A sewer network clogging analysis method and system based on internet of things monitoring

Abstract

The application discloses a kind of based on the drainage pipe network silt analysis method and system of monitoring of Internet of Things, and the application can effectively solve the deviation problem caused by single factor by obtaining comprehensive silt coefficient by fusing pipe flow rate related silt degree coefficient and sump well liquid level related pipe silt coefficient, effectively lock the drainage pipeline with serious silt, avoid misjudgment phenomenon caused by single liquid level parameter, flow rate parameter.The application can be applied to the dredging and desilting work of drainage pipe network, avoid carrying out desilting work when very serious silt occurs, cause urban waterlogging, waterlogging and other phenomena, harm city development, property safety and people's life safety;And comprehensively improve the phenomenon of traditional dredging and desilting of entire city drainage pipe network, effectively reduce the loss caused by large workload, large investment and other problems without specific target.Through the application, the pipe section and sump well with serious silt can be accurately positioned, and the relevant pipe can be desilted and treated.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) technology, specifically relating to a method and system for analyzing siltation in drainage pipe networks based on IoT monitoring. Background Technology

[0002] In recent years, the Internet of Things (IoT) technology has developed rapidly. Monitoring equipment technology that can monitor and reflect the real-time operation status of drainage pipe networks, such as water quality, flow rate, flow velocity, and liquid level, has become very mature. Based on related equipment, the operation monitoring of drainage pipe networks is becoming increasingly important.

[0003] Drainage pipe networks are one of the vital lifelines of cities, bearing the important mission of treating sewage, preventing floods, and draining waterlogged areas. Due to the harsh drainage environment, these networks frequently suffer from siltation and blockages. In recent years, frequent extreme weather events have also caused severe urban flooding and waterlogging. While cities regularly conduct large-scale dredging and clearing of urban drainage networks, this method suffers from drawbacks such as long cycles, high investment, and weak targeting. There is a lack of methods, equipment, and systems that can monitor drainage networks in real time based on the Internet of Things (IoT), providing precise siltation analysis and location, and enabling real-time early warning of abnormal conditions to ensure the healthy operation of the drainage network. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a method and system for analyzing siltation in drainage pipe networks based on Internet of Things (IoT) monitoring. This invention is based on real-time monitoring of drainage pipe networks using IoT and provides a method for precise location of siltation analysis, providing real-time early warning of abnormal conditions in drainage pipe networks and ensuring the healthy operation of drainage pipe networks.

[0005] To achieve the desired effect, the present invention adopts the following technical solution:

[0006] This invention discloses a method for analyzing siltation in drainage pipe networks based on Internet of Things (IoT) monitoring, comprising:

[0007] Obtain data on the layout of monitoring points in the drainage pipe network and monitoring parameters;

[0008] The deployment data and monitoring parameters are optimized using optimization algorithms in IoT monitoring devices;

[0009] The first model was obtained by calibrating the SWMM model of the drainage network using optimized layout data and monitoring parameters.

[0010] The first model is used to analyze the siltation of the drainage network.

[0011] Furthermore, the IoT monitoring device includes: a collector, a memory, a processor, a communication module, and a concentrator; the IoT monitoring device centrally collects various monitoring data of the drainage pipe network operation through the collector and stores them in the memory; the processor processes the data by running the program in the memory for the deployment, analysis, and optimization of drainage pipe network monitoring points; and the communication module wirelessly transmits the processed data remotely to the concentrator for storage and analysis.

[0012] Furthermore, the data collector integrates multiple types of communication protocol interfaces to collect different types of monitoring data.

[0013] Furthermore, the monitoring points of the drainage network are initially deployed according to the topological structure of pollution sources, branch pipes, trunk pipes, main trunk pipes, sewage treatment plants, and drainage outlets.

[0014] Furthermore, the monitoring parameters include water level, flow rate, and flow velocity at each monitoring point.

[0015] Furthermore, the optimization algorithm is at least one of a correlation analysis algorithm and a flow ratio algorithm;

[0016] The correlation analysis algorithm specifically includes: using Pearson correlation coefficient and Spearman correlation coefficient to analyze the correlation between the target monitoring point and other monitoring points, and deleting any one of the two monitoring points whose correlation is greater than the first threshold;

[0017] The flow ratio algorithm specifically includes: comprehensively analyzing the ratio of the inflow to the transfer flow of each manhole in the drainage network, and setting up monitoring points at manholes with a ratio greater than a second threshold.

[0018] Furthermore, the step of performing siltation analysis and location analysis on the drainage pipe network using the first model specifically includes: based on the first model, comprehensively considering the influence of pipeline operating flow rate, flow velocity and manhole liquid level when the drainage pipe network is silted up, calculating the comprehensive siltation degree coefficient using a weighted average coefficient algorithm, and analyzing the siltation degree of the drainage pipes based on the comprehensive siltation degree coefficient.

[0019] Furthermore, the comprehensive siltation degree coefficient X is expressed as:

[0020] X = aX1 + bX2,

[0021] In this case, both a and b are positive numbers, and a + b = 1;

[0022] X1 is a sedimentation coefficient related to the pipe flow velocity;

[0023] X2 is the pipe siltation coefficient related to the manhole liquid level.

[0024] Furthermore, the sedimentation coefficient X1 related to the pipe flow velocity is expressed as:

[0025]

[0026] The pipeline siltation coefficient X2 related to the manhole liquid level is expressed as:

[0027]

[0028] This invention also discloses a drainage pipe network siltation analysis system based on Internet of Things monitoring, comprising:

[0029] The monitoring data query module is used to obtain the layout data and monitoring parameters of the drainage pipe network monitoring points;

[0030] The optimization module uses optimization algorithms in the IoT monitoring device to optimize the deployment data and the monitoring parameters;

[0031] The drainage network hydraulic model simulation module is used to calibrate the drainage network SWMM model using optimized layout data and monitoring parameters to obtain the first model.

[0032] The drainage pipe network siltation analysis module performs siltation analysis on the drainage pipe network using the first model.

[0033] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a method and system for analyzing drainage pipe network siltation based on Internet of Things (IoT) monitoring. This invention comprehensively considers that after siltation occurs in the drainage pipe network, parameters such as fluid flow rate, water level, and flow velocity within the network will undergo regular changes. It fully integrates the rapidly developing IoT technology, enabling real-time monitoring and analysis of the degree of siltation in the drainage pipe network, and breaking down data transmission barriers between the Internet and government dedicated networks. By fusing the siltation degree coefficient related to pipe flow velocity and the siltation coefficient related to manhole liquid level to obtain a comprehensive siltation coefficient, it can effectively solve the deviation problem caused by a single factor, effectively pinpoint severely silted drainage pipes, and avoid misjudgments due to single liquid level or flow velocity parameters. This invention can be applied to the dredging and cleaning of drainage pipe networks, avoiding the need to carry out siltation work only when severe siltation occurs, which can cause urban flooding and waterlogging, endangering urban development, property safety, and people's lives. It also comprehensively improves the traditional practice of dredging and cleaning the entire urban drainage pipe network, effectively reducing losses caused by a lack of specific targets, resulting in large workloads and investments. This invention allows for the precise location of severely silted pipe sections and manholes, enabling targeted dredging and treatment of the relevant pipelines. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart of a drainage pipe network siltation analysis method based on Internet of Things monitoring provided in an embodiment of the present invention.

[0036] Figure 2 This is a schematic diagram of a drainage pipe network siltation analysis system based on Internet of Things monitoring, provided in an embodiment of the present invention.

[0037] Figure 3 This is a schematic diagram of the longitudinal profile of the water level along a drainage pipe section from upstream to downstream, provided by an embodiment of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] See Figures 1 to 2 This invention discloses a method for analyzing siltation in drainage pipe networks based on Internet of Things (IoT) monitoring, comprising:

[0040] Step 1: Obtain the layout data and monitoring parameters of the drainage pipe network monitoring points;

[0041] Furthermore, the monitoring points of the drainage network are initially deployed according to the topological structure of pollution sources, branch pipes, trunk pipes, main trunk pipes, sewage treatment plants, and drainage outlets.

[0042] Furthermore, the monitoring parameters include water level, flow rate, and flow velocity at each monitoring point.

[0043] Specifically, based on the layout principle of drainage pipe network monitoring points, the preliminary layout plan of monitoring points is carried out according to the topological structure of drainage users (pollution sources), branch pipes, main pipes, sewage treatment plants, and drainage outlets. The monitoring points mentioned in this invention are mixed monitoring points composed of three parameters: water level, flow rate, and flow velocity. The water level refers to the manhole water level, and the flow rate and flow velocity are the parameters of the downstream main pipe corresponding to the manhole.

[0044] It is worth noting that, to improve the accuracy of drainage network siltation analysis, monitoring points should be set up and data collected as much as possible, where conditions permit. Generally, data collection frequency for monitoring points is once every 5 minutes, with data uploaded every 15 minutes; this can be adjusted according to actual needs.

[0045] Step 2: Optimize the deployment data and monitoring parameters using the optimization algorithm in the IoT monitoring device;

[0046] Furthermore, the IoT monitoring device includes: a data collector, a memory, a processor, a communication module, and a concentrator. The data collector centrally collects various monitoring data (from devices such as flow sensors, water level sensors, and flow velocity sensors) of the drainage network operation and stores it in the memory. The processor processes the data by running programs stored in the memory for the deployment, analysis, and optimization of drainage network monitoring points. The communication module wirelessly transmits the processed data remotely to the concentrator for storage and analysis. Users can configure parameters, automatically collect and analyze optimized deployment schemes for relevant monitoring points, and receive prompts. The communication module enables wireless remote transmission of all monitoring data, centralized storage and analysis at the concentrator, and provides a unified interface for all monitoring data, allowing direct connection to network gateways, switches, and other devices to push data to the upper-level government network system.

[0047] Furthermore, the data collector integrates multiple types of communication protocol interfaces to collect different types of monitoring data.

[0048] Furthermore, the optimization algorithm is at least one of a correlation analysis algorithm and a flow ratio algorithm;

[0049] The correlation analysis algorithm specifically includes: using Pearson correlation coefficient and Spearman correlation coefficient to analyze the correlation between the target monitoring point and other monitoring points, and deleting any one of the two monitoring points whose correlation is greater than the first threshold;

[0050] The flow ratio algorithm specifically includes: comprehensively analyzing the ratio of the inflow to the transfer flow of each manhole in the drainage network, and setting up monitoring points at manholes with a ratio greater than a second threshold.

[0051] Specifically, this invention provides optimization suggestions for the deployment of monitoring points in drainage networks by comprehensively analyzing the ratio of inbound flow to outbound flow through an algorithm service.

[0052] The correlation analysis algorithm service specifically includes: following the principle of upstream to downstream, a fixed upstream monitoring point is selected and pipeline operation parameters are collected in real time. The system automatically analyzes the correlation between this monitoring point and other monitoring points within a certain range (generally set to 2 kilometers based on experience; beyond this distance, one more monitoring point can be deployed). This avoids the situation where the monitoring parameters of a certain monitoring point, such as flow rate, liquid level, and flow velocity, can reflect the monitoring parameters of other nearby monitoring points, thus reducing the investment in deploying monitoring equipment. Pearson correlation coefficient and Spearman correlation coefficient can be used for analysis.

[0053] Specifically,

[0054] Where X and Y represent the flow rate, flow velocity, and liquid level variables between two monitoring points, μx and μy are the mean (expectation) values ​​of variables X and Y respectively, and σx and σy are the standard deviations of variables X and Y respectively, with a value range of [-1, 1]. The larger the absolute value, the stronger the correlation. The alarm threshold (first threshold) of the correlation can be configured according to the actual situation on site.

[0055] Flow ratio algorithm service: Comprehensively analyzes the ratio of incoming flow to transferred flow in each manhole of the drainage network. Monitoring points can be set up for those with larger ratios, because the larger inflow has a greater impact on the downstream.

[0056] The two algorithms mentioned above are integrated into the monitoring equipment, and the relevant monitoring points are optimized by comprehensively analyzing the actual situation on site.

[0057] Step 3: The SWMM model of the drainage network is calibrated using the optimized layout data and monitoring parameters to obtain the first model;

[0058] Specifically, with a sufficient number of monitoring points, the overall operation of the drainage network can be comprehensively reflected. To optimize the analysis of the overall operation of the drainage network, the SWMM model of the drainage network can be integrated, and the model can be calibrated using the monitoring parameters of each IoT monitoring point. This allows for a comprehensive analysis and reflection of the flow rate, velocity, and liquid level parameters of each pipe section and manhole in the drainage network, providing auxiliary support for the analysis of siltation in unmonitored drainage networks.

[0059] Step 4: Perform siltation analysis on the drainage network using the first model.

[0060] Furthermore, the step of performing siltation analysis and location analysis on the drainage pipe network using the first model specifically includes: based on the first model, comprehensively considering the influence of pipeline operating flow rate, flow velocity and manhole liquid level when the drainage pipe network is silted up, calculating the comprehensive siltation degree coefficient using a weighted average coefficient algorithm, and analyzing the siltation degree of the drainage pipes based on the comprehensive siltation degree coefficient.

[0061] Specifically, the drainage network consists of pipe points and pipe sections. Taking into account the influence of multiple parameters such as pipe flow rate, flow velocity, and manhole liquid level during drainage network siltation, a weighted average coefficient algorithm service is provided. The degree of siltation in the drainage pipeline is analyzed by calculating a comprehensive siltation degree coefficient. Siltation analysis of the drainage network can be performed by setting up monitoring points. The model extends to the entire network for analysis, including networks without monitoring points. In other words, the first model includes both networks with and without monitoring points.

[0062] To comprehensively consider the relationship between the degree of siltation in the drainage pipe network and operating parameters such as flow velocity, flow rate, and liquid level, a weighted average method is used to analyze the siltation situation of the drainage pipe network. The degree of siltation in the drainage pipe network is divided into slight siltation, moderate siltation, and severe siltation. Pipes judged to be severely silted up require timely dredging measures.

[0063] Furthermore, the comprehensive siltation degree coefficient X is expressed as:

[0064] X = aX1 + bX2,

[0065] Where a and b are both positive numbers, and a+b=1, they can be assigned values ​​according to the actual situation;

[0066] X1 is a sedimentation coefficient related to the pipe flow velocity;

[0067] X2 is the pipe siltation coefficient related to the manhole liquid level.

[0068] Specifically, the degree of siltation is judged based on the range of the comprehensive siltation coefficient X. For example, less than 0.2 indicates slight siltation, greater than 0.2 and less than 0.6 indicates moderate siltation, and greater than 0.6 indicates severe siltation.

[0069] Furthermore, the sedimentation coefficient X1 related to the pipe flow velocity is expressed as:

[0070]

[0071] In this system, the siltation coefficient related to flow velocity is 0 when there is no siltation and 1 when the pipe is completely blocked. A higher value indicates more severe siltation. Specifically, drainage networks operate on gravity flow; the flow velocity is highest when there is no siltation, reaching near the design velocity. As siltation gradually increases, the flow velocity gradually decreases, reaching 0 when the pipe is completely blocked. The system automatically acquires parameters such as design fullness, flow rate, design velocity, and gradient of drainage pipes at different locations. It compares the design velocity of the corresponding pipe with the actual monitored velocity (simulated velocity is used for unmonitored pipes). When a certain limit is exceeded, the pipe is identified as silted up, and the severity of siltation is analyzed based on the degree of deviation.

[0072] The pipeline siltation coefficient X2 related to the manhole liquid level is expressed as:

[0073]

[0074] When there is no siltation in the pipe, X2 = 0; when the pipe gradually becomes silted up to the point of complete blockage, the difference in liquid level between the upstream and downstream gradually increases, and X2 gradually approaches 1 (but will not equal 1).

[0075] Specifically, such as Figure 3 As shown, starting from the upstream pipe point, a longitudinal profile of the drainage pipe section from upstream to downstream is established according to the topological relationship of branch pipe, trunk pipe and main trunk pipe. Based on the elevation data and water level data of each manhole along the route, the water level gradient data of the pipeline is calculated, and standard limit parameters such as normal water level line, alarm water level line and overflow water level line along the route are generated. A monitoring and alarm mechanism is established, and the following sedimentation analysis is triggered only when an alarm information is generated.

[0076] When pipes become silted up, their internal transport space decreases, water flow resistance increases, and the pipe's discharge capacity decreases, causing uneven drops in water levels in upstream and downstream manholes. Without siltation, the level difference between adjacent manholes only considers elevation and water level gradient; however, when pipes are silted up or completely blocked, the water level in the upstream manhole drops more slowly, while the downstream manhole has a relatively lower level, resulting in a larger level difference. Generally, the greater the degree of siltation in a pipe section, the greater the resulting level difference.

[0077] When the flow rate in the pipeline is from the designed full capacity to full flow, the liquid level difference between adjacent manholes reaches its maximum. At this time, the degree of siltation in the pipeline is the same as when there is no siltation, and its water level difference is represented by ΔH1, which is a fixed value; if there is siltation in the pipeline, the maximum liquid level difference between its upstream and downstream is represented by ΔH2, and the larger the value, the more severe the siltation in the pipeline.

[0078] In practical applications, drainage pipe networks may not experience siltation upon initial design, installation, and operation. However, due to the poor water quality, siltation gradually develops. This invention comprehensively considers the regular changes in parameters such as fluid flow rate, water level, and flow velocity within the drainage pipe network after siltation. It fully integrates rapidly developing Internet of Things (IoT) technology to achieve real-time monitoring and analysis of the degree of siltation in drainage pipe networks, and breaks down data transmission barriers between the Internet and government networks. By combining the siltation degree coefficient related to pipe flow velocity and the siltation coefficient related to manhole liquid level to obtain a comprehensive siltation coefficient, it effectively solves the deviation problem caused by a single factor, effectively identifies severely silted drainage pipes, and avoids misjudgments due to a single liquid level or flow velocity parameter.

[0079] This invention can be applied to the dredging and cleaning of drainage pipe networks, avoiding the situation where dredging is only carried out when severe blockages occur, thus preventing urban flooding and waterlogging that endanger urban development, property safety, and people's lives. It also comprehensively improves upon the traditional practice of dredging and cleaning the entire urban drainage pipe network, effectively reducing losses caused by large workloads and investments due to a lack of specific targets. Through this invention, severely silted pipe sections and manholes can be precisely located, allowing for targeted dredging treatment.

[0080] For example, if the siltation in a manhole is less than 30cm, the dredging cost is calculated at 80 yuan per manhole. If the pipe diameter is 400-600mm and the siltation depth is no more than 1 / 2, the dredging cost is calculated at 5000 yuan per 100 meters. This can greatly reduce the annual dredging work and investment in urban drainage pipe networks.

[0081] This invention also discloses a drainage pipe network siltation analysis system based on Internet of Things monitoring, comprising:

[0082] The monitoring data query module is used to obtain the layout data and monitoring parameters of the drainage pipe network monitoring points;

[0083] The optimization module uses optimization algorithms in the IoT monitoring device to optimize the deployment data and the monitoring parameters;

[0084] The drainage network hydraulic model simulation module is used to calibrate the drainage network SWMM model using optimized layout data and monitoring parameters to obtain the first model.

[0085] The drainage pipe network siltation analysis module performs siltation analysis on the drainage pipe network using the first model.

[0086] The system embodiments described herein can be implemented one-to-one with the aforementioned method embodiments, and will not be repeated here.

[0087] The drainage network siltation analysis system of this invention has a built-in drainage network siltation analysis and location program. Considering the confidentiality of the data, it is generally deployed on a government intranet. The drainage network siltation analysis system includes a monitoring data query module, a drainage network hydraulic model simulation module, and a drainage network siltation analysis (and location) module. The drainage network siltation analysis system is based on the drainage network GIS system and integrates the SWMM storm flood model. It combines the flow rate, water level, and flow velocity monitoring points of the drainage network to calculate and analyze in real time the siltation degree coefficient X1 related to the pipe flow velocity, the siltation coefficient X2 related to the manhole liquid level, and the comprehensive siltation coefficient X. Based on the range of the comprehensive siltation coefficient, it analyzes the degree of siltation of the pipe, locates silted pipes, and issues alarms.

[0088] Specifically, the drainage pipe network siltation analysis system designs the location of monitoring points based on the principles of drainage pipe network monitoring point layout. It then optimizes the monitoring point layout using built-in monitoring point correlation analysis algorithms and manhole access flow to transfer flow ratio optimization algorithms. By integrating the drainage pipe network SWMM model, it rationally analyzes the flow rate, velocity, and liquid level parameters of unmonitored pipe sections and manholes in the drainage pipe network, extracting different parameters for comprehensive analysis and judgment of drainage pipe network siltation. Based on the actual and designed flow velocities of the drainage pipe network, it obtains the siltation degree coefficient of the pipes. Then, based on the liquid level values ​​of the manholes upstream and downstream of the pipe section, it analyzes the liquid level difference between the upstream and downstream sides of the pipe to obtain the siltation coefficient. Taking into account regional factors, the type and quantity of monitoring equipment, it calculates the comprehensive siltation coefficient of the drainage pipes. Combined with the comprehensive siltation coefficient alarm threshold, it outputs the siltation degree and alarm information of the drainage pipe network in real time and supports location tracking.

[0089] The Storm Water Management Model (SWMM) is a dynamic precipitation-runoff simulation model primarily used to simulate the quantity and quality of water during single precipitation events or long-term urban periods. It comprehensively handles precipitation, runoff, and pollution loads across various sub-basins, and tracks water transport through pipe networks, canals, storage facilities, and treatment systems. SWMM can simulate the runoff quality and quantity of each sub-basin at different time steps, as well as the flow, depth, and quality of water in pipes and channels.

[0090] Each communication module corresponds to several data collectors, several memory units, and several processors; each data collector corresponds to several flow sensors, several water level sensors, and several flow velocity sensors; each communication module stores information centrally on a concentrator, which collects and transmits the data to the network gate, which then transmits it to the switch and outputs it to the drainage network siltation analysis system for siltation analysis.

[0091] Based on the same inventive concept, this invention also discloses an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus. The processor can call logical instructions in the memory to execute a drainage pipe network siltation analysis method based on Internet of Things monitoring, including:

[0092] Obtain data on the layout of monitoring points in the drainage pipe network and monitoring parameters;

[0093] The deployment data and monitoring parameters are optimized using optimization algorithms in IoT monitoring devices;

[0094] The first model was obtained by calibrating the SWMM model of the drainage network using optimized layout data and monitoring parameters.

[0095] The first model is used to analyze the siltation of the drainage network.

[0096] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] On the other hand, embodiments of the present invention also provide a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to execute a drainage pipe network siltation analysis method based on Internet of Things monitoring provided in the above-described method embodiments, including:

[0098] Obtain data on the layout of monitoring points in the drainage pipe network and monitoring parameters;

[0099] The deployment data and monitoring parameters are optimized using optimization algorithms in IoT monitoring devices;

[0100] The first model was obtained by calibrating the SWMM model of the drainage network using optimized layout data and monitoring parameters.

[0101] The first model is used to analyze the siltation of the drainage network.

[0102] In another aspect, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a drainage pipe network siltation analysis method based on Internet of Things monitoring provided in the above embodiments, comprising:

[0103] Obtain data on the layout of monitoring points in the drainage pipe network and monitoring parameters;

[0104] The deployment data and monitoring parameters are optimized using optimization algorithms in IoT monitoring devices;

[0105] The first model was obtained by calibrating the SWMM model of the drainage network using optimized layout data and monitoring parameters.

[0106] The first model is used to analyze the siltation of the drainage network.

[0107] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing siltation in drainage pipe networks based on Internet of Things (IoT) monitoring, characterized in that, include: Obtain data on the layout of monitoring points in the drainage pipe network and monitoring parameters; The deployment data and monitoring parameters are optimized using an optimization algorithm in the IoT monitoring device. The optimization algorithm is at least one of a correlation analysis algorithm and a flow ratio algorithm. The first model was obtained by calibrating the drainage network SWMM model using optimized layout data and monitoring parameters. The first model includes a network with monitoring points and a network without monitoring points. The first model is used to analyze the siltation of the drainage pipe network. Specifically, it includes: based on the first model, taking into account the influence of pipeline flow rate, flow velocity and manhole liquid level when the drainage pipe network is silted up, the weighted average coefficient algorithm is used to calculate the comprehensive siltation degree coefficient, and the degree of siltation of the drainage pipe is analyzed based on the comprehensive siltation degree coefficient. The comprehensive siltation degree coefficient X is expressed as: X = aX1 + bX2, Where a and b are both positive numbers, and a+b=1; X1 is a sedimentation coefficient related to the pipe flow velocity; X2 is the pipe siltation coefficient related to the manhole liquid level; The sedimentation coefficient X1, which is related to the pipe flow velocity, is expressed as: ； The pipeline siltation coefficient X2 related to the manhole liquid level is expressed as: 。 2. The method as described in claim 1, characterized in that, The IoT monitoring device includes: a data collector, a memory, a processor, a communication module, and a concentrator. The IoT monitoring device collects various monitoring data of the drainage pipe network operation through the data collector and stores them in the memory. The processor processes the data by running the program in the memory for the deployment, analysis, and optimization of drainage pipe network monitoring points. The communication module wirelessly transmits the processed data remotely to the concentrator for storage and analysis.

3. The method as described in claim 2, characterized in that, The data collector integrates multiple types of communication protocol interfaces to collect different types of monitoring data.

4. The method as described in claim 1, characterized in that, The monitoring points for the drainage network are initially laid out according to the topological structure of pollution sources, branch pipes, trunk pipes, main trunk pipes, sewage treatment plants, and drainage outlets.

5. The method as described in claim 4, characterized in that, The monitoring parameters include water level, flow rate, and flow velocity at each monitoring point.

6. The method as described in claim 1, characterized in that, The correlation analysis algorithm specifically includes: using Pearson correlation coefficient and Spearman correlation coefficient to analyze the correlation between the target monitoring point and other monitoring points, and deleting any one of the two monitoring points whose correlation is greater than the first threshold; The flow ratio algorithm specifically includes: comprehensively analyzing the ratio of the inflow to the transfer flow of each manhole in the drainage network, and setting up monitoring points at manholes with a ratio greater than a second threshold.

7. A drainage pipe network siltation analysis system based on Internet of Things (IoT) monitoring, characterized in that, include: The monitoring data query module is used to obtain the layout data and monitoring parameters of the drainage pipe network monitoring points; The optimization module uses optimization algorithms in the IoT monitoring device to optimize the deployment data and the monitoring parameters; The drainage network hydraulic model simulation module is used to calibrate the drainage network SWMM model using optimized layout data and monitoring parameters to obtain the first model. The drainage pipe network siltation analysis module performs siltation analysis on the drainage pipe network using the first model. Specifically, it includes: based on the first model, comprehensively considering the influence of pipeline flow rate, flow velocity and manhole liquid level when the drainage pipe network is silted up, calculating the comprehensive siltation degree coefficient using a weighted average coefficient algorithm, and analyzing the degree of siltation of the drainage pipes based on the comprehensive siltation degree coefficient. The comprehensive siltation degree coefficient X is expressed as: X = aX1 + bX2, In this case, both a and b are positive numbers, and a + b = 1; X1 is a sedimentation coefficient related to the pipe flow velocity; X2 is the pipe siltation coefficient related to the manhole liquid level; The sedimentation coefficient X1, which is related to the pipe flow velocity, is expressed as: ； The pipeline siltation coefficient X2 related to the manhole liquid level is expressed as: 。

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