43 results about "Source tracing" patented technology

This disclosure provides a method for unknown malicious source tracing analysis based on cross-platform behavior graph fusion. The method includes: inducing the collection of collaborative behaviors of multiple processes across platforms through an adversarial sandbox; semantically reorganizing the collected raw behavior data into a set of collaborative behavior events; constructing a subject-attribution-enhanced heterogeneous behavior graph by adding collaborative attribution edges; performing subject attribution determination on the subject-attribution-enhanced heterogeneous behavior graph based on a graph neural network model, merging the dispersed collaborative processes into a unified attack subject subgraph set; identifying high-risk behavior subgraphs and their stage propagation chains from the unified attack subject subgraph set; performing reverse tracing along the time sequence, resource transfer sequence, and control transfer sequence; and combining cross-platform same-source comparison to shrink into a single-source attack chain, outputting a source tracing conclusion. This method can solve the technical problems of multiple false starting points and the inability to form a single attack source attribution in the source tracing results.

This invention discloses a method and system for identifying abnormal behavior in wind farms based on multi-model cascaded calls, belonging to the field of abnormal behavior identification technology. The method includes: acquiring real-time video streams and determining scene semantic information; constructing an anomaly identification task graph; adaptively filtering and arranging a wind farm model library; obtaining a model cascaded call chain; performing multi-level progressive detection on the real-time video stream according to the model cascaded call chain; obtaining the output sequences of each model; performing error assessment and source tracing; and establishing a wind farm identification interference tree; performing multi-dimensional redundancy verification on the output sequences of each model; and establishing a wind farm identification redundancy verification space; and performing credibility enhancement processing on the output sequences of each model based on the wind farm identification interference tree and the wind farm identification redundancy verification space; and establishing a wind farm abnormal behavior heatmap. This invention solves the technical problem of insufficient accuracy in wind farm abnormal behavior identification in existing technologies, achieving the technical effect of improving the accuracy of wind farm abnormal behavior identification.

This invention provides a method and system for collaborative environmental monitoring and pollution source tracing using UAV swarms in complex terrain, relating to the fields of UAV environmental monitoring and pollution source tracing. The method includes: a ground control center planning layered and altitude-based tasks for the target area and sending them to each group containing master and sub-UAVs; the master UAV decomposing and encoding the sub-tasks and transmitting the results back; the ground-based optimization of flight paths being distributed; the sub-UAVs collecting pollution source data, which is then preprocessed by the master UAV and uploaded; ground-based analysis results being fed back; and the master UAV calculating the contribution value C using a source tracing model. en Once the source tracing is complete, the results are uploaded to the server, and the drone returns to base. The system includes a ground control center and at least one drone swarm; each swarm consists of a master drone and sub-drones. This invention can plan flight paths based on the geographical environment of the monitored target area, avoiding detection blind spots and conflicts, and coordinate actions based on the flight path, thus improving the convenience and accuracy of environmental monitoring and pollution source tracing.

This invention discloses a deep learning-based method for VOCs pollution feature analysis and source tracing. The method includes acquiring online monitoring time-series data of VOCs and synchronous meteorological parameters of the monitoring area, constructing a multidimensional input tensor after preprocessing, building a deep neural network model containing a spatiotemporal feature extraction module and a source apportionment regression module, extracting nonlinear chemical coupling features and temporal evolution features between VOCs components using a one-dimensional convolutional neural network and a bidirectional long short-term memory network, and weighting and fusing them using meteorological parameters, and finally decoding and outputting the source component spectrum and temporal contribution rate that conform to physical constraints through the source apportionment regression module to generate a pollution feature map. This invention effectively solves the problem that traditional receptor models struggle to handle nonlinear chemical transformations and complex meteorological couplings, improves the accuracy and physical interpretability of VOCs pollution source tracing, and has online update capabilities to adapt to environmental changes.

This application discloses a machine learning-based intelligent source tracing method and system for pesticide pollutants. The method includes: acquiring multi-source data and constructing a graph structure representing the migration and diffusion relationships of pollutants; calculating the deviation between the theoretical and actual migration times of pollutants between monitoring points based on historical monitoring data, using this deviation as a correction factor; constructing and training a machine learning model that integrates the correction factor for predicting the spatial distribution of pollutants in conventional scenarios; monitoring the data stream in real time, calculating data anomaly indicators using an anomaly detection model, determining an unknown pollution event when the indicator falls below a threshold, and automatically switching to physical model inversion and unknown substance structure analysis modes to deduce the location of the pollution source and identify the category of the unknown pollutant, respectively. This application enhances the response capability to unknown events such as sudden leaks through the synergistic collaboration of conventional source tracing and emergency unknown source tracing modes, and can be widely applied to intelligent early warning and source tracing of pesticide pollution in surface water.

The invention relates to a fire tracing and diffusion prediction method and system based on multi-source data fusion and a storage medium, and relates to the technical field of disaster monitoring and early warning. The fire tracing and diffusion prediction method comprises the following steps: collecting and cleaning multi-dimensional environment information of a disaster scene, and obtaining multi-modal environment data; constructing a disaster situation multi-dimensional map according to the multi-modal environment data, and dividing a field dangerous area in combination with a preset disaster situation threshold value; analyzing the disaster situation multi-dimensional map according to the real-time environment data collected by the intelligent carrier, determining the disaster situation category, and positioning the disaster situation generation source; monitoring environment change data of a disaster occurrence source, and constructing a disaster time sequence deduction model in combination with disaster categories; according to the disaster situation time sequence deduction model in combination with the disaster situation multi-dimensional map, pre-judging and determining the regional disaster situation level of the field dangerous region, and drawing a disaster situation diffusion rendering graph; and planning a rescue path of a disaster scene according to the position of the intelligent carrier in combination with the regional disaster level, and providing disaster early warning.

This application discloses a method and system for multimodal content source tracing analysis of social media propagation chains, including: collecting and preprocessing multimodal propagation data; encoding visual and semantic modal data through a deep feature extraction network and projecting it into a cross-modal alignment space to generate a fused semantic representation vector; calculating the weighted cosine distance through a modality adaptive attention mechanism to obtain the semantic fidelity attenuation, and combining it with platform encoding degradation estimation to obtain the net semantic attenuation; constructing a propagation dynamic graph based on propagation metadata with the net semantic attenuation as edge weights; aggregating propagation features through an edge-weight-aware graph convolutional network; tracing and locating the source node by performing reverse semantic gradient tracing through a source likelihood scoring network; and achieving specialized source tracing of adversarial mutation propagation through semantically invariant region detection and deep semantic structure correlation mining. This invention achieves deep coupling source tracing of content-level semantic information and propagation topology information.

This invention provides a method for tracing the source of large language model text based on virtual prompt word embedding, belonging to the fields of artificial intelligence natural language processing technology and digital content security technology. The method includes: (1) constructing chat input command text with role format; (2) obtaining embedded vector input; (3) obtaining system prompt word embedding representation; (4) obtaining generated text with user identity information watermark; (5) restoring user identity information to achieve source tracing of generated text. This invention achieves real-time embedding of traceable identity information in the large language model generation process without modifying the main parameters of the large language model or affecting the model text generation quality and response efficiency. At the same time, it ensures the robustness of the embedded watermark information in text transformation scenarios, and can stably extract verifiable and reproducible identity information from the generated text, providing reliable technical support for tracing the source of generated text.

This invention provides a method and system for precise source tracing of heavy metals in sediments based on background value stripping, relating to the fields of environmental monitoring and pollution source tracing. The invention optimizes sampling based on a three-level zoning method using watershed boundaries, hydrological models, and lake bay morphology, establishing pollution time series by 1cm stratification combined with 210Pb dating. By constructing a background sediment database and employing the Savitzky-Golay filter curvature extremum method and robust double standard deviation method to determine zoning background values, the anthropogenic source content of different eras is obtained after stripping the natural background, achieving precise identification and source tracing of heavy metal pollution evolution.

The invention discloses an authority management system and method for multi-source authority aggregation and dynamic inheritance decoupling, and belongs to the technical field of DevOps, the system comprises an authority storage module for realizing user affiliation judgment of constant time complexity through non-expansion storage and bitmap indexing; the reverse index module is used for asynchronously updating the authority in batches through a secondary hash reverse index and a message queue; the permission aggregation module is used for obtaining an aggregation permission list through union set operation; the source tracing module is used for obtaining directly added permissions through difference set operation; the inheriting and decoupling module is used for realizing dynamic inheriting and decoupling through an inheriting flag bit and a user-defined mask; and the cache synchronization module realizes increment synchronization through three-layer version number and inheritance chain propagation. According to the method, the problems of difficulty in source tracing, low dynamic decoupling efficiency, poor batch change performance and poor cache consistency are solved, and efficient, accurate and extensible multi-source authority management is realized.

This invention belongs to the field of water pollution source tracing technology, and specifically relates to a source tracing method for water pollution at monitoring sections in river basins. The method includes collecting seven types of basic data, such as industrial and agricultural sources; screening out exceeding pollution standards and corresponding catchment area pollution sources through standardized preprocessing; constructing standardized topological relationships between four types of pollution sources, discharge channels, and exceeding pollution standards, and marking pollution sources without topological relationships; calculating the correlation between nodes based on topological type differences, and calculating spatial correlation for nodes without topological relationships; designing differentiated methods to calculate relative emissions for industrial, agricultural, and domestic sources; combining correlation and relative emissions to calculate a comprehensive correlation, and outputting the source tracing results after sorting. This invention solves the problems of existing water pollution source tracing technologies, such as the lack of systematic topological relationships, biased calculation dimensions, and inaccurate results.

This invention relates to the field of industrial waste gas monitoring and source tracing technology, specifically to an intelligent monitoring and source tracing system for VOCs emissions from cement plants based on multispectral sensing and edge computing. The system includes a distributed multispectral sensor array, an edge computing cluster, a spatiotemporal source tracing engine, and a full-link communication module. The sensor array employs a mid-infrared multi-channel narrowband filter structure matched to the characteristic VOCs components of cement plants. The edge computing cluster incorporates an improved spectral preprocessing and concentration inversion algorithm. The spatiotemporal source tracing engine uses a multi-node spatiotemporal calibration and correction Gaussian plume diffusion source tracing algorithm. This invention achieves in-situ high-precision monitoring of multi-component VOCs in cement plants and rapid and accurate source tracing of emission sources. Through a three-level edge architecture, it significantly reduces response latency and constructs a closed-loop control system of monitoring-source tracing-early warning-linkage, suitable for routine VOCs emission control and emergency response in cement plants.

This application relates to the field of data governance technology, and in particular to a method and system for tracing the source of abnormal rights data by integrating communication behavior profiling. The method includes: acquiring rights service event streams and communication link event streams and generating a unified event sequence; extracting number lifecycle features, SMS link features, call forwarding / call transfer features, and behavioral response features based on the unified event sequence to construct a communication behavior profile; calculating a verification code link integrity score and outputting a high-contribution evidence factor; constructing a causal relationship identification attack chain stage using key rights events as anchors; and executing tiered loss mitigation measures when triggering conditions are met, simultaneously generating an evidence package set, constructing a verifiable summary, and outputting the source tracing result. This application achieves interpretable source tracing of verification code link attacks, avoids difficulties in post-incident evidence collection through a real-time loss mitigation and evidence solidification synchronization mechanism, and uses a hash chain structure to ensure the immutability and verifiability of evidence, enhancing trust in cross-departmental collaboration.

A method for online monitoring and tracing of hardware Trojans implanted in AI accelerators based on heterogeneous side-channel perception is proposed. This method collaboratively schedules heterogeneous native monitoring resources on FPGAs to capture multi-dimensional side-channel feature fingerprints in situ. It utilizes deep neural networks for spatiotemporal joint modeling and behavior reconstruction, and integrates adaptive compensation strategies to resist thermal drift interference. This enables autonomous monitoring of hardware Trojan behavior and precise physical region tracing within the AI ​​accelerator. This method significantly improves the robustness of hardware Trojan detection under complex conditions without requiring external audit intervention or significantly impacting accelerator inference performance. It also demonstrates good detection generalization ability for various unknown triggering mechanisms, providing a key technical path for the security assessment and risk hardening of convolutional neural network accelerators.

1. Name of the product in this design: Water Quality Early Warning and Source Tracing Analyzer (Three-Dimensional Fluorescence Synchronous Absorption). 2. Purpose of this design: For use in a water quality monitoring device based on three-dimensional fluorescence spectroscopy technology. 3. The key design features of this product are the combination of the overall shape and pattern of the product. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The invention discloses an automatic reconciliation and difference analysis system for cross-platform heterogeneous financial data, and relates to the technical field of data processing of finance, insurance or tax affairs. According to the system, heterogeneous platform financial messages are extracted in real time through an integrated bus module; the standardization module carries out semantic alignment and format normalization processing; the time sequence association module realizes time sequence pre-association by using a sliding window model; the automatic reconciliation engine adopts a mixed matching strategy to calculate confidence and generate a result; and the traceability analysis module locates difference reasons based on financial logic. The technical problems that heterogeneous data formats are different, the account checking false alarm rate is high, and tracing is difficult are solved, data islands are effectively eliminated, the false difference rate is reduced, and meanwhile the financial account checking automation level and the internal control treatment capacity in a complex transaction scene are remarkably improved.

This invention relates to the field of power quality monitoring technology and provides a power quality disturbance tracing method based on a workflow platform using an intelligent diagnostic model. The method includes: collecting three-phase voltage and current waveform data from each monitoring node; when a power quality indicator exceeds a preset threshold, the workflow platform performs feature extraction to obtain a disturbance feature vector; performs disturbance pattern discrimination and selects the corresponding tracing sub-algorithm branch based on the discrimination result to obtain branch tracing results; performs hierarchical event mapping to aggregate abnormal indicators of each monitoring node into disturbance events according to spatiotemporal clustering and constructs a directed acyclic graph-like disturbance propagation chain based on the branch tracing results; inputs the disturbance feature vector and disturbance propagation chain into a trained target hybrid model; outputs the corrected disturbance propagation chain and root cause node; and finally outputs the tracing results. This invention effectively improves the tracing accuracy and efficiency in complex disturbance scenarios.

This invention relates to harmonic source tracing in converter stations, specifically to a multi-harmonic source tracing method for converter stations based on filter adaptive switching disturbances. When a passive filter state switch is detected, multiple sets of harmonic voltage phasors and harmonic current phasors are continuously collected in the steady-state intervals before and after the switch. The phasor sequences collected before and after the switch are cross-paired to construct a full-combination sample data. The background harmonic voltage variation coefficient is calculated, and background harmonic voltage stability is verified using a verification threshold. For the full-combination sample data that passes the background harmonic voltage stability verification, a harmonic responsibility quantification index is calculated. For the harmonic responsibility quantification index obtained from multiple passive filter state switches, a weighting factor based on the background harmonic voltage variation coefficient is introduced, and harmonic source tracing is performed by calculating the comprehensive harmonic responsibility quantification index. This invention effectively overcomes the shortcomings of achieving accurate quantitative harmonic source tracing in converter stations under multi-source coupling and dynamic time-varying scenarios.

This invention relates to the field of industrial Internet of Things (IoT) technology, and in particular to an anomaly detection and source tracing analysis system and method based on an information model. The system includes a data acquisition and storage module, an information model construction and management module, an indicator construction and calculation module, an anomaly detection module, a source tracing analysis module, and a result output and visualization module. This invention integrates multi-dimensional data to construct a graph-structured information model, unifying data from different sources into the information model. Through entities, attributes, and relationships within the information model, the dependencies between various indicator data are clarified, achieving comprehensive source tracing of anomalies. Compared to traditional methods that only focus on a single type of data or local indicator correlations, this invention can uncover all potential data correlations related to anomalies from a holistic system perspective, avoiding incomplete source tracing problems caused by missing data dimensions.

This invention relates to a method, system, and storage medium for diagnosing defects in drainage pipe networks. The method of this invention overcomes the limitations of existing technologies that can only provide a single alarm signal or perform deterministic matching based on numerous assumptions by explicitly defining the inversion parameters as "defect node, defect occurrence time, and defect severity," and using the MCMC sampling algorithm to jointly invert these parameters. Through the above design, the method of this application can simultaneously pinpoint the precise location, accurate time, and quantify the intensity of defects, achieving multi-dimensional and precise source tracing of defects in drainage pipe networks, and solving the problem of "inability to accurately locate the defect location, occurrence time, and severity" in existing technologies.

This invention discloses a method and system for tracing water pollution sources in a watershed using a fused graph neural network, belonging to the field of regional water environment supervision technology. Based on initial theoretical pollutant flux, water quality concentration data, hydrological data, and known discharge flux from sewage outlets, this invention generates an initial dynamic mass residual field and calculates its global deviation feature data to determine if it exceeds a threshold. If so, the initial dynamic mass residual field is updated to obtain a target dynamic mass residual field; otherwise, the initial dynamic mass residual field is used as the target dynamic mass residual field. Then, the target dynamic mass residual field and areal rainfall data are processed to obtain dynamic background baseline data and local anomaly feature data for non-point source pollution. Finally, based on these, a weighted fusion is performed to generate and output point source illegal discharge probability distribution data, improving the accuracy of water pollution source tracing and solving the problem in existing technologies where non-point source pollution is misclassified as multiple discrete point sources, leading to a decrease in the accuracy of tracing the source of water pollution in a watershed.

This invention discloses an early warning and source tracing method for CO2 microleakage in saline aquifers based on isotope tracing, comprising: an injection device including an air compressor, a tracer storage tank, and an injection pump; a monitoring well device including a wellbore, a wellhead device, and monitoring instruments, arranged around the injection device; a sampling device including a sampler and a sample bottle, connected to the monitoring well device; analytical instruments including an inductively coupled plasma mass spectrometer, an isotope ratio mass spectrometer, an X-ray fluorescence spectrometer, and an atomic absorption spectrometer; and a data processing and early warning system. This invention solves the problem that conventional monitoring technologies cannot identify early microleakage, achieving accurate early identification and timely warning of CO2 microleakage. Furthermore, through optimized design of the structure of each device, the integrity and pollution-free nature of the saline aquifer water samples are ensured, enabling accurate and rapid analysis of water quality parameters.

The present application belongs to the technical field of tunnel construction intelligent analysis and management, and relates to a tunnel large machine supporting operation control analysis method based on multi-source data fusion. First, the process duration data of continuous multiple construction cycles of a tunnel operation surface is obtained, and a process tree structure containing longitudinal membership and horizontal connection is constructed. Then, the deviation duration of each component unit in the process tree structure relative to the historical cycle is calculated, and the abnormal source node is identified according to the threshold value performance. Subsequently, the data is processed according to the abnormal source level selection resolution dimension, the abnormal type and confidence are traced back, and the optimization suggestion is generated and pushed according to the rules. Finally, the adoption state is detected in the subsequent cycle and closed-loop feedback is performed. The present application improves the accuracy and interpretability of abnormal source tracing through hierarchical modeling, quantifies the abnormal reasons through multi-dimensional analysis, realizes the iteration of optimization strategies relying on closed-loop verification, and improves the tunnel construction diagnosis efficiency and collaborative operation level.

This invention discloses a method for tracing the source of generated images based on spatial-frequency cross-domain second-order statistics. This method extracts spatial domain residual anomalous features and frequency domain anomalous features from the generated image, and constructs a cross-domain second-order statistical coupling relationship between them, forming a cross-domain statistical fingerprint for source tracing. The cross-domain statistical fingerprint matrix is ​​obtained by calculating the cross-domain cross-correlation matrix, and then normalized and compressed to obtain a cross-domain second-order statistical feature representation. Based on the similarity matching or distance measurement results between this feature representation and the statistical fingerprint templates of each source category, the source category or confidence score of the generated image is output. This invention can explicitly characterize the coupling relationship between spatial anomalies and frequency anomalies, and still has strong source discrimination ability and good interpretability and generalization performance even when the generation models are highly similar.

This invention discloses a method and apparatus for precise source tracing and analysis of atmospheric ozone pollution. Based on the concentration data of each VOC species among multiple species monitored at ambient air quality monitoring stations, the ozone generation potential of each species is determined; the ozone generation reactivity of each species is determined; key VOC species for ozone generation are screened based on both ozone generation potential and ozone generation reactivity; mobile VOC monitoring is conducted around the ambient air quality monitoring stations, and automatic offline sampling and laboratory analysis are performed at high-value TVOC sites; key pollution sources for ozone generation at the ambient air quality monitoring stations are identified and subject to control. This invention provides a more comprehensive analysis of atmospheric ozone pollution, accurately identifying specific pollution sources and better providing technical support for environmental management.

This invention discloses a method for water quality remote sensing inversion and rapid source tracing, relating to the field of water environment remote sensing monitoring and pollution source tracing technology. It includes data acquisition and differentiated preprocessing, acquiring multi-source remote sensing data from the sky and ground, measured data, and auxiliary data, and performing differentiated preprocessing based on different data sources, climate zones, and water body type characteristics. This invention utilizes the S1 differentiated preprocessing scheme, designing specific processing procedures for scenarios such as temperate freezing, tropical high-algae, and water bodies with different turbidity levels, effectively avoiding interference caused by differences in climate zones and water body types, and providing reliable and stable basic data for water quality inversion.

This invention discloses an event aggregation and attribution method based on attack chain homology analysis, comprising the following steps: Step 1, multi-source heterogeneous data fusion and standardization processing; Step 2, dynamic subject-object relationship modeling; Step 3, attack event homology analysis; Step 4, homology attack event aggregation; Step 5, attack path deduction and visualization; Step 6, dynamic threat adaptive optimization. This invention improves the completeness of attack path reconstruction and solves the defect of static modeling in failing to capture attack chain evolution by using a dynamic subject-object relationship model and homology analysis algorithm; it presents the attack process through visualization technology, shortening the analysis time and improving the efficiency of source tracing; it improves the robustness of homology determination and the accuracy of entity relationship identification by using a multi-feature weighted fusion hierarchical clustering mechanism combined with code string features and vulnerability exploitation methods for auxiliary verification; and it enhances the adaptability of dynamic threats and shortens the response window for new threats through a dynamic weight update mechanism.

This invention discloses a method for assessing enterprise ESG environmental indicators based on IoT data, belonging to the field of intelligent industrial environmental management technology. It addresses the technical problems of ambiguous correlations between production factors and environmental indicators, lack of scientific basis for anomaly judgment, and low efficiency in source tracing and handling in existing technologies. The method establishes a ledger for the coding of equipment, processes, and ESG environmental indicators throughout the entire production process; collects IoT data to filter core correlation groups; builds a three-level correlation mapping table and establishes a dynamic update and traceability mechanism; establishes a threshold ledger for different operating conditions; constructs an abnormal feature library of enterprise ESG environmental indicators; and uses threshold comparison and cosine similarity methods to determine true anomalies; generates a source tracing list based on the three-level correlation mapping table; filters abnormal elements hierarchically; and integrates information to form an anomaly root cause location report. This invention achieves standardized and quantitative binding of production factors and ESG indicators, improves the rationality and accuracy of anomaly judgment, and effectively enhances the efficiency and scientific basis of enterprise ESG environmental indicator assessment and anomaly handling.

The present application relates to the technical field of power distribution network harmonic monitoring, and particularly relates to a power distribution network multi-harmonic source tracing quantification method, system, device and medium, comprising joint extraction of harmonic space-time characteristics based on power distribution network multi-node power quality time sequence data, obtaining multi-scale harmonic local space-time characteristics, and capturing harmonic long-range space-time propagation correlation coupling characteristics of multi-harmonic sources in global space-time dimensions, obtaining harmonic global space-time correlation characteristics; cross-level fusion of multi-scale harmonic local space-time characteristics and harmonic global space-time correlation characteristics, obtaining multi-scale space-time fusion characteristics, and through double-branch shared feature joint decoding, synchronous generation of initial harmonic source positioning decoding results and initial harmonic responsibility quantification results, followed by bidirectional joint constraint optimization, generation of power distribution network harmonic pollution tracing quantification results. Through the space-time correlation modeling architecture and the bidirectional joint constraint mechanism, the present application synchronously realizes accurate positioning and high-precision tracing of harmonic sources in complex time-varying working conditions.