326 results about "Trace analysis" patented technology

The invention discloses an SCL field safety accident analysis and early warning method and system, a medium and a program product, and the method comprises the steps: carrying out the distributed cleaning and feature extraction of multi-source heterogeneous monitoring data, and obtaining a multi-dimensional time sequence feature flow; constructing a dynamic association network of a security state; identifying a key influence factor chain, and establishing a semantic analysis model; constructing a safety threshold system; evaluating the multi-dimensional time sequence characteristic flow by using a safety threshold system; when the multi-dimensional time sequence characteristic flow triggers early warning, performing tracing analysis by using a semantic analysis model to obtain a root event triggering the early warning, and simulating the development trend of the root event based on a preset evolution model; determining a predicted risk range and a risk type according to the tracing cause analysis result and the accident development trend; and the corresponding accident early warning scheme is matched in the preset database according to the risk range and the risk type, and the accident early warning scheme is sent to the monitoring terminal, so that the accuracy of judgment and classified early warning of different types of safety accidents is improved.

The invention discloses a high-accuracy threat intelligence assisted network threat tracing method, which comprises the following steps: S1, collecting and preprocessing multi-source network security data, and constructing a time-marked event sequence set; s2, constructing an optimized Transform network model, and processing an attack event sequence by using position coding and time embedding; s3, a black swan optimization algorithm is initialized, and a Transform structure hyper-parameter is dynamically optimized; s4, outputting an attack event semantic vector, and constructing an attack path semantic map; s5, the intelligence information vector is embedded into a Transform hidden space; s6, calculating semantic similarity and dependency intensity, and generating an attack source candidate set and a traceability path; s7, outputting an attack traceability path, a starting point node and an information label, and generating a structured traceability report; and S8, according to the traceability result feedback, updating the black swan algorithm and the Transform model. The method is used for realizing intelligent modeling of multi-source network attack events and high-accuracy traceability analysis of attack source nodes.

The invention relates to the technical field of regional fire-fighting analysis, in particular to a regional fire-fighting risk analysis method and system based on multi-modal AI analysis. The method comprises the following steps: collecting a multi-modal fire-fighting related sensing source in an area, carrying out cross-modal fusion processing and abnormal event detection, and identifying a fire-fighting risk mutation point; carrying out risk propagation trend evolution on the fire risk mutation points, and carrying out fire source high-dimensional traceability analysis to obtain regional fire risk traceability points; performing risk evolution time estimation on the regional fire-fighting risk tracing point, and then performing fire risk situation evolution prediction to obtain a fire-fighting simulation situation map; and performing dynamic risk level change tracking according to the fire-fighting simulation situation map, and performing comprehensive emergency demand adjustment to obtain comprehensive risk intervention decision parameters. Through refined resource matching, the utilization rate of fire-fighting resources is improved, and the regional fire prevention and control quality and safety efficiency are improved.

The invention provides a food production information traceability management method and system, and relates to the technical field of food production monitoring. The method comprises the steps of obtaining multiple groups of multi-process production record data of target baked food, performing production process characteristic linkage analysis on the target baked food, and generating a plurality of process joint characteristic matrixes; performing mode clustering based on production process distance perception on the multiple groups of multi-process production record data to generate a plurality of abnormal production mode class clusters; performing process anomaly analysis and back propagation traceability on the to-be-analyzed batch to generate a process anomaly detection result of the to-be-analyzed batch; according to the plurality of production abnormity mode class clusters, mode abnormity tracing is carried out on the to-be-analyzed batch, a production abnormity mode matching result of the to-be-analyzed batch is generated, and a production abnormity tracing analysis result of the to-be-analyzed batch is generated through fusion. According to the invention, the positioning precision of the abnormal source process of the baked food is improved.

The invention relates to the field of methane change dynamic traceability analysis, and discloses a methane change dynamic traceability analysis and early warning system based on artificial intelligence, and the system comprises a data collection module which collects methane concentration data, constructs a two-state three-dimensional monitoring network, and calculates a relative standard deviation; the multi-source analysis module is used for merging calibration environment data, generating a three-dimensional matrix, iteratively updating a class cluster mean value and judging whether a burst leakage traceability process is triggered or not; capturing a time sequence dependency relationship, adding abnormal data optimization, analyzing and triggering residual drive and retraining; the anomaly recognition module is used for constructing a four-dimensional source feature matrix and dynamically adjusting the source feature weight; constructing an observation data matrix, and solving the contribution proportion of each source to the methane concentration; iteratively optimizing the source intensity parameter until convergence; and the early warning response module is used for dynamically updating a historical residual mean value and a standard deviation, judging a response level, reversely tracking an air mass track, positioning a potential emission source coordinate, and triggering a corresponding early warning response if an abnormal degree condition is met.

The invention provides a water affair informatization safety monitoring system, method and device based on the Internet of Things and machine learning, and belongs to the technical field of water affair monitoring. Through real-time data collection, intelligent early warning of abnormal operation of a water affair system, device health assessment and water volume scheduling optimization are carried out; through combination of multi-source data fusion analysis and a machine learning model, water resource optimization scheduling and equipment energy efficiency management and control are realized. Through a multi-level early warning threshold setting mechanism, an intelligent matching emergency plan library, a multi-mode notification unit and an emergency command decision support unit, and in combination with a historical early warning event tracing analysis interface, identification and response of abnormal events of a water affair system are realized. Classification statistics according to departments, personnel or equipment is supported to generate a visual chart; a machine learning algorithm is utilized to predict future trend change, and hydrological data distribution conditions are displayed on a map based on GIS integration. And the level and efficiency of water affair management are improved.

The invention relates to the technical field of cloud computing, and discloses a log intelligent aggregation and threat traceability analysis method and system for a multi-cloud environment, and the method comprises the following steps: collecting original logs of the multi-cloud environment, injecting a unified service identifier and a cloud platform label, analyzing a heterogeneous log format into structured data, and storing the structured data in a database; sorting and complementing missing log entries according to timestamps; constructing a service dependency graph, quantifying the calling relation weight between services, fusing log semantic features and statistical features, and generating a feature matrix after standardization; according to the method, standardized aggregation of multi-cloud logs is realized through unified service identifiers, the problem of heterogeneous log processing is solved, and the accuracy of complex threat identification and the propagation path verification capability are remarkably improved based on a feature fusion and cascade integration detection mechanism of a service dependency graph; the cross-cloud attack path is accurately positioned in combination with tracing analysis of the service identifier, and root cause diagnosis and security protection in a multi-cloud environment are effectively supported.

The invention relates to the technical field of artificial intelligence, in particular to a remote medical inquiry system based on AI recognition, which comprises a voice structure analysis module, a semantic association mapping module, a knowledge graph reasoning module, an inquiry path adjustment module and a disease traceability analysis module. According to the method, the logic relation of a description chain is accurately obtained by performing specific value mapping on the time delay and the semantic frequency of the voice information, the structural perception ability of the semantic information is enhanced, the symptom frequency and the health index trend are combined, accurate measurement is performed on abnormal fluctuation dependency, inquiry priority grading is optimized, and the key symptom recognition sensitivity is improved; through induction of a periodic fluctuation trend, formation of a dynamic evolution sequence, enhancement of the monitoring capability of health risk evolution, improvement of decision precision and optimization of a diagnosis chain, overall processing is performed through time sequence and semantic association analysis, the recognition and intervention capability of a complex health state is enhanced, and the health assessment adaptability and accuracy in multiple rounds of interaction are improved.

The invention discloses an IGBT driver fault prediction method and a safety system, and relates to the technical field of power electronics, and the method comprises the steps: constructing a dynamic causal reasoning matrix based on degradation characteristics, and carrying out the reverse reasoning of an accidental fault mode of an IGBT driver through the dynamic causal reasoning matrix; establishing a multi-stage fault prediction model based on the accidental fault mode of the IGBT driver, and predicting the fault occurrence frequency of each life cycle stage of the IGBT driver by using the multi-stage fault prediction model; and identifying a vulnerable element in the IGBT driver based on the fault occurrence frequency, performing fault tracing analysis on the vulnerable element, and optimizing the multi-stage fault prediction model according to a tracing analysis result. According to the method, the causal structure model with the dynamic evolution ability and the causal tracing ability is constructed, the search space of the causal relationship is effectively compressed, and the accuracy and expression ability of the causal path are improved.

The invention discloses an equipment anomaly tracing method and system based on a digital twinborn and graph neural network, and belongs to the technical field of industrial intelligent operation and maintenance and fault diagnosis. The invention provides an innovative solution integrating digital twin high-fidelity simulation and a graph structure deep learning algorithm, aiming at the technical bottlenecks that the generalization performance of an existing data driving method is insufficient under the conditions of fault sample scarcity and category imbalance and the traceability accuracy of unknown and composite faults is poor. The method comprises the following steps: constructing a high-fidelity digital twin integrating multi-dimensional physical attributes and a system topology structure; based on a fault mode, influence and harmfulness analysis method system, constructing a fault mode library comprising a plurality of single fault modes and composite fault modes, and generating an enhanced training data set with accurate labels through an automatic fault injection mechanism; training a graph neural network model with a multi-level attention mechanism by using the data set so as to learn a propagation rule of a fault in a complex system topology; and finally deploying the model to carry out abnormity traceability analysis on real-time industrial Internet of Things monitoring data. According to the method, the fault diagnosis generalization ability and the positioning precision under the sample imbalance condition are remarkably improved.

The invention relates to the technical field of air pollution tracing, and particularly discloses an air pollution source accurate tracing method and system, and the system comprises a multi-source data monitoring module, a multi-source data analysis module, a pollution tracing comprehensive analysis module, a pollution diffusion model verification optimization module, and a pollution tracing analysis module. By comprehensively considering various factors, setting a proportionality coefficient to calculate a pollution traceability comprehensive index and comparing the pollution traceability comprehensive index with a threshold value, a pollution traceability area and a potential area can be effectively marked, simulation concentration is calculated according to actual parameters in combination with the verification and optimization process of a pollution diffusion model, a root-mean-square error is reduced by adjusting a source intensity value, and a pollution source diffusion coefficient is increased. According to the method, the model is continuously optimized, so that the model better fits the actual situation, finally, the real pollution source is determined according to the acceptable threshold value, scientific and accurate basis and method are provided for air pollution control, and improvement of the control efficiency and effect and improvement of the atmospheric environment quality are facilitated.

The invention relates to the technical field of mode recognition, in particular to a hydraulic engineering monitoring method and system based on water wave analysis, and the method comprises the following steps: based on a time sequence of a fluid particle motion trail, combining with the spatial change of a water flow velocity field, carrying out the difference calculation of the position and the velocity, and carrying out the superposition operation of fusing the curl and shear. According to the method, the local dynamic feature matrix is generated by combining entropy screening, and the local change capturing precision is improved. An overall dynamic feature matrix is generated based on convolution operation of time distribution and a position matrix, and the dynamic distribution and time correlation expression ability is enhanced. Efficient fusion of feature data sets is realized through sensitive weight distribution and distribution matching processing of eddy current distribution, and the accuracy of trend analysis is enhanced. A multi-scale dynamic rule is captured by using a rule matrix constructed by multi-frequency decomposition and a time window, dynamic trend prediction is completed by combining offset recursion extraction, and comprehensive analysis and high-precision prediction of water area changes are realized.

The invention provides a traceability analysis method, device and system based on electroencephalogram signals collected by a mechanical arm. The method comprises the steps that coordinate information of all electroencephalogram electrodes in a first coordinate system is obtained; according to the coordinate information of each electroencephalogram electrode in the first coordinate system and the coordinate transformation relation between the first coordinate system and the second coordinate system, obtaining the first coordinate information of each electroencephalogram electrode in the second coordinate system; calibrating the first coordinate information of each electroencephalogram electrode in the second coordinate system according to the calibration matrix to obtain second coordinate information of each electroencephalogram electrode in the second coordinate system; electroencephalogram signals are acquired through each electroencephalogram electrode; and performing traceability analysis according to the second coordinate information of each electroencephalogram electrode in the second coordinate system and the electroencephalogram signal. According to the traceability analysis method, device and system based on the electroencephalogram signals collected by the mechanical arm, the accuracy of traceability analysis is improved.

The invention discloses a raising dust traceability treatment method and system for a construction area, and relates to the related technical field of raising dust treatment, and the method comprises the steps: monitoring and collecting environment feature information in a current construction area; raising dust monitoring is carried out at raising dust monitoring points of the construction area, and raising dust feature information is obtained; obtaining traceability dust raising scale information and traceability particulate matter distribution information, and carrying out earthwork feature information traceability analysis to obtain traceability earthwork feature information; an earthwork construction image in the construction area is collected, and multiple traceability matching degrees are obtained; and generating a plurality of flying dust treatment schemes of the plurality of earthwork areas, and carrying out flying dust treatment. The technical problems that the dust source in a construction area cannot be accurately identified and traced due to the fact that comprehensive analysis is difficult to combine with environmental characteristics in existing dust traceability treatment, the dust treatment efficiency is insufficient, and the treatment effect is poor are solved. The technical effects of accurately tracing the source of dust raising, enhancing the treatment effect of construction dust raising and improving the treatment efficiency are achieved.

The invention discloses an underground physical characteristic prediction method and system combining seismic waves and drilling data, and relates to the technical field of geophysical exploration, and the method comprises the steps: collecting original seismic records, carrying out the preprocessing, obtaining a three-dimensional seismic data body, carrying out the complex channel analysis of the three-dimensional seismic data body, extracting the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes, and carrying out the calculation of the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes. Fault features are identified through a coherent body algorithm, an earthquake feature matrix is obtained, depth alignment and correction are carried out on density logging, interval transit time and neutron porosity curves of drill holes, and a standardized drill hole data table is generated; and based on the seismic feature matrix and the standardized drilling data table. According to the method, deep complementation of the earthquake macroscopic information and the borehole local truth value is realized through the geometric perception multi-modal graph neural network and the dynamic gating fusion mechanism, the problems of insufficient cross-modal feature fusion and misalignment of physical rules in the prior art are solved, and the prediction precision and reliability under complex geological conditions are remarkably improved.

The invention belongs to the technical field of material surface structure detection, and provides a paper defect detection method and system.The height information of all pixel points on the surface of paper is obtained through an optical distance measuring sensor, then a height distribution diagram is constructed, the fluctuation mode of the surface structure contour of the paper is analyzed in combination with the convolution difference value, the local trend and the change intensity, and the paper defect detection result is obtained. Through curve fitting and trend clustering of structural features, typical surface structure types, such as periodic corrugations and non-periodic wrinkles, can be effectively distinguished, so that automatic identification and structural cause traceability analysis of paper surface microstructures are realized, and meanwhile, accurate identification of abnormal regions is realized. And the universality and the practicability of the system in an actual industrial scene are improved.

The invention discloses a rainwater and sewage mixing traceability analysis method based on a municipal pipe network BIM model. The rainwater and sewage mixing traceability analysis method comprises the steps of data acquisition and preprocessing, BIM model construction and integration, rainwater and sewage mixing traceability analysis and result display and report generation. According to the method, pipe network BIM geometric data and operation monitoring data are integrated to construct a digital twinborn model, hydraulic simulation and data mining are combined to identify mixed connection suspicious points, and a Bayesian network probability inference algorithm is utilized to trace along a pipe network topological structure to determine a mixed connection source and path. And finally, an analysis result is displayed through BIM three-dimensional visualization, and a comprehensive report is generated. According to the method, BIM, hydraulic simulation and an intelligent algorithm are fused, the rain and sewage mixed connection troubleshooting efficiency and traceability accuracy are remarkably improved, and reliable technical support is provided for intelligent operation and maintenance and precise rectification of a municipal pipe network.

The invention discloses an SERS (Surface Enhanced Raman Scattering) substrate with a needle-like diagram nanostructure and a preparation method of the SERS substrate, and belongs to the field of nanotechnology and detection. The preparation method of the needle-like Turing nano-structure SERS substrate comprises the following steps: by taking a silver needle as a carrier, carrying out electrochemical etching on the surface of the silver needle to induce to form a Turing nano-structure with fractal topological characteristics, and then inducing to conformally grow a layer of zeolite imidazole framework-8 material as a shell layer by utilizing the nano curvature characteristics of the fractal structure, so as to prepare the needle-like Turing nano-structure SERS substrate. And a composite SERS enhanced platform with an electromagnetic field hot spot aggregation effect and a molecular screening and enriching capability is constructed. The platform has the advantages of high structural uniformity, expandability in manufacturing, synergy of signal enhancement and molecular recognition and the like, remarkably improves the detection sensitivity and the signal reproducibility, and is suitable for trace analysis in a complex sample system, such as related application in the fields of food safety, environmental pollution monitoring, biomedicine and the like.

The invention discloses a high-altitude parabolic positioning traceability analysis method and system, and particularly relates to the technical field of positioning traceability. Motion parameters of an object are calculated by adopting a multi-frame image analysis method, rotary motion is detected by utilizing a deep learning algorithm, if rotation exists, a Magnus effect compensation model is introduced to correct trajectory calculation, meanwhile, wind speed and wind direction data are collected in real time, airflow change is simulated in combination with a computational fluid dynamics model, and the influence of wind power on the trajectory is corrected. Based on the corrected trajectory data, the initial throwing position of the object is reversely deduced in combination with a building three-dimensional structure model, a target person in charge is determined, and a complete traceability report is generated by comparing an analysis result with a monitoring video, so that the accuracy and traceability of traceability analysis are ensured. The method can effectively reduce the calculation error caused by the rotation effect, wind field interference and other factors, improves the precision of high-altitude parabolic traceability, and provides a scientific basis for responsibility determination.

The invention provides a traffic jam intelligent identification method and system based on deep learning. The method comprises the steps of obtaining a multi-source traffic recognition optimized data set by obtaining and optimizing a multi-source traffic recognition data set, then constructing traffic scene label feature data, recognizing dynamic traffic scene category feature data, evaluating a traffic jam initial evaluation index, analyzing a traffic jam recognition influence factor, and optimizing to obtain a traffic jam correction index. And finally, performing threshold value comparison with a preset dynamic traffic jam identification threshold value, judging whether traffic jam exists or not according to a threshold value comparison result, analyzing and identifying a jam cause, and generating an intelligent traffic jam identification report at the same time. According to the invention, multi-source heterogeneous data is collected, data fusion and feature extraction are completed through the deep learning model, congestion identification is realized by combining dynamic scene classification and adaptive threshold determination, and congestion cause tracing analysis is completed by using the deep learning classification model, so that traffic congestion intelligent identification based on deep learning is realized.

The invention discloses a voltage transformer state influence factor analysis method, system and device and a medium, and belongs to the technical field of voltage fault analysis, and the method comprises the steps: collecting state parameters of a voltage transformer, carrying out the preprocessing of the state parameters, and constructing a nonlinear coupling relation between multidimensional feature tensor capture features; analyzing voltage fluctuation characteristics of the voltage transformer, and automatically correcting the insulation state evaluation model; and inputting abnormal feature vectors obtained by monitoring into a recursive attribution decision tree model, carrying out multi-dimensional abnormal clustering and causal analysis, generating a tracing report, and carrying out multi-dimensional evaluation and verification on an analysis result. According to the invention, continuous dynamic full-coverage monitoring of the equipment insulation health condition is realized, the accuracy and robustness of anomaly detection are improved, the phenomena of missing detection and false alarm are reduced, the scientificity of anomaly tracing analysis and the interpretation of early warning decision are improved, and the reliability of the system is improved. And the intelligent level of the monitoring and early warning system is improved by continuously adapting to new abnormal types and complex operation environments.

The invention discloses an industrial park pollution source rapid identification method based on fuzzy clustering, and the method comprises the following steps: S1, collecting pollution data in an industrial park, and carrying out the preprocessing of the pollution data; s2, identifying the pollution data by adopting a fuzzy clustering algorithm combining a possibility C mean value and a fuzzy rough C mean value; s3, constructing a pollution diffusion model, calculating a diffusion path of pollutants, and performing correction in combination with an identification result; s4, dynamically adjusting parameters of the possibility C mean value and the fuzzy rough C mean value by adopting an improved differential evolution algorithm; s5, when abnormal fluctuation occurs in the pollution data, dynamically adjusting the membership degree, and correcting the pollution source identification result; and S6, outputting an optimized pollution source identification result, and generating a pollution traceability analysis report. According to the industrial park pollution source identification method, the possibility C mean value algorithm and the fuzzy rough C mean value algorithm are combined, the improved differential evolution algorithm is used for optimizing parameters, and the industrial park pollution source identification accuracy, the anti-interference performance and the self-adaptive optimization capacity are improved.

The invention discloses a network security malicious traffic detection and tracing method based on a generative adversarial network, and the method comprises the following steps: S1, collecting normal traffic samples and malicious traffic samples, and forming a training data set; s2, training a malicious traffic detection model based on a generator and a discriminator of the improved generative adversarial network; s3, analyzing the input flow data by using the improved gating cycle unit network; s4, analyzing the input traffic data through the malicious traffic detection model, and outputting a malicious traffic alarm; s5, performing source tracking on the identified malicious traffic, and analyzing a propagation path of the malicious traffic; s6, dynamically adjusting malicious traffic detection model parameters based on the malicious traffic sample generated by the generator and the traceability result; and S7, continuously optimizing the generator and the discriminator. According to the method, the accuracy and robustness of malicious traffic detection are improved by combining the improved generative adversarial network and the improved gating circulation unit and through malicious traffic sample generation and traceability analysis.

The invention relates to a device and a method of a high-sensitivity suspension single-droplet sensor for monitoring PM2.5 pollution, belongs to the technical field of aerosol microdroplet spectrum trace analysis, and solves the problem that ppb-magnitude gas cannot be monitored in the prior art. The method comprises the following steps: pumping aerosol into a sample cell of a droplet suspension system in a single droplet form; introducing required reaction gas into the sample pool through an atmosphere control system; scattered light signals of the single liquid drops are respectively input into the dynamic signal processing system and the microdroplet imaging system; acquiring the characteristic peak displacement change of the metal ion sensing microdroplet; establishing a gas particle distribution Mie scattering inversion model based on micro-droplet dynamic response; inverting the radius of the liquid drop according to the stimulated Raman scattering / stimulated elastic scattering signal; according to the quantitative relationship of the reaction, calculating the generation of sulfate by the droplet suspension system (sensor); and judging which gas in the actual atmosphere is a main oxidation pathway for oxidizing sulfur dioxide to generate sulfate pollutants according to a calculation result.

The invention discloses a privacy leakage traceability method based on a data consanguinity map, and relates to the technical field of privacy leakage traceability, and the method comprises the steps: obtaining any node in a block chain network, and enabling the node to correspond to any enterprise; performing blood relationship analysis on the plurality of privacy data of any enterprise to generate a data blood relationship map; obtaining leaked privacy data, recording the data as a tracing starting point, and performing tracing analysis to obtain a tracing record; generating a visual traceability chart; performing prediction analysis on a first tracing path in the plurality of tracing paths to obtain a first leakage probability; and generating a troubleshooting priority list, and performing leakage troubleshooting and traceability on the private data. The technical problems that in the prior art, it is difficult to quickly locate the source for privacy data leakage, the checking process is lack of accuracy, and data security management is lack of effective means for accurately evaluating the leakage risk are solved, and the technical effects of achieving efficient tracing and accurate checking of privacy data leakage and improving the data security management capacity are achieved.

The invention relates to the technical field of power system power quality analysis and control, and discloses a power distribution network power quality multi-disturbance-source positioning and traceability analysis method, system and device and a medium, and the method comprises the steps: optimally configuring a monitoring point network according to a power distribution network topological structure; collecting three-phase voltage and current data of each monitoring point, and extracting multi-dimensional features after preprocessing; identifying disturbance types by adopting a hierarchical classification strategy, and separating composite disturbance; the disturbance information and the time sequence of each monitoring point are fused, and accurate space positioning of harmonic waves and transient disturbance sources is realized by combining parameters of the power distribution network; establishing a disturbance propagation model, and analyzing a propagation path and an influence range; and finally, generating a comprehensive traceability report containing a positioning result, disturbance characteristics, influence evaluation and governance suggestions, and calculating the overall traceability confidence. According to the method, the problems of accurate positioning and tracing in a multi-disturbance-source coexistence scene are effectively solved, and the positioning accuracy and the recognition reliability are improved.

The invention provides a project quality tracing and early warning method based on data analysis, and relates to the technical field of data analysis, and the method comprises the steps: obtaining a project quality data set, carrying out the traversal matching of the data set according to a key factor corresponding to a project stage, carrying out the replacement and recombination of a predetermined project quality data set through key feature parameters, and obtaining a project quality data set; taking the real-time data set as input information of a project quality prediction middle station and obtaining output information of the project quality prediction middle station, if the real-time prediction quality does not meet a preset project quality threshold value, sending out a primary early warning signal, and building a completed project stage based on the primary early warning signal; and a predetermined tracing early warning mechanism is called to perform tracing analysis on the completed project stage to obtain a real-time quality abnormity category, and a fine-grained early warning signal of the predetermined project is sent out, so that the problem that the discovery and control of the project quality problem lags behind the construction process and potential quality risks cannot be timely and accurately identified is solved; and the effect of improving the timeliness, accuracy and comprehensiveness of project quality management is achieved.

The invention discloses a semantic unification and anomaly traceability analysis method and system for heterogeneous program logs, and the method comprises the steps: firstly collecting original logs in various formats, and packaging the original logs into standard objects in a unified manner; thirdly, identifying fields through a rule and a semantic model, uniformly mapping and labeling semantic tags, eliminating semantic differences, and constructing log data with uniform semantics; automatically constructing a cross-system call chain based on the request identifier and the like; secondly, extracting log features and comparing the log features with a normal behavior model to carry out anomaly detection, and generating an abnormal event; combining time, calling and dependency to construct an anomaly propagation path, and reversely backtracking and positioning an anomaly source; abnormal priority scores are calculated according to the propagation range, the influence degree and the like, and sorting and visual display are carried out; through unified semantic modeling and automatic association analysis of heterogeneous logs, accurate tracing and intelligent positioning of cross-service link anomalies are realized, the operation and maintenance efficiency is effectively improved, and the dependence on artificial experience is reduced.

The invention provides a livestock number identification method and system combining AI vision and multi-target tracking, and the method comprises the steps: collecting a continuous video stream of a livestock activity region, generating a video frame sequence with a timestamp mark, carrying out the dynamic background modeling operation, extracting a contour feature set of a livestock object in each frame image, generating a multi-dimensional motion representation vector, and carrying out the recognition of the number of livestock. The method comprises the following steps: performing adaptive multi-scale feature enhancement on a livestock cluster region, decoupling pixel-level features of the livestock cluster region, outputting a form-motion joint coding sequence of each independent livestock individual, constructing a space-time incidence matrix based on the form-motion joint coding sequence, and extracting topological features of a livestock motion track through a lightweight feature extraction network. And generating a global ID mapping table containing the unique identifier of the individual, carrying out real-time statistics on the number of the livestock based on the global ID mapping table, and carrying out behavior tracing analysis on the individual livestock corresponding to the abnormal movement track to obtain a number statistics result and an abnormal behavior identification result. The precision of livestock quantity statistics can be improved.

The invention discloses a pathogen transmission rapid early warning and traceability analysis method based on multi-source data fusion. The method comprises the steps of S1, collecting multi-source data such as drug sales, network behaviors, social media, outpatient diagnosis and traffic time and space; s2, through cleaning and standardized preprocessing, social text disease features are extracted by adopting BERT; s3, mining a comprehensive weak signal through single-source anomaly detection and multi-source correlation analysis; s4, fusing the features by using an Attention-LSTM model, and outputting a regional risk index; s5, training an early warning model based on historical data, and setting a three-level threshold to trigger early warning; and S6, positioning a propagation starting point in combination with the spatio-temporal data, and constructing a propagation chain through a graph neural network. The early warning is advanced by 3-7 days, the traceability precision reaches the community level, and the prevention and control precision is improved.