237 results about "Trace analysis" patented technology

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 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 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 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 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 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 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.

The invention discloses a high-interaction deception defense and attack tracing method based on an RASP technology, and the method specifically comprises the following steps: S1, RASP implantation and bait deployment, S2, attack detection and simulation triggering, S3, attack session redirection and high-interaction deception, S4, attack behavior monitoring and tracing data collection, and S5, tracing analysis and threat disposal. The invention relates to the technical field of network and information security. According to the high-interaction deception defense and attack tracing method based on the RASP technology, a deception mechanism is embedded into a real application / system in operation, and high fusion of a real business environment and a false trapping environment is achieved. Different from a traditional'main-auxiliary separation 'mode that a honeypot is placed in an independent network or a host, the method has the advantage that the application can be changed to generate a'trap' during running through the RASP. The seamless embedded spoofing makes attackers difficult to distinguish true and false: the attackers attack a part of a production system initially, and then are introduced into a parallel simulation space unconsciously.

The invention discloses a lake pollution traceability analysis method based on multi-source data assimilation and reverse diffusion, and relates to water pollution traceability analys.The lake pollution traceability analysis method comprises the steps that chemical pollution is monitored at the downstream of a lake, monitoring data are imported into a hydrodynamic force-water quality model after Kalman filtering, and spatial and temporal distribution data of pollutants in a water body are obtained; pollution source inversion is carried out based on a differential evolution Markov chain Monte Carlo method, and pollution source parameters are obtained; forward simulation is carried out, a flow velocity field and water depth are solved through a hydrodynamic force-water quality model, then a pollutant transport model is generated, pollution source parameters are used as input, a finite volume method discrete control equation is used based on the pollutant transport model to predict pollutant concentration space-time evolution, and pollution duration under natural attenuation is evaluated; and comparing real-time monitoring data with an expected effect, and evaluating a pollution traceability analysis effect. Pollution source positioning errors are reduced, pollution contribution rate calculation accuracy is improved, traceability efficiency is improved, the risk of excessive reagents is avoided, and treatment cost is reduced.

The invention discloses a flood season pollution source-tracing evidence-obtaining monitoring system and method based on multi-source evidence chain fusion, and relates to the cross technical field of environment monitoring and judicial evidence-obtaining, and the system comprises an intelligent cooperative monitoring network unit, a full evidence chain collection evidence-storage module, a multi-modal source-tracing analysis engine unit and a block chain evidence-fixing platform module. Multi-source heterogeneous information such as water quality spectrums, drain outlet thermodynamic diagrams, enterprise production logs and Beidou hydrological data is integrated through a millisecond-level timestamp synchronization technology, a unique'pollution event fingerprint 'is generated, the problems that a traditional monitoring system is single in data dimension and lacks relevance are solved, a complete evidence chain of time-space relevance is formed, and the system has a good application prospect. And meanwhile, a block chain double-channel architecture is innovatively adopted, and an electronic evidence packet which conforms to judicial expertise specifications and is attached with an electronic signature is automatically generated, so that the legal credibility and the credibility of data are remarkably enhanced, and the core defects that traditional electronic data are easy to tamper and have no legal endorsement are overcome.

The invention relates to the field of drainage pipe network traceability, in particular to a drainage pipe network inflow and seepage traceability analysis method and system. The method comprises the steps that a drainage pipe network time sequence monitoring data set is acquired, water quality parameter concentration changes in the drainage pipe network time sequence monitoring data set are analyzed, and pollutant instantaneous dilution is generated; obtaining an infiltration water quality fingerprint database, performing correlation analysis on the pollutant instantaneous dilution degree and the infiltration water quality fingerprint database, and determining water quality infiltration fingerprint similarity; on the basis of the water quality infiltration fingerprint similarity, the contribution change of external water inflow to the drainage pipe network flow is speculated, and a flow rainfall response coefficient is generated; and based on the flow rainfall response coefficient and the water quality infiltration fingerprint similarity, carrying out comprehensive traceability analysis, and generating an inflow and infiltration traceability analysis report. According to the method, in the inflow and infiltration traceability process, dynamic contributions of different external water bodies can be accurately stripped and quantified, and economic waste and potential safety hazards are avoided.

The application belongs to the technical field of material surface structure detection, and provides a paper defect detection method and system, height information of each pixel point on the surface of the paper is acquired through an optical distance measuring sensor, and then a height distribution map is constructed, and the fluctuation mode of the surface structure contour of the paper is analyzed by combining convolution difference, local trend and change intensity, through curve fitting and trend clustering of the structure characteristics, typical surface structure types such as periodic corrugation and non-periodic wrinkle can be effectively distinguished, so that automatic identification of the microstructure of the paper surface and structure cause tracing analysis are realized, and meanwhile, accurate identification of the abnormal area is realized, and the universality and practicality of the system in the actual industrial scene are improved.

The invention discloses a garden environment informatization management system based on diversified analysis, which belongs to the technical field of garden environment management and comprises a management platform, a parameter monitoring module, a vegetation growth evaluation module, an environment influence analysis module, an early warning unit and a decision execution module. According to the method, a partition management mode is adopted, the vegetation growth state of a local area is comprehensively analyzed based on soil data and vegetation apparent images of a single area, the vegetation abnormal growth type of an abnormal management area is obtained through abnormal recognition, and historical data tracing analysis is combined, so that the vegetation abnormal growth state of the abnormal management area is obtained. The method specifically comprises the steps of performing root cause judgment based on historical meteorological data and facility data so as to generate different strategy information according to a root cause judgment result, constructing a closed-loop management system from anomaly recognition to data attribution to decision execution, and performing decision execution by using diversified data and an intelligent analysis technology and taking data as driving. And comprehensive monitoring, multi-dimensional analysis and accurate decision making of environmental elements are realized.

The invention relates to the technical field of valve detection, and discloses a ball valve airtightness detection method and system. The method comprises the following steps: in a pressurization and pressure maintaining test, synchronously acquiring multi-channel pressure information flow, and constructing a three-dimensional pressure field dynamic evolution sequence reflecting a sealing interface pressure transmission process; time-frequency domain cross comparison is carried out on the sequence and a leakage signal simulation library, potential leakage characteristic signals are extracted, reverse tracing analysis of a leakage energy propagation path is carried out on the potential leakage characteristic signals, and therefore space coordinates and energy intensity of a suspected leakage source are determined. And finally, calculating an overall leakage state index, and generating a sealing state evaluation map and a maintenance operation sequence. According to the method, the transformation from single-point pressure monitoring to three-dimensional pressure field dynamic analysis is realized, the position of a leakage source can be accurately positioned, and the detection intuition and the maintenance pertinence are improved.

The invention discloses a water pollution traceability analysis and ecological restoration decision support system. The system comprises a data acquisition layer, a data management and fusion layer, a pollution traceability analysis layer, an ecological restoration decision layer and a user interaction and visualization layer. The system collects multi-source water quality, hydrology and discharge data by deploying a distributed water quality sensing network, remote sensing and unmanned aerial vehicle monitoring equipment and a pollution discharge source online access module; through data cleaning, fusion and knowledge modeling, a pollution propagation model and a traceability inversion algorithm are constructed, and a pollution source and a contribution rate are identified; and simulating a multi-class repair measure response process in the digital twin water environment, and automatically recommending an optimal repair scheme in combination with a multi-objective optimization algorithm. The method has the advantages of accurate traceability, scientific decision, eco-friendliness, dynamic optimization and the like, and is suitable for pollution treatment and ecological restoration scenes of various water bodies such as drainage basins, lakes, urban rivers and the like.

The invention discloses an industrial fault traceability analysis method based on neural causal discovery and causal feature screening, which introduces a neural Granger component-by-component long and short-term memory network with group sparse characteristics, and can effectively capture sparse causal relationships in a system. Meanwhile, in combination with an arrangement feature importance method, the extracted causal relationships are screened by using model characteristics, false causals are removed, a causal adjacency matrix is obtained, and the robustness and credibility of the result are enhanced. And constructing a reachable matrix through the causal adjacency matrix, and expressing and positioning a root node in a causal path in combination with position scoring, thereby realizing accurate tracing of the industrial fault. The method is based on a data driving method, does not need to depend on mechanism knowledge, can be effectively applied to an industrial system with a complex control loop, and aims to improve the effectiveness of causal analysis, better perform root cause analysis and determine a fault source, and improve the safety and reliability of an industrial production process.

The invention discloses an intelligent sewage treatment monitoring system and method, and relates to the technical field of sewage treatment.The intelligent sewage treatment monitoring system and method achieve transformation from parameter standard exceeding alarm to efficiency health degree evaluation by establishing a standard efficiency curve of each key unit and calculating a real-time health index; the degradation trend of the unit performance can be captured earlier and more accurately; the overall process efficiency coefficient based on the environmental parameter dynamic adjustment weight is introduced, so that the evaluation is more suitable for the actual operation condition, and the influence of bottleneck units under different conditions is highlighted; a cross-unit root cause tracing analysis mechanism can effectively distinguish unit faults from upstream impact conduction, so that the accuracy and efficiency of abnormality diagnosis are greatly improved, and wrong regulation and control are avoided; a coordinated regulation instruction set is generated based on root cause analysis, systematicness and collaboration of regulation actions are ensured, current abnormity is solved, and it is more emphasized to maintain stability and efficiency of the whole process; the intelligent operation level of a sewage treatment plant is obviously improved.

The invention provides a voltage stability risk dynamic assessment method and system based on data driving, and belongs to the technical field of voltage stability risk assessment. Constructing a power grid state vector based on the preprocessed power grid multi-source operation data; learning a causal dependency relationship among parameters in the power grid state vector by using a causal discovery algorithm, and generating a dynamic causal graph representing an internal causal structure of current power grid operation; performing risk traceability analysis based on a dynamic causal graph, and performing causal effect deduction on regulation and control strategies associated with the key nodes by using a causal intervention model; and generating a voltage stability risk dynamic assessment report according to a risk traceability analysis result and a causal effect deduction result. According to the invention, by constructing the dynamic causal graph, real-time and accurate traceability and quantitative evaluation of the voltage stability risk are realized, and causal effect deduction can be carried out on the regulation and control strategy, so that the intervention effect is predicted in advance.

The invention discloses an automatic AI memory leak intelligent positioning and root cause traceability analysis system, and relates to the technical field of memory leak intelligent positioning and root cause traceability analysis. The automatic AI memory leak intelligent positioning and root cause traceability analysis method includes the following steps: S1, data acquisition: performing index acquisition through a multi-dimensional data acquisition unit, and supporting physical memory and virtual memory utilization rate, heap and stack memory allocation and log release, object creation, track destruction and thread execution state index acquisition; according to the automatic AI memory leak intelligent positioning and root cause tracing analysis system, the leak position is positioned, the root cause can be analyzed from dimensions such as code defects, improper configuration and resource competition, the leak recurrence rate is reduced, normal memory fluctuation and a real leak mode are effectively distinguished through sliding window time sequence analysis, exception screening and feature engineering optimization, and the reliability of the system is improved. False alarms are reduced, and the accuracy of abnormal event identification is improved.

The invention discloses an error traceability analysis method and system for an electric energy meter, and relates to the technical field of electric energy metering error analysis, and the method comprises the steps: constructing a multi-source error data set covering the full operation cycle of the electric energy meter, and the multi-source error data set comprises field operation errors, indoor verification errors and full-life state data; performing time alignment and space mapping on the set to generate a space-time associated error data cube; calling a pre-training model to perform hierarchical analysis on the data cube, and extracting features to obtain a multi-dimensional error feature vector and a preliminary traceability hypothesis; quantifying the error contribution degree, and identifying key influence factors; and iteratively optimizing the traceability path, and generating a final traceability conclusion and a confidence evaluation report. According to the method, the problems of single data and inaccurate traceability in the prior art are solved, and the integrity and reliability of error traceability are improved.

The invention relates to the technical field of knowledge maps, and provides a food safety index abnormity identification method and system based on a knowledge map. The method comprises the following steps: extracting an index safety limit value from a multi-source standard text; networking to obtain an abnormal record, performing association analysis, and quantifying a troubleshooting priority coefficient; dividing K levels of risk nodes and constructing an inverted graph; taking the limit value as a knowledge base injection map; performing small-batch pilot production detection on the hierarchical trigger atlas to generate a defect vector set; performing cross-sample clustering analysis on the defect vector set, identifying N common defects, and generating a fault hypothesis and confidence probability set. The technical problems that traditional food safety detection depends on a single standard, risk identification is difficult to carry out on complex production line links, detection results are fragmented, and the abnormity tracing efficiency is low are solved, and the purposes of realizing multi-source standard fusion, abnormity risk grading and defect tracing analysis through the knowledge graph and improving the detection accuracy are achieved. And the technical effects of comprehensiveness of food safety index anomaly identification and high efficiency of traceability are improved.

The present application relates to the technical field of molecular detection, and particularly relates to a specific primer combination for distinguishing Enteromorpha prolifera and related species, application and molecular identification method thereof. The primer combination comprises an upstream primer SEQ1 and a downstream primer SEQ2. The primer sequence of SEQ1 is shown as SEQ ID NO. 01, and the primer sequence of SEQ2 is shown as SEQ ID NO. 02. The primer combination of the present application is designed based on the genomic difference of two kinds of algae, has high specificity and sensitivity, can effectively avoid cross reaction, and ensures the accuracy of the detection result. Meanwhile, combined with the real-time fluorescent quantitative PCR (qPCR) technology, the method can effectively quantify the abundance of Enteromorpha prolifera in environmental samples or laboratory samples, and provides reliable technical support for early warning, tracing analysis and prevention and control strategy of green tide outbreak.

The invention provides a power grid bus protection method and system combined with voltage sag traceability and a medium, and relates to the technical field of bus protection, and the method comprises the steps: collecting a first bus protection device and a second bus protection device of an upstream circuit and a downstream circuit, carrying out the bidirectional voltage sag wrong conduction relation analysis under the triggering of voltage sag protection, and generating a bidirectional wrong conduction relation; establishing a data sharing channel; monitoring the voltage sag behavior of the first bus circuit by the first bus protection device, performing traceability analysis, sharing a traceability result with the second bus protection device through the sharing channel, and calling a bidirectional false conduction relation to judge whether false conduction exists or not; and if not, the first bus protection device performs circuit protection operation based on the traceability result. The technical problem that unnecessary protection actions are caused due to the fact that the fault source cannot be accurately judged due to lack of voltage sag traceability analysis in the prior art is solved, the bus protection accuracy and reliability are improved, misoperation is reduced, and then the stability of a power grid is improved.

The invention relates to the technical field of historical building structure health monitoring, and discloses a historical building safety performance evaluation method and system. The method comprises the steps of obtaining a structure response signal through monitoring of a multi-position sensor, and generating a fusion feature map through a multi-scale feature sensing network. And performing three-dimensional reconstruction based on the atlas, adaptively dividing difference precision entity units according to feature density, and constructing the high-fidelity digital twinborn body. And driving the twin to perform time-history evolution under simulation of long-term environment excitation, and extracting damage evolution behavior fingerprints. And performing multi-round iterative comparison and reverse traceability analysis on the behavior fingerprint and a safety baseline containing material time-varying degradation and component interaction influence, identifying a failure mode and an evolution path, calculating a residual bearing capacity margin of a key component, and generating a structural life map in combination with a component interaction network. According to the method, high-fidelity modeling based on monitoring data and reverse intelligent diagnosis of dynamic damage evolution are realized.