172 results about "Trace analysis" patented technology

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 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 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 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 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 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 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 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 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 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 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.

This invention belongs to the field of SERS detection technology and relates to the application of an Ag / Bi4O5Br2 / Ni-Fe double hydroxide composite SERS substrate in the detection of CV in wastewater. First, a Bi4O5Br2 / Ni-Fe double hydroxide is prepared, and silver nanoparticles are loaded onto its surface using a liquid-phase chemical reduction method. This material combines the LSPR effect of silver nanoparticles with the interfacial coupling and multiple active sites of the Ni-Fe double hydroxide, enabling the regulation of interfacial charge transfer and enhancing molecular adsorption. Utilizing the excellent adsorption and enrichment properties of Bi4O5Br2, a highly efficient charge transport system is synergistically constructed from multiple components, improving the SERS chemical enhancement effect and amplifying the CV Raman signal. This invention overcomes the shortcomings of weak signals and insufficient stability of single substrates, achieving sensitive detection of trace CV in water. The substrate preparation process is simple, environmentally friendly, and highly practical, showing broad application prospects in the field of trace CV analysis in aquatic environments.

The application provides a kind of information security event's tracking and tracing analysis method and system, method includes: real-time monitoring information security event whether there is alarm information, when monitoring the alarm information, obtain the original data message of the information security event;The attribute information of the industrial control protocol of the information security event is obtained by identifying the original data message;According to the attribute information, the malicious operation of the information security event is searched and matched with the malicious operation in the plan library, and the information security event is tracked and traced according to the matching result.The application provides a set of event alarm-quick message analysis-tracing tracking information security event tracking and tracing processing procedure, which can shorten the response time and improve the efficiency of emergency tracing tracking.

The application discloses a security vulnerability tracing analysis method and system for application software, and relates to the technical field of network security.The application solves the technical problem that it is difficult to quickly and accurately locate the source of the vulnerability and assess the risk level, thereby leading to the difficulty in formulating an effective security protection strategy.The application determines the source and propagation path of the vulnerability attack by performing correlation analysis on abnormal traffic, performs reverse DNS query and authenticity verification on the source IP address, and further improves the accuracy of the tracing result, thereby providing strong support for quickly locating the attack source.In terms of vulnerability risk level calculation, basic index scores, time index scores and environment index scores are calculated respectively, and the vulnerability score index is calculated in combination with respective weights, so that the actual situation is fully considered, the evaluation result is more in line with the actual risk, more accurate risk early warning, more targeted vulnerability repair and protection strategies can be provided, and the application software security protection level is improved.

A method includes grouping signal traces based on signal trace characteristics. The method includes sampling at least one of a set of signal traces to a uniform time sequence and generating an image including visual indicators associated with the set of signal traces with signal trace characteristics. Each visual indicator corresponds to a signal characteristic of a respective signal trace of the set of signal traces and a time value based on the uniform time sequence. The method includes detecting a defect in operation of one or more components of manufacturing equipment based on a deviation of one of the visual indicators of the image. The method includes classifying the defect based on the respective signal trace corresponding to the image.

The present application relates to a kind of network security CTF competition multi-class cheating identification and trace analysis method, belong to network security technical field.The present application first monitors and records log to the process of player competition answering question;Then use finite state machine to analyze the logic abnormality of player of answering question step, based on normal distribution to judge the time abnormality of answering question step of player, and according to flag submission time, propose player correlation degree calculation method, find suspicious high similarity player, analyze and trace back the source of player cheating;Finally, collect and comprehensively judge suspicious player Writeup, answering question log under new and old environment, output cheating identification result and analysis report.The method effectively identifies the cheating behaviors in CTF competition, such as proxy participation, multiple accounts, plagiarism of other players' flags or solution ideas, and locates suspicious similar players.

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.

The invention discloses a BIM fusion carbon footprint mine complete cycle carbon emission metering system and method, and relates to the intelligent mine and green low carbon technology field, and the system comprises a carbon footprint enhanced BIM model module, a dynamic multi-source data acquisition and fusion module, a complete cycle carbon metering and traceability engine module, and a carbon flow visualization and intelligent analysis platform module; according to the system, a carbon footprint enhanced BIM model is constructed, dynamic multi-source data are fused, and carbon emission simulation, real-time metering and traceability analysis of the whole process of planning design, construction, operation production and decommissioning closing are achieved through a carbon metering engine. According to the invention, delicacy management of carbon footprints from enterprise level to component level is realized, the problems of incomplete cycle coverage, extensive measurement, difficulty in traceability and the like in the prior art are solved, and core technical support is provided for mine full life cycle low-carbon optimization and carbon asset accurate management.

Apparatuses, methods, and computer programs for detecting anomalies in distributed systems by distributed tracking data and self-attention based deep learning are disclosed. Distributed traces are received from a monitoring entity that monitors execution of functions in a distributed system. The distributed traces are analyzed for anomaly detection. Results of the anomaly detection are provided to a control entity for evaluation. The anomaly detection is performed by applying a self-attention based deep learning process to judge whether the distributed traces involve anomalies. Since the self-attention based deep learning only uses information from the traces of system monitoring, unsupervised anomaly detection can be performed by it. Since the self-attention based deep learning has a memory of the whole trace, end-to-end trace analysis can also be performed by it.

The utility model discloses a portable traditional Chinese medicine olfactory diagnosis instrument, which comprises a sample collection device, a gas enrichment device, a mass spectrometer, a data processor and a display, the mass spectrometer is sequentially connected with the data processor and the display, the gas enrichment device is test paper with an enrichment reagent encapsulating nano-metal particles, the enrichment efficiency is effectively improved, and the detection accuracy is improved. Therefore, the detection sensitivity and accuracy of the mass spectrometer are improved; the device is provided with the ion trap mass spectrometer, has the characteristics of high detection speed, high resolution and high sensitivity, and has performance advantages in qualitative and trace analysis aspects; and due to small volume and strong portability, the device can be conveniently integrated into portable or field detection equipment, can be used for auxiliary diagnosis of heart failure and asthma, and also has great clinical application value in the aspects of disease evolution and disease prognosis evaluation.

The invention discloses a power system oscillation traceability analysis method and system based on multi-method probability fusion, which can effectively solve the problem of oscillation source positioning error caused by sparse measurement data and compressed sensing. Firstly, the oscillation source probability of each node is calculated through an oscillation energy method, a frequency spectrum characteristic method, a correlation analysis method, a voltage fluctuation method and a phase angle gradient method; secondly, calculating the adaptive weight of each method based on an information entropy theory, and completing multi-method probability fusion; the fusion probability is optimized in combination with a power grid topological structure, and an oscillation source candidate sequence is determined; and finally, a comprehensive probability distribution diagram and a detailed analysis report are output through a visual module, and reliable technical support is provided for safe and stable operation of a power system.

The embodiment of the invention provides a test method and device for an operating system kernel based on a Rust language, and the method comprises the steps: obtaining a source code of the operating system kernel based on the Rust language, compiling the source code into an intermediate representation, and generating a function call graph among functions contained in the operating system kernel according to the intermediate representation; determining a target position corresponding to a preset potential anomaly in the intermediate representation and a target variable corresponding to the potential anomaly in the target position through a preset recognition rule; performing backward tracking analysis on the target variable according to the function call graph and the target position, and determining a system call interface associated with the target position in an operating system kernel and a call variable associated with the target variable in the system call interface; and according to the system call interface and the call variable, generating a test case for the operating system kernel.

The invention relates to the technical field of aero-engines, and discloses a cartridge receiver quality problem tracing method, which improves the tracing analysis efficiency and reduces the influence of human subjective factors. A data confidence coefficient formula is adopted to quantify data reliability, so that interference of low-quality data on a traceability result is avoided, and data support is more scientific; through a Bayesian network posterior probability formula, quantitative sorting of problem reasons is realized, manual subjective judgment errors are reduced, and traceability precision is improved; by integrating data of a casing design stage, a processing stage and a detection stage, a'data island 'is broken, a constructed knowledge graph realizes historical experience reuse, and a systematic knowledge accumulation and reuse mechanism is established, so that a historical solution can be quickly called when similar problems appear repeatedly, and the tracing efficiency is improved; through dual verification of an evidence chain and a relation chain, accuracy and interpretability of problem positioning are provided.

The invention discloses a multi-source heterogeneous data pollution traceability and management system based on end-edge-cloud collaboration. The system comprises a multi-source heterogeneous data acquisition module, a cloud-edge collaboration data management and control module, an environment traceability graph embedded sensing module, an interpretable traceability analysis module, a closed-loop regulation and control decision module and a pollution abatement feedback module. A sensing end multi-dimensional sensor collects various types of data, edge end preprocessing and cloud deep analysis cooperation are carried out, an abnormal sensing algorithm is embedded based on an environment traceability graph, a dynamic pollution source identification mechanism is constructed by an interpretable environment pollution traceability analysis model, and a strategy is adjusted in combination with a data pollution closed-loop regulation and control decision model. And the links of early warning, patrol, traceability, disposal and feedback are covered. According to the system, the robustness and maintainability of a multi-source data system are improved, rapid response, accurate traceability and intelligent closed-loop management of pollution events are realized, the problems of insufficient monitoring data collaboration and poor traceability treatment closed-loop performance are effectively solved, and the fine management requirement of environment monitoring is met.