243 results about "Cause analysis" patented technology

The invention discloses a road traffic accident cause analysis and responsibility judgment method and system, and relates to the technical field of traffic management data processing, and the method comprises the steps: carrying out the credible collection of multi-source data, and constructing an evidence chain; performing multi-source data preprocessing and cross-modal fusion optimization; performing multi-dimensional cause intelligent analysis; performing responsibility judgment based on a quantification rule; and the responsibility judgment result is subjected to multi-dimensional rechecking and rule iteration adaptation. According to the road traffic accident cause analysis and responsibility judgment method and system, through a full-process closed-loop design of data acquisition, preprocessing, cause analysis, responsibility judgment, re-checking iteration and report evidence storage, technologies such as multi-source perception and an AI algorithm are integrated; the method solves the problems of uncredible evidence chain, one-sided cause traceability, non-uniform judgment standard, low cooperation efficiency and the like in traditional accident processing, realizes intelligence, standardization, compliance and traceability of accident processing, remarkably improves the credibility, processing efficiency and judicial suitability of a judgment result, and provides core technical support for modernization of traffic control.

The invention provides a traffic abnormity and congestion cause analysis method and system based on a knowledge graph, and relates to the technical field of intelligent traffic perception, and the method comprises the steps: carrying out the target detection through employing preprocessed laser radar point cloud data, recognizing traffic participants, carrying out the continuous frame tracking of the traffic participants, and extracting the motion track and behavior characteristics in an event dimension; identifying a behavior event of the traffic target based on the motion trail and the behavior characteristics, binding the identified behavior event of the traffic target to a corresponding target entity, and performing target behavior modeling to form standardized structured information; based on structured information of a traffic target, an intersection-oriented traffic state knowledge graph is constructed, a rule-based abnormal event judgment module is utilized to perform semantic analysis on behavior and event nodes in the traffic state knowledge graph, abnormal traffic events are identified, and potential causes causing traffic congestion are traced. According to the invention, refined understanding and active perception of the intersection traffic state can be realized.

The invention belongs to the technical field of petroleum and natural gas development engineering, discloses a shale reservoir three-dimensional dessert prediction method based on physical-data dual drive, and solves the defects of an existing shale reservoir dessert evaluation method in the aspect of space continuity. The prediction method comprises the following steps: (1) constructing a three-dimensional attribute field, and forming a multi-channel standardized three-dimensional data volume through voxelization processing; (2) fusing multi-parameter attributes of the shale reservoir and constructing a multi-channel three-dimensional feature body; (3) constructing a dessert prediction model based on the physically constrained space-attribute joint attention three-dimensional convolutional network; and (4) calculating a comprehensive dessert index, performing dessert grading and cause analysis, analyzing and quantifying the contribution degree of each attribute to dessert grading, and analyzing the dessert connectivity and the advantage extension direction. The predicted productivity potential and fracturing potential dessert field keeps reasonable gradient change and continuous distribution in the inter-well area, and the inter-well prediction error is small.

The invention provides a bridge monitoring data abnormal value intelligent judgment and processing system, which comprises a monitoring module used for acquiring dynamic displacement data of a plurality of monitoring points of a bridge in a non-contact manner; the preprocessing module is used for performing time synchronization and quality fusion on the displacement, load and environment data to generate multi-dimensional time sequence data; the anomaly recognition module is used for extracting collaborative deformation and power fingerprint features and performing anomaly recognition and initial classification based on the feature deviation degree; the abnormality diagnosis module is used for performing abnormality cause analysis in combination with the load and the environmental condition and outputting an abnormality diagnosis result; and the strategy generation module is used for generating a grading early warning and disposal strategy according to the diagnosis result and the structural key index usage degree. The accuracy and efficiency of bridge structure safety monitoring can be improved, and safe operation of a bridge is guaranteed.

The invention provides an energy storage device damage diagnosis and interaction system based on penetration vision and a large model, and relates to the technical field of energy storage device damage diagnos.The energy storage device damage diagnosis and interaction system comprises a penetration type 3D sensing module, a periodic topology analysis module, a feature mapping and retrieval module and a large model reasoning and interaction module, and the energy storage device is scanned and reconstructed; the method comprises the following steps: constructing a time-space decoupling periodic Transform network, introducing a periodic mask matrix to force the network to pay attention to a repeatability rule of an internal structure of a battery, and identifying internal tiny deformation and structural damage by calculating topological consistency under the condition that a large amount of negative sample training is not needed; visual defect features are mapped into text embedding by utilizing a feature projection technology, a diagnostic report containing physical cause analysis and maintenance suggestions is generated by combining a retrieval enhancement generation technology and a large language model, and a user is supported to perform interactive questions and answers in a natural language. The method can solve the problems that in the prior art, three-dimensional deformation is difficult to quantify, small samples are difficult to train, and intelligent decision-making ability is lacked.

The invention relates to the technical field of wind power operation and maintenance, and discloses a wind power blade crack diagnosis method and system based on knowledge graph large model enhancement, and the method comprises the steps: S1, edge end image collection and crack semantic recognition: an edge end carries out the crack detection and semantic feature extraction of a collected wind power blade image sequence through an ECL-Net lightweight network, generating structured semantic information and transmitting the structured semantic information to a cloud; s2, cloud knowledge graph retrieval and semantic reasoning: the cloud performs semantic retrieval and causal reasoning on the structured semantic information through a KFR-Net knowledge graph reasoning network based on a pre-constructed wind power blade operation and maintenance knowledge graph, and outputs a knowledge retrieval result; and S3, multi-modal information is fused through an MMR-Cogntive multi-modal memory retrieval-cognitive generation network, and a structured maintenance report including crack detection summarization, cause analysis, maintenance measures and risk assessment is generated. The method has the advantage that intelligent closed-loop management of wind power blade cracks from detection to maintenance suggestions is realized.

The invention relates to the technical field of medical intelligent decision, in particular to an operating room nursing key node decision support system, which comprises the following steps: establishing a nursing operation reference containing a standard execution sequence and a parameter interval based on historical cases; by integrating discrete operation and continuous monitoring data in a real-time operation, a nursing process event chain with unified semantics is formed. The system compares the chain of events with an execution criterion to identify potentially risky segments and automatically extracts, for each segment, a complete operating room multi-dimensional state snapshot within a specific time window before and after its occurrence. And dynamically constructing a cause deduction network according to the state snapshots, and speculating cause chains possibly causing risks by traversing the network. And integrating all deviation information and cause analysis results, and generating a structured decision intervention prompt. According to the invention, deepening from automatic risk identification to root intelligent diagnosis can be realized, and accurate and efficient decision support is provided for operating room nursing.

The invention discloses an automatic driving traffic accident intelligent analysis system based on sand table simulation, and relates to the technical field of vehicle automatic driving. The system comprises a sand table simulation scene construction module which is used for simulating a high-risk scene of an automatic driving traffic accident and providing a physical and digital twinborn combined experiment environment; the automatic driving data acquisition and perception module is used for acquiring vehicle sensor data in real time and realizing environment perception and behavior decision simulation; the traffic accident intelligent analysis core module is used for realizing accident cause analysis and responsibility determination based on multi-source data and a knowledge base; and the cloud control platform and data visualization module is used for monitoring sand table simulation data in real time and providing a visual interface and an interaction function. The system can simulate and analyze the performance of automatic driving in various complex traffic environments and perform analysis and prediction, the analysis and prediction accuracy is high, and the analysis effect is better.

The invention discloses a land subsidence area permeable channel identification and cause analysis method based on a high-density electrical method, which overcomes the limitation of traditional single profile data interpretation by arranging a three-dimensional observation grid and fusing and collecting multi-source data. Through combination of finite element three-dimensional inversion and the improved convolutional neural network model, accurate description of the three-dimensional spatial morphology, the extension direction and the connectivity of the permeable channel is realized, and the accuracy and the spatial resolution of low-resistance abnormal region identification are improved. Compared with the prior art, the method has remarkable advantages in the aspects of permeable channel recognition accuracy, cause analysis comprehensiveness, result application practicability and the like, and effective support can be provided for long-acting prevention and control work of geological disasters in land subsidence areas.

The invention discloses an atmospheric pollution case system, a case analysis method and a medium, and the system comprises a basic information layer which is used for storing the whole-process feature data and treatment measure data of a pollution event; the logic analysis layer is used for carrying out quality control on the data, carrying out source analysis and cause analysis on the whole-process feature data of the pollution event after the quality control, and carrying out effect evaluation on the treatment measures after the quality control; and the comprehensive decision-making layer is used for comparing historical cases according to the retrieval conditions and matching similar historical cases, and finding out treatment measures corresponding to the similar historical cases according to the similar historical cases, or formulating targeted new treatment measures according to the corresponding treatment measures. The invention provides a'basic information-logical analysis-comprehensive decision 'three-layer progressive architecture case library, so that other modules can be conveniently expanded on the architecture of the case library subsequently; and multi-source data are systematically integrated, so that the problem of fragmentation of existing case data is solved.

The invention discloses an aviation equipment intelligent pricing method and system and a medium, and the method comprises the steps: carrying out the fusion processing and feature extraction of multi-source heterogeneous data, such as aviation material internal transaction data, external market information data, supply chain state data, and regulation event text data, and generating a standardized feature vector; utilizing a natural language model to extract structured event features from the law and regulation event text data; inputting the standardized feature vector into a pre-training price prediction model adopting a mixed architecture of an integrated learning algorithm and a time sequence deep learning algorithm, and outputting a reference price prediction value and a price prediction interval; according to a pricing strategy instruction selected by a user, a final pricing result is generated through a strategy mapping module, and an interpretable price cause analysis report is output, so that the accuracy and adaptability of aerial material pricing are effectively improved, the dynamic response of various pricing strategies is supported, the transparency and interpretability of a decision-making process are enhanced, and the user experience is improved. And scientific and reliable intelligent decision support is provided for aviation equipment pricing.

The invention relates to a multi-agent-based personalized opera action teaching assistance method and system. The method comprises the steps of inputting a student portrait into a planning agent to generate a personalized learning plan; collecting student follow-up training videos in real time, extracting to obtain a key point sequence, inputting the key point sequence into the lightweight action matching model, calculating the similarity between student follow-up training actions and standard actions in real time, and outputting an action similarity score; inputting key point data obtained after the follow-up training is finished into an execution agent to obtain opera action characteristics, performing deviation judgment and reason analysis through a large language model, generating correction suggestions, and realizing teaching assistance; and after a learning cycle is completed, inputting deviation judgment and reason analysis results into the reflection agent, carrying out statistics on a deviation trend and a skill improvement amplitude, outputting a portrait updating suggestion, updating the student portrait through an index moving average algorithm, and triggering a new round of learning planning. Compared with the prior art, the intelligent level and effectiveness of Chinese opera action teaching assistance are remarkably improved.

The invention discloses an intelligent substation state evaluation method and system based on digital twinning, and relates to the technical field of state evaluation, and the method comprises the steps: firstly constructing a heterogeneous knowledge graph capable of precisely mapping a physical entity logic relation based on the ontology definition, asset information and topological data of a substation; then, massive real-time fast-change data streams are introduced into the atlas framework, and node states are dynamically mapped by using a time sequence feature coding technology; and a complex message passing and aggregation mechanism is carried out in the graph structure through the relation perception graph neural network, and node embedding representation with local personality and global generality is generated. And finally, synchronously finishing equipment risk prediction at a micro level and total-station comprehensive evaluation at a macro level based on the high-dimensional representation, and carrying out path backtracking and cause analysis on a high-risk state based on map relevance. Therefore, the problem of equipment health and system operation state separation can be solved, and intelligent substation panoramic state perception and risk early warning with interpretability can be realized.

The invention belongs to the technical field of power system safety monitoring, and particularly relates to an electric leakage and electricity larceny prevention positioning system and device.The system comprises a multi-modal information acquisition module, a data preprocessing and fusion module, a feature extraction module, a cause analysis and distinguishing module, a precise positioning module and a differentiation response module; the multi-modal information acquisition module acquires electrical parameters and environment multi-dimensional data, and extracts a feature set after preprocessing and fusion; the cause analysis and distinguishing module is used for realizing accurate judgment on abnormal types and causes through feature classification and dual threshold matching; the precise positioning module is combined with topological data and multi-parameter correction to realize high-precision positioning; the differential response module triggers exclusive treatment measures for electric leakage and electricity stealing, and the device is embedded equipment and bears all functions of the system. According to the method, the problems of fuzzy anomaly judgment and low positioning precision in the traditional technology are solved, the anomaly handling precision and efficiency are improved, and the power grid safety and the power supply enterprise rights and interests are guaranteed.

A data processing system implements performing a root cause analysis that includes identifying a first anomalous signal data predictive of a root cause of a first anomaly in signal data received from a computing system, analyzing the sub-signals of the first anomalous signal data to generate labeled training data, training a gradient boosted tree model using the labeled training data, generating a decision tree based approximating a predictive performance of the gradient boosted tree model, determining insights data predictive of the root cause of the first anomaly based on the gradient boosted tree model and the decision tree, aggregating the insights and analyzing the aggregated insights data to determine a predicted root cause for the first anomaly, determining a confidence level associated with the predicted root cause, and categorizing the predicted root cause into one of a plurality of categories based on the confidence level.

The invention discloses a multi-source data fusion-oriented comprehensive geological cause analysis method and system. The method comprises the following steps of 1, generating semantic mapping data; 2, constructing a cause characteristic pedigree tree, and outputting cause main chain data; step 3, generating geological space-time interlacing data; 4, limiting a time window and a space window, and generating spectrum fusion modeling data through a spectrum-guided fusion converter; 5, constructing a three-dimensional voxel grid, and carrying out dynamic clustering to generate a three-dimensional construction model; step 6, generating geological evolution field data; and 7, inputting the geological evolution field data into the improved CrossViT model, setting an interlayer cross attention unit, reconstructing a Token interaction mode, introducing a cause field constraint attention modulation mechanism, and outputting a geological cause analysis result. According to the method, high-precision analysis of complex geological causes is realized through multi-source data semantic mapping, cause pedigree reasoning and CrossViT model improvement.

A system and method for temporal cause analysis of cybersecurity events. A method includes: creating a plurality of time series pairs for a computing environment, wherein each time series pair includes a first time series and a second time series, wherein each time series includes a series of data points arranged by time; determining a temporal relationship for at least one first time series pair of the plurality of time series pairs based on the series of data points of each time series of each of the plurality of time series pairs; identifying a root cause of a cyber event based on the temporal relationship of the at least one first time series pair; and remediating the cyber event based on the identified root cause.

The embodiment of the invention provides an external risk factor cause quantitative analysis method and system, and belongs to the technical field of risk assessment. The method comprises the steps that historical accident information is collected, accident cause analysis is performed on the historical accident information, and an external risk factor set is constructed and obtained; obtaining a corresponding relation matrix; constructing a directed weighted network based on the external risk factor set and the relation matrix, and constructing an evaluation system corresponding to the directed weighted network; constructing a corresponding external factor cause quantitative evaluation model; and evaluating the current operation state information of the target chemical enterprise based on the external factor cause quantitative evaluation model to obtain a safety risk evaluation result. According to the scheme, the problem that the external risk factor causes cannot be quantified in the safety risk assessment process of the chemical production process at present is solved, and the accuracy of the safety risk assessment result of the chemical production process is improved.

The invention discloses a road traffic anomaly detection method and system based on multi-modal data fusion, and relates to the field of traffic data processing, and the method comprises the steps: monitoring and obtaining multi-modal road traffic data, carrying out the traffic anomaly recognition based on the road traffic data of each modal, and obtaining a candidate abnormal event; obtaining candidate causes based on the space-time range of the candidate abnormal events, the similar historical abnormal events and the multi-modal road traffic data; based on the candidate abnormal events and the candidate causes, contribution degree analysis is carried out, multiple analysis results are obtained in combination with the multi-modal road traffic data, the analysis result with the highest credibility in the analysis results is extracted to serve as a road traffic anomaly detection result, and the analysis results comprise the abnormal events and the main causes. According to the invention, the technical problems of inaccurate road traffic abnormity detection result and insufficient reliability of cause analysis result in the prior art are solved.

This disclosure relates to a system (106) and a method (300) for blockchain-based root cause analysis of faults in a vehicle (102). In response to fault detection, the system (106) receives one or more DTCs, time-series data, and fault-related metadata from an electronic control unit (104). The system (106) generates one or more blocks (110) based on the received DTCs, time-series data, and metadata. The blocks (110) are sequentially added to a blockchain ledger (116), where they are validated to form a chain. The system (106) receives an update signal configured to address a root cause of the faults. This update signal is determined based on a root cause analysis of the chain formed by the validated blocks (110) in the blockchain ledger (116).

This application relates to the field of program repair processing technology, and in particular to an automatic program repair method and system based on a multi-level tree structure of a large language model. The method includes: using an information retrieval algorithm to retrieve a set of similar code examples corresponding to the program code to be repaired from a pre-defined code library; determining the model input content based on the set of similar code examples and the program code to be repaired; constructing a thought forest based on the model input content; performing error cause analysis based on the thought forest to obtain a set of high-confidence error causes; constructing a repair forest based on the set of high-confidence error causes; formulating repair strategies based on the repair forest to obtain a set of repair strategies; and performing autoregressive decoding operations based on the program code to be repaired, the set of similar code examples, and the set of repair strategies to obtain the patch code corresponding to the program code to be repaired. This application facilitates improvements in the accuracy and stability of the program repair process.

The invention relates to an automated method for data-based error cause analysis for at least one deviation in a container treatment process in a container treatment machine of the beverage / filling industry from a target container treatment process. The method comprises: determining at least one deviation in a container treatment process in a container treatment machine of the beverage / filling industry from a target container treatment process by analysing sensor data and / or measurement data from sensors and / or measuring devices provided in the container treatment machine; bringing the determined at least one deviation into context with a most probable error cause; outputting the most probable error cause for an error which causes the at least one deviation and an action instruction for rectifying the error; and applying the action instruction to rectify the error.

The application provides an enterprise resource planning system data flow conversion monitoring method, relates to the technical field of abnormal cause analysis in an enterprise resource planning system, and aims to solve problems such as difficult extraction of abnormal propagation causality, unexplainable traceability path, and low efficiency of quasi-real-time cause analysis in a multi-node complex business process. The core technical scheme includes the following steps: collecting and standardizing time series data of business key state indicators, establishing a local causal correlation cache by using a sliding window and a causal reasoning algorithm, recursively tracing back to an abnormal node as a terminal to form a preliminary causal path, screening high-confidence causal links through multi-dimensional time consistency evaluation, generating a simplified dynamic path by using a node semantic aggregation mechanism, and finally embedding the path in an operation and maintenance interface in the form of a visual trajectory to realize interactive cause analysis verification. The scheme improves the accuracy and explainability of abnormal cause analysis, realizes full-process automation and man-machine fusion optimization from data to root cause links, and significantly enhances the intelligent level of system abnormality handling.

The present invention provides an intelligent predictive-diagnosis system based on an ultra-large language model. The intelligent predictive-diagnosis device based on an ultra-large language model, according to the present invention, comprises: a preprocessing unit for extracting, upon occurrence of an early warning corresponding to closed-type industrial equipment, tag information related to the early warning, then converting the extracted tag information into a predetermined standard signal name, converting, on the basis of signal data included in the tag information, a result of analyzing a data trend into text information, and generating predictive-diagnosis request information including the converted standard signal name and text information; a first processing unit for outputting warning cause analysis information as a result of inquiring and analyzing, by means of the predictive-diagnosis request information, a first predictive-diagnosis database pre-stored in correspondence to the closed-type industrial equipment; and a second processing unit for outputting a predictive diagnosis for the occurrence of the early warning and one or more pieces of equipment failure action information corresponding thereto by inputting the predictive-diagnosis request information and / or the warning cause analysis information into a diagnosis prediction module based on the ultra-large language model.

The invention discloses a track circuit fault cause analysis method based on text record data mining, and belongs to the technical field of track circuit fault diagnosis and analysis in a railway signal system, and the method comprises the following steps: S1, collecting a track circuit fault text, and processing the track circuit fault text into a standardized fault text R; s2, semantic features and word order features of the R are extracted and fused, and a feature set S is output after optimization of an SMOTE algorithm; s3, inputting the S into an FEML model, and outputting a large-class label corresponding to the fault text through parameter optimization, parallel training of a base learner and integration of a meta learner; s4, in combination with the weight value and a Dirichlet multi-term hybrid model, extracting and outputting a fine-grained fault type and a cause thereof under the large-class label; and S5, constructing a visual map by using Neo4j, and realizing association analysis through a Cyber query language. According to the method, the analysis model and the visual knowledge graph are constructed, fault causes are accurately mined, and the operation and maintenance efficiency is improved.

Information required for analyzing a future failure cause has been sometimes insufficient in a failure cause analysis system when inputting content of executed work in free description sentences and recording the content.　Provided is a work record management device for managing a work record indicating content of work for a target system, the work record management device comprising a work recording unit that includes: an interface unit that receives the work record indicating work of a worker for the target system and described in a free description format; a storage unit that stores a work record data model indicating a record item to be described as the work record; a work record structuring unit that structures the received work record by using a generative AI; and a work record confirmation unit that identifies insufficient information in the work record by using the record item.

The present application relates to the technical field of coal mine accident cause analysis, and proposes a coal mine accident cause analysis method based on a knowledge graph.The specific steps of the present application include: collecting coal mine roof accident reports and cleaning the text to obtain a roof accident word set;secondly, by extracting the accident cause nodes, accident attributes and their relationships of the word set, a roof accident cause knowledge graph is constructed;then, the similarity of the accident cause nodes is calculated, and node merging is performed based on the similarity;then, the cause attribute code and the accident attribute code are obtained, the cause correlation index of the accident cause node is calculated, and the single-factor risk index and the multi-factor risk index are obtained in combination with the accident attribute code;finally, according to the single-factor risk and the multi-factor risk, the comprehensive risk index is determined, and the danger degree of the accident cause node is sorted.The present application can effectively analyze the risk factors of coal mine roof accidents, thereby reducing the potential risk of roof accidents.

This invention relates to an optimization method and system for preventing coking in waste incinerators based on CFD and solid-phase combustion coupled simulation, belonging to the field of solid waste energy utilization and combustion optimization technology. The technical solution is as follows: S1. Full-process coupled numerical modeling and baseline operating condition simulation; S2. Coking risk diagnosis and dominant cause analysis; S3. Two-level sequential collaborative optimization adjustment; S4. Numerical verification and engineering implementation of the optimization scheme; S5. Adaptive operation and closed-loop feedback control. This invention has the following beneficial effects: breakthroughs in the realism and reliability of numerical simulation, greatly improving the accuracy of diagnostic results and the reliability of optimization guidance; the control strategy possesses high innovation and synergy, the technical solution is specific, quantifiable, and highly operable, with extremely significant industrial application effects, outstanding economic benefits, improved boiler operating efficiency and overall economic benefits, and greatly enhanced equipment operating safety; and it forms a complete technical system that is replicable and scalable.

The invention belongs to the technical field of detection and analysis, and provides an HPLC detection method for taurocholic acid in snake gall bulbus fritilariae liquid, which comprises the following steps: collecting chromatograms of a test sample and a reference substance through HPLC, and judging whether a co-outflow phenomenon exists or not; if the co-outflow phenomenon occurs, interference proportion analysis is carried out, and main interferents are identified; the judged main interferents are removed; carrying out retention analysis to obtain a taurocholic acid retention rate, judging whether the taurocholic acid retention rate accords with expectation or not based on the taurocholic acid retention rate, and if not, judging whether a trailing signal is triggered or not; if the trailing signal is triggered, performing cause analysis to determine the cause of triggering the trailing signal; when the optimal methanol proportion is obtained, a comprehensive detection signal is triggered; if the comprehensive detection signal is triggered, performing comprehensive adaptation analysis of the HPLC detection method to obtain an adaptation index, and evaluating the comprehensive performance of the HPLC detection method according to the adaptation index; and the accuracy of HPLC taurocholic acid detection is effectively improved.

The present application relates to the technical field of sewage treatment, and particularly relates to a method for evaluating purification efficiency of urban sewage pipeline. Based on the topological structure and historical operation data of the target sewage pipeline network, the key internal nodes are selected and the monitoring points are arranged through in-situ process sensitivity sorting; the concentration data of the reference water quality indexes of all water quality monitoring nodes and the hydraulic flow velocity data between adjacent nodes are synchronously collected within a preset evaluation period; based on the above data and in combination with the geometric parameters of the pipeline, the purification efficiency of each pipe section is characterized and quantified to generate a pipe section purification characteristic data table; the dynamic simulation and spatial interpolation of the internal purification process of the pipeline network are performed to generate a dynamic quantification atlas; based on the atlas and the characteristic data table, the pipe section efficiency is evaluated, and the abnormal cause analysis is performed in association with the sensitivity characteristics of the key internal nodes. The multi-dimensional dynamic correlation of the in-situ purification process inside the pipeline network is described and visualized, and the fine and intelligent level of the pipeline network management is improved.