87 results about "Data stream analysis" patented technology

The invention discloses a fully homomorphic encryption application-oriented ciphertext level parallelization method, which comprises the following steps of: for fully homomorphic encryption FHE ciphertext vectors corresponding to ciphertexts, specifying initial cavitation bubble positions in the FHE vectors corresponding to input ciphertexts and output ciphertexts by utilizing a specific field computer language; analyzing and tracking the dynamically changing cavitation bubble position corresponding to each ciphertext by using the data stream, and confirming the final cavitation bubble position of the ciphertext; the operation sequence of the ciphertext is fused to the maximum extent by adopting a global fusion strategy, a plurality of ciphertext vectors are packaged into an integral FHE vector by utilizing cavitation bubble positions in the FHE ciphertext vectors, and the maximum profitability of ciphertext level parallelism is obtained. The invention provides an accurate analysis method, and the cavitation positions of all ciphertexts are automatically analyzed to ensure the legality of ciphertext level parallelism and the profitability of ciphertext level parallelism.

The invention relates to the technical field of data center remote operation, and discloses a data center remote operation method and system based on collection gateway analysis, and the method comprises the steps: obtaining gateway resource data and data flow priority attributes; quantifying according to the resource data to obtain a cache length; when the cache length is greater than a length threshold value, processing by adopting a dynamic load balancing algorithm to obtain a migration data stream; analyzing according to the data stream to obtain a timestamp and a serial number; measuring according to the timestamp and the serial number to obtain transmission delay and packet loss rate; performing evaluation according to the transmission time delay and the packet loss rate, and performing link dynamic switching to obtain a backup transmission path when an evaluation result is a main link fault; according to the backup transmission path, adopting a distributed routing protocol to calculate and obtain a data transmission path; monitoring according to the data transmission path to obtain monitoring data; predicting according to the monitoring data to obtain an adjustment result; and performing optimization according to an adjustment result, and dynamically switching the main link and the standby link to perform data transmission. The method can solve the problem of low overall data transmission efficiency.

The invention relates to the technical field of artificial intelligence operation and maintenance, in particular to an AIOps multi-task scheduling method and system cooperatively driven by large and small models. The method comprises the steps of obtaining a heterogeneous operation and maintenance time sequence data set, performing data consistency calibration on the heterogeneous operation and maintenance time sequence data set, and determining a multi-source standardized operation and maintenance data stream; analyzing the multi-source standardized operation and maintenance data flow, determining operation and maintenance index dynamic causal information, and determining optimized target event information according to the operation and maintenance index dynamic causal information; determining a real-time operation and maintenance task chain according to the optimized target event information, and determining a large and small model collaborative scheduling strategy according to the real-time operation and maintenance task chain; and performing real-time operation and maintenance adjustment on the system state according to the large and small model collaborative scheduling strategy, and determining and outputting a collaborative operation and maintenance report. According to the method, the problem of over-optimization traps existing in the operation and maintenance process by using the AIOps technology is effectively solved, and the operation and maintenance process is highly matched with the high dynamic response requirement of the system.

The invention relates to the technical field of the Internet of Things, and discloses a security assessment method and system based on the Internet of Vehicles, and the method comprises the steps: obtaining multi-dimensional original data, context data and a traffic data flow; attack event features are generated through data classification, cleaning, standardization and PCA feature extraction; constructing spatial and temporal distribution characteristics in combination with context data, determining a damaged object and a damage radius through a K-means clustering and morphological expansion algorithm, and outputting a damage area; analyzing abnormal behavior distribution based on the area and the traffic data flow, and generating a distribution diagram; analyzing the threat level according to the distribution map, the damaged object and the hazard radius, distributing resources by combining a greedy algorithm, and planning a time sequence to construct a dynamic defense strategy; optimizing a protection scheme according to the real-time feedback data; finally, behavior changes are continuously monitored, and a risk thermodynamic diagram and a safety gap list are generated. According to the method, the Internet of Vehicles safety assessment capability is improved.

The invention provides a sparse and dense state calculation method, device and equipment and a readable storage medium, and the method comprises the steps: receiving an input data stream, analyzing the feature information of the input data stream, and marking a sensitivity level for the data stream; according to the sensitivity level of the data stream, a corresponding calculation strategy is called, and the calculation strategy at least comprises secret state calculation, hybrid calculation and plaintext calculation; and executing a calculation strategy for calculation associated with the data stream according to the calculation strategy corresponding to the data stream in AI training and / or AI reasoning. Through the technical scheme of the specification, the sensitivity level is marked for the data stream, and the secret state, mixing or plaintext calculation strategy is dynamically matched, so that the overall calculation overhead is remarkably reduced while the high-sensitivity data security is ensured, and the AI training and reasoning efficiency is improved.

The invention relates to the technical field of network security, and provides a network threat detection method and system under protocol dynamic recombination. The method comprises the following steps: capturing network traffic, and obtaining a traffic feature set; introducing dynamic recombination fitness as an effect evaluation index, and carrying out protocol dynamic recombination optimization by taking the index and a flow parameter as constraints to obtain a dynamic protocol; capturing a data stream of a dynamic protocol, and analyzing to obtain a protocol feature set; and activating a threat detector to analyze the protocol feature set to obtain a real-time threat type, and calling a corresponding defense strategy to perform network processing. According to the invention, the technical problems of threat detection lagging and high missing report rate caused by the fact that a traditional static protocol analysis technology is difficult to adapt to a protocol dynamic recombination scene are solved, and real-time accurate threat identification and adaptive defense based on dynamic recombination optimization are realized; and the detection efficiency and the protection response speed of unknown threats in a complex network environment are improved.

The invention discloses an engineering construction digital intelligent supervision management and control system, and relates to the technical field of engineering construction supervision. The system comprises an information acquisition module which is used for acquiring equipment state, image and environment data; the data fusion layer module is used for carrying out cleaning, alignment and Kalman filtering fusion on the multi-source data to generate a multi-modal construction data flow; a progress calculation and prediction unit, a deviation judgment unit and a digital modeling unit are arranged in the analysis monitoring module, and the analysis monitoring module is used for updating a progress state based on an XGBoost model, analyzing a node dependency relationship by using a graph neural network, calculating a deviation propagation risk coefficient and generating a hierarchical deviation signal in combination with three-level judgment conditions; and the management and control layer module executes hierarchical response according to the level of the deviation signal, and feeds back execution effect data to the front-end module to realize model optimization and closed-loop iteration. According to the invention, intelligent perception, risk prediction and adaptive management and control of the construction process are realized, and the real-time performance, accuracy and autonomy of project supervision are improved.

The invention is suitable for the field of vehicle-road cooperation, and provides a real-time intelligent data stream analysis and management method and system, and the method comprises the steps: obtaining multi-type data streams transmitted by a vehicle terminal, roadside equipment and a cloud platform in a vehicle-road cooperation scene; assigning priority levels to the multi-type data streams; carrying out parallel processing on the data streams with different priorities by adopting a self-adaptive analysis algorithm, and extracting environment perception features, vehicle state features and traffic event features; performing integrity and accuracy verification on the analyzed feature data through a real-time verification model, marking abnormal data and triggering a correction mechanism; according to the priority level and a data quality evaluation result, dynamically distributing, calculating and transmitting targeted resources, and pushing effective feature data to a corresponding decision node to support real-time response; priority transmission of key data is guaranteed through dynamic priority scheduling, abnormal data is verified and corrected in real time, and the problems of poor multi-source data fusion, key data delay and the like are effectively solved.

The invention relates to the technical field of data processing, and discloses a safety distance monitoring method and system for net rack and pipe truss construction. The method comprises the following steps: acquiring key node safety distance data, construction state data and environment interference data, and integrating the data into a safety distance monitoring data set; performing anomaly screening and error compensation on the data set to generate a standardized data stream; analyzing the data flow, establishing a deformation feature table, and generating a deformation state report; positioning a dangerous area based on report matching early warning conditions, and outputting early warning information; determining an intervention node according to the early warning information, and generating an adjustment instruction; and in combination with the adjustment instruction and historical data, establishing an associated network to generate a management knowledge base. Intelligent monitoring of the safety distance in the construction process of the net rack and the pipe truss is achieved, an early warning mechanism is established through fusion analysis of multi-source data, a scientific disposal scheme is provided based on historical experience data, and therefore the accuracy and reliability of construction safety management are improved.

The invention provides an automatic object-level authorization vulnerability detection method based on a large language model, which comprises the following steps of: firstly, identifying a sensitive resource table through SQL (Structured Query Language) grammar analysis, and analyzing an operation path from a tracking request parameter to the sensitive resource table by utilizing a forward data stream; then, performing context sensing analysis on the database operation by adopting LLM to infer object-level sensitive operation; thirdly, recognizing program logic and a conditional protection mechanism in database query through static analysis, and recognizing custom verification logic such as method-level annotation and administrator permission in combination with an LLM and character string matching method; and finally, judging whether the sensitive operation lacks user-defined verification, ownership SQL constraint and access control conditions at the same time or not by integrating the mechanism, and generating a vulnerability report according to the judgment result. The invention further provides an automatic object-level authorization vulnerability detection system based on the large language model. Therefore, the detection precision can be effectively improved, and false alarm and missing alarm are remarkably reduced.

The present invention relates to the field of risk identification technology, specifically a big data-driven financial risk identification system, which includes a time data synchronization module, a multidimensional feature mapping module, a financial behavior analysis module, and a risk dynamic identification module. In the present invention, by synchronizing financial data streams in real time and applying multidimensional scaling transformations, the capture and processing of financial data are optimized, and more efficient data stream analysis is achieved. It is possible to accurately capture subtle changes and key forms of time series data in complex financial environments, greatly improving the sensitivity and accuracy of risk identification. Refined similarity calculations and behavior identification help the system discover potential risks earlier, ensuring that financial institutions can respond quickly, thereby effectively preventing and mitigating risk events, and then responding to dynamically changing markets, which can significantly improve the formulation of risk management strategies and the rationality of resource allocation.

The invention relates to an industrial control system PLC control logic decompilation method and application thereof, and the method comprises the following steps: carrying out simulation execution on a PLC control application program, realizing memory relocation and variable initialization processing on the control application program, and extracting an executable instruction and corresponding data; converting the extracted executable instruction and data into a unified intermediate representation to obtain an IR instruction; on the basis of a heuristic data flow analysis algorithm, variables in the IR instruction sequence are identified, forward backtracking analysis is carried out starting from the output variables, the accurate dependency relationship between the variables is recorded and constructed step by step, and a complete variable dependency graph is formed; and based on the variable dependency graph, combining branch and loop structure information in a control program, and reconstructing a complete PLC control logic in a structured text language. Compared with the prior art, the method has the advantages that the positioning precision and the recovery efficiency of PLC control logic tampering are improved, and the safety and the maintenance efficiency of an industrial control system are remarkably improved.

The present application relates to the technical field of building automation, in particular to an outdoor louver canopy automatic control method and system, comprising the following steps: based on historical and real-time environmental data, using an autoregressive integrated moving average model, analyzing the trend and seasonal variation of illumination, temperature and wind speed data, using a random forest algorithm to process the nonlinear relationship of the data, constructing a model to predict environmental changes, analyzing and predicting the future trend of illumination and temperature parameters, and generating environmental prediction data.In the present application, the autoregressive integrated moving average and random forest algorithm accurately predict environmental changes, the gradient boosting machine algorithm reduces prediction error, the long short-term memory network timely identifies structural damage and functional failure, enhances reliability and safety, the fuzzy logic control handles input uncertainty, improves control accuracy, the PID algorithm adjusts the control amount according to real-time data, realizes accurate adjustment, real-time data stream analysis monitors the state, and ensures effective execution of the strategy.

The invention relates to a method and a system for identifying an instant call type service and an intelligent terminal, and relates to the field of network instant call technology, and the method comprises the following steps: obtaining an instant call data flow and participant information; identifying a call message and a message frame number; determining the content of the message; defining a common message content when the same message content exists between different call messages; determining a common number of common message contents; determining a common proportion based on the common number and the message frame number; defining the message content as key data when the common proportion is greater than the key critical proportion; screening instant call data streams with the same key data and marking the instant call data streams as the same call data stream; and analyzing the participant information corresponding to the same conversation data flow to obtain the same conversation participant. The method has the advantages that participants of the same session can be recognized by analyzing the same content without deeply analyzing specific content, and illegal behaviors of instant communication of illegal persons through a network can be detected.

This invention relates to the field of distributed streaming data processing and dynamic reconfiguration of cross-domain processing logic, and particularly to a real-time data stream analysis method and system for the international automotive market. The method includes: acquiring pure physical kinematic state data of the vehicle and pre-authorized original environmental data from a pre-defined system; calculating and generating a transient symmetric key; encrypting the original environmental data into ciphertext data and writing it into a memory buffer; inputting the kinematic data into a gradient descent optimization model, fine-tuning the feature transformation computation graph weight matrix and bias terms, and outputting a new rule domain matching operator path; reading the hardware clock cycle value and injecting it into the header of the ciphertext data tensor to generate a routing marker, guiding the data flow to the new operator path; after confirming the path switch, sending an overwrite command to replace the transient symmetric key with an all-zero physical state, blocking the decryption and restoration path of the remaining ciphertext data. This invention solves the problem of feature data leakage or computational interruption caused by differences in feature transformation standards during cross-regional driving.

The invention discloses a measurement acquisition equipment information flow monitoring system based on a virtualization technology, which relates to the technical field of measurement information monitoring and comprises an acquisition terminal, an operation sensing module, a communication uploading module, a data preprocessing module, a data flow analysis module and a log management module. And the acquisition terminal is used for monitoring and acquiring the flow information of the equipment information, and the acquired data of the acquisition terminal is divided into a first type of data, a second type of data and a third type of data according to time attributes. According to the invention, the online process of the uploading module is intelligently analyzed, and is concisely displayed on a terminal liquid crystal, so that operation and maintenance personnel can conveniently check and process on site, the pertinence of terminal operation and maintenance and the efficiency of on-site troubleshooting are improved, and information such as terminal version information, software and hardware information, batch information, manufacturer information and the like is collected in combination. Big data analysis is carried out on information sensed by the acquisition terminal, and field terminal operation quality evaluation is given from multiple dimensions, so that development of work such as acquisition terminal replacement and quality evaluation is supported.

Techniques for data flow analysis of cloud-based software applications are described. A first portion of source code of a software application is determined to obtain data from a data source identified by a first resource identifier of a cloud provider network, the determination based on a mapping of a first statement in the first portion of the source code to an application programming interface (API) call. A trace of a data flow from the first portion of the source code to a second portion of the source code is obtained. A data sink identified by a second resource identifier of the cloud provider network is determined based on a mapping of a second statement in the second portion of the source code to another API call. A result that includes an identification of a data flow from the data source identified by the first resource identifier to the data sink identified by the second resource identifier is generated.

The invention relates to the field of intelligent analysis, and particularly discloses a binary code-oriented static taint analysis system and method, which introduces an on-demand micro-symbol execution mechanism on the basis of basic data flow analysis through binary lifting and control flow diagram construction, that is, aiming at memory ambiguous nodes in a data dependence diagram, a micro-symbol execution mechanism is introduced on the basis of the basic data flow analysis; refined alias analysis is achieved through reverse instruction slicing and constraint solution, then an enhanced data dependency graph is generated, and path-sensitive taint propagation iteration is conducted on the basis to generate a final taint state table. In this way, complex dynamic memory addressing can be effectively recognized, taint chain breakage and misinformation explosion caused by ambiguity ignoring or excessive approximation are avoided, and resource allocation of an analysis engine is remarkably optimized.

The invention relates to the technical field of distributed streaming data processing and cross-domain processing logic dynamic reconstruction, in particular to a real-time data stream analysis method and system oriented to an automobile internationalization market, and the method comprises the steps: obtaining vehicle pure physical kinematics state data and preset system authorization original environment data; calculating and generating a transient symmetric key, encrypting original environment data into ciphertext data, and writing the ciphertext data into a memory buffer space; inputting kinematics data into a gradient descent optimization model, finely adjusting a feature conversion calculation graph weight matrix and an offset item, and outputting a new rule field matching operator path; reading a hardware clock period numerical value, injecting a ciphertext data tensor head to generate a routing mark, and guiding data to flow to a new operator path; and after path switching is confirmed, an overwriting instruction is sent to replace the transient symmetric key to be in an all-zero physical state, and a decryption restoration path of the residual ciphertext data is blocked. According to the method, the problem of feature data leakage or calculation interruption caused by feature conversion standard difference in cross-regional driving is solved.

Provided are a method and device for analyzing a data stream. According to the method, one of a plurality of sampling periods having a same sampling duration and different start times is allocated to an audio and video stream. A first arrival data packet of the audio and video stream within the allocated sampling period is copied as a first sample data packet based on a sample stream entry. A last arrival data packet of the audio and video stream within the allocated sampling period is copied as a second sample data packet. A sender delay and a receiver delay of the audio and video stream are calculated based on respective timestamps of the first and second sample data packets. A jitter of the audio and video stream is analyzed based on the sender delay and the receiver delay.

The invention relates to the technical field of data stream processing, and discloses an intelligent decision command system based on data stream analysis, comprising: a corpus collection unit used for deploying a plurality of corpus collection agents used for collecting text data on each process node; the corpus database is used for storing the corpora in the database in a classified manner according to the correlation between the corpora and the decision-making scene; the decision-making scene unit is used for collecting decision-making demand text data input by a decision maker, the intelligent analysis unit takes the decision maker demand text data and all data related to the decision-making scene in the corpus database as input data of a decision-making model, and the decision-making output unit optimizes an anomaly detection model through incremental learning. Multi-dimensional information visualization display of first-line decision and central command is realized; the technical problems that in the prior art, the data island phenomenon is serious, and intelligent data analysis is lacked are solved.

The application discloses a high-position remote landslide disaster early warning method and system based on multi-source data fusion, and relates to the technical field of early warning. First, a hardware risk perception unit is deployed at the data acquisition front end of an early warning server, running data streams of each sensor data channel are obtained through the hardware risk perception unit, and multi-dimensional labels of each sensor data channel are synchronously acquired, then according to the running data streams, interruption risk index values of each sensor data channel are analyzed and obtained, the multi-dimensional labels are analyzed in real time, early warning results of each sensor data channel are marked, and then based on the interruption risk index values and the early warning results of each sensor data channel, the running mode of an early warning dispatcher of each sensor data channel is adjusted, based on the adjusted running mode of the early warning dispatcher, scheduling execution adjustment of each sensor data channel is carried out, and high-position remote landslide disaster early warning is carried out after the scheduling execution adjustment, thereby improving the reliability and timeliness of high-position remote landslide disaster early warning.

The invention relates to a misuse detection method and device of an encrypted API and terminal equipment. The method comprises the following steps: constructing an encrypted API knowledge graph; extracting an encrypted API file from the to-be-detected item file; respectively performing control flow analysis and data flow analysis on the encrypted API file to obtain function call information and static variable information of the encrypted API file; and inputting the encrypted API knowledge graph, the encrypted API file, the function call information and the static variable information into an encrypted API agent for encrypted API misuse detection to obtain a detection report of the to-be-detected item file. Through application of the method and the device, the problems of relatively high false alarm rate and missing report rate of an encrypted API misuse detection method depending on rules in related technologies are solved, and the technical effect of improving misuse detection accuracy of the encrypted API is realized.

A system and method for encrypted data compression with hardware management combines data compression techniques with real-time hardware optimization. The system receives an input data stream comprising data blocks, analyzes frequency distributions within the stream, and generates a conditioned data stream by identifying portions that deviate from target frequency distributions and applying conditioning rules. A hardware management layer continuously monitors the system resource utilization including processing latency, memory consumption, and field-programmable gate array (FPGA) resource usage during data processing operations. When resource utilization exceeds performance thresholds, the system generates hardware optimization plans and implements configuration changes including memory reallocation, processor adjustments, and FPGA resource management. The system applies Burrow's-Wheeler transforms (BWT) to improve data compressibility and creates error streams through XOR operations between original and conditioned data. The hardware management layer enables dynamic adaptation to changing processing demands while maintaining optimal system performance for data compression operations.

The invention discloses a data-driven extensible communication protocol analysis system, method and device and a medium, and relates to the technical field of communication protocol processing and software automation, and the system comprises a log preprocessing module which is used for forming a binary protocol frame to be analyzed; the frame analysis module is used for carrying out structured analysis on the binary protocol frame and comprises a protocol atom library used for storing and analyzing an atom analysis function of a basic data field; the atomic type mapping table is used for associating an atomic type identifier declared in a protocol description file with the atomic analytic function; and the data stream analysis and visualization module is used for generating visual output based on the data stream context formed in the frame analysis process. According to the invention, the analysis of the newly added protocol field only needs to register a new atomic function in the protocol atomic library without modifying the core architecture of the system, and the newly added protocol type only needs to establish a new frame structure mapping relation, so that the system can quickly adapt to the evolution requirements of various communication protocols.

The present invention discloses a cloud-edge collaborative adaptive data transmission management method and system based on a fence mechanism. The IoT management platform receives the service data characteristics sent by IoT terminals, generates corresponding data fence insertion strategies for the IoT terminals according to the service data characteristics, and the IoT terminals insert data fences into the data stream according to the data fence insertion strategies and output the data stream. The IoT management platform obtains the data stream, captures the data fences in the data stream, for any two adjacent data fences, counts the amount of data between the adjacent data fences, and parses the information carried by the data fences according to the corresponding data fence insertion strategies to obtain the theoretical value of the amount of data between the adjacent data fences, compares it with the counted amount of data, determines whether there is data loss, and if so, generates corresponding data transmission fault records. The present invention can achieve high-efficiency and low-occupation power big data statistics through the message fence mechanism, realize the accurate positioning of service data, and improve the efficiency of data stream analysis.

The invention discloses a privacy routing forwarding method, device and system, and a storage medium, and introduces a homomorphic tree HTree structure to realize homomorphic routing query, so that a router carries out routing matching in a ciphertext space under the condition of not knowing a plaintext destination address, data flow analysis of a malicious router is prevented, and privacy forwarding of a user data packet is ensured. By adopting the technical scheme of the invention, the method is applied to the field of Internet, provides an encrypted routing query and forwarding function, and ensures the privacy of a data packet.

Network architectures interface with several real-time streams that control network operation. The network can analyze stream data by storing incoming raw stream data, processing the data, and controlling network operations based thereon. Specific packets, bits, flags, fields, or messages may be targeted, including HL7 ADTs. Data is queued for processing in a FIFO manner to avoid timeline mistakes. The network sets a configuration value based on the data analyzed in this time ordering, and a relational database can be updated in real-time with these values as stream data is incoming. Separate clean-up functions separately manage the database by adjusting the values as they become old. Timeline information is generated and selectively broadcast based on the operations value, potentially with additional information from the targeted data. The network selectively provides this information to users interfaces and displays as dictated by the operating value and information calculated therefrom.

The invention discloses a multi-source time series data stream concept drift detection method based on dual inspection, and belongs to the field of data stream mining. The method comprises the following steps: acquiring a prediction result of a prediction model and a real observation value of multi-source time series data, and dividing a time window; judging whether the current window error obviously changes or not through first hypothesis testing; matching a window most similar to the current data from the historical data, calculating the error change degree between the two windows, and judging whether the prediction error of the current window obviously exceeds the prediction error of the similar historical window through secondary inspection; and if the two tests are both significant, determining that the concept drift occurs. Through double inspection and similar window comparison, the problem that the misjudgment rate is high in multi-source data flow concept drift detection in a traditional method is solved, the accuracy and robustness of drift detection are improved, and the method is suitable for dynamic data flow analysis in the fields of finance, industrial monitoring and the like.

Inter-kernel dataflow analysis and deadlock detection includes, for each kernel of a plurality of kernels of a design, including, using computer hardware, a signal for the kernel that is asserted in response to all processes inside the kernel stalling, wherein the plurality of kernels form a strongly connected component. For each kernel of the plurality of kernels, the signal is asserted during operation of the design in response to each process in the kernel stalling. A notification is generated indicating that the strongly connected component is deadlocked in response to each kernel of the strongly connected component asserting the signal.