Underground drainage pipe channel abnormity identification and positioning method based on topology high-speed exploration

Abstract

The invention relates to the technical field of drainage pipe channel monitoring, and discloses an underground drainage pipe channel abnormity identification and positioning technology based on topology high-speed exploration. The method comprises the following steps: S1, carrying out topological structure construction on GIS data of an underground drainage pipe channel, and obtaining a topological connection relation of the drainage pipe channel; s2, based on a technology of combining a cache and a search engine, carrying out topological high-speed tracing on the drainage pipe channel, and outputting an upstream and downstream topological relation of a pipeline; and S3, automatically identifying abnormal conditions, including mixed connection, clogging, rainfall inflow, end breakage, reverse slope and pipe diameter matching problems, in the drainage pipe channel by using a topology high-speed exploration technology. By adopting the technical scheme based on the combination of topological modeling and high-speed exploration, the technical effect of quickly analyzing and dynamically updating the topological structure of the underground drainage pipe channel is achieved; the defects that a topology model is slow in updating and difficult to adapt to real-time changes of a pipe network are overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of drainage pipe network monitoring, and particularly to an abnormal identification and positioning method for underground drainage pipe networks based on topological high-speed exploration. Background Art

[0002] Underground drainage pipe networks are an indispensable part of urban infrastructure, and their operating conditions are directly related to the urban flood prevention ability and water resource management. However, there are many deficiencies in the existing technology for the monitoring and management of pipe networks, especially in the efficient management and abnormal handling of large-scale complex drainage systems, and the technical means are inadequate.

[0003] The current topological structure of drainage pipe networks is usually generated by manual drawing or simple static algorithms. This method can be applied when the scale of the pipe network is small, but in the face of the huge underground pipe network system of modern cities, the efficiency and accuracy of topological modeling are extremely easy to decline. Especially after new pipelines are added or modified, it is difficult to update the topological relationship in a timely manner, resulting in the disconnection between the pipe network model and the actual operating state, and affecting the accuracy of subsequent analysis.

[0004] Most traditional pipe network topological tracing methods rely on relational databases or step-by-step recursive query modes, and there are significant efficiency bottlenecks. In scenarios with a large amount of data and frequent requests, the system response time often fails to meet the real-time requirements. In addition, the existing technology lacks a large-scale parallel optimization scheme for complex topological structures, resulting in a sharp decline in performance during high-concurrency access, and it is extremely easy to cause data query delays or even interruptions.

[0005] Currently, the handling of abnormal points mostly relies on manual judgment or experience accumulation, lacking the ability to generate systematic analysis and transformation suggestions. This method is inefficient and prone to subjective biases. Especially in the face of large-scale drainage pipe networks, the positioning and analysis results often lag behind the occurrence of problems and cannot timely guide maintenance or optimization and transformation. In addition, the existing system is difficult to be linked with CCTV detection equipment and other devices, lacking a closed-loop mechanism from abnormal detection to on-site rectification, and the overall operation process is inefficient. Summary of the Invention

[0006] In view of the deficiencies of the existing technology, the present invention provides an abnormal identification and positioning method for underground drainage pipe networks based on topological high-speed exploration, which solves the problems of low efficiency of drainage pipe network topological modeling, incomplete abnormal detection, untimely dynamic data update, and insufficient abnormal point positioning.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: An abnormal identification and positioning method for underground drainage pipe networks based on topological high-speed exploration, including the following steps; S1. Construct a topological structure for the GIS data of the underground drainage pipe network to obtain the topological connection relationship of the drainage pipe network; S2, based on the technology combining high-speed cache and search engine, the drainage pipelines are traced back at high speed to output the upstream and downstream topological relationship of the pipelines; S3. Use topological high-speed exploration technology to automatically identify abnormal conditions in drainage pipes and canals, including mixed connections, blockages, rainfall inflows, breakages, reverse slopes, and pipe diameter matching problems; S4. Link and update the data of the runtime database and the standard database in the dual databases through the dual database mechanism; S5. Correct the abnormal data in real time in the operation database, and synchronize the corrected data to the standard database after approval; S6. Based on the traceability analysis results of the pipeline topology, provide location information and modification suggestions for abnormal points in the drainage pipeline.

[0008] Preferably, the topological structure in step S1 specifically includes the following steps: S1.1. Establish a topological connection model of drainage pipes based on GIS data analysis; S1.2, construct the topological node relationship between pipelines and pipe points, and determine the upstream and downstream connectivity relationship through a recursive algorithm; S1.3. The drainage channel topology structure includes pipelines, pipe points and their attribute information. The attribute information at least includes pipe diameter, flow direction and connection status.

[0009] Preferably, the topology high-speed tracing in step S2 specifically includes the following steps: S2.1. Store and call the topological data of drainage pipes based on Redis cache technology; S2.2. Use Elasticsearch search engine technology to quickly query pipeline topology data and analyze its upstream and downstream paths; S2.3. The response time for high-speed tracing of drainage channel topology shall not exceed 4 seconds.

[0010] Preferably, the abnormal situation in step S3 specifically includes the following steps: S3.1. Identification of abnormal mixing, including rainwater and sewage mixing, rainwater joint flow, and sewage joint flow; S3.2. Identification of abnormal flow direction, including reverse slope pipelines and abnormal inclination angles; S3.3, identification of abnormal pipe diameter matching, including large pipes connected to small pipes or sudden changes in pipe diameter; S3.4. Identification of abnormal connections, including broken pipelines and isolated pipe points; S3.5. Identification of abnormal blockage, including comparison of flow monitoring data with pipeline design flow; S3.6. Identification of rainfall inflow anomalies, including analysis of anomalous inflow points based on rainfall and flow data.

[0011] Preferably, in step S4, the dual-database mechanism includes a runtime library and a standard library. The runtime library is used to record dynamically updated data and support daily business operations, while the standard library is used to store measured and audited measurement data for outputting authoritative data. After the dynamic data is updated in the runtime library, it needs to be audited and synchronized to the standard library. If the audit fails, the corresponding modification record in the runtime library is deleted.

[0012] Preferably, the update of the GIS data specifically includes the following: Field personnel identify the on-site verification status through the field App and modify the data in the runtime library. After the dynamic data is submitted, it enters the audit process, and technicians conduct audits based on the data source and modification basis. The data that passes the audit is synchronized to the standard library for outputting official data. The data that fails the audit is rolled back to the temporary state of the runtime library or directly deleted.

[0013] Preferably, the traceability based on the pipeline topology structure in step S6 specifically includes the following steps: S6.1. Upward traceability: Analyze all upstream connected pipelines of the target pipeline. S6.2. Downward traceability: Analyze all downstream connected pipelines of the target pipeline. S6.3. Comprehensive up-and-down traceability: Analyze the complete topological relationship of the target pipeline in the upstream and downstream directions.

[0014] Preferably, the positioning of abnormal points in step S6 includes: Mark the abnormal positions of the drainage pipelines and channels on the GIS map. Provide detailed information about the abnormal points, including the specific location, abnormal type, and influence range. The information of the abnormal points is used to guide CCTV detection and subsequent engineering rectification.

[0015] Preferably, the topological high-speed exploration realizes resource allocation through dynamic scheduling optimization, specifically including: Based on Kubernetes containerized deployment, dynamic load balancing is achieved, and system computing resources are allocated on demand. During the topological traceability process, the computing nodes are dynamically expanded or reduced according to the request volume to ensure the system response efficiency under high-concurrency requests.

[0016] Preferably, the results of the abnormal point positioning can be used to guide the operation and maintenance of the drainage system, specifically including: Generate a task list for the identified abnormal points, and the task list includes the abnormal type, specific location, and priority level for handling. Support linkage with the CCTV detection system, push the abnormal point positioning results to the detection equipment, and accurately carry out the detection work; Generate renovation suggestions according to the task list, assist in formulating the optimization renovation plan for the drainage system, and track the implementation of the renovation tasks.

[0017] The present invention provides a method for identifying and positioning underground drainage pipe anomalies based on topological high-speed exploration. It has the following beneficial effects: 1. By adopting a technical solution combining topological modeling and high-speed exploration, the present invention achieves the technical effect of quickly parsing and dynamically updating the topological structure of underground drainage pipe networks. Compared with the method of manually drawing or offline calculating the topological structure in the prior art, it solves the problem of slow update of the topological model and difficulty in adapting to the real-time changes of the pipe network.

[0018] 2. By introducing Redis high-speed cache and Elasticsearch search engine technology, the present invention achieves the technical effect of realizing second-level upstream and downstream path tracing in large-scale pipe network data. Compared with the prior art solutions relying on single-threaded queries or low-efficiency database indexes, it significantly improves the query efficiency and solves the problem of excessive system response time in a multi-concurrent environment.

[0019] 3. By using a dual-database linkage mechanism, the present invention establishes an efficient data review and synchronization strategy between the runtime library and the standard library, ensuring the technical effect of real-time management of dynamic data and stable output of standard data. Compared with the deficiency of the single-database structure in the prior art being vulnerable to data conflicts and incorrect operations, this solution improves the security and consistency of data through the review and version management mechanism.

[0020] 4. By combining the topological analysis results, the present invention accurately locates the abnormal points of the drainage pipe network and automatically generates renovation suggestions, realizing the technical effects of rapid abnormal response and efficient decision-making for subsequent maintenance. Compared with the prior art solutions relying on manual investigation of anomalies or single anomaly detection algorithms, it solves the problems of lagging anomaly positioning, inaccurate scope, and lack of pertinence in the renovation plan. Description of the Drawings

[0021] Figure 1 It is the flowchart of the method of the present invention; Figure 2 It is the logic diagram of the method of the present invention; Figure 3 It is the dual-database operation logic diagram of the present invention; Figure 4 It is the topological high-speed tracing flowchart of the present invention. Detailed Embodiments

[0022] Next, in combination with the accompanying drawings of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to the attached Figure 1 - attached Figure 4 , the embodiment of the present invention provides an abnormal identification and positioning method for underground drainage pipelines based on topological high-speed exploration, including the following steps; S1. Construct a topological structure for the GIS data of the underground drainage pipeline to obtain the topological connection relationship of the drainage pipeline; Specifically, step S1 is the basic module in the entire technical process, ensuring the implementation of subsequent functional modules. In the specific implementation of this step, based on GIS data, by analyzing the association relationship between pipelines and pipe points, a structured topological model is generated to provide high-quality data support for abnormal exploration.

[0024] In this embodiment, by analyzing the GIS data of the underground drainage pipeline, a topological connection relationship is constructed. GIS data usually includes spatial information and attribute information. Spatial information refers to the geographical location information of pipelines and pipe points, and attribute information includes pipe diameter, flow direction, material, and connection status; Specifically, first, based on the GIS data, a topological connection model of the drainage pipeline is constructed. The topological model refers to a network structure with pipe points as nodes and pipelines as connections. Pipe points represent the endpoints, intersection points, or facility points of pipelines, and pipelines represent the flow paths of drainage pipelines.

[0025] In this embodiment, a recursive algorithm is used to gradually analyze the upstream and downstream relationships between pipelines and pipe points. The system starts from the starting pipe point and sequentially traces the connected pipelines and adjacent pipe points until all paths are parsed. The set of pipelines connected to the pipe point is , where represents the th pipeline connecting the pipe point . For any pipe point , the system uses the recursive formula; ; ; where: represents the set of direct adjacent nodes of the pipe point . The complete topological connection relationship is obtained through multiple iterations; represents the intersection operation, indicating taking out the common elements; Denote the union operation for all possible to cover all possible sets of adjacent pipelines.

[0026] In this step, the upstream and downstream relationships are further defined by combining the flow direction attributes of the pipelines. The flow direction attributes of the pipelines can be determined through slope analysis. Specifically, assume that the starting coordinate of the pipeline is , and the ending coordinate is , then the flow direction can be calculated by the following formula; ; where: Slope represents the slope or inclination; are the height values of two points respectively; , are the coordinates of two points respectively; When Slope > 0, the starting point is higher than the ending point, and the flow direction is from the starting point to the ending point. When Slope , the flow direction is marked as abnormal and needs further verification.

[0027] This step also introduces a data cleaning mechanism. Specifically, first, a consistency check is performed on the spatial position data of the pipelines and pipeline points. If the starting or ending point of a pipeline does not match any pipeline point, it is marked as an isolated pipeline and added to the list for manual verification. The connection radius of the pipeline points is restricted using a spatial algorithm, and the restriction formula is as follows; ; where, is the coordinate of the pipeline point, is the coordinate of the target node, represents the distance between two points. When exceeds the threshold, the connection relationship is considered incorrect.

[0028] This step also supports dynamic updates. Specifically, when new or modified pipeline data is added, the system can automatically adjust the topological structure. The system adopts an incremental update mechanism and only analyzes the newly added or modified part without recalculating the entire topological relationship. When a new pipeline is added, the system adds its starting and ending points to the topological network and updates the node sets of adjacent pipelines and pipeline points according to its connection attributes; The system stores this information in matrix form, where the rows represent the pipeline numbers and the columns represent the attribute fields of the pipelines; ; where: represents the th attribute of pipeline , such as pipe diameter, material, slope.

[0029] In this step, through the construction of the topological model, the original GIS data is transformed into a structured topological relationship, laying a solid foundation for subsequent high-speed exploration and anomaly identification. By introducing recursive algorithms, data cleaning mechanisms, and dynamic update capabilities, the accuracy and efficiency of the topological model are ensured. At the same time, combined with the matrix storage of pipeline attribute information, the application scope of the topological model is expanded, making the global analysis and local exploration of drainage pipe channels more efficient and accurate.

[0030] S2. Based on the technology combining cache and search engine, conduct high-speed topological tracing on the drainage pipe channels and output the upstream and downstream topological relationships of the pipelines; Specifically, after the construction of the topological structure in step S1 is completed, the system needs to conduct high-speed tracing on the topological data of the underground drainage pipe channels. This process is a key link for subsequent anomaly identification and data update. The purpose of topological tracing is to quickly obtain the upstream and downstream relationships of the target pipeline or node and form a complete flow path.

[0031] In this embodiment, Redis cache and Elasticsearch search engine technology are used to trace the topological data. Redis, as an efficient in-memory database, is mainly used to store temporary data of the topological structure, supporting fast reading and writing of large-scale data. Elasticsearch is used to search and analyze topological relationships and is particularly suitable for processing unstructured data.

[0032] The system first loads the topological structure information from the GIS database and stores this data in Redis according to the upstream and downstream relationships of the pipelines and pipe points. The relationships between pipelines and pipe points are all identified in the form of key-value pairs; The pipeline number is and its upstream pipe point is and the downstream pipe point is , then the key-value pair stored in Redis is: ; The system can read the connection information of any pipeline or pipe point with extremely high efficiency. Specifically, in topological tracing, the dynamic generation of upstream and downstream paths is realized through a recursive algorithm. Taking a certain target pipe point as an example, its upstream and downstream relationships can be represented by the following recurrence formula.

[0033] Specifically, in topological tracing, the dynamic generation of upstream and downstream paths is realized through a recursive algorithm. Taking a certain target pipe point as an example, its upstream and downstream relationships can be represented by the following recurrence formula; ; Where: Represents the set of inflow pipelines; Represents a pipeline 's upstream pipe point; Represents the set of outflow pipelines; Represents a pipeline 's downstream pipe point.

[0034] The system combines Elasticsearch to perform path analysis on topological data. Elasticsearch supports distributed search and can quickly locate the target path among millions of pipelines. By setting search conditions, users can specify the starting pipeline or pipe point, and the system automatically returns the upstream and downstream paths related to it. When the user specifies the starting point as , and the flow direction is "downward", the results returned by Elasticsearch will include all downstream paths connected to .

[0035] In this embodiment, the storage structure of Redis cache is designed. The topological data is stored in the form of a two-dimensional array, with one row corresponding to one pipeline, and the columns representing the attribute fields of the pipeline, including the starting point, ending point, flow direction, and pipe diameter; ; The system can complete the upstream and downstream queries of a single pipeline within milliseconds; This step also supports the dynamic adjustment of path tracing. After the flow direction attribute of the pipeline is updated, the system will automatically recalculate the pipeline and its upstream and downstream paths and synchronize them to the Redis cache. The time complexity of the update process is determined by the original topological scale. For topological data with a scale of , the update complexity is , which can meet the response requirements in a high-concurrency environment; Specifically, after the abnormal point is identified, the user can choose to trace only the paths directly related to this point, rather than the complete upstream and downstream relationships. When it is found that a certain pipeline has a broken end phenomenon, the system will quickly locate the starting and ending positions of the broken end point through topological tracing without affecting the analysis of other normal paths.

[0036] The system also allows users to set the depth limit of tracing. The depth limit is used to avoid generating redundant paths in a large pipeline network. The tracing results are displayed in a graphical form through the GIS platform, and users can intuitively see the connection relationships between the target pipeline and its upstream and downstream.

[0037] Through the implementation of this step, topological tracing can be quickly completed among millions of pipeline network data and the results can be provided in the form of second-level response. Combining the technologies of Redis cache and Elasticsearch search engine, the system realizes efficient and flexible topological data processing capabilities, and at the same time supports dynamic updates and path-oriented analysis.

[0038] S3. Utilize the topological high-speed exploration technology to automatically identify abnormal conditions in drainage pipelines, including problems of misconnection, siltation, rainfall inflow, dead end, reverse slope, and pipe diameter matching; Specifically, after the topological high-speed tracing in step S2 is completed, the system automatically identifies abnormal conditions in drainage pipelines based on the tracing results. Abnormality identification is an important link in drainage system management, aiming to discover potential hidden problems that may affect the functions of the pipeline network through the analysis of topological structures and attribute data.

[0039] In this embodiment, by combining topological structures and attribute information, multi-dimensional detection of abnormal conditions in pipelines is carried out. Specifically, misconnection abnormality identification depends on the attribute information and connection relationships of pipelines. Pipeline attributes usually include pipe diameter, pipe material, flow direction, and drainage type. The system classifies the connection relationships of different types of pipelines and determines the misconnection types according to preset rules. If a rainwater pipe is connected to a sewage pipe, it is identified as a "rain-to-sewage" abnormality; if a sewage pipe is connected to a combined sewer pipe, it is identified as a "sewage-to-combined" abnormality. This determination process can be expressed as; ; Where: is the misconnection abnormality set; and are respectively the drainage types of pipelines and .

[0040] The system can also verify misconnection abnormalities through topological tracing results. For a pipeline starting with a rainwater pipe, if its tracing path contains a sewage pipe, it is automatically marked as an abnormal path. Misconnection detection based on path verification can improve the accuracy of determination.

[0041] In some embodiments, the flow direction abnormality detection combines the slope information of pipelines. The slope calculation formula is; ; Where: is the slope value; and are respectively the elevations of the starting point and the ending point of the pipeline; is the horizontal length of the pipeline.

[0042] The slope should be positive; if a negative value appears, the system determines it as an abnormal reverse slope. The system combines the topological structure to further check whether the reverse slope pipeline will affect the downstream flow direction. When a reverse slope pipeline is connected to a normal pipeline, the system marks the connection point as a high-risk area.

[0043] For the identification of blockage anomalies, the system compares the flow monitoring data with the designed pipeline flow rate. Assume the pipeline has a designed flow rate of , and the real-time monitored flow rate is , then the degree of blockage can be defined as; ; When exceeds the set threshold, the system determines that there is a blockage problem in this pipeline.

[0044] A dead-end pipeline refers to an isolated pipeline that is not connected to any upstream or downstream pipe points. By analyzing the connection status of topological nodes, the system can quickly identify all dead-end pipelines. Assume the pipeline has an empty set of upstream and downstream nodes, then the system will determine it as a dead-end anomaly; ; Among them, represents the set of upstream and downstream connection nodes of the pipeline , is the set of dead-end anomalies. The diameter of the upstream pipeline should not be less than that of the downstream pipeline, otherwise it may lead to insufficient drainage capacity. The system realizes the matching determination by comparing the diameter values of the upstream and downstream pipelines. Assume the upstream diameter is , and the downstream diameter is ， Then when , the system determines it as a pipe diameter mutation anomaly; For rainfall inflow anomalies, first collect and preprocess the original data.

[0045] Monitoring data; First, collect the real-time monitoring data on site, mainly including information such as the flow rate, pressure, and water level in the pipeline. This data comes from sensors installed in the pipeline.

[0046] Weather data; Obtain real-time rainfall data, usually from weather monitoring stations. These data include rainfall amount and rainfall intensity indicators; Monitoring data; Includes the real-time flow rate in the pipeline ( ), water level ( ) and pressure and other data; Weather data; Obtain real-time rainfall data (rainfall amount ) from the meteorological system.

[0047] Identification of high-value status of monitoring data; Process time series data through the Hidden Markov Chain algorithm to infer the hidden state of the system. For the changes in flow rate and water level in the monitoring data, the Hidden Markov Chain can effectively identify the high-value status and set the hidden state May be in a normal state or an abnormal state; Transition probability of the Hidden Markov Chain; ; Where: Is the hidden state at time , representing the current state of the system. The transition probability Is inferred based on historical data and the current state; Is a constant or a scaling factor used to adjust the probability value; Represents the prior probability of the state at time ; Represents the prior probability of the state at time ;

[0048] Observation probability (observed values of flow rate and water level); ; Represents the probability of observing the measured value at time given the state ; Is the observed value of the system at time , specifically referring to measurement data such as flow rate and water level; Is the hidden state of the system at time ; Represents the joint conditional probability of flow rate and water level at time given the hidden state ;

[0049] Where: Are the observed values (flow rate and water level); Is the hidden state of the system at time ; Infer the optimal path of the hidden state; ; represents the maximum probability of obtaining the state sequence given the entire observed sequence ; represents all hidden state sequences from time 1 to time ; represents all observed data from time 1 to time ; represents the product operation on the joint probabilities for all time steps from 1 to ; represents selecting the state sequence that maximizes the entire joint probability.

[0050] This formula determines the most likely hidden state sequence through Bayesian inference method, based on the given observed data .

[0051] Hidden state identification; Hidden state identification is achieved by judging whether the data conforms to a high-value state (such as a sudden increase in pipeline water flow caused by rainfall). Hidden states are identified through two different high-value distributions; Normal state distribution; ; Abnormal state distribution; ; where: and are the mean and standard deviation of the normal and abnormal states respectively.

[0052] Introduction of real-time rainfall data; Weather data integration: Real-time rainfall data is used to assist in the analysis of flow and water level. By comparing with the real-time rainfall amount, it is analyzed whether there is external water flow entering caused by rainfall.

[0053] Wavelet transform formula; ; where: is the wavelet function; is the rainfall data or pipeline flow data; is the scale factor, which controls the frequency of analysis.

[0054] Online alarm; When the system detects an inflow anomaly, the real-time alarm mechanism will push it to pipeline managers via online alarms. The warning content includes the type of anomaly, the time period when the anomaly occurred, and the pipeline area that may be affected.

[0055] Decision support systems; Bayesian online inflection point identification: Bayesian algorithms are used to determine turning points in data and help identify the start or end of traffic anomalies; ; in: It is the turning point of the present moment; It is historical data.

[0056] Wavelet Analysis: It is used to detect the impact of rainfall inflow on flow, especially when rainfall changes dramatically. The sudden changes or anomalies in rainfall data are extracted through wavelet transform to assist in determining whether there is rainfall inflow anomaly.

[0057] S4. Link and update the data of the runtime database and the standard database in the dual databases through the dual database mechanism; Specifically, after the abnormal situation is identified in step S3, the dynamic data of the drainage pipe needs to be further processed. The present invention proposes a dual database mechanism for linking the dynamic update data in the running database with the measurement data in the standard database.

[0058] In this embodiment, the runtime library is used to store real-time data and dynamically updated data, while the standard library is used to store verified and audited data. Since any modification to the standard library may have a significant impact, the update process is divided into two stages: first, data modification and verification are performed in the runtime library, and after verification, the verified data is synchronized and updated to the standard library.

[0059] Processing and marking of dynamic data: Dynamic data comes from real-time monitoring and field updates, and these data are initially recorded in the runtime library. The data includes pipeline addition, modification, and deletion operations, as well as the spatial coordinates, flow direction information, pipe diameter, and abnormal point detection results of the pipeline. In the runtime library, these data will be marked as "pending review" and can only be submitted to the standard library after review.

[0060] Data consistency check and verification: In the runtime library, all newly added or modified data will undergo consistency checks to ensure compatibility with the existing pipe network structure and standard data. For example, for newly added pipes, the system will calculate their start and end coordinates, verify the pipe length, and compare it with the lengths of adjacent pipelines. If there are significant deviations, the system will mark it as "abnormal" and require manual verification.

[0061] Audit process for dynamic data: After passing the consistency check, the data will enter the audit process. At this stage, the data will be carefully examined, especially for key data such as pipe diameter, flow direction, and pipeline connections. Only the data that passes the audit will be marked as "audited" and prepared for synchronous update to the standard library.

[0062] Trigger conditions for standard library updates: In the runtime library, the audited data will be synchronized to the standard library periodically or based on specific conditions. The update of the standard library is subject to strict conditions because modifying the standard library may have a significant impact on the system. Therefore, the standard library will only be updated after multiple audits and confirmations.

[0063] Data verification: The system will confirm that the key data such as pipe diameter, flow rate, and pipeline path meet the design standards and requirements through inspections.

[0064] Data conflict handling: When there is a conflict between the data in the runtime library and the existing data in the standard library, the system will automatically mark the conflict records, give priority to retaining the data in the standard library, and the conflicting data in the runtime library will be temporarily stored in a temporary table for manual verification.

[0065] Data synchronization and version management: When data is synchronized from the runtime library to the standard library, the system will perform batch updates according to the preset synchronization period, and each synchronization will generate a version snapshot to record the attribute values before and after the data update. The version management system can effectively trace the historical changes of the data and ensure that it can be quickly restored to the previous version if problems occur.

[0066] Version snapshot: Each data update will generate a new version identifier and save the states before and after the update. This ensures the traceability and recovery ability of the data.

[0067] Data conflict handling: If there is an inconsistency between the data in the runtime library and the standard library, the system will resolve it through conflict handling rules (giving priority to retaining the data in the standard library) to ensure data consistency.

[0068] Through this dual-database mechanism, the present invention effectively solves the consistency and stability problems during the synchronization of dynamic data and standard library data, and enhances data security and management efficiency through measures such as data auditing, conflict handling, and version management. This solution provides a solid technical guarantee for the long-term management and optimization of the drain pipe network system, and can efficiently support the real-time monitoring and intelligent decision-making of the system.

[0069] S5. Real-time correction of abnormal situation data in the runtime library, and the corrected data is synchronized to the standard library after passing the audit; Specifically, after completing the linkage mechanism with the standard library in step S4, the correction of abnormal data becomes a key link for further optimizing the data quality of the system. The present invention corrects abnormal data in the runtime library in real time and combines the audit mechanism to ensure data accuracy. After the data correction is completed, only the data that passes the audit can be synchronized to the standard library.

[0070] In this embodiment, the correction of abnormal data is based on the topological relationship, combined with pipeline attributes and upstream and downstream connection information. The abnormal data in the runtime library has been marked through the foregoing steps and stores relevant abnormal types; Specifically, the correction of reverse slope data can be completed by adjusting the elevation information of the pipeline. In a possible implementation, the system will recalculate the heights of the starting point and the ending point according to the average elevation of the upstream and downstream pipe points. The starting point and ending point elevations of the reverse slope pipeline are respectively and , and its corrected elevation value can be calculated by the following formula; ; Where: is the preset normal slope value; is the horizontal distance of the pipeline.

[0071] After adjustment, the system will recalculate the slope of the pipeline and verify whether it meets the requirements of the normal flow direction.

[0072] For the correction of the pipe diameter matching problem, the system will readjust the pipe diameter value in combination with the flow requirements of the upstream and downstream pipelines. The pipe diameter of the upstream pipeline needs to meet the flow requirements of the downstream pipeline; The system calculates the total flow requirements of the upstream and downstream pipelines , and then determines the new pipe diameter value according to the design standard , and the calculation formula of the pipe diameter value is; ; Where: represents at time The flow rate, the unit may be volume flow rate or mass flow rate; is the design flow velocity, usually determined by the operating conditions of the system.

[0073] The problem of mixed connections needs to be handled manually or by engineering means, but the system can provide detailed correction suggestions. The system will generate a corresponding pipe network renovation plan, and mark the location and type of the connection points that need to be adjusted. For a mixed connection point of rainwater connected to sewage, the system will automatically recommend a splitting plan to connect it to the nearest rainwater pipe and sewage pipe for separate treatment.

[0074] The system comprehensively checks the corrected data through preset rules to ensure that it meets the consistency requirements of topology and attributes. The system will regenerate the topological connection model for the corrected pipeline and verify whether it forms a complete upstream and downstream path. If a connection break or an incomplete path is found, the system will automatically return the data to the runtime library and mark it as "not passed the review"; The data that passes the review will be synchronized to the standard library and become the official pipe network data. At the same time, the system will generate a version record for the corrected data to save its status before and after correction. This record not only facilitates subsequent traceability management but also provides a reference basis for the long-term maintenance of the data.

[0075] This step realizes the closed-loop data flow between the runtime library and the standard library through the real-time correction and strict review of abnormal data. While ensuring the accuracy of the correction results, it enhances the transparency and traceability of data management. This correction mechanism provides an important guarantee for the continuous optimization and efficient management of drainage pipes and channels, and at the same time lays a solid foundation for the digital operation and maintenance of complex pipe networks.

[0076] S6. Based on the traceability analysis results of the pipeline topological structure, provide the location information and renovation suggestions for the abnormal points of the drainage pipes and channels; Specifically, after completing the data correction and review in step S5, it is necessary to apply the results of the topological structure analysis to the location of the abnormal points in the drainage system and the generation of renovation suggestions. The accurate location and reasonable renovation of the abnormal points are the key links to solve the functional defects of the drainage system. The present invention comprehensively analyzes the problem areas in the drainage system through topological traceability technology, combined with pipeline attributes and abnormal characteristics.

[0077] In this embodiment, the location of the abnormal points is based on the GIS system. By visualizing the topological relationship and attribute data of the pipelines, specifically, the location process combines the type and spatial coordinate information of the abnormal points; The abnormal points are marked as independent layers and displayed on the GIS map by differences in color or symbols. For reverse slope abnormal points, the identification of the reverse slope; through elevation data, the system first calculates and identifies whether there is a reverse slope phenomenon in the pipeline. If the calculation result shows the existence of a reverse slope, the system will mark this position as a "problem point" and prompt that further verification is required.

[0078] Preliminary calculation of the reverse slope; The identification of the reverse slope problem is based on the elevation data of the starting point and the ending point of the pipeline. By calculating the slope of the pipeline, it can be initially judged whether there is a reverse slope phenomenon in the pipeline. Set the starting point coordinates of the pipeline as , and the ending point coordinates as , where and represent the elevations of the starting point and the ending point of the pipeline respectively; Slope calculation formula; ; Where: is the horizontal length of the pipeline, and the calculation formula is as follows; ; If the calculation result Slope < 0, it means the pipeline has a reverse slope.

[0079] On-site verification: For the reverse slope positions marked as "problem points", on-site verification by front-line staff is required. The staff will confirm whether there is indeed a reverse slope phenomenon in the pipeline according to the on-site situation.

[0080] Confirmation and handling: If on-site verification confirms the existence of a reverse slope in the pipeline, the staff will mark the data as "verified" and update its status to a "problem point" that has been processed. Once the problem is confirmed and handled, this problem point is considered solved; The positioning result of the abnormal point includes not only the spatial location information but also detailed attribute information. When the abnormal point involves a connection problem, the system will mark its specific connection type and attach relevant pipeline attributes, including pipe diameter, material, and connection angle, etc. The system also generates a detailed abnormal point report, and the report content may include the following information; The unique identifier of the abnormal point; The upstream and downstream pipeline numbers associated; The abnormal type and its influence range; The recommended treatment plan.

[0081] The system further expands the analysis scope of the abnormal point through topological tracing technology. The system can trace all upstream drainage units and generate a list of all abnormal points within this drainage unit. The tracing process is based on the aforementioned topological relationship and recursively searches all upstream nodes related to the target point; The target discharge port number is , and the set of upstream drainage units is , then the inventory generation formula is; ; Where: is the list of abnormal points; represents the set of abnormal points within the drainage unit .

[0082] The system also supports priority sorting of the positioning results. The sorting basis includes the type of abnormal points, the influence range, and the urgency. The sorting process combines the analysis results of the topological path. When an abnormal point is located on the main drainage pipeline, its influence range will be estimated by the following formula; ; Where: is the influence range; is the total flow of the drainage pipeline; is the length of the downstream discharge path.

[0083] This step provides comprehensive data support for the transformation of the drainage system through the positioning and correlation analysis of abnormal points. On the basis of combining topological tracing and visualization technology, this step realizes the accurate identification of abnormal points, the quantitative analysis of the influence range, and the automatic generation of transformation plans. By linking with detection equipment and maintenance systems, this step further improves the efficiency of abnormal handling in the drainage system and provides technical support for the efficient management of urban drainage networks.

[0084] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made therein without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An abnormal identification and location method for underground drainage pipelines based on topological high-speed exploration, characterized in that The steps include: S1. Construct the topological structure of the GIS data of underground drainage pipes and channels to obtain the topological connection relationship of the drainage pipes and channels; S2, based on the technology combining high-speed cache and search engine, the drainage pipelines are traced back at high speed to output the upstream and downstream topological relationship of the pipelines; S3. Use topological high-speed exploration technology to automatically identify abnormal conditions in drainage pipes and canals, including mixed connections, blockages, rainfall inflows, breakages, reverse slopes, and pipe diameter matching problems; S4. Link and update the data of the runtime database and the standard database in the dual databases through the dual database mechanism; S5. Correct the abnormal data in real time in the operation database, and synchronize the corrected data to the standard database after approval; S6. Based on the traceability analysis results of the pipeline topology, provide location information and modification suggestions for abnormal points in the drainage pipeline.

2. The method for abnormity identification and location of underground drainage pipelines based on topological high-speed exploration according to claim 1, wherein The topological structure in step S1 specifically includes the following steps: S1.

1. Establish a topological connection model of drainage pipes based on GIS data analysis; S1.2, construct the topological node relationship between pipelines and pipe points, and determine the upstream and downstream connectivity relationship through a recursive algorithm; S1.

3. The drainage channel topology structure includes pipelines, pipe points and their attribute information. The attribute information at least includes pipe diameter, flow direction and connection status.

3. A method for identifying and locating anomalies in underground drainage pipelines based on topological high-speed exploration according to claim 1, characterized in that, The topology high-speed tracing in step S2 specifically includes the following steps: S2.

1. Store and call the topological data of drainage pipes based on Redis cache technology; S2.

2. Use Elasticsearch search engine technology to quickly query pipeline topology data and analyze its upstream and downstream paths; S2.

3. The response time for high-speed tracing of drainage channel topology shall not exceed 4 seconds.

4. The abnormal identification and positioning method of an underground drainage pipe network based on topological high-speed exploration according to claim 1, characterized in that, The abnormal conditions in step S3 specifically include: S3.

1. Identification of abnormal mixing, including rainwater and sewage mixing, rainwater joint flow, and sewage joint flow; S3.

2. Identification of abnormal flow direction, including reverse slope pipelines and abnormal inclination angles; S3.3, identification of abnormal pipe diameter matching, including large pipes connected to small pipes or sudden changes in pipe diameter; S3.

4. Identification of abnormal connections, including broken pipelines and isolated pipe points; S3.

5. Identification of abnormal blockage, including comparison of flow monitoring data with pipeline design flow; S3.

6. Identification of rainfall inflow anomalies, including analysis of anomalous inflow points based on rainfall and flow data.

5. The abnormal identification and location method of underground drainage pipelines based on topological high-speed exploration according to claim 1, characterized in that, The dual database mechanism in step S4 includes a runtime database and a standard database, wherein the runtime database is used to record dynamically updated data and support daily business operations, and the standard database is used to store measured and audited measurement data and output authoritative data; After dynamic data is updated in the runtime library, it must be reviewed and synchronized to the standard library; If the review fails, the corresponding modification record in the runtime library will be deleted.

6. The method for identifying and locating anomalies in underground drainage pipes based on topological high-speed exploration according to claim 1, wherein The updating of the GIS data specifically includes: Field personnel use the field app to identify the on-site inspection status and modify the data in the runtime library; After the dynamic data is submitted, it enters the review phase, and the technical staff reviews it according to the data source and modification basis; The approved data is synchronized to the standard library for outputting official data; Data that fails the review will be rolled back to a temporary state in the runtime library or deleted directly.

7. A method for identifying and locating anomalies in underground drainage pipes based on topological high-speed exploration according to claim 1, characterized in that, The tracing based on pipeline topology structure in step S6 specifically includes the following steps: S6.

1. Upward Tracing: Analyze all upstream connected pipelines of the target pipeline; S6.

2. Downward Tracing: Analyze all downstream connected pipelines of the target pipeline; S6.

3. Comprehensive Up and Down Tracing: Analyze the complete topological relationship of the target pipeline in the upstream and downstream directions.

8. A method for identifying and locating anomalies in underground drainage pipelines based on topological high-speed exploration according to claim 1, characterized in that, The positioning of abnormal points in step S6 includes; Mark the abnormal points of the drainage pipe network on the GIS map; Provide detailed information about the abnormal points, including specific locations, abnormal types, and influence ranges; The abnormal point information is used to guide CCTV detection and subsequent engineering rectification.

9. The method for identifying and positioning anomalies in underground drainage pipelines based on topological high-speed exploration according to claim 1, wherein The topological high-speed exploration realizes resource allocation through dynamic scheduling optimization, specifically including; Based on Kubernetes containerized deployment, achieve dynamic load balancing and allocate system computing resources on demand; During the topological tracing process, dynamically expand or reduce computing nodes according to the request volume to ensure the system response efficiency under high-concurrency requests; Through the container hot update mechanism, ensure that the system does not interrupt normal services during updates or maintenance.

10. A method for identifying and locating anomalies in underground drainage pipes based on topological high-speed exploration according to claim 1, characterized in that, The results of the abnormal point positioning can be used to guide the operation and maintenance of the drainage system, specifically including; Generate a task list for the identified abnormal points. The task list includes abnormal types, specific locations, and priority levels for handling; Support linkage with the CCTV detection system, push the abnormal point positioning results to the detection equipment, and accurately carry out detection work; Generate renovation suggestions according to the task list, assist in formulating an optimization renovation plan for the drainage system, and track the implementation of renovation tasks.

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