Pipeline network online monitoring method and system based on Internet of Things

Abstract

The present application relates to the field of sewage pipe network monitoring, and discloses a method and system for online pipe network monitoring based on the Internet of Things. The method includes: constructing a sewage pipe network diagram consisting of multiple monitoring points, the sewage pipe network diagram includes multiple sewage pipe scale monitoring nodes and sewage pipe scale monitoring edges connecting different sewage pipe scale monitoring nodes, and obtaining sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge; through a sewage pipe scale monitoring model, according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, determining multiple sewage pipe scale monitoring abnormal pipelines; according to multiple sewage pipe scale monitoring abnormal pipelines, determining a sewage pipe scale cleaning plan. The present application can efficiently monitor urban and rural sewage pipe networks.

Description

Technical Field

[0001] The present application relates to the technical field of sewage pipe network monitoring, and more specifically, to an online pipe network monitoring method and system based on the Internet of Things. Background Art

[0002] At present, the technologies for monitoring dirt inside sewage pipes mainly include physical monitoring technology and intelligent monitoring technology. Physical monitoring technologies include ultrasonic detection, laser ranging and video monitoring, which can obtain the dirt situation inside the pipe through reflected waves, distance changes or image recognition. At the same time, intelligent monitoring technologies such as sensor networks, the Internet of Things (IoT) and big data analysis have begun to be widely used. Sensors can monitor parameters such as flow rate, flow, temperature and chemical composition of sewage in real time. Combined with big data analysis technology, it helps to identify the occurrence and development trend of dirt and improve the accuracy and efficiency of monitoring. However, the structure of urban and rural sewage pipe networks is complex and the distribution of pipes is relatively scattered. It is difficult for existing technologies to monitor urban and rural sewage pipe networks efficiently. Summary of the invention

[0003] The purpose of this application is to provide an online monitoring method and system for urban and rural sewage pipe networks based on the Internet of Things, which solves the technical problem of difficulty in efficiently monitoring urban and rural sewage pipe networks and achieves the technical effect of efficiently monitoring urban and rural sewage pipe networks.

[0004] An Internet of Things-based pipe network online monitoring method provided in an embodiment of the present application comprises: constructing a sewage pipe network diagram consisting of multiple monitoring points, the sewage pipe network diagram comprising multiple sewage pipe scale monitoring nodes and sewage pipe scale monitoring edges connecting different sewage pipe scale monitoring nodes, and obtaining sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge; wherein the sewage pipe scale monitoring parameters comprise pipe scale type and pipe scale thickness; through a sewage pipe scale monitoring model, according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, determining multiple sewage pipe scale monitoring abnormal pipelines; wherein each sewage pipe scale monitoring abnormal pipeline comprises multiple sewage pipe scale monitoring nodes, and the sewage pipe scale monitoring model is a graph neural network model; and determining a sewage pipe scale cleaning plan according to the multiple sewage pipe scale monitoring abnormal pipelines.

[0005] In a possible implementation, a plurality of abnormal sewage pipe scale monitoring pipelines are determined through a sewage pipe scale monitoring model according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, including: determining the time series thickness increment of the pipe scale thickness according to the historical data of the pipe scale thickness; and determining a plurality of abnormal sewage pipe scale monitoring pipelines through a sewage pipe scale monitoring model according to the pipe scale type corresponding to each sewage pipe scale monitoring node, the time series thickness increment and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge.

[0006] In another possible implementation, the method also includes: obtaining regional data corresponding to each sewage pipe scale monitoring node, the regional data including the sewage discharge type, sewage discharge volume and historical sewage pipe maintenance times discharged through the sewage pipe in the area where the sewage pipe scale monitoring node is located; through the sewage pipe scale monitoring model, according to the pipe scale type corresponding to each sewage pipe scale monitoring node, the time series thickness increment, the regional data, and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, determining multiple sewage pipe scale monitoring abnormal pipelines.

[0007] In another possible implementation, a sewage scale cleaning plan is determined based on multiple sewage scale monitoring abnormal pipelines, including: determining the intersections of different sewage scale monitoring abnormal pipelines, and obtaining the sewage scale monitoring parameters corresponding to each intersection; when the first sewage scale monitoring parameter corresponding to the first intersection meets the first sewage scale parameter threshold, cleaning the scale in the first sewage scale monitoring abnormal pipeline including the first intersection.

[0008] In another possible implementation, the method also includes: obtaining predicted area data corresponding to each sewage pipe scale monitoring node in a future time period, the predicted area data including the type and amount of sewage discharged through the sewage pipe in the area where the sewage pipe scale monitoring node is located in the future time period; adjusting the first sewage pipe scale parameter threshold according to the change amplitude of the predicted area data corresponding to each sewage pipe scale monitoring node; wherein the first sewage pipe scale parameter threshold includes a sewage pipe scale thickness threshold and a sewage pipe scale thickness increase threshold.

[0009] In another possible implementation, the first sewage pipe scale parameter threshold is adjusted according to the change range of the predicted area data, including: determining the sewage discharge volume increase corresponding to each sewage discharge type in the predicted area data; for multiple sewage discharge types, respectively calculating the sum of the first sewage pipe scale parameter threshold / (sewage discharge volume increase corresponding to the sewage discharge type + 1) × type adjustment coefficient as the adjusted first sewage pipe scale parameter threshold.

[0010] In another possible implementation, the method also includes: obtaining the number of sewage pipes in the area where each sewage pipe scale monitoring node is located; through the sewage pipe scale monitoring model, according to the scale type corresponding to each sewage pipe scale monitoring node, the time series thickness increment, the regional data, the number of sewage pipes in the area and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, determining multiple sewage pipe scale monitoring abnormal pipelines.

[0011] In another possible implementation, determining a sewage pipe scale cleaning plan based on multiple sewage pipe scale monitoring abnormal pipelines also includes: determining the number of intersections between each sewage pipe scale monitoring abnormal pipeline and other pipelines; and inspecting the multiple sewage pipe scale monitoring abnormal pipelines in descending order of the number of intersections between the multiple sewage pipe scale monitoring abnormal pipelines and other pipelines.

[0012] In another possible implementation, the method also includes: obtaining the construction age of the sewage pipe and the number of historical sewage pipe maintenance times corresponding to each sewage pipe scale monitoring edge; determining multiple sewage pipe reconstruction lines through a sewage pipe reconstruction monitoring model according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node, the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, the construction age of the sewage pipe and the number of historical sewage pipe maintenance times; wherein each sewage pipe reconstruction pipeline includes one sewage pipe scale monitoring edge, and the sewage pipe reconstruction monitoring model is a graph neural network model; determining a sewage pipe reconstruction plan based on the multiple sewage pipe reconstruction pipelines.

[0013] In another possible implementation, a sewage pipe reconstruction plan is determined based on multiple sewage pipe reconstruction lines, including: obtaining sewage pipe flow parameters of each sewage pipe reconstruction line, and determining the sewage pipe reconstruction plan for the multiple sewage pipe reconstruction lines according to the order of the sewage pipe flow parameters from large to small.

[0014] An embodiment of the present application also provides an online pipe network monitoring system based on the Internet of Things, including a unit for executing any of the above methods.

[0015] An embodiment of the present application also provides an online pipe network monitoring system based on the Internet of Things, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the computer program.

[0016] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above items is implemented.

[0017] An embodiment of the present application also provides a computer program product, including a computer program, which implements the steps of any of the methods described above when executed by a processor.

[0018] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0019] The embodiment of the present application provides an online monitoring method for pipe networks based on the Internet of Things, and the method includes: constructing a sewage pipe network diagram composed of multiple monitoring points, the sewage pipe network diagram includes multiple sewage pipe scale monitoring nodes and sewage pipe scale monitoring edges connecting different sewage pipe scale monitoring nodes, and obtaining sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge; wherein, the sewage pipe scale monitoring parameters include pipe scale type and pipe scale thickness; through the sewage pipe scale monitoring model, according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, multiple sewage pipe scale monitoring abnormal pipelines are determined; wherein, each sewage pipe scale monitoring abnormal pipeline includes multiple sewage pipe scale monitoring nodes, and the sewage pipe scale monitoring model is a graph neural network model; according to multiple sewage pipe scale monitoring abnormal pipelines, a sewage pipe scale cleaning plan is determined. The online monitoring method for sewage pipe networks in the embodiment of the present application can effectively monitor sewage pipes, improve the efficiency of identifying dangerous pipe networks in urban and rural sewage pipe networks, and improve the monitoring effect of urban and rural sewage pipe networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 A schematic diagram of a flow chart of a first method for online monitoring of a pipe network based on the Internet of Things provided in an embodiment of the present application;

[0022] Figure 2 A schematic diagram of a sewage pipe network diagram provided in an embodiment of the present application;

[0023] Figure 3 A schematic diagram of a second method for online monitoring of a pipe network based on the Internet of Things provided in an embodiment of the present application;

[0024] Figure 4 A schematic diagram of a flow chart of a third method for online monitoring of a pipe network based on the Internet of Things provided in an embodiment of the present application;

[0025] Figure 5A schematic diagram of the logical structure of a first online pipe network monitoring system based on the Internet of Things provided in an embodiment of the present application;

[0026] Figure 6 A schematic diagram of the physical structure of the first Internet of Things-based pipe network online monitoring system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0028] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0029] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0030] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0032] The structure of urban and rural sewage pipe networks is complex and the pipes are distributed relatively scatteredly, making it difficult to carry out efficient monitoring of urban and rural sewage pipe networks using existing technologies.

[0033] Based on the above reasons, the embodiment of the present application provides an online monitoring method for pipe networks based on the Internet of Things, the method comprising: constructing a sewage pipe network diagram consisting of multiple monitoring points, the sewage pipe network diagram comprising multiple sewage pipe scale monitoring nodes and sewage pipe scale monitoring edges connecting different sewage pipe scale monitoring nodes, and obtaining sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge; wherein, the sewage pipe scale monitoring parameters include pipe scale type and pipe scale thickness; through the sewage pipe scale monitoring model, according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, multiple sewage pipe scale monitoring abnormal pipelines are determined; wherein, each sewage pipe scale monitoring abnormal pipeline includes multiple sewage pipe scale monitoring nodes, and the sewage pipe scale monitoring model is a graph neural network model; according to multiple sewage pipe scale monitoring abnormal pipelines, a sewage pipe scale cleaning plan is determined. The online monitoring method for sewage pipe networks in the embodiment of the present application can effectively monitor sewage pipes, improve the efficiency of identifying dangerous pipe networks in urban and rural sewage pipe networks, and improve the monitoring effect of urban and rural sewage pipe networks.

[0034] In some scenarios, an online pipe network monitoring method based on the Internet of Things of an embodiment of the present application can be applied to large-scale monitoring of urban and rural sewage pipe networks. When the distribution of urban and rural pipes is relatively dispersed, it can improve the monitoring efficiency of urban and rural sewage pipe networks.

[0035] The following is a specific example of an online monitoring method for a pipe network based on the Internet of Things provided in an embodiment of the present application.

[0036] Figure 1 A schematic diagram of a process flow of an online monitoring method for a pipe network based on the Internet of Things provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes S110 to S130, and S110 to S130 are described in detail below.

[0037] S110, construct a sewage pipe network diagram consisting of multiple monitoring points, the sewage pipe network diagram including multiple sewage pipe scale monitoring nodes and sewage pipe scale monitoring edges connecting different sewage pipe scale monitoring nodes, and obtain sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge. Among them, the sewage pipe scale monitoring parameters include pipe scale type and pipe scale thickness.

[0038] like Figure 1 As shown, when monitoring urban and rural sewage pipe networks, a sewage pipe network map consisting of multiple monitoring points can be constructed. The sewage pipe network map includes multiple sewage pipe scale monitoring nodes and sewage pipe scale monitoring edges connecting different sewage pipe scale monitoring nodes. The sewage pipe network map represents the characteristics of urban and rural sewage pipe networks, and the sewage pipe network can be monitored through the sewage pipe network map.

[0039] Figure 2 A schematic diagram of a sewage pipe network diagram provided in an embodiment of the present application is shown as follows: Figure 2 As shown, a sewage pipe network diagram consisting of multiple monitoring points can be constructed. The sewage pipe network diagram includes multiple sewage pipe scale monitoring nodes A1, A2, A3... and sewage pipe scale monitoring edges K1, K2, K3 connecting different sewage pipe scale monitoring nodes are constructed... The sewage pipe network diagram represents the connection and sewage flow characteristics of urban and rural sewage pipe networks, and thus the working status of the sewage pipe network can be monitored through the sewage pipe network diagram.

[0040] Exemplarily, the sewage pipe scale monitoring node may be a monitoring point for setting a sensor to monitor the sewage pipe, and the sewage pipe scale monitoring node may upload the monitoring data of the sewage pipe network to a data processing center through the Internet of Things.

[0041] Structurally, the sewage pipe scale monitoring edges K1, K2, K3... connected to different sewage pipe scale monitoring nodes can correspond to the pipes connected to different sewage pipe scale monitoring nodes, and then the working status of the sewage pipe can be monitored by the sewage flow in the pipes connected to different sewage pipe scale monitoring nodes.

[0042] After obtaining the sewage pipe network diagram, the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge can be obtained, and then the characteristics of the sewage pipe network diagram can be identified according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge.

[0043] Exemplarily, the sewage pipe scale monitoring parameters may include pipe scale type and pipe scale thickness. The pipe scale type may include oil pipe scale, solid pipe scale and other pipe scale types. The pipe scale thickness may be the thickness of oil pipe scale and solid pipe scale.

[0044] Exemplarily, the type and thickness of pipe scale can be detected by an ultrasonic sensor installed on the outer wall of the sewage pipe. During detection, an ultrasonic pulse can be emitted by the ultrasonic sensor. The ultrasonic wave propagates in the medium. When encountering different media (such as pipe walls and pipe scale), the ultrasonic wave will be reflected and refracted. By measuring the time difference from the emission to the reception of the ultrasonic wave, the propagation speed and thickness of the ultrasonic wave in the pipe wall and pipe scale are calculated. By analyzing the characteristics of the echo signal, different types of pipe scale (such as calcification, organic matter, etc.) can be identified, and then they can be distinguished based on information such as the intensity, frequency and shape of the echo.

[0045] S120. Determine multiple abnormal sewage scale monitoring pipelines through the sewage scale monitoring model according to the sewage scale monitoring parameters corresponding to each sewage scale monitoring node and the sewage pipe flow parameters corresponding to each sewage scale monitoring edge. Each abnormal sewage scale monitoring pipeline includes multiple abnormal sewage scale monitoring nodes, and the sewage scale monitoring model is a graph neural network model.

[0046] When monitoring the sewage pipe network, the sewage pipe scale monitoring model can be used to characterize the sewage pipe network status in the sewage pipe network diagram according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge. Multiple sewage pipe scale monitoring abnormal pipelines can then be determined, thereby identifying abnormal sewage pipe scale monitoring pipelines.

[0047] When identifying abnormal sewage pipe scale monitoring pipelines, each abnormal sewage pipe scale monitoring pipeline may include multiple sewage pipe scale monitoring nodes, and the sewage pipe scale monitoring model is a graph neural network model, so that the abnormal sewage pipe scale monitoring pipeline can be identified through the graph neural network.

[0048] When training the sewage pipe scale monitoring model, the sewage pipe scale monitoring parameters of each sewage pipe scale monitoring node (such as pipe scale type, pipe scale thickness), the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge (such as flow rate) and the label of the sewage pipe scale monitoring pipeline can be obtained by training on the graph neural network.

[0049] S130, determining a sewage pipe scale cleaning plan based on multiple sewage pipe scale monitoring abnormal pipelines.

[0050] After obtaining multiple sewage pipe scale monitoring abnormal pipelines, a sewage pipe scale cleaning plan can be determined based on the multiple sewage pipe scale monitoring abnormal pipelines to achieve maintenance and cleaning of urban and rural sewage pipe networks.

[0051] The beneficial effect brought about by the above-mentioned implementation method is that, by combining the scale type and scale thickness of the monitoring nodes of the sewage pipe and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge through the graph neural network, the working status of the sewage pipe can be identified, thereby realizing the monitoring of the working status of the sewage pipe and improving the monitoring efficiency of the sewage pipe.

[0052] The beneficial effect brought about by the above implementation method is that it avoids the accidental errors caused by monitoring the sewage pipe network based on the data of each sewage pipe monitoring point, and improves the accuracy of fault judgment on the sewage pipe network.

[0053] The beneficial effect brought about by the above-mentioned implementation method is that, in a complex and criss-crossed sewage network, it is possible to accurately identify sewage networks that may fail, thereby improving the monitoring efficiency of the sewage network; when maintaining the sewage network, it is possible to identify and clean abnormal sewage pipe scale monitoring pipelines, avoiding the poor cleaning effect caused by only cleaning a certain node in the abnormal sewage pipe scale monitoring pipeline in the prior art.

[0054] In some implementations, in the above S120, through the sewage pipe scale monitoring model, according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, multiple sewage pipe scale monitoring abnormal pipelines are determined, including S121 to S122. S121 to S122 are described in detail below.

[0055] S121. Determine the time series thickness increment of the pipe scale thickness according to the historical data of the pipe scale thickness.

[0056] After obtaining the pipe scale thickness in the sewage pipe, the time series thickness increment of the pipe scale thickness can also be determined based on the historical data of the pipe scale thickness, so as to monitor the status of the sewage pipe through the time series thickness increment.

[0057] For example, the time series thickness increment may include the scale thickness increment in the sewage pipe within 1 month or 3 months.

[0058] S122. Determine multiple sewage pipe scale monitoring abnormal pipelines through the sewage pipe scale monitoring model according to the pipe scale type corresponding to each sewage pipe scale monitoring node, the time series thickness increment, and the sewage pipe flow parameter corresponding to each sewage pipe scale monitoring edge.

[0059] During operation, through the sewage pipe scale monitoring model, according to the pipe scale type corresponding to each sewage pipe scale monitoring node, the time series thickness increment and the sewage pipe flow parameter corresponding to each sewage pipe scale monitoring edge, multiple sewage pipe scale monitoring abnormal pipelines are determined to realize the identification of the sewage pipe scale monitoring abnormal pipelines.

[0060] When training the sewage pipe scale monitoring model, the scale type corresponding to each sewage pipe scale monitoring node, the time series thickness increment, the sewage pipe flow parameter corresponding to each sewage pipe scale monitoring edge, and the label of the sewage pipe scale monitoring pipeline can be used as sample data and obtained through graph neural network training.

[0061] The beneficial effect brought about by the above-mentioned implementation method is that, by combining the scale type and scale thickness increment of the monitoring nodes of the sewage pipe, as well as the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, the working status of the sewage pipe can be identified, thereby realizing the monitoring of the working status of the sewage pipe and improving the monitoring efficiency of the sewage pipe.

[0062] The beneficial effect brought about by the above-mentioned implementation method is that, by using the scale thickness increment as a reference, the abnormal sewage scale monitoring pipelines in the sewage network can be identified, which can avoid accidental errors in the absolute value detection of the scale thickness and improve the monitoring accuracy of the abnormal sewage scale monitoring pipelines.

[0063] The beneficial effect brought about by the above-mentioned implementation method is that, taking the pipe scale thickness increment as a reference quantity and combining it with the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, the pipe scale thickness increment has a greater impact on the sewage pipe flow. By exploring the relationship between the pipe scale thickness increment and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, the monitoring accuracy of abnormal sewage pipe scale monitoring pipelines can be improved.

[0064] Figure 3 A flow chart of a second method for online monitoring of a pipe network based on the Internet of Things provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the above method further includes S210 to S220, and S210 to S220 are described in detail below.

[0065] S210. Acquire regional data corresponding to each sewage pipe scale monitoring node, where the regional data includes sewage discharge type, sewage discharge volume, and historical sewage pipe maintenance times discharged through sewage pipes in the area where the sewage pipe scale monitoring node is located.

[0066] During operation, in order to improve the accuracy of identifying abnormal pipelines for sewage pipe scale monitoring, the regional data corresponding to each sewage pipe scale monitoring node can also be obtained. The regional data includes the sewage discharge type, sewage discharge volume and historical sewage pipe maintenance times discharged through the sewage pipe in the area where the sewage pipe scale monitoring node is located. The sewage discharge type and sewage discharge volume characterize the sewage discharge characteristics in the area where the sewage pipe network is located, and the historical sewage pipe maintenance times characterize the maintenance characteristics of the sewage pipe in the area where the sewage pipe network is located.

[0067] Exemplarily, a plurality of regions may be divided in the map data, and regional data may be acquired for each region, and then the regional data corresponding to each sewage pipe scale monitoring node may be determined.

[0068] Exemplarily, the types of sewage discharge corresponding to different areas may be different. For example, the sewage in an industrial zone is mainly industrial sewage; the sewage in a district is mainly sewage containing oil.

[0069] Exemplarily, the historical sewage pipe maintenance times in the regional data may include the specific times that the sewage pipes in the region have been repaired, and the historical sewage pipe maintenance times represent the frequency characteristics of blockages in the sewage pipes in the region.

[0070] S220. Determine multiple sewage pipe scale monitoring abnormal pipelines through the sewage pipe scale monitoring model according to the pipe scale type, time series thickness increment, regional data, and sewage pipe flow parameters corresponding to each sewage pipe scale monitoring node.

[0071] When identifying abnormal sewage pipe scale monitoring pipelines, the sewage pipe scale monitoring model can be used to determine multiple abnormal sewage pipe scale monitoring pipelines based on the scale type, time series thickness increment, regional data, and sewage pipe flow parameters corresponding to each sewage pipe scale monitoring node, thereby achieving accurate identification of abnormal sewage pipe scale monitoring pipelines.

[0072] The beneficial effect brought about by the above-mentioned implementation method is that, through the regional data of the sewage pipe network and the characteristics of the sewage pipe network itself, the abnormal pipelines for sewage pipe scale monitoring in the sewage pipe network can be accurately identified, thereby realizing the identification of abnormal pipelines for sewage pipe scale monitoring in combination with regional characteristics.

[0073] Figure 4 A schematic diagram of a third method for online monitoring of a pipe network based on the Internet of Things provided in the present application is shown in FIG. Figure 4 As shown, in the above S130, a sewage pipe scale cleaning plan is determined based on a plurality of sewage pipe scale monitoring abnormal pipelines, including S131 to S132, and S131 to S132 are specifically described below.

[0074] S131. Determine the intersections of different sewage pipe scale monitoring abnormal pipelines, and obtain sewage pipe scale monitoring parameters corresponding to each intersection.

[0075] During operation, in order to further improve the efficiency of cleaning the sewage pipes, the intersection points of different sewage pipe scale monitoring abnormal pipelines can be determined. The intersection points of different sewage pipe scale monitoring abnormal pipelines are the intersections of sewage in different sewage pipe scale monitoring abnormal pipelines. The sewage in different sewage pipe scale monitoring abnormal pipelines gathers at the intersection, which may cause the scale thickness of different sewage pipe scale monitoring abnormal pipelines at the intersection to be greater.

[0076] After obtaining the intersections of different sewage pipe scale monitoring abnormal pipelines, the sewage pipe scale monitoring parameters corresponding to each intersection can be obtained, and then the sewage pipe scale monitoring abnormal pipelines can be further screened according to the sewage pipe scale monitoring parameters corresponding to each intersection to determine the sewage pipe scale monitoring abnormal pipelines that need to be cleaned.

[0077] S132: When the first sewage pipe scale monitoring parameter corresponding to the first intersection meets the first sewage pipe scale parameter threshold, the pipe scale in the first sewage pipe scale monitoring abnormal pipeline including the first intersection is cleaned.

[0078] When screening abnormal sewage pipe scale monitoring pipelines, when the first sewage pipe scale monitoring parameter corresponding to the first intersection is greater than the first sewage pipe scale parameter threshold, it means that the scale at the first intersection has reached the requirement for cleaning, and the scale in the first sewage pipe scale monitoring abnormal pipeline including the first intersection can be cleaned, so as to achieve timely cleaning of the first sewage pipe scale monitoring abnormal pipeline with thicker scale at the intersection.

[0079] Exemplarily, the first sewage pipe scale monitoring parameter may include pipe scale thickness and pipe scale thickness increase.

[0080] The beneficial effect brought about by the above-mentioned implementation method is that the sewage pipe scale monitoring parameters at the intersection of different sewage pipe scale monitoring abnormal pipelines are screened, so as to determine the sewage pipe scale monitoring abnormal pipeline that needs to be cleaned according to the scale status at the sewage intersection in different sewage pipe scale monitoring abnormal pipelines, thereby improving the identification effect of the sewage pipe scale monitoring abnormal pipeline.

[0081] The beneficial effect brought about by the above-mentioned implementation method is that by first identifying the abnormal sewage pipe scale monitoring pipelines, and determining the abnormal sewage pipe scale monitoring pipelines that need to be cleaned first at the intersection of different abnormal sewage pipe scale monitoring pipelines, the cleaning efficiency of the abnormal sewage pipe scale monitoring pipelines is improved, and the maintenance of the abnormal sewage pipe scale monitoring pipelines is avoided.

[0082] In urban and rural sewage pipe networks, the sewage discharge volume in the area will fluctuate due to festivals, holidays, etc. Therefore, in order to predict and maintain the working status of the sewage pipes in advance, the sewage pipes can be maintained in combination with the changing status of the sewage.

[0083] In some implementations, the above method further includes S310 to S320, and S310 to S320 are described in detail below.

[0084] S310. Obtain the predicted area data corresponding to each sewage pipe scale monitoring node in a future time period, the predicted area data including the type and amount of sewage discharged through the sewage pipe in the area where the sewage pipe scale monitoring node is located in the future time period.

[0085] During operation, the predicted area data corresponding to each sewage pipe scale monitoring node in the future time period can be obtained. The predicted area data includes the sewage discharge type and sewage discharge amount discharged through the sewage pipe in the area where the sewage pipe scale monitoring node is located in the future time period, and then the sewage pipe status can be predicted according to the sewage discharge type and sewage discharge amount in the predicted area data.

[0086] Exemplarily, the sewage discharge type and the sewage discharge amount may include a first sewage discharge type and a first sewage discharge amount corresponding to the first sewage discharge type, a second sewage discharge type and a second sewage discharge amount corresponding to the second sewage discharge type, and a third sewage discharge type and a third sewage discharge amount corresponding to the third sewage discharge type.

[0087] Exemplarily, when obtaining the predicted area data corresponding to each sewage pipe scale monitoring node in the future time period, the predicted area data of each sewage pipe scale monitoring node in the future time period can be predicted through historical data.

[0088] S320: Adjust the first sewage pipe scale parameter threshold according to the change range of the prediction area data corresponding to each sewage pipe scale monitoring node, wherein the first sewage pipe scale parameter threshold includes a sewage pipe scale thickness threshold and a sewage pipe scale thickness increase threshold.

[0089] When monitoring the sewage pipe network, the first sewage pipe scale parameter threshold can be adjusted according to the change amplitude of the predicted area data corresponding to each sewage pipe scale monitoring node, and then the sewage pipe status can be monitored according to the adjusted first sewage pipe scale parameter threshold. The adjusted first sewage pipe scale parameter threshold represents the risk resistance threshold of the sewage pipe after changes in sewage type and sewage flow.

[0090] Exemplarily, the first sewage pipe scale parameter threshold may include a sewage pipe scale thickness threshold and a sewage pipe scale thickness increase threshold.

[0091] The beneficial effect brought about by the above-mentioned implementation method is that it can monitor the status of sewage pipes in combination with the fluctuations in sewage discharge in the area due to festivals, holidays, etc., thereby improving the robustness of sewage pipe monitoring and preventing sewage pipe failures in advance.

[0092] The beneficial effect brought about by the above-mentioned implementation method is that it is only necessary to monitor the sewage pipe status in advance according to the adjusted first sewage pipe scale parameter threshold. The adjusted first sewage pipe scale parameter threshold can screen and monitor the sewage pipe network status threshold in advance according to the sewage change in the area, thereby improving the reliability of sewage pipe network monitoring.

[0093] In some implementations, in the above S320, the first sewage pipe scale parameter threshold is adjusted according to the change range of the predicted regional data, including S321 to S322, and S321 to S322 are specifically described below.

[0094] S321. Determine the increase in sewage discharge volume corresponding to each sewage discharge type in the prediction area data.

[0095] When adjusting the first sewage pipe scale parameter threshold, the first sewage pipe scale parameter threshold can be adjusted according to different sewage discharge types to improve the scientificity of adjusting the first sewage pipe scale parameter threshold.

[0096] During operation, the increase in sewage discharge volume corresponding to each sewage discharge type in the forecast area data can be obtained. For example, the sewage discharge volume corresponding to the sewage discharge type of residential water use and the sewage discharge volume corresponding to the sewage discharge type of industrial water use can be specifically included.

[0097] S322. For multiple sewage discharge types, respectively calculate the sum of the first sewage pipe scale parameter threshold value / (increase in sewage discharge volume corresponding to the sewage discharge type+1)×type adjustment coefficient as the adjusted first sewage pipe scale parameter threshold value.

[0098] During operation, for multiple sewage discharge types, the sum of the first sewage pipe scale parameter threshold / (sewage discharge volume increase corresponding to the sewage discharge type+1)×type adjustment coefficient can be calculated respectively as the adjusted first sewage pipe scale parameter threshold.

[0099] Exemplarily, when determining the type adjustment coefficient corresponding to each sewage discharge type, it can be determined based on empirical values.

[0100] After adjusting the first sewage pipe scale parameter threshold to the sewage pipe scale parameter threshold obtained after summing, the sewage pipe network can be maintained through the above S310 to S320 according to the adjusted first sewage pipe scale parameter threshold, thereby improving the scientific nature of the sewage pipe network maintenance.

[0101] The beneficial effect brought about by the above-mentioned implementation method is that, for multiple sewage discharge types, the sum of the first sewage pipe scale parameter threshold / (sewage discharge volume increase corresponding to the sewage discharge type + 1) × type adjustment coefficient is calculated respectively as the adjusted first sewage pipe scale parameter threshold, which combines the characteristics of different types of sewage and provides early warning of the sewage pipe status during sewage pipe maintenance, thereby improving the monitoring effect of the sewage pipe.

[0102] In some implementations, the above method further includes S410 to S420, and S410 to S420 are described in detail below.

[0103] S410: Obtain the number of sewage pipes in the area where each sewage pipe scale monitoring node is located.

[0104] When monitoring the sewage network, the number of sewage pipes in the area where each sewage pipe scale monitoring node is located can be obtained. The number of sewage pipes represents the sewage discharge capacity in the area where the sewage pipe scale monitoring node is located, and then the status of the sewage pipe can be monitored according to the number of sewage pipes in the area where each sewage pipe scale monitoring node is located.

[0105] S420. Determine multiple sewage pipe scale monitoring abnormal pipelines through the sewage pipe scale monitoring model according to the pipe scale type corresponding to each sewage pipe scale monitoring node, the time series thickness increment, the regional data, the number of sewage pipes in the area, and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge.

[0106] When monitoring the status of sewage pipes according to the number of sewage pipes in the area where each sewage pipe scale monitoring node is located, the sewage pipe scale monitoring model can be used to determine multiple sewage pipe scale monitoring abnormal pipelines according to the pipe scale type, time series thickness increment, regional data, the number of sewage pipes in the area and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge corresponding to each sewage pipe scale monitoring node. The regional data includes the sewage discharge type, sewage discharge volume and historical sewage pipe maintenance times discharged through the sewage pipe in the area where the sewage pipe scale monitoring node is located, thereby achieving the effect of monitoring the sewage pipe in combination with the sewage discharge capacity.

[0107] The beneficial effect brought about by the above-mentioned implementation method is that it can monitor the status of the sewage pipe in combination with the sewage discharge capacity in the area, avoid the occurrence of excessive scale in the sewage pipe in a short period of time, and improve the monitoring effect of the sewage pipe.

[0108] In some implementations, in the above S130, abnormal pipelines are monitored based on multiple sewage pipe scales, and a sewage pipe scale cleaning plan is determined, which also includes S133 to S134. S133 to S134 are described in detail below.

[0109] S133. Determine the number of intersections between each sewage pipe scale monitoring abnormal pipeline and other pipelines.

[0110] When multiple groups of abnormal sewage scale monitoring pipelines intersect, the number of intersections of each abnormal sewage scale monitoring pipeline and other pipelines can be determined, and the abnormal sewage scale monitoring pipelines can be screened according to the intersection of multiple groups of intersecting abnormal sewage scale monitoring pipelines to determine the maintenance priority of the abnormal sewage scale monitoring pipelines.

[0111] S134. In order of the number of intersections between the multiple sewage pipe scale monitoring abnormal pipelines and other pipelines from high to low, inspect and repair the multiple sewage pipe scale monitoring abnormal pipelines.

[0112] When determining the maintenance priority of abnormal sewage scale monitoring pipelines, the maintenance priority may be determined in descending order according to the number of intersections between multiple abnormal sewage scale monitoring pipelines and other pipelines. The more intersections there are between multiple abnormal sewage scale monitoring pipelines and other pipelines, the greater the scope of impact after a sewage pipe failure occurs. Therefore, the maintenance priority may be determined in order according to the number of intersections between multiple abnormal sewage scale monitoring pipelines and other pipelines to improve the overall reliability of the sewage network.

[0113] The beneficial effect brought about by the above-mentioned implementation method is that multiple sewage pipe scale monitoring abnormal pipelines are inspected and repaired in the order of the number of intersections between multiple sewage pipe scale monitoring abnormal pipelines and other pipelines, thereby improving the overall reliability of the sewage pipe network, reducing the impact range of sewage pipe failures, and improving the maintenance effect of the sewage pipe network.

[0114] In some implementations, the above method further includes S510 to S520, and S510 to S520 are described in detail below.

[0115] S510, obtaining the construction age of the sewage pipe and the number of historical sewage pipe maintenance times corresponding to each sewage pipe scale monitoring edge.

[0116] When monitoring sewage pipes, old pipelines can be monitored and evaluated as a research basis for pipeline upgrades and renovations. It can also more reasonably distinguish the extended service life of old pipelines so that scientific investment planning for pipeline reconstruction can be carried out to save unnecessary public utility investment and waste.

[0117] During operation, the construction age of the sewage pipe and the number of historical sewage pipe maintenance times corresponding to each sewage pipe scale monitoring edge can be obtained. The construction age of the sewage pipe represents the construction age of the sewage pipe corresponding to each sewage pipe scale monitoring edge, and the number of historical sewage pipe maintenance times represents the number of maintenance times of the sewage pipe corresponding to each sewage pipe scale monitoring edge.

[0118] Exemplarily, the first sewage pipe corresponding to the first sewage pipe scale monitoring edge may be built for 1 year, 3 years or 5 years.

[0119] S520. Determine multiple sewage pipe reconstruction lines through the sewage pipe reconstruction monitoring model according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node, the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, the age of the sewage pipe and the number of historical sewage pipe maintenance times; wherein each sewage pipe reconstruction line includes 1 sewage pipe scale monitoring edge, and the sewage pipe reconstruction monitoring model is a graph neural network model.

[0120] When monitoring pipeline reconstruction, the sewage pipe reconstruction monitoring model can be used to determine multiple sewage pipe reconstruction lines based on the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node, the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, the age of the sewage pipe and the number of historical sewage pipe maintenance times. Each sewage pipe reconstruction line includes one sewage pipe scale monitoring edge to achieve monitoring of the sewage pipe reconstruction lines that need to be rebuilt.

[0121] Exemplarily, the sewage pipe reconstruction monitoring model is a graph neural network model, and the sewage pipe reconstruction process can be planned through the graph neural network model.

[0122] S530. Determine a sewage pipe reconstruction plan based on multiple sewage pipe reconstruction pipelines.

[0123] After obtaining multiple sewage pipe reconstruction pipelines, a sewage pipe reconstruction plan can be determined based on the multiple sewage pipe reconstruction pipelines to achieve planning of the sewage pipe reconstruction plan.

[0124] The beneficial effect brought about by the above-mentioned implementation method is that it can determine multiple sewage pipe reconstruction lines according to the sewage pipe scale status corresponding to each sewage pipe scale monitoring node, the sewage flow corresponding to each sewage pipe scale monitoring edge, the construction age of the sewage pipe and the number of historical sewage pipe maintenance times. Compared with simply planning the pipeline reconstruction based on the construction age of the sewage pipe, the scientific nature of the sewage pipe reconstruction plan is improved.

[0125] In some implementations, in the above method, a sewage pipe reconstruction plan is determined based on multiple sewage pipe reconstruction lines, including: obtaining sewage pipe flow parameters of each sewage pipe reconstruction line, and determining the sewage pipe reconstruction plan for multiple sewage pipe reconstruction lines according to the order of the sewage pipe flow parameters from large to small.

[0126] When reconstructing the pipeline, the sewage pipe flow parameters of each sewage pipe reconstruction pipeline can be obtained. The sewage pipe flow parameters characterize the sewage flow flowing in each sewage pipe reconstruction pipeline. The larger the sewage flow, the more important the sewage pipe is. Then, the sewage pipe reconstruction plan of multiple sewage pipe reconstruction pipelines can be determined according to the order of sewage pipe flow parameters from large to small.

[0127] The beneficial effect brought about by the above-mentioned implementation method is that the greater the sewage flow rate, the more important the sewage pipe is. The sewage pipe can be rebuilt in order of sewage pipe flow parameters from large to small, thereby improving the scientific nature of the sewage pipe reconstruction plan and avoiding failures of pipes with large sewage flow rates that affect the sewage network.

[0128] An embodiment of the present application also provides an online pipe network monitoring system based on the Internet of Things, including a unit for executing any of the above methods.

[0129] Figure 5 A schematic diagram of the logical structure of an online monitoring system for a pipe network based on the Internet of Things is provided in an embodiment of the present application, such as Figure 5 As shown, the system 1 of this embodiment includes a processing unit 11, a storage unit 12 and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12 and the transceiver unit 13 cooperate with each other to implement the above method. The beneficial effects brought by the embodiment of the present application have been described in the above method and will not be repeated here.

[0130] An embodiment of the present application also provides an online pipe network monitoring system based on the Internet of Things, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the computer program.

[0131] Figure 6 A schematic diagram of the physical structure of an online monitoring system for a pipe network based on the Internet of Things is provided in an embodiment of the present application, such as Figure 6 As shown, the system 2 of this embodiment includes: at least one processor 20 ( Figure 6 Only one processor 20 is shown in the figure), a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20, and the processor 20 implements the steps in any of the above-mentioned method embodiments when executing the computer program 22. The beneficial effects brought about by the embodiments of the present application have been described in the above-mentioned methods and will not be repeated here.

[0132] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0133] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0134] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0135] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0136] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0137] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0138] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0139] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0140] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for online monitoring of pipe networks based on the Internet of Things, characterized in that: The method comprises: Construct a sewage pipe network diagram consisting of multiple monitoring points, the sewage pipe network diagram includes multiple sewage pipe scale monitoring nodes and sewage pipe scale monitoring edges connecting different sewage pipe scale monitoring nodes, and obtain sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge; wherein the sewage pipe scale monitoring parameters include pipe scale type and pipe scale thickness; Through the sewage pipe scale monitoring model, according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, multiple sewage pipe scale monitoring abnormal pipelines are determined; wherein each sewage pipe scale monitoring abnormal pipeline includes multiple sewage pipe scale monitoring nodes, and the sewage pipe scale monitoring model is a graph neural network model; Determine the sewage pipe scale cleaning plan based on multiple sewage pipe scale monitoring abnormal pipelines; Among them, through the sewage pipe scale monitoring model, according to the sewage pipe scale monitoring parameters corresponding to each sewage pipe scale monitoring node and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge, multiple sewage pipe scale monitoring abnormal pipelines are determined, including: Determine the time series thickness increment of pipe scale thickness based on historical data of pipe scale thickness; Through the sewage pipe scale monitoring model, according to the pipe scale type corresponding to each sewage pipe scale monitoring node, the time series thickness increment and the sewage pipe flow parameter corresponding to each sewage pipe scale monitoring edge, multiple sewage pipe scale monitoring abnormal pipelines are determined; Based on the abnormal pipelines of multiple sewage pipe scale monitoring, determine the sewage pipe scale cleaning plan, including: Determine the intersections of different sewage scale monitoring abnormal pipelines and obtain the sewage scale monitoring parameters corresponding to each intersection; When the first sewage pipe scale monitoring parameter corresponding to the first intersection meets the first sewage pipe scale parameter threshold, the pipe scale in the first sewage pipe scale monitoring abnormal pipeline including the first intersection is cleaned.

2. The method according to claim 1, characterized in that The method further comprises: Obtain the regional data corresponding to each sewage pipe scale monitoring node, the regional data including the sewage discharge type, sewage discharge volume and historical sewage pipe maintenance times discharged through the sewage pipe in the area where the sewage pipe scale monitoring node is located; Through the sewage pipe scale monitoring model, multiple sewage pipe scale monitoring abnormal pipelines are determined according to the pipe scale type, time series thickness increment, regional data corresponding to each sewage pipe scale monitoring node, and the sewage pipe flow parameters corresponding to each sewage pipe scale monitoring edge.

3. The method according to claim 2, characterized in that The method further comprises: Obtaining the predicted regional data corresponding to each sewage pipe scale monitoring node in a future time period, the predicted regional data including the sewage discharge type and sewage discharge amount discharged through the sewage pipe in the area where the sewage pipe scale monitoring node is located in the future time period; The first sewage pipe scale parameter threshold is adjusted according to the variation range of the prediction area data corresponding to each sewage pipe scale monitoring node; wherein the first sewage pipe scale parameter threshold includes a sewage pipe scale thickness threshold and a sewage pipe scale thickness increase threshold.

4. An online monitoring system for pipe networks based on the Internet of Things, characterized in that: The method comprises means for performing the method according to any one of claims 1 to 3.

5. An online monitoring system for a pipe network based on the Internet of Things, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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