A method and system for monitoring areas of pipeline blockage mitigation

Abstract

This invention relates to a pipeline network monitoring method, and more particularly to a method and system for monitoring pipeline network slow-flow and overflow areas. The method includes the following steps: selecting monitoring nodes, installing online liquid level monitoring devices, and establishing a pipeline network node liquid level monitoring network; combining urban pipeline network design, construction survey maps, and geographic information base data to complete coding, classification, and spatial data collection, and establishing a basic information database; constructing a spatial data equidistant calculation model to screen for discrepancies; identifying overflow nodes; and on-site handling. This invention, through IoT-based pipeline network node monitoring and using grid computing to determine the extent of slow-flow and overflow areas in urban underground pipeline networks, solves the problems of high difficulty, high cost, and long cycle in existing urban underground pipeline network blockage detection, significantly improving the transport efficiency of urban underground drainage networks and achieving the goal of improving the quality and efficiency of urban drainage network supervision.

Description

Technical Field

[0001] This invention relates to a pipeline network monitoring method, and more particularly to a pipeline network slow-blockage area monitoring method and system. Background Technology

[0002] Urban underground drainage networks are crucial municipal infrastructure ensuring the normal operation of cities. With the continuous advancement of urbanization, the management of these networks is becoming increasingly challenging, encompassing all aspects of their design, construction, and maintenance. Due to the concealed nature of underground networks, issues such as unauthorized connections, cross-connections, ruptures, and blockages often go undetected during routine maintenance. These problems are frequently only discovered after urban water quality deteriorates, sewage overflows, and urban flooding occur, often only revealed through regional network surveys. This process consumes significant human, material, and financial resources for urban infrastructure maintenance, while also contributing to water quality deterioration and the occurrence of floods. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides a method for monitoring pipeline network blockage mitigation areas. By installing online liquid level monitoring devices at key nodes and accurately locating blockage mitigation areas through grid node analysis, coupled with on-site verification to confirm the blockage location, this method can significantly reduce urban pipeline network operating costs, decrease maintenance resource requirements, and effectively improve urban pipeline network efficiency. The specific technical solution is as follows:

[0004] A method for monitoring areas of pipeline network blockage includes the following steps: selecting monitoring nodes, installing online liquid level monitoring devices, and establishing a pipeline network node liquid level monitoring network; combining urban pipeline network design, construction survey maps and geographic information basic data to complete coding, classification, and spatial data collection, and establishing a basic information database; constructing a spatial data equidistant calculation model and screening out differential nodes; identifying overflow nodes; and handling the situation on-site.

[0005] Preferably, the selection of monitoring nodes includes: head and tail node constraints, key node deployment, and auxiliary node deployment: wherein, the head and tail node constraints include rainwater and sewage inlet manholes, rainwater outfalls, and nodes before the sewage treatment and storage tank; the key node deployment includes rainwater collection and lifting nodes; and the auxiliary node deployment includes: selecting relatively equidistant nodes around the key nodes within a range of 500m to 1km for auxiliary node deployment.

[0006] Preferably, the installation monitoring device further includes setting reporting rules, which include: data acquisition frequency ≥ 5 minutes, data transmission adopts fixed-point reporting and strategy reporting, the interval of fixed-point reporting is 4 hours, and the strategy reporting is a volatility greater than or equal to 5%FS or an equidistant range of more than 1 cm.

[0007] Preferably, when constructing the spatial data equidistant calculation model, the burial depth, inclination angle, and manhole depth of the pipeline network are used, and the existing pipeline distance is constructed into an equidistant monitoring numerical network matrix based on the standard key value distance of the standard equidistant mapping using the proportional algorithm sh'2 = h'1 / h'2 (CP(W1:SW1)).

[0008] Furthermore, when screening the difference nodes, a node difference list is constructed using the difference algorithm of liquid level difference △sh2=h'1-sh'2-tanθw'1. After completion, the node data with difference ≥0 is screened using the bubble calculation method, and the values ​​of the nodes with preceding and following relationships are obtained. Then, the current point data is obtained after judging based on the well depth and well spacing.

[0009] The tree algorithm model is established based on the front and back height difference and the equidistant node network model. Then, the difference list is filtered using sorting algorithms such as bubble sort. The height difference is determined by the ratio of the difference list. Nodes with large height differences are nodes with smooth gravity flow, and nodes with small height differences are nodes with slow gravity flow blockage. The list is formed by filtering through the tree algorithm.

[0010] Furthermore, when determining the overflow node, the basic information of the key node is obtained, the spatial relationship data of the node is retrieved from the database table, the spatial relationship data is compared with the actual pipeline network data, and the offset algorithm consistent with the map system is used to process the blockage data to obtain the key area where the pipeline network is located.

[0011] Furthermore, during the on-site handling, the location information of key areas is transmitted to maintenance personnel via handheld terminals. The maintenance personnel then carry equipment such as CCTV and ground-penetrating radar to conduct on-site pipeline network internal detection, pipeline flaw detection analysis, and pipeline quality verification.

[0012] A monitoring system for pipeline network blockage mitigation areas includes: an online liquid level monitoring device for monitoring liquid levels; a cloud server that communicates with the online liquid level monitoring device via a wireless network; and a handheld terminal that connects to the cloud server via a wireless network.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] This invention provides a method for monitoring areas of slow-flow and overflow in urban underground pipe networks. It constructs a monitoring network by deploying online liquid level monitoring devices in a grid of nodes, and uses cloud-based grid data algorithms combined with spatial data to determine areas of slow flow and overflow. The results are then fed back to the maintenance team for on-site investigation and verification. This invention, through IoT-based monitoring of pipe network nodes and grid computing, determines the extent of slow-flow and overflow areas in urban underground pipe networks. It solves the problems of high difficulty, high cost, and long cycle in existing urban underground pipe network blockage detection methods, significantly improving the transport efficiency of urban underground drainage networks and achieving the goal of improving the quality and efficiency of urban drainage network supervision. Attached Figure Description

[0015] Figure 1 This is a diagram of the pipeline network nodes;

[0016] Figure 2 It is a cross-sectional view of the pipeline network;

[0017] Figure 3 It is a well logging layout diagram;

[0018] Figure 4 It is a slope relationship diagram;

[0019] Figure 5 It is a slope node mapping diagram;

[0020] Figure 6 It is an abstract diagram of slope relationships;

[0021] Figure 7 It is a tree-like node diagram;

[0022] Figure 8 This is a procedure for monitoring areas of pipeline blockage. Detailed Implementation

[0023] The present invention will now be further described with reference to the accompanying drawings.

[0024] 1. Deployment method of pipeline node liquid level monitoring network

[0025] Urban drainage networks are divided into stormwater and sewage networks. Stormwater networks primarily utilize pathways such as household connection wells, pipe networks, connection wells, lift wells, interceptor wells, and river outfalls. Sewage networks primarily utilize pathways such as household connection wells, pipe networks, connection wells, lift pump stations (wells), regulating reservoirs, and sewage treatment plants. Due to limitations in construction conditions, construction year, and construction standards, strict separation of stormwater and sewage networks is generally difficult in existing urban drainage networks. Newly built residential areas or main roads, and primary and secondary trunk lines, typically employ separate stormwater and sewage systems; older residential areas and restricted areas may use combined stormwater and sewage systems. Furthermore, issues such as unauthorized connections, incorrect connections, and mixed connections in primary and secondary trunk lines can lead to sewage flowing into rivers or stormwater entering treatment plants. Based on these circumstances, a liquid level monitoring network is established by installing liquid level monitoring devices at key points in areas affected by these problems.

[0026] (1) Node selection: such as Figure 1 As shown, the node selection criteria adopt the principles of first and last node constraints, key node control, and relational node assistance. First and last node constraints refer to nodes before stormwater and sewage inlets, stormwater outfalls, and sewage treatment and storage tanks; key node control refers to stormwater runoff collection and lifting nodes; key node assistance refers to selecting relatively equidistant nodes within a certain area (500m to 1km) for auxiliary judgment in key areas; at road junctions, monitoring groups can be appropriately formed for directional assistance.

[0027] (2) Online liquid level monitoring device: such as Figure 3 As shown, the online liquid level monitoring device is an integrated online monitoring system with built-in battery, acquisition, sensing, processing, and transmission modules. The network supports both NB narrowband and 4G / 5G mobile internet transmission. The device is installed in the selected monitoring well using a wall-mounted installation method. A minimum of 5cm is provided between the bottom sensor and the well bottom to allow for siltation, and a minimum of 10cm is provided between the top sensor and the well cover to allow for monitoring and maintenance of the well cover.

[0028] (3) Reporting Rules: The monitoring equipment is an integrated device. To reduce power consumption and ensure that the battery can operate stably for more than 3 years, a strategic reporting rule is adopted. The data acquisition frequency is ≥5 minutes, and data transmission adopts both fixed-point reporting and strategic reporting methods. The fixed-point reporting interval is 4 hours, and the strategic reporting is based on volatility greater than or equal to 5%FS or an equidistant range of more than 1 cm.

[0029] (4) Basic data: such as Figure 2 As shown, after the aforementioned nodes, online liquid level monitoring devices, and reporting rules are arranged as required, attribute data is recorded. Simultaneously, combined with urban pipeline network design, construction survey maps, and geographic information base data, coding, classification, and spatial data collection are completed. A basic information database and a GIS information management system are established to achieve graphical management and spatial data analysis of the data.

[0030] (5) Network setup: such as Figure 1 As shown, based on the above-mentioned node selection, monitoring equipment, reporting rules, and construction requirements within the basic data, the deployment of the pipeline node liquid level monitoring network is completed, providing basic data and structure for the calculation of node overflow data.

[0031] 2. Pipeline Node Overflow Data Algorithm

[0032] The pipeline node overflow data algorithm is mainly based on the node monitoring network to eliminate network path differences, construct a spatial data equidistant calculation model, use a tree node algorithm to filter out differences, and use the correlation between differences to determine the overflow node.

[0033] (1) Eliminating network path differences using equidistant mapping: such as Figure 4 , Figure 5 and Figure 6 As shown, data screening based on spatial data requires the standardization of data screening criteria. During the construction of the monitoring network, the impact of monitoring accuracy may cause certain analytical discrepancies. Furthermore, the influence of pipeline flow rate, elevation difference, and distance will further amplify these discrepancies, leading to a loss of judgment basis. A fundamental condition for model calculation is to minimize and control these discrepancies within a reasonable range. While differences in monitoring accuracy cannot be completely eliminated due to the inherent limitations of sensing equipment, the impact of distance differences can be mitigated to reduce adverse conditions. Path differences can be eliminated through equidistant mapping, enabling the construction of an equidistant model. Figure 4 This is a diagram showing the relationship between slope nodes in the pipeline network. Figure 5 The relationship between adjacent nodes can be abstracted. Figure 6 Abstract diagram of the relationship slope. In the diagram, h1 is the well depth (relative elevation value) of the previous node, h'1 is the water level height (absolute height value) of the previous node, h2 is the well depth (relative elevation value) of the next node, h'2 is the water level height (absolute value) of the next node, W1 is the lateral distance between the two nodes, and W1 distance is the actual node distance. To eliminate differences in the equidistant node network, the nodes need to be mapped to a standard SW1 (any fixed value within the range of 500-1000m). This can be achieved by mapping h'2 to a standard distance node S h'2. Figure 6 The slope relationship diagram can be constructed based on the existing geometric relationships of a, b, c, d and ∠β. The relationship algorithm x = cmp(a:b&c:d, ∠β) can be used to obtain the mapping x value, thereby realizing the elimination of network path differences by using equidistant mapping.

[0034] (2) Constructing an equidistant node network model for spatial data: After eliminating the differences in influence in the above steps, the spatial data, node type, and monitoring data of the existing nodes are used as unit node data to construct an equidistant node network model with the paths of service connections, aggregation points, auxiliary points, and endpoints. In the network model, the node key value is a fixed distance (fixed value), the spatial data is the actual location data of the nodes, and the monitoring data is the difference in liquid level after equidistant mapping. The difference between preceding and following nodes is calculated as follows: Figure 5 As shown, the liquid level difference Δsh2 = h'1 - sh'2 - tanθw'1. The relative elevation of the liquid level difference to the preceding and following liquid levels is the key basis for judgment.

[0035] (3) Use a tree algorithm to filter the list of differing nodes: such as Figure 7As shown, a tree algorithm model (binary and multi-branch) is established based on the front and back height difference and the equidistant node network model. Then, a list of differences is filtered using sorting algorithms such as bubble sort. The height difference is determined by the ratio of the difference list. Nodes with large height differences are nodes with smooth gravity flow, and nodes with small height differences are nodes with slow gravity flow blockage. The list is formed by filtering through the tree algorithm.

[0036] 3. Method for determining the slow-clogging area of ​​the pipeline network

[0037] After filtering the height difference data of nodes before and after the overflow algorithm, the node is identified as a critical node that may have pipeline leakage or blockage. The possible causes of blockage at critical nodes are artificial and natural. Artificial blockage is an effective way to improve pipeline transportation efficiency and prevent tools such as airbags from creating conditions for diversion and diversion in underground pipelines. In this case, as long as the absolute value of the liquid level is below the pipeline well depth limit based on the engineering data reported on site, it can be ignored. Natural blockage refers to the actual blockage area caused by rupture, blockage, engineering, etc. For this type of blockage node, the pipeline between the associated pipeline and the upstream node in the spatial correlation data is the pipeline with a blockage area, which needs to be verified on site.

[0038] 4. Pipeline blockage mitigation system

[0039] according to Figure 8 The processing flow is as follows: the monitoring network generates monitoring data and reports it to the platform. After the platform uses the above-mentioned system and calculation methods to determine the congestion mitigation area, it can assign the congestion mitigation area to maintenance personnel through the task system. The maintenance personnel carry detection tools to conduct on-site verification and evidence collection, and then upload the on-site survey materials to the platform. The platform organizes the analysis of the plan and completes the approval before issuing it to the site for disposal. After the disposal is completed, the data is sent back to the platform to complete the data update.

[0040] CCTV Closed-Circuit Television Pipeline Inspection: Closed-circuit television (CCTV) pipeline inspection technology is a tool specifically designed for the inspection of underground pipelines. It is one of the most widely used and commonly employed inspection technologies in drainage pipe network inspection. During inspection, the system is remotely controlled by ground operators to manage video recording within the pipeline. Images are acquired through video surveillance, and the recorded images are displayed and viewed via wired transmission. The video monitoring system is used to assess and analyze the internal condition of the pipeline. This inspection technology is easy to operate, provides accurate and intuitive image registration, and avoids potential injury to personnel entering the pipeline. However, before testing, it is necessary to temporarily lower the water level within the pipeline and pre-clean the inner walls. CCTV pipeline inspection technology has been widely applied in drainage pipe network inspection both domestically and internationally.

[0041] A method for monitoring areas prone to blockage in urban drainage networks primarily utilizes existing spatial data mapping results, based on the location and purpose of the junctions and manholes in the city's primary and secondary drainage networks, to install online liquid level monitoring equipment at the locations of access points, nodes, junctions, and auxiliary judgment node manholes, selecting locations according to approximately equidistant spacing combined with node auxiliary rules. This achieves automated liquid level monitoring and constructs an automated monitoring network for pipeline nodes. The liquid level monitoring equipment employs a pressure sensor structure, is powered by a lithium battery, and reports real-time liquid level monitoring data using both heartbeat and strategy-based methods.

[0042] The pipeline node overflow data algorithm mainly forms a pipeline twin digital network by comprehensively considering data such as the foundation of pipeline node wells, network node locations, pipeline space, and pipeline monitoring data. It utilizes basic algorithms such as equidistant mapping, difference calculation, and tree-structured calculation to construct difference data for key pipeline network-related nodes. Based on the difference data, spatial data, and the relationships and ratios between related nodes, nodes of interest are selected.

[0043] (1) Equidistant Mapping: Since manhole nodes are selected to replace internal monitoring of the pipeline network when constructing the pipeline node monitoring network, the pipeline node association is disordered. In order to satisfy the association calculation, it is necessary to construct an associated pipeline network to eliminate the difference and construct an equidistant network. To achieve the above algorithm, the basic data such as the burial depth, inclination angle and manhole depth of the pipeline network are used. The equidistant monitoring numerical network matrix is ​​constructed by using the proportional algorithm sh'2 = h'1 / h'2 (CP(W1:SW1)) to map the existing pipeline distance according to the standard key value distance of the standard proportional mapping (500m-1000m).

[0044] (2) Difference calculation: After mapping all network nodes to equidistant keys, the difference algorithm of △sh2=h'1-sh'2-tanθw'1 is used to construct a node difference list. After completion, the bubble calculation method is used to filter the node data with difference ≥0, and the values ​​of the nodes with previous and subsequent relationships are obtained. The current point data is obtained after judging according to the well depth and well spacing.

[0045] (3) Tree structure calculation: The equidistant mapping and difference are used to construct a multi-branch tree relationship list, forming a multi-level multi-branch tree list with the access well as the drainage outlet as the end. The relationship spectrum between the above nodes and the difference is identified by combining the digital twin graphical identification method. Finally, the bubble sort algorithm is used to select key interest nodes and verify them. After the verification is completed, the nodes are identified based on the basic tree structure of the node data.

[0046] The basic information of key nodes is obtained, and the spatial relationship data of the nodes is retrieved from the database table. The spatial relationship data is compared with the actual pipeline network data. Due to the regulatory requirements of high-precision geographic information data, there is a certain deviation in the map system. An offset algorithm consistent with the map system is used to process the congestion mitigation data and obtain the key area where the pipeline network is located.

[0047] Example 1

[0048] like Figure 1 As shown,

[0049] A method for monitoring areas of pipeline blockage is implemented through four steps: on-site deployment, cloud deployment, grid assessment, and on-site handling.

[0050] 1. On-site deployment

[0051] On-site deployment includes four parts: data organization, grid design, node design, and network operation.

[0052] (1) During the facility preparation stage, it is necessary to retrieve the urban drainage network graphic electronic data of the area and complete the basic network data organization based on the network direction, surrounding impact, and facility and equipment content of the graphic electronic data.

[0053] (2) After organizing the pipeline network data, the data is processed using geographic information system tools to form standard geographic information system layer data.

[0054] (3) Using the electronic data of drainage pipe network graphics, the connection points, junctions, nodes and outlets are designed according to the node network monitoring method to complete the monitoring grid design.

[0055] (3) Based on the manhole categories marked on the drawings, design the monitoring nodes according to their categories, taking into account parameters such as the material, diameter, depth, and type of the nodes, and determine the battery capacity, external dimensions, cable depth, and wall mounting method of the monitoring equipment.

[0056] (4) Based on the grid and node design scheme, complete the on-site node deployment.

[0057] 2. Cloud deployment

[0058] (1) Build basic data services, complete the construction of classification, coding, basic database tables, business database tables, and analysis database tables, and start basic data services.

[0059] (2) Build a data aggregation service, complete the parsing and storage of sensor IoT protocols. The data aggregation service supports the exchange of sensor data, gateway data and application data from devices, gateways and interfaces, and starts the service to complete the data aggregation function.

[0060] (3) Construct spatial data services. Use GEO Server to build online spatial data management services. Convert existing graphic spatial data files into spatial data layer data with elevation using data conversion tools and publish spatial data services.

[0061] (4) Geographic Information System Services: Geographic Information System Services are built using GEO Server, and existing basic, spatial, and attribute data are used to support geographic information services for business applications through GIS services.

[0062] (5) Construct an equidistant network calculation model, adopt the J2EE specification, and use code to complete the development of the pipeline network congestion mitigation algorithm model and release the interface and service.

[0063] (6) Build business algorithm services, adopt the J2EE specification, use code to complete the development of business algorithm services, and combine network settlement model to publish call interfaces and services.

[0064] (7) Development and deployment of business applications.

[0065] (8) Complete the input and association processing of basic data, spatial data and attribute data of field nodes.

[0066] (9) Obtain policy reporting data from front-end monitoring equipment and complete the networking service for on-site monitoring equipment.

[0067] (10) Complete the verification of nodes, equipment and data, and complete the equipment association and monitoring data calibration based on monitoring data, spatial data, attribute data and original pipeline spatial data.

[0068] (11) Select test network nodes, use airbags to complete backflow isolation in the test area, and complete reverse correlation and monitoring data calibration.

[0069] (12) Complete the calibration of the reporting time of the monitoring equipment and verify the consistency of the monitoring and reporting time to ensure that the deviation of the equipment monitoring and reporting data is kept within ±5s.

[0070] 3. Grid Detection

[0071] (1) Select test network nodes. Select 3-5 nodes in the test area to form a monitoring grid. Set the horizontal spacing value to 500m-1000m (select 500m as the characteristic value).

[0072] (2) Use pipe airbags to seal the test pipe network to reduce the impact of water flow deviation.

[0073] (3) Introduce municipal irrigation water into the pipeline network to reach more than 50% of the pipeline network capacity.

[0074] (4) After the water level in the pipeline network reaches equilibrium, the accuracy of the pipeline network monitoring data, network model, and simulation algorithm is verified using a reporting cycle.

[0075] (5) Verify the percentage of overflow nodes through the overflow algorithm results.

[0076] (6) Verify the state of the congestion-relief node through the results of the congestion-relief algorithm.

[0077] (7) Reintroduce municipal irrigation water until water overflows from the manhole cover at a certain point.

[0078] (8) Verify the percentage of overflow nodes through the overflow algorithm results.

[0079] (9) Verify the state of the congestion-relief node through the results of the congestion-relief algorithm.

[0080] (10) After verifying the accuracy of the above model and algorithm, the parameters are fixed to form a stable version and officially released to the public.

[0081] 4. On-site handling

[0082] (1) Utilize cloud deployment services to complete system platform applications and perform routine on-call services.

[0083] (2) Use cloud deployment services to complete the on-site support application release and enable terminal access.

[0084] (3) When the field sensor generates policy or over-limit data, the duty service will automatically handle it and remind the duty personnel to handle it.

[0085] (4) The on-duty personnel will issue the congestion relief area to the on-site personnel through the system task.

[0086] (5) On-site personnel will arrive at the site to conduct detection and video inspection of the pipeline based on the on-site location description and defect area analysis data.

[0087] (6) After the inspection is completed, the data is reported to the duty system through the terminal.

[0088] (7) Backend professionals complete the solution by using the video and data transmitted from the site and release it to the duty center. On-site personnel then complete the repair work according to the solution.

[0089] (8) After the on-site personnel complete the repair, they will send the on-site repair comparison data back to the back-end for archiving.

[0090] Example 2

[0091] A monitoring system for pipeline network blockage mitigation areas includes: an online liquid level monitoring device for monitoring liquid levels; a cloud server that communicates with the online liquid level monitoring device via a wireless network; and a handheld terminal that connects to the cloud server via a wireless network.

[0092] The technical principles of the present invention have been described above with reference to specific embodiments. These descriptions are merely for explaining the principles of the invention and should not be construed as limiting the scope of protection of the invention in any way. Based on this explanation, those skilled in the art can readily conceive of other specific embodiments of the invention without inventive effort, and these embodiments will all fall within the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring areas of pipeline network with mild blockage, characterized in that, Includes the following steps; Select monitoring nodes, install online liquid level monitoring devices, and establish a liquid level monitoring network for pipeline nodes; By combining urban pipeline network design and construction survey maps with geographic information basic data, coding, classification, and spatial data collection are completed to establish a basic information database; Construct an equidistant spatial data calculation model and filter out discrepancies. Determine the overflow node; On-site handling; When constructing the spatial data equidistant calculation model, the burial depth, inclination angle, and manhole depth of the pipeline network are used. The equidistant monitoring numerical network matrix is ​​constructed by using the proportional algorithm sh'2=h'1 / h'2(CP(W1:SW1)) to map the existing pipeline distances according to the standard key value distance of the standard proportional mapping. When screening the difference nodes, a node difference list is constructed using the difference algorithm of liquid level difference △sh2=h'1-sh'2-tanθw'1. After completion, the bubble calculation method is used to screen the node data with difference ≥0, and the values ​​of the nodes with previous and subsequent relationships are obtained. Then, the current point data is obtained after judging based on the well depth and well spacing. A tree-structured algorithm model is established based on the front and back height difference and the equidistant node network model. Then, the bubble sort algorithm is used to filter the difference list. The height difference is determined by the ratio of the difference list. Nodes with large height differences are nodes with smooth gravity flow, and nodes with small height differences are nodes with slow gravity flow blockage. The list is formed by filtering through the tree algorithm. When determining the overflow node, the basic information of the key node is obtained, the spatial relationship data of the node is retrieved from the database table, the spatial relationship data is compared with the actual pipeline network data, and the offset algorithm consistent with the map system is used to process the blockage data to obtain the key area where the pipeline network is located.

2. The method for monitoring pipeline network slow-clogging areas according to claim 1, characterized in that, The selected monitoring nodes include: head and tail node constraints, key node control, and auxiliary node control. The constraints on the first and last nodes include the nodes before the stormwater and sewage inlet manholes, stormwater outlets, and sewage treatment and storage tanks. The key node deployment includes rainwater collection and lifting nodes; The auxiliary node deployment includes: selecting relatively equidistant nodes around the key node within a range of 500m to 1km for auxiliary node deployment.

3. The method for monitoring pipeline network slow-clogging areas according to claim 1, characterized in that, The monitoring device also includes setting reporting rules, which include: data acquisition frequency ≥ 5 minutes, data transmission adopts fixed-point reporting and strategy reporting, the interval of fixed-point reporting is 4 hours, and the strategy reporting is a volatility greater than or equal to 5%FS or an equidistant range of more than 1 cm.

4. The method for monitoring the slow-clogging area of ​​a pipeline network according to claim 1, characterized in that, During the on-site handling, the location information of key areas is released to the maintenance personnel via handheld terminal data. The maintenance personnel then carry CCTV pipeline closed-circuit television detection and ground penetrating radar equipment to complete the on-site pipeline network internal detection, pipeline flaw detection analysis, and pipeline quality verification.

5. A pipeline network slow-clogging area monitoring system, used in the pipeline network slow-clogging area monitoring method according to claim 1, characterized in that, include: Online liquid level monitoring device, used to monitor liquid level; The cloud server communicates with the online liquid level monitoring device via a wireless network; A handheld terminal, which connects to the cloud server via a wireless network.

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