Urban inland inundation monitoring safety studying and judging system based on Internet of Things

Abstract

The invention is suitable for the technical field of flood monitoring, and particularly relates to an urban inland inundation monitoring safety research and judgment system based on the Internet of Things, and the system comprises an Internet of Things data collection module which is used for collecting monitoring point water level data and monitoring point elevation data of an urban drainage system; the drainage system model building module is used for acquiring an urban drainage map and building an urban drainage system model based on the elevation data of the monitoring points; the monitoring point prediction module is used for constructing a water level prediction function according to the monitoring point water level data and generating a water level prediction value of each monitoring point based on the water level prediction function, and the waterlogging research and judgment module is used for traversing each monitoring point, performing abnormal area positioning according to the water level prediction value and the monitoring point water level data acquired in real time and generating a research and judgment result. According to the invention, the water level of each monitoring point in the city can be obtained based on the Internet of Things equipment, the waterlogging position can be positioned according to the constructed urban drainage system model, and the response speed is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of flood monitoring, and in particular relates to an urban waterlogging monitoring and safety analysis system based on the Internet of Things. Background Art

[0002] Urban waterlogging monitoring is a system of technologies and measures that monitor and manage temporary waterlogging within urban areas due to factors such as rainfall and inadequate drainage systems. By deploying sensor networks, leveraging remote sensing technology and geographic information systems (GIS), and combining them with meteorological data forecasts, it collects and analyzes hydrological information from various urban areas in real time, such as rainfall, waterlogging depth, and flow rate. This allows for the assessment of waterlogging risks and provides decision-making support to relevant departments, enabling them to take timely measures to mitigate the impacts, safeguarding the lives and property of urban residents and ensuring the normal operation of the city.

[0003] Existing urban waterlogging monitoring mainly relies on manual analysis and judgment. It is impossible to determine waterlogging based on data from urban drainage monitoring points, and it is difficult to locate waterlogging points. Summary of the Invention

[0004] The purpose of the present invention is to provide an urban waterlogging monitoring and safety assessment system based on the Internet of Things, aiming to solve the problem that existing urban waterlogging monitoring mainly relies on manual assessment, cannot make waterlogging assessments based on data from urban drainage monitoring points, and is difficult to locate waterlogging points.

[0005] The present invention is implemented as follows: an urban waterlogging monitoring and safety assessment system based on the Internet of Things, the system comprising: IoT data acquisition module, used to collect water level data and elevation data of monitoring points in the urban drainage system; Drainage system model building module, used to obtain urban drainage maps and build urban drainage system models based on monitoring point elevation data; The monitoring point prediction module is used to construct a water level prediction function based on the water level data of the monitoring points, and generate the water level prediction value of each monitoring point based on the water level prediction function; The waterlogging analysis module is used to traverse various monitoring points, locate abnormal areas based on water level prediction values ​​and real-time water level data from monitoring points, and generate analysis results.

[0006] Preferably, the drainage system model building module includes: A data acquisition unit, the data acquisition unit is used to acquire a city drainage map and extract a drainage system plane model based on the city drainage map; A data retrieval unit, the data retrieval unit is used to retrieve the elevation data of the monitoring points, and determine the position of each monitoring point in the drainage system plane model according to the elevation data of the monitoring points; The three-dimensional model construction unit is used to raise the riverbed in the drainage system plane model according to the direction of water flow to obtain the urban drainage system model.

[0007] Preferably, the monitoring point prediction module includes: A data preprocessing unit, configured to retrieve water level data from monitoring points at preset time intervals and preprocess the water level data from monitoring points; A function fitting unit, the function fitting unit is used to generate the water level coordinates of the monitoring points according to the preprocessed water level data of the monitoring points, and perform function fitting based on the water level coordinates of the monitoring points to obtain a water level prediction function; The water level prediction unit is used to predict the water level within a preset time through a water level prediction function to generate a water level prediction value for each monitoring point.

[0008] Preferably, the waterlogging analysis module includes: A simulation unit is used to obtain water level prediction values ​​at all monitoring points, perform fluid simulation in the urban drainage system model based on the water level prediction values, and obtain a theoretical water level prediction value at each monitoring point; A data comparison unit is used to obtain the real-time water level data of each monitoring point and compare it with the theoretical water level prediction value. If the difference is greater than a preset value, it is determined that there is an abnormality at the monitoring point; The waterlogging positioning unit is used to determine the abnormal interval based on the monitoring point where the abnormality exists, perform sampling simulation in the abnormal interval, determine the location of the waterlogging according to the simulation result, and generate the analysis result.

[0009] Preferably, the monitoring point is set at the intersection of water flows.

[0010] Preferably, the elevation data of the monitoring point is the positioning data of the detection point.

[0011] Preferably, the urban drainage map includes at least the width of the water flow channel and the direction of the water flow.

[0012] The IoT-based urban waterlogging monitoring and safety assessment system provided by the present invention can obtain the water levels of various monitoring points in the city based on IoT devices, and can locate the location of waterlogging based on the constructed urban drainage system model, thereby improving the response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is an architecture diagram of the urban waterlogging monitoring and safety assessment system based on the Internet of Things provided by an embodiment of the present invention; Figure 2 This is an architectural diagram of a drainage system model construction module provided by an embodiment of the present invention; Figure 3 This is an architecture diagram of a monitoring point prediction module provided by an embodiment of the present invention; Figure 4 This is an architecture diagram of the waterlogging analysis module provided in an embodiment of the present invention; Figure 5 A schematic diagram of waterlogging monitoring provided by an embodiment of the present invention; Figure 6 A schematic diagram of water flow monitoring in urban channels provided by an embodiment of the present invention; Figure 7 A first schematic diagram of river water level monitoring provided by an embodiment of the present invention; Figure 8 A schematic diagram of drainage network monitoring provided by an embodiment of the present invention; Figure 9 A second schematic diagram of river water level monitoring provided by an embodiment of the present invention; Figure 10 A first installation diagram of the drainage network monitoring provided by an embodiment of the present invention; Figure 11 A second installation diagram of the drainage network monitoring provided by an embodiment of the present invention; Figure 12 A third installation diagram of the drainage network monitoring system provided by an embodiment of the present invention; Figure 13 A fourth installation diagram of the drainage network monitoring system provided by an embodiment of the present invention; Figure 14 This is a network architecture diagram of the urban waterlogging monitoring and safety assessment system based on the Internet of Things provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0015] like Figure 1 and Figure 14 FIG. 1 is an architecture diagram of an urban waterlogging monitoring and safety assessment system based on the Internet of Things provided by an embodiment of the present invention. The system includes: The Internet of Things data acquisition module 100 is used to collect water level data and elevation data of monitoring points in the urban drainage system.

[0016] In this system, the IoT data acquisition module 100 obtains the water level values ​​monitored by the IoT devices set at each monitoring point in real time to obtain the water level data of the monitoring point. Specifically, a water level meter is used to measure the water level at the monitoring point. The water level meter can be a float type water level meter, a pressure type water level meter or an ultrasonic water level meter. The monitoring point is set at the intersection of the flow channel, such as Figure 6 、 Figure 7 and Figure 9 As shown, the flow channel includes a river channel and a drainage pipe and other channels for water flow. All interconnected flow channels in the monitored area are divided into a drainage system. When abnormal weather occurs, such as when the rainfall reaches a preset value, data is collected through the Internet of Things data collection module 100. The monitoring point elevation data is the height of the monitoring point. If the monitoring point is set at a river intersection, the altitude of the riverbed of the monitoring point is the monitoring point elevation data. Figure 8 This is a schematic diagram of drainage network monitoring, such as Figure 10 、 Figure 11 、 Figure 12 and Figure 13 The figure shows the installation diagram of the detection devices at each monitoring point.

[0017] The drainage system model building module 200 is used to obtain a city drainage map and build a city drainage system model based on the monitoring point elevation data.

[0018] In this system, the drainage system model construction module 200 obtains the urban drainage map, records the position of each flow channel in the urban drainage map, and records the width of each flow channel at various locations and the direction of water flow in the flow channel. The edges of each flow channel are extracted according to the urban drainage map, and the urban drainage system model is constructed in the three-dimensional coordinate system according to the edge data of the flow channel and the corresponding monitoring point elevation data. The urban drainage system model is imported into the fluid simulation software, and fluid simulation is subsequently performed based on the urban drainage system model.

[0019] The monitoring point prediction module 300 is used to construct a water level prediction function according to the water level data of the monitoring points, and generate a water level prediction value for each monitoring point based on the water level prediction function.

[0020] In this system, the monitoring point prediction module 300 constructs a water level prediction function based on the water level data of the monitoring point, and the Internet of Things data acquisition module 100 continuously collects water level data from each monitoring point to determine the water level changes at each monitoring point, and characterizes the water level changes at the monitoring point through the water level prediction function. By inputting the corresponding time into the water level prediction function, the water level of the monitoring point in a short time can be predicted to obtain the water level prediction value of each monitoring point in the short time in the future.

[0021] The waterlogging analysis module 400 is used to traverse various monitoring points, locate abnormal areas based on water level prediction values ​​and real-time water level data of monitoring points, and generate analysis results.

[0022] In this system, the waterlogging assessment module 400 traverses each monitoring point, selects one monitoring point each time, assesses the monitoring point, imports the urban drainage system model into fluid simulation software such as MIKE FLOOD, and sets the water level of each monitoring point based on the water level prediction value. The water levels between adjacent monitoring points are determined in a gradient increasing manner. For example, if monitoring point A is located upstream of monitoring point B, the water level prediction value of monitoring point A is a, the water level prediction value of monitoring point B is b, the flow channel length between monitoring point A and monitoring point B is L0, and a point between monitoring point A and monitoring point B is defined as C. The distance between C and point A is L, then the water level prediction value at C is set to Based on this, the water level of any point in any flow channel in the urban drainage system model is determined, and fluid simulation is performed in combination with real-time rainfall to predict the theoretical water level prediction value of each monitoring point in a short period of time. The theoretical water level prediction value is stored, and when the real-time monitoring point water level data at that moment is measured, it is compared with the theoretical water level prediction value. If the difference between the two is greater than the preset value, it is determined that there is a flow channel abnormality near the monitoring point, so as to generate an analysis result and notify the staff to conduct an inspection.

[0023] like Figure 2 As shown, as a preferred embodiment of the present invention, the drainage system model building module 200 includes: The data acquisition unit 201 is used to acquire a city drainage map and extract a drainage system plane model based on the city drainage map.

[0024] In this module, the data acquisition unit 201 obtains the urban drainage map, which records the location and width information of each flow channel in the urban drainage system, and includes the water flow direction of the flow channel. The drainage system plane model is extracted from it. The drainage system plane model only includes the area of ​​the flow channel, and other areas are discarded.

[0025] The data retrieving unit 202 is used to retrieve the elevation data of the monitoring points and determine the position of each monitoring point in the drainage system plane model according to the elevation data of the monitoring points.

[0026] In this module, the data retrieval unit 202 retrieves the monitoring point elevation data, which at least includes the positioning data of the monitoring point and the flow channel height at the monitoring point. The positioning data of the monitoring point is matched with the position in the drainage system plane model.

[0027] The three-dimensional model building unit 203 is used to raise the riverbed in the drainage system plane model according to the direction of water flow to obtain the urban drainage system model.

[0028] In this module, the three-dimensional model construction unit 203 takes the plane model of the drainage system as the basis, extracts the edge data of each flow channel, and constructs a three-dimensional model of each flow channel in the three-dimensional coordinate system. The bottom elevation of the flow channel is determined according to the elevation data of the monitoring point, and the wall height of the flow channel is determined according to the flow channel height at the monitoring point.

[0029] like Figure 3 As shown, as a preferred embodiment of the present invention, the monitoring point prediction module 300 includes: The data pre-processing unit 301 is used to retrieve the water level data of the monitoring point according to a preset time interval and pre-process the water level data of the monitoring point.

[0030] In this module, the data preprocessing unit 301 retrieves the water level data of the monitoring point according to the preset time interval. Specifically, the time interval is determined by the rainfall. The greater the rainfall, the smaller the time interval is set, so that it can respond faster. When the rainfall is small, a smaller time interval is used. After the water level data of the monitoring point is retrieved, the data is cleaned when the water level of the urban river is monitored: first, it is necessary to check the errors and outliers in the original data, including identifying and correcting or deleting obviously erroneous data points (such as negative water level data), and processing missing values ​​due to sensor failure or other reasons. For missing data, interpolation can be used to fill in the data based on the data of the previous and next time periods; time synchronization: since the data comes from multiple different devices, ensure that the timestamps of all data are consistent.

[0031] The function fitting unit 302 is used to generate the water level coordinates of the monitoring points according to the pre-processed water level data of the monitoring points, and perform function fitting based on the water level coordinates of the monitoring points to obtain a water level prediction function.

[0032] In this module, the function fitting unit 302 extracts the monitoring point water level data corresponding to each monitoring point, retrieves the generation time of the monitoring point water level data and the corresponding water level height, and constructs the monitoring point water level coordinates (t, h), where t is the generation time of the monitoring point water level data, and h is the water level height obtained by monitoring. Using the preset function fitting tool, the monitoring point water level coordinates (t, h) are imported into the corresponding function fitting tool to fit the corresponding water level prediction function. The function fitting tool can use MATLAB.

[0033] The water level prediction unit 303 is used to predict the water level within a preset time by using a water level prediction function to generate a water level prediction value for each monitoring point.

[0034] In this module, the water level prediction unit 303 predicts the water level within a preset time through a water level prediction function. According to a preset time gradient, such as one minute, taking one minute as an example, the time value is substituted into each water level prediction function to predict the predicted water level of each monitoring point when it arrives at the preset time in the future, and obtain the water level prediction value.

[0035] like Figure 4 As shown, as a preferred embodiment of the present invention, the waterlogging analysis module 400 includes: The simulation unit 401 is used to obtain the water level prediction values ​​of all monitoring points, perform fluid simulation in the urban drainage system model according to the water level prediction values, and obtain the theoretical water level prediction value of each monitoring point.

[0036] In this module, the simulation unit 401 retrieves the water level prediction values ​​of all monitoring points at the same time, and imports the urban drainage system model into the fluid simulation software. The urban drainage system model is initialized according to the predicted water level prediction values ​​of each monitoring point, and the water level distribution in each flow channel is determined. During the simulation, the rainfall gain is determined according to the real-time rainfall, that is, the water flow at each location is increased in real time. The simulation time is a preset value, such as one minute, that is, the water level of each monitoring point is simulated after one minute to obtain the theoretical water level prediction value, and the prediction time corresponding to the theoretical water level prediction value is recorded.

[0037] The data comparison unit 402 is used to obtain the real-time monitoring point water level data of each monitoring point, and compare it with the theoretical water level prediction value. If the difference is greater than a preset value, it is determined that there is an abnormality in the monitoring point.

[0038] In this module, when the predicted time arrives, the data comparison unit 402 extracts the real-time monitoring point water level data corresponding to the predicted time, and extracts the actual water level of each monitoring point from it. If the difference between the two is greater than the preset value, it is determined that there is an abnormality at the monitoring point, which means that there is an abnormality in the flow channel connected to the monitoring point. The monitoring point with the abnormality is defined as an abnormal monitoring point, and other normal monitoring points are defined as normal monitoring points.

[0039] The waterlogging positioning unit 403 is used to determine the abnormal interval based on the monitoring point where the abnormality exists, perform sampling simulation in the abnormal interval, determine the location of the waterlogging according to the simulation result, and generate an analysis result.

[0040] In this module, if Figure 5FIG4 is a schematic diagram of waterlogging monitoring. The waterlogging locating unit 403 determines an abnormal interval based on abnormal monitoring points, locates the abnormal monitoring points according to the flow direction, and determines the flow path between adjacent abnormal monitoring points and upstream normal monitoring points in the same flow path as an abnormal interval. Multiple sampling points are selected in the abnormal interval at preset intervals, and each sampling point is considered a waterlogging location. A simulated drainage location is set in the urban drainage system model, and simulation is performed based on the initialization data of the current prediction. All simulation results are stored. Each simulation result includes a theoretical water level prediction value for each monitoring point. The theoretical water level prediction value in each simulation result is compared with the actual water level data of the monitoring point. Specifically, the difference between the theoretical water level prediction value and the actual water level of all abnormal monitoring points is calculated. If the difference is less than a preset value, the abnormal monitoring point is considered to have been successfully simulated. The proportion of successful simulations among the abnormal monitoring points is calculated. When the proportion is greater than the preset value, such as the number of successfully simulated abnormal monitoring points accounts for 90% of the total number of abnormal monitoring points, the sampling point corresponding to the current simulation scheme is determined to be an abnormal point, and water leakage occurs at this location. An analysis result is generated.

[0041] The present invention also provides a method for urban waterlogging monitoring and safety assessment based on the Internet of Things, the method comprising: S100, collecting water level data and elevation data of monitoring points of the urban drainage system.

[0042] In this step, the water level values ​​monitored by the Internet of Things devices set at each monitoring point are obtained in real time to obtain the water level data of the monitoring point. Specifically, the water level at the monitoring point is measured using a water level gauge. The water level gauge can be a float-type water level gauge, a pressure-type water level gauge or an ultrasonic water level gauge. The monitoring point is set at the intersection of the flow channel. The flow channel includes a river channel and a drainage pipe and other channels for water flow. All interconnected flow channels in the monitored area are divided into a drainage system. When abnormal weather occurs, such as when the rainfall reaches a preset value, data is collected through the Internet of Things data acquisition module 100. The monitoring point elevation data is the height of the monitoring point. If the monitoring point is set at a river intersection, the altitude of the riverbed at the monitoring point is the monitoring point elevation data.

[0043] S200: Obtain an urban drainage map and construct an urban drainage system model based on the elevation data of the monitoring points.

[0044] In this step, an urban drainage map is obtained, in which the position of each flow channel is recorded, as well as the width of each flow channel at various locations and the direction of water flow in the flow channel are recorded. The edges of each flow channel are extracted according to the urban drainage map, and an urban drainage system model is constructed in a three-dimensional coordinate system based on the edge data of the flow channel and the corresponding monitoring point elevation data. The urban drainage system model is imported into the fluid simulation software, and fluid simulation is subsequently performed based on the urban drainage system model.

[0045] S300: constructing a water level prediction function according to the water level data of the monitoring points, and generating water level prediction values ​​of each monitoring point based on the water level prediction function.

[0046] In this step, a water level prediction function is constructed based on the water level data of the monitoring points, and the water level data of each monitoring point is continuously collected to determine the water level changes at each monitoring point. The water level changes at the monitoring points are characterized by the water level prediction function. By inputting the corresponding time into the water level prediction function, the water level of the monitoring point in a short time can be predicted to obtain the water level prediction values ​​of each monitoring point in the short time in the future.

[0047] S400, traverse each monitoring point, locate the abnormal area based on the water level prediction value and the real-time water level data of the monitoring point, and generate the analysis result.

[0048] In this step, each monitoring point is traversed, and one monitoring point is selected each time to analyze and judge the monitoring point. The urban drainage system model is imported into fluid simulation software such as MIKE FLOOD. The water level of each monitoring point is set according to the water level prediction value of the monitoring point. The water level between adjacent monitoring points is determined by a gradient increase method. For example, monitoring point A is located upstream of monitoring point B, the water level prediction value of monitoring point A is a, the water level prediction value of monitoring point B is b, the flow channel length between monitoring point A and monitoring point B is L0, and a point between monitoring point A and monitoring point B is defined as C. The distance between C and point A is L, then the water level prediction value at C is set to Based on this, the water level of any point in any flow channel in the urban drainage system model is determined, and fluid simulation is performed in combination with real-time rainfall to predict the theoretical water level prediction value of each monitoring point in a short period of time. The theoretical water level prediction value is stored, and when the real-time monitoring point water level data at that moment is measured, it is compared with the theoretical water level prediction value. If the difference between the two is greater than the preset value, it is determined that there is a flow channel abnormality near the monitoring point, so as to generate an analysis result and notify the staff to conduct an inspection.

[0049] In one embodiment of the present invention, the step of obtaining an urban drainage map and constructing an urban drainage system model based on monitoring point elevation data includes: S201: Obtain a city drainage map, and extract a drainage system plane model based on the city drainage map.

[0050] In this step, the urban drainage map is obtained. The urban drainage map records the location and width information of each flow channel in the urban drainage system, and includes the water flow direction of the flow channel. The drainage system plane model is extracted from it. The drainage system plane model only contains the area of ​​the flow channel, and other areas are discarded.

[0051] S202: Retrieve the elevation data of the monitoring points, and determine the position of each monitoring point in the drainage system plane model according to the elevation data of the monitoring points.

[0052] In this step, the elevation data of the monitoring point is retrieved. The elevation data of the monitoring point includes at least the positioning data of the monitoring point and the height of the flow channel at the monitoring point. The positioning data of the monitoring point is matched with the position in the drainage system plane model.

[0053] S203: raising the height of the riverbed in the drainage system plane model according to the direction of water flow to obtain an urban drainage system model.

[0054] In this step, the drainage system plane model is used as the basis to extract the edge data of each flow channel, and a three-dimensional model of each flow channel is constructed in the three-dimensional coordinate system. The bottom elevation of the flow channel is determined according to the elevation data of the monitoring point, and the wall height of the flow channel is determined according to the flow channel height at the monitoring point.

[0055] In one embodiment of the present invention, the step of constructing a water level prediction function based on the water level data of the monitoring points and generating the water level prediction value of each monitoring point based on the water level prediction function includes: S301, retrieve the water level data of the monitoring points according to a preset time interval, and pre-process the water level data of the monitoring points.

[0056] In this step, the water level data of the monitoring points is retrieved at preset time intervals. Specifically, the time interval is determined by the rainfall. The greater the rainfall, the smaller the time interval is set, allowing for a faster response. When the rainfall is small, a smaller time interval is used. After the water level data of the monitoring points is retrieved, the water level of the urban river is monitored. Data cleaning is performed: first, the raw data needs to be checked for errors and outliers, including identifying and correcting or deleting obviously erroneous data points (such as negative water level data), as well as handling missing values ​​due to sensor failure or other reasons. Missing data can be filled using interpolation based on data from previous and subsequent time periods. Time synchronization: Since the data comes from multiple different devices, ensure that the timestamps of all data are consistent.

[0057] S302 , generating the water level coordinates of the monitoring points according to the pre-processed water level data of the monitoring points, performing function fitting based on the water level coordinates of the monitoring points, and obtaining a water level prediction function.

[0058] In this step, the monitoring point water level data corresponding to each monitoring point is extracted, and the generation time of the monitoring point water level data and the corresponding water level height are retrieved to construct the monitoring point water level coordinates (t, h), where t is the generation time of the monitoring point water level data and h is the water level height obtained by monitoring. The preset function fitting tool is used to import the monitoring point water level coordinates (t, h) into the corresponding function fitting tool to fit the corresponding water level prediction function. The function fitting tool can use MATLAB.

[0059] S303: Predicting the water level within a preset time using a water level prediction function to generate a water level prediction value for each monitoring point.

[0060] In this step, the water level within the preset time is predicted through the water level prediction function. According to the preset time gradient, such as one minute, taking one minute as an example, the time value is substituted into each water level prediction function to predict the predicted water level of each monitoring point when it arrives at the preset time in the future, and obtain the water level prediction value.

[0061] In one embodiment of the present invention, the step of traversing each monitoring point, locating the abnormal area based on the water level prediction value and the real-time acquired water level data of the monitoring point, and generating the analysis result includes: S401, obtaining water level prediction values ​​of all monitoring points, performing fluid simulation in the urban drainage system model according to the water level prediction values, and obtaining a theoretical water level prediction value of each monitoring point.

[0062] In this step, the water level prediction values ​​of all monitoring points at the same time are retrieved, and the urban drainage system model is imported into the fluid simulation software. The urban drainage system model is initialized according to the predicted water level prediction values ​​of each monitoring point, and the water level distribution in each flow channel is determined. During the simulation, the rainfall gain is determined according to the real-time rainfall, that is, the water flow at each location is increased in real time. The simulation time is a preset value, such as one minute, that is, the water level of each monitoring point after one minute is simulated to obtain the theoretical water level prediction value, and the prediction time corresponding to the theoretical water level prediction value is recorded.

[0063] The theoretical water level prediction value is retrieved and compared with the theoretical water level prediction value. If the difference is greater than the preset value, it is determined that there is an abnormality at the monitoring point.

[0064] In this step, when the predicted time arrives, the real-time monitoring point water level data corresponding to the predicted time is extracted, and the actual water level of each monitoring point is extracted from it. If the difference between the two is greater than the preset value, it is determined that there is an abnormality at the monitoring point, which means that there is an abnormality in the flow channel connected to the monitoring point. The monitoring point with the abnormality is defined as an abnormal monitoring point, and other normal monitoring points are defined as normal monitoring points.

[0065] Based on the abnormal monitoring points, the abnormal interval is determined, sampling simulation is performed in the abnormal interval, the location of the waterlogging is determined according to the simulation results, and the analysis results are generated.

[0066] In this step, an abnormal interval is determined based on the monitoring points with abnormalities. The abnormal monitoring points are located according to the flow direction of the water flow. In the same flow path, the flow path between the abnormal monitoring points and the normal monitoring points upstream of the adjacent abnormal monitoring points is determined to be an abnormal interval. Multiple sampling points are selected in the abnormal interval according to a preset length interval. Each sampling point is regarded as a waterlogging location. The simulated drainage location is set in the urban drainage system model. The simulation is performed based on the initialization data of this prediction. All simulation results are stored. Each simulation result includes the theoretical water level prediction value of each monitoring point. The theoretical water level prediction value in each simulation result is compared with the actual water level data of the monitoring point. Specifically, the difference between the theoretical water level prediction value and the actual water level of all abnormal monitoring points is calculated. If the difference is less than a preset value, the abnormal monitoring point is considered to have been successfully simulated. The proportion of successful simulations among the abnormal monitoring points is calculated. When the proportion is greater than the preset value, such as the number of successfully simulated abnormal monitoring points accounts for 90% of the total number of abnormal monitoring points, the sampling point corresponding to the current simulation scheme is determined to be an abnormal point, and water leakage occurs at this location. An analysis result is generated.

[0067] 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 and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The urban waterlogging monitoring and safety assessment system based on the Internet of Things is characterized by: The system comprises: IoT data acquisition module, used to collect water level data and elevation data of monitoring points in the urban drainage system; Drainage system model building module, used to obtain urban drainage maps and build urban drainage system models based on monitoring point elevation data; The monitoring point prediction module is used to construct a water level prediction function based on the water level data of the monitoring points, and generate the water level prediction value of each monitoring point based on the water level prediction function; The waterlogging analysis module is used to traverse various monitoring points, locate abnormal areas based on water level prediction values ​​and real-time water level data from monitoring points, and generate analysis results.

2. The urban waterlogging monitoring and safety assessment system based on the Internet of Things according to claim 1 is characterized in that: The drainage system model building module includes: A data acquisition unit, the data acquisition unit is used to acquire a city drainage map and extract a drainage system plane model based on the city drainage map; A data retrieval unit, the data retrieval unit is used to retrieve the elevation data of the monitoring points, and determine the position of each monitoring point in the drainage system plane model according to the elevation data of the monitoring points; The three-dimensional model construction unit is used to raise the riverbed in the drainage system plane model according to the direction of water flow to obtain the urban drainage system model.

3. The urban waterlogging monitoring and safety assessment system based on the Internet of Things according to claim 1 is characterized in that: The monitoring point prediction module includes: A data preprocessing unit, configured to retrieve water level data from monitoring points at preset time intervals and preprocess the water level data from monitoring points; A function fitting unit, the function fitting unit is used to generate the water level coordinates of the monitoring points according to the preprocessed water level data of the monitoring points, and perform function fitting based on the water level coordinates of the monitoring points to obtain a water level prediction function; The water level prediction unit is used to predict the water level within a preset time through a water level prediction function to generate a water level prediction value for each monitoring point.

4. The urban waterlogging monitoring and safety assessment system based on the Internet of Things according to claim 1 is characterized in that: The waterlogging analysis module includes: A simulation unit is used to obtain water level prediction values ​​at all monitoring points, perform fluid simulation in the urban drainage system model based on the water level prediction values, and obtain a theoretical water level prediction value at each monitoring point; A data comparison unit is used to obtain the real-time water level data of each monitoring point and compare it with the theoretical water level prediction value. If the difference is greater than a preset value, it is determined that there is an abnormality at the monitoring point; The waterlogging positioning unit is used to determine the abnormal interval based on the monitoring point where the abnormality exists, perform sampling simulation in the abnormal interval, determine the location of the waterlogging according to the simulation result, and generate the analysis result.

5. The urban waterlogging monitoring and safety assessment system based on the Internet of Things according to claim 1 is characterized in that: The monitoring point is set at the intersection of water flows.

6. The urban waterlogging monitoring and safety assessment system based on the Internet of Things according to claim 1 is characterized in that: The monitoring point elevation data is the positioning data of the detection point.

7. The urban waterlogging monitoring and safety assessment system based on the Internet of Things according to claim 1 is characterized in that: The urban drainage map at least includes the width of the water flow channel and the direction of water flow.

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