data storage and relevance expression are more flexible and efficient; the prediction module performs reasoning on the atlas by adopting a graph neural network to generate predictive risk distribution, and a correlation analysis set generated by the prediction module is stored back to the atlas in a structured manner; by introducing a multi-thread concurrent write-in and lock mechanism, the atlas supports complex combination query based on a Cypher query language, and supports multi-level and traceability query.

The invention provides a semi-aviation transient electromagnetic acquisition device and method for calibrating data drift in real time, and relates to the technical field of data calibration, and the device comprises an electromagnetic data acquisition unit which receives a secondary induction signal generated by a ground transmitting end through a transient electromagnetic hollow induction coil disposed in a coil storage cabin of an unmanned aerial vehicle, and transmits the secondary induction signal to an unmanned aerial vehicle; self-adaptive gain control is carried out according to the signal intensity to obtain real-time electromagnetic data; the flight control unit monitors the swing amplitude and frequency of the coil in real time according to the inclination angle and acceleration data fed back by the sensor in the coil storage cabin, and carries out flight attitude adjustment and control; the drift calibration unit adopts a static reference mode for calibration at the beginning stage of flight; and switching to a dynamic reference mode in the flight process, and carrying out self-adaptive calibration on the real-time data stream based on a sliding window. According to the invention, drift calibration of traditional software post-processing is migrated to a hardware acquisition stage, so that synchronous completion of data acquisition and drift compensation is realized.

A correlation-aware adaptive codebook compaction system for multi-modal data compression that preserves cross-modal relationships while providing enhanced reconstruction quality. The system analyzes temporal and spatial relationships between different data modalities to generate correlation maps that guide compression decisions. A virtual management layer performs stream characterization and adaptive routing, while a processing pipeline implements primary codebook compression with mismatch handling for novel data blocks. High-entropy data segments receive pre-compression processing before codebook compression. Sequential registration data is processed through matrix factorization and dedicated matrix codebooks. The system continuously monitors data distribution characteristics and automatically retrains codebooks when drift thresholds are exceeded. A neural upsampling subsystem uses correlation information to guide cross-modal enhancement processes through modality-specific networks and attention mechanisms. The unified output includes compressed data streams, correlation maps, synchronization metadata, neural model parameters, and updated codebooks, enabling synchronized reconstruction with preserved cross-modal relationships and enhanced quality through correlation-guided neural upsampling.

A computer system for compacting video data. The system acquires a video stream, reduces redundancy through pre-processing, and analyzes the stream to identify patterns and irregularities. It detects spatial or temporal anomalies in the video and produces three outputs: a conditioned video stream based on statistical analysis, an error stream reflecting adjustments made during conditioning, and an anomaly meta-stream containing metadata about detected anomalies. The system communicates with one or more remote systems to synchronize and negotiate a compatible compression codebook, optionally exchanging compact updates that represent differences between local and remote codebooks. The conditioned video stream is then compressed using the agreed codebook. The system outputs a compacted representation of the video that includes the compressed stream, the error stream, and the anomaly metadata, supporting efficient storage or transmission while maintaining the ability to detect, trace, and reconstruct key information within the video.

The invention discloses a business data quality treatment method supporting system hidden danger identification, and relates to the technical field of computer information processing, and the method comprises the steps: S1, collecting a real-time batch tracking data stream from an enterprise inventory system through a multi-source data interface, and obtaining an initial data set containing inconsistent formats and missing batch numbers; s2, scanning a data stream according to the initial data set by applying a preset automatic detection rule, judging positions and types of abnormal points by utilizing a rule matching mode and a threshold comparison mechanism to obtain an abnormal point set, and activating a real-time stream analyzer to process field complete verification to generate an abnormal log record; according to the business data quality treatment method supporting system hidden danger identification, real-time and accurate anomaly detection and repair are realized, the consistency and reliability of inventory data are improved, and the enterprise inventory management efficiency is remarkably optimized.

The invention discloses a method for aggregating ocean buoy data, which comprises the following steps of: establishing a space-time correlation model according to the spatial distribution density of buoy clusters and the dynamic characteristics of an ocean environment, and adaptively dividing buoy groups with a data compression cooperative relationship; performing a data-related compression operation within the buoy group; constructing a transmission priority dynamic sorting engine, and calculating a dynamic weight coefficient; performing hierarchical packaging processing on the differential coding data, distributing enhanced forward error correction coding resources for high-priority data, and reserving redundant transmission time slots; the method comprises the following steps: receiving a hierarchical data packet through a low-orbit satellite and executing on-satellite preprocessing, reconstructing an original data stream by using a distributed decoder, and meanwhile, fusing multi-dimensional space-time related data to generate an aggregated data product; and finally, the aggregated data product is transmitted to a ground data center through a satellite downlink. According to the invention, uplink data volume is reduced, disaster monitoring timeliness is improved, ground station processing load is reduced, and system environment adaptability is enhanced.

The invention discloses an artificial intelligence and Internet of Things smart park integrated management method and system, and belongs to the technical field of smart park management, and the method comprises the following steps: S1, collecting multi-source operation data of Internet of Things smart park equipment in real time, and generating a multi-source data stream; s2, generating a multi-dimensional feature set based on the multi-source data stream; s3, analyzing a behavior mode of an administrator of the Internet of Things smart park based on the historical operation record, and generating an administrator behavior portrait and an event priority evaluation result; s4, dynamically constructing a visual management interface according to an event priority evaluation result, and generating a one-key operation instruction set; and S5, collecting operation response data of the administrator on the visual management interface, and updating the administrator behavior portrait. The system comprises a data acquisition module, a feature analysis module, a priority evaluation module, a generation module and a feedback module. According to the method, the complex state of the park is comprehensively captured through the steps S1 to S5, and the problem of information fragmentation is solved.

The invention provides an environment supervision method and system based on an edge cloud architecture. The method comprises an edge end device and a cloud platform, wherein the edge end device is arranged on an environment supervision site; the execution steps of the side end device comprise: collecting a pollution source multi-modal data stream; according to the pollution source multi-mode data flow, triggering an alarm through a grading abnormity identification model; in response to the alarm, generating an evidence chain with a timestamp; extracting a monitoring deviation feature D, a behavior anomaly feature B and a semantic feature S according to the evidence chain; compressing the monitoring deviation feature D, the behavior anomaly feature B and the semantic feature S to generate a feature packet Z; and uploading the feature packet Z to a cloud platform. The cloud data matching performance can be effectively improved, and the false alarm rate is reduced.

The invention provides a ship-based unified storage and management design data method, which comprises the following steps that: an operation end generates and sends an editing request with a node identifier, an editing type, a timestamp and a digital signature, and a WEB front end receives and verifies the legality of the request through an encryption channel; the storage service generates an intention lock based on the permission table and version number matching, and concurrent access control is achieved. Then, the WEB front end carries out content definition partitioning (CDC) and Hash fingerprint calculation on a design file stream, the storage service realizes partitioning duplicate removal and incremental storage according to a fingerprint matching result, and a Merkle tree is utilized to construct a file version integrity Hash root; on this basis, the system analyzes model differences, constructs a semantic difference chart, and calls a rule engine to perform automatic pre-check. According to the method, efficient storage, collaborative editing, security and credibility and hierarchical scheduling management of ship design data are realized, and the method has the technical advantages of high data consistency, high resource utilization rate, traceability auditing and the like.

The invention relates to the technical field of digital twinning, physical modeling and multi-source data fusion, and provides a digital twinning dynamic construction method based on multi-source data fusion and physical simulation. The method comprises the following steps: acquiring a multi-source heterogeneous data stream from a preset sensor array, a numerical simulation result and a historical database, identifying a key feature mode of a dominant physical process in the multi-source heterogeneous data stream, acquiring a key feature mode time-varying physical field evolution rule corresponding to the key feature mode by using a time sliding window and a forgetting mechanism, extracting a low-dimensional sparse characteristic parameter set reflecting dynamic behaviors from a high-dimensional observation space, and constructing a reduced-order proxy model by adopting Gaussian process regression, a neural network proxy model or an intrinsic orthogonal decomposition combined interpolation technology; and receiving a corresponding real-time observation data stream to establish a full-closed-loop feedback link from model prediction, high-fidelity solution verification to observation data correction in combination with the reduced-order proxy model so as to complete the construction of the digital twin.

The invention discloses an energy corridor forest fire risk prediction method and system based on multi-source data fusion, and the method comprises the steps: constructing a fusion data set, and determining risk evolution parameters; if the risk evolution parameter exceeds a preset threshold value, triggering a vegetation difference analysis function to obtain a section heterogeneity label; after section heterogeneity labels are obtained, time-space non-uniformity compensation correction is conducted on historical data through a time sequence change tracking method, the risk transition probability of each section is calculated, and potential critical points are judged; if the potential critical point is judged to be high in probability, activating a difference early warning mechanism, generating a targeted risk level map according to a section heterogeneity label and a risk transition probability, and obtaining a dynamic early warning signal; and carrying out iterative verification on the dynamic early warning signal by adopting a real-time update flow in the fusion data set, adjusting a risk level in combination with prediction output of the long and short-term memory network, and determining a final risk prediction result. According to the invention, the timeliness and accuracy of fire risk prediction are improved.

An all-binary neural network system and method for processing and analyzing multi-source time series data is disclosed. The system employs a shared codebook to encode input streams into binary codewords, which are then processed through a series of binary convolutional layers, binary LSTM layers, and binary fully connected layers. The system maintains binary representations throughout, enabling efficient computation and reduced memory requirements while effectively capturing temporal and inter-source relationships in the data.

The invention relates to a three-dimensional reconstruction-oriented Gaussian point cloud lossless coding and decoding method, a product and a storage medium. The method comprises the following steps: extracting a first Gaussian point cloud corresponding to anhor data in a numpy array format at a coding side; converting the first Gaussian point cloud in the numpy array format into a first Gaussian point cloud in a ply format; meanwhile, through a Morton code-based spatial sorting method, performing Morton code sorting on geometric data and attribute data of the first Gaussian point cloud in the numpy array format, and generating a Morton sequence index table; and carrying out AVS coding on the first Gaussian point cloud in the ply format to obtain compressed data in a binary code stream form. On the decoding side, AVS decoding is carried out when the compressed data in the binary code stream form and the Morton sequence index table are received, and a second Gaussian point cloud in the ply format is obtained through reduction; performing format conversion on the second Gaussian point cloud in the ply format to obtain a second Gaussian point cloud in a numpy array format; and rearranging the attribute data and the geometric data based on the Morton sequence index table to realize one-to-one correspondence of the geometric data and the attribute data between the first Gaussian point cloud and the second Gaussian point cloud before and after coding and decoding. Compared with an existing method, the data consistency before and after coding is improved.

The invention discloses a multi-dimensional time sequence data compression and rapid retrieval method and system, and relates to the technical field of large data compression retrieval. The multi-dimensional time sequence data compression and rapid retrieval method comprises the following steps: S1, collecting and preprocessing multi-source data in a monitoring abstract video stream, and constructing a standardized time sequence behavior data set; s2, analyzing behavior characteristics of frame segments in the sliding window, and dynamically adjusting anchor point labeling and compression strategies; s3, evaluating the coverage integrity of anchor point information in a compression section, and driving generation of an index path; s4, comprehensively evaluating the path behavior association strength and dynamically adjusting a loading decision; and S5, verifying the matching integrity of the behavior anchor point field and the jump pointer, and guaranteeing the localizability and jump stability of the event in the compression structure. The problems that in an intelligent monitoring scheme, video abstract compression is not bound with behavior semantics, a fine-grained positioning mechanism based on behavior labels is lacked, and key action segments cannot be directly positioned during user playback are solved.

The invention discloses a practical training evaluation method and system based on multi-modal data fusion. The method comprises the following steps: firstly, constructing a practical training task topology based on a knowledge graph, mapping elements such as knowledge points into nodes, and defining a relationship through directed edges; in response to task starting, dynamically deploying a multi-modal data fusion agent pre-loaded with a targeted model and a rule; in the operation process, the agent synchronously collects and processes original data from the machine vision and sensor network in real time, and a standardized feature flow is generated; then, on-line space-time correlation and reasoning are carried out on the multi-modal features based on a fusion strategy, and a task scene understanding model is dynamically constructed and maintained; after the task is completed, packaging a program code, a report and process abstract data exported by the model to form an enhanced practical training result package; and finally, multi-dimensional automatic comparison is carried out by calling the rule base and the case model, and an evaluation result is generated. According to the invention, intelligent perception and comprehensive evaluation of the whole practical training operation process are realized.

The invention belongs to the technical field of wireless communication, and particularly relates to a communication method and device based on MQAM-OFDM modulation, a medium and equipment, and the method comprises the steps: generating a random data source, carrying out MQAM modulation, generating a serial modulation signal, carrying out serial-parallel conversion and subcarrier mapping, and obtaining frequency domain subcarrier data; performing inverse fast Fourier transform on the frequency domain subcarrier data to generate a time domain OFDM symbol, and adding a cyclic prefix to obtain a sending signal; transmitting the sending signal based on a multipath channel model; receiving a sending signal and removing a cyclic prefix part in the sending signal to obtain an effective OFDM signal; performing fast Fourier transform on the effective OFDM signal to recover frequency domain data, and obtaining a frequency domain receiving symbol sequence; performing channel estimation and equalization on the frequency domain receiving symbol sequence to obtain a frequency domain symbol estimation value; and performing MQAM demodulation on the frequency domain symbol estimation value, and recovering the parallel data stream into the serial bit stream to complete data recovery.

The invention provides an Internet server anomaly detection method and system based on multi-modal data fusion, and relates to the technical field of data processing, and the method comprises the steps: combining a fused multi-modal feature with a time sequence feature, and classifying the combined features to obtain a preliminary anomaly detection result; on the basis of the preliminary anomaly detection result, calculating inter-class distinction degree weights of session stream fusion features, and evaluating a quality coefficient after multi-modal feature fusion and a consistency coefficient of time sequence features respectively to obtain a dynamic adjustment factor; performing weighted fusion on the dynamic adjustment factor and the initial anomaly detection result to generate a final anomaly detection result; and generating a risk assessment level based on the final anomaly detection result in combination with the protocol type of the traffic and the activeness context information of the target port. According to the invention, the comprehensiveness and accuracy of anomaly detection are improved.

The invention provides a non-deep traffic analysis streaming matching search analysis method, which comprises the following steps of: constructing a deep data packet detection architecture on the basis of a data platform development kit (DPDK); aiming at the non-encrypted traffic, establishing a multi-mode recognition mechanism combining a regular expression and a feature bit stream mode; aiming at the encrypted traffic, constructing an encrypted traffic feature library according to the statistical features, the protocol features and the behavior features; real-time risk detection of network traffic is realized by adopting a streaming matching search technology; and constructing an intelligent decision and response mechanism, and integrating flow analysis and identification results. According to the non-deep traffic analysis streaming matching search analysis method provided by the invention, real-time analysis, risk identification and supervision of network non-encrypted traffic and encrypted traffic are realized by taking a data platform development kit (DPDK) as a basis and combining a high-performance streaming regular expression engine and a finite-state machine principle; the method can be widely applied to scenes of network communication supervision, data security protection, malicious traffic monitoring and the like.

The invention relates to the technical field of embedded data storage, and discloses a data storage method and system for an embedded storage chip. The method comprises the following steps: acquiring a to-be-stored original data stream through a data acquisition interface and performing segmentation marking; performing hierarchical decomposition on the data segments by adopting a dynamic decomposition algorithm to generate a plurality of data components; calculating an information density index of each data component, and screening key data components and non-key data components according to the information density index; key data components are stored in a high-speed cache region, and non-key data components are stored in a conventional storage region; performing redundancy check on the key data components in the cache region and generating a check result; after correcting the key data component based on the verification result, recombining the key data component with the non-key data component to generate an optimized data stream; writing the optimized data stream into a physical storage medium and recording a storage position; and monitoring the storage load state, and dynamically adjusting the distribution proportion of the two storage areas.