Urban waterlogging monitoring and safety analysis system based on internet of things

Abstract

The application is suitable for the technical field of flood monitoring, and particularly relates to a city waterlogging monitoring and safety research and judgment system based on the Internet of Things, which comprises: an Internet of Things data acquisition module, which is used for acquiring water level data and monitoring point elevation data of a city drainage system monitoring point; a drainage system model construction module, which is used for acquiring a city drainage map and constructing a city drainage system model based on the monitoring point elevation data; a monitoring point prediction module, which is used for constructing a water level prediction function according to the monitoring point water level data and generating water level prediction values of each monitoring point based on the water level prediction function; and a waterlogging research and judgment module, which is used for traversing each monitoring point, positioning an abnormal area according to the water level prediction values and the real-time acquired monitoring point water level data, and generating a research and judgment result. The application can acquire the water levels of each monitoring point in the city based on the Internet of Things equipment, can position the position of waterlogging occurrence according to the constructed city drainage system model, and can improve the response speed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of flood monitoring, and particularly relates to a city waterlogging monitoring and safety research and judgment system based on the Internet of Things. BACKGROUND

[0002] City waterlogging monitoring is a technology and measure system for monitoring and managing temporary waterlogging phenomena in urban areas caused by rainfall, imperfect drainage systems, etc. By deploying sensor networks, using remote sensing technology and geographic information systems (GIS), combined with weather data forecasts, real-time collection and analysis of hydrological information of each area in the city, such as rainfall, water depth, flow rate, etc. to assess waterlogging risks and provide decision support for relevant departments to take timely measures to mitigate the impact of waterlogging, protect the life and property safety of urban residents and the normal operation of the city.

[0003] Existing city waterlogging monitoring mainly relies on manual research and judgment, and cannot determine waterlogging according to the data of city drainage monitoring points, making it difficult to locate waterlogging points. SUMMARY

[0004] The purpose of the present application is to provide a city waterlogging monitoring and safety research and judgment system based on the Internet of Things, which aims to solve the problem that existing city waterlogging monitoring mainly relies on manual research and judgment, and cannot determine waterlogging according to the data of city drainage monitoring points, making it difficult to locate waterlogging points.

[0005] The present application is implemented as follows: the city waterlogging monitoring and safety research and judgment system based on the Internet of Things, the system comprises:

[0006] An Internet of Things data acquisition module for acquiring city drainage system monitoring point water level data and monitoring point elevation data;

[0007] A drainage system model construction module for obtaining a city drainage map and constructing a city drainage system model based on monitoring point elevation data;

[0008] A monitoring point prediction module for constructing a water level prediction function based on monitoring point water level data and generating water level prediction values for each monitoring point based on the water level prediction function;

[0009] A waterlogging research and judgment module for traversing each monitoring point, locating abnormal areas based on water level prediction values and real-time monitoring point water level data, and generating research and judgment results.

[0010] Preferably, the drainage system model construction module comprises:

[0011] A data acquisition unit for obtaining a city drainage map and extracting a drainage system planar model based on the city drainage map;

[0012] a data retrieval unit configured to retrieve monitoring point elevation data and determine the position of each monitoring point in the drainage system plan model according to the monitoring point elevation data;

[0013] a three-dimensional model construction unit configured to lift the riverbed in the drainage system plan model according to the direction of the water flow to obtain the urban drainage system model.

[0014] Preferably, the monitoring point prediction module comprises:

[0015] a data preprocessing unit configured to retrieve the monitoring point water level data at preset time intervals and preprocess the monitoring point water level data;

[0016] a function fitting unit configured to generate monitoring point water level coordinates according to the preprocessed monitoring point water level data, perform function fitting based on the monitoring point water level coordinates, and obtain a water level prediction function;

[0017] a water level prediction unit configured to predict the water level in a preset time through the water level prediction function and generate water level prediction values of each monitoring point.

[0018] Preferably, the waterlogging research and judgment module comprises:

[0019] a simulation unit configured 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 theoretical water level prediction values of each monitoring point;

[0020] a data comparison unit configured to obtain real-time monitoring point water level data of each monitoring point, compare the real-time monitoring point water level data with the theoretical water level prediction values, and determine that the monitoring point is abnormal if the difference is greater than a preset value;

[0021] a waterlogging positioning unit configured to determine an abnormal interval based on the monitoring point that is abnormal, perform sampling simulation in the abnormal interval, determine the position of the waterlogging according to the simulation result, and generate a research and judgment result.

[0022] Preferably, the monitoring points are arranged at the intersection of the water flow.

[0023] Preferably, the monitoring point elevation data is the positioning data of the monitoring points.

[0024] Preferably, the urban drainage map at least contains the width of the water flow channel and the direction of the water flow.

[0025] The application provides a city waterlogging monitoring and safety research and judgment system based on the Internet of Things, which can obtain water levels of various monitoring points in the city based on Internet of Things equipment, and can locate the position of waterlogging occurrence based on a constructed city drainage system model, thereby improving response speed. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 An architectural diagram of the city waterlogging monitoring and safety research and judgment system based on the Internet of Things is provided for the embodiment of the application.

[0027] Figure 2 An architectural diagram of the drainage system model construction module is provided for the embodiment of the application.

[0028] Figure 3 An architectural diagram of the monitoring point prediction module is provided for the embodiment of the application.

[0029] Figure 4 An architectural diagram of the waterlogging research and judgment module is provided for the embodiment of the application.

[0030] Figure 5 A schematic diagram of the waterlogging monitoring is provided for the embodiment of the application.

[0031] Figure 6 A schematic diagram of the city flow channel water flow monitoring is provided for the embodiment of the application.

[0032] Figure 7 A first schematic diagram of the river water level monitoring is provided for the embodiment of the application.

[0033] Figure 8 A schematic diagram of the drainage pipe network monitoring is provided for the embodiment of the application.

[0034] Figure 9 A second schematic diagram of the river water level monitoring is provided for the embodiment of the application.

[0035] Figure 10 A first installation schematic diagram of the drainage pipe network monitoring is provided for the embodiment of the application.

[0036] Figure 11 A second installation schematic diagram of the drainage pipe network monitoring is provided for the embodiment of the application.

[0037] Figure 12 A third installation schematic diagram of the drainage pipe network monitoring is provided for the embodiment of the application.

[0038] Figure 13 A fourth installation schematic diagram of the drainage pipe network monitoring is provided for the embodiment of the application.

[0039] Figure 14 A network architectural diagram of the city waterlogging monitoring and safety research and judgment system based on the Internet of Things is provided for the embodiment of the application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0041] As shown in Figure 1 and Figure 14 , the architecture diagram of the urban waterlogging monitoring and safety research and judgment system based on the Internet of Things provided by the embodiment of the present application is shown, the system comprises:

[0042] The Internet of Things data acquisition module 100 is used to acquire the water level data of the monitoring points of the urban drainage system and the elevation data of the monitoring points.

[0043] In the present system, the Internet of Things data acquisition module 100 acquires the water level values monitored by the Internet of Things devices set at each monitoring point in real time to obtain the water level data of the monitoring points. Specifically, the water level of the monitoring point is measured by using a water level gauge, which 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, as shown in Figure 6 、 Figure 7 and Figure 9 , the flow channel includes a river channel and a drainage pipeline and other channels for water flow, all the 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 acquisition is performed by the Internet of Things data acquisition module 100. The elevation data of the monitoring point is the height of the monitoring point. If the monitoring point is set at the intersection of the river channel, the elevation of the river bottom of the monitoring point is the elevation data of the monitoring point. Figure 8 is a drainage pipe network monitoring schematic diagram, as shown in Figure 10 、 Figure 11 、 Figure 12 and Figure 13 , it is a schematic diagram of the installation of the detection device of each monitoring point.

[0044] The drainage system model construction module 200 is used to acquire the urban drainage map and construct the urban drainage system model based on the elevation data of the monitoring points.

[0045] In the present system, the drainage system model construction module 200 acquires the urban drainage map, records the positions of each flow channel in the urban drainage map, and records the widths of each flow channel at each place and the flow direction in the flow channel. According to the urban drainage map, the edges of each flow channel are extracted, and the urban drainage system model is constructed in a three-dimensional coordinate system according to the edge data of the flow channel and the corresponding elevation data of the monitoring points. The urban drainage system model is imported into the fluid simulation software, and subsequent fluid simulation is based on the urban drainage system model.

[0046] The monitoring point prediction module 300 is configured to construct a water level prediction function according to the water level data of the monitoring points, and generate water level prediction values of the monitoring points based on the water level prediction function.

[0047] In the system, the monitoring point prediction module 300 constructs a water level prediction function according to the water level data of the monitoring points, and the Internet of Things data acquisition module 100 continuously acquires the water level data of the monitoring points, so as to determine the water level change of each monitoring point. The water level change of the monitoring points is characterized by the water level prediction function, and the water level of the monitoring point in a short time can be predicted by inputting the corresponding time into the water level prediction function, so as to obtain the water level prediction values of the monitoring points in a short time in the future.

[0048] The waterlogging research and judgment module 400 is configured to traverse the monitoring points, locate the abnormal area according to the water level prediction values and the real-time acquired water level data of the monitoring points, and generate a research and judgment result.

[0049] In the system, the waterlogging research and judgment module 400 traverses the monitoring points, selects one monitoring point each time, and researches and judges the monitoring point. The city drainage system model is imported into a fluid simulation software such as MIKE FLOOD, the water level of the monitoring point at this time is set according to the water level prediction value of each monitoring point, and the water levels between adjacent monitoring points are determined in a gradient increasing manner. For example, the A monitoring point is located upstream of the B monitoring point, the water level prediction value of the A monitoring point is a, the water level prediction value of the B monitoring point is b, the flow channel length between the A monitoring point and the B monitoring point is L0, a point between the A monitoring point and the B monitoring point is defined as C, the distance between C and A is L, and the water level prediction value at C is set to Accordingly, the water level of any point in any flow channel in the city drainage system model is determined, fluid simulation is performed in combination with real-time rainfall, the theoretical water level prediction value of each monitoring point in a short time is predicted, the theoretical water level prediction value is stored, and when the real-time monitoring point water level data at this time is measured, it is compared with the theoretical water level prediction value. If the difference between the two is greater than a preset value, it is determined that there is a flow channel abnormality near the monitoring point, so as to generate a research and judgment result and notify the staff to check.

[0050] As shown in Figure 2 , as a preferred embodiment of the present application, the drainage system model construction module 200 comprises:

[0051] The data acquisition unit 201 is configured to acquire a city drainage map, and extract a drainage system plane model based on the city drainage map.

[0052] In the module, the data acquisition unit 201 acquires a city drainage map, the city drainage map records the positions and width information of each flow channel in the city drainage system, and contains the water flow direction of the flow channel, from which a drainage system plane model is extracted, the drainage system plane model only contains the area of the flow channel, and other areas are discarded.

[0053] The data retrieval unit 202 is used to retrieve monitoring point elevation data, and the position of each monitoring point in the drainage system plane model is determined according to the monitoring point elevation data.

[0054] In the module, the data retrieval unit 202 retrieves monitoring point elevation data, which at least includes positioning data of the monitoring point and the height of the flow channel at the monitoring point, and matches the positioning data of the monitoring point with the position in the drainage system plane model.

[0055] The three-dimensional model construction unit 203 is used to lift the riverbed in the drainage system plane model according to the direction of the water flow, to obtain a city drainage system model.

[0056] In the module, the three-dimensional model construction unit 203 takes the drainage system plane model as the basis, extracts the edge data of each flow channel, constructs a three-dimensional model of each flow channel in a three-dimensional coordinate system, and determines the bottom elevation of the flow channel according to the monitoring point elevation data and the wall height of the flow channel according to the height of the flow channel at the monitoring point.

[0057] As shown in Figure 3 As a preferred embodiment of the present application, the monitoring point prediction module 300 includes:

[0058] The data preprocessing unit 301 is used for data preprocessing, and is used for retrieving monitoring point water level data at a preset time interval and preprocessing the monitoring point water level data.

[0059] In the module, the data preprocessing unit 301 retrieves monitoring point water level data at a preset time interval, specifically, the time interval is determined according to the rainfall, the greater the rainfall, the smaller the time interval is set, so that a faster response can be obtained, when the rainfall is small, a smaller time interval is used, after the monitoring point water level data is retrieved, the city river water level is monitored, and data cleaning is performed: first, the errors and abnormal values in the original data need to be checked, including identifying and correcting or deleting obviously erroneous data points (such as negative water level data), and processing missing values caused by sensor failure or other reasons. For the missing data, interpolation method can be used to fill in according to the data of the previous and subsequent time periods; time synchronization: since the data comes from multiple different devices, the time stamps of all data are ensured to be consistent.

[0060] The function fitting unit 302 is configured to generate monitoring point water level coordinates according to the preprocessed monitoring point water level data, and perform function fitting based on the monitoring point water level coordinates to obtain a water level prediction function.

[0061] In the 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 monitoring point water level coordinates (t, h), where t is the generation time of the monitoring point water level data, and h is the monitoring water level height. The monitoring point water level coordinates (t, h) are imported into a preset function fitting tool, and a corresponding water level prediction function is fitted by using the function fitting tool. The function fitting tool can use Matlab.

[0062] The water level prediction unit 303 is configured to predict the water level in a preset time period by using the water level prediction function, and generate a water level prediction value of each monitoring point.

[0063] In the module, the water level prediction unit 303 predicts the water level in a preset time period by using the water level prediction function. According to a preset time gradient, for example, one minute, the time value is substituted into each water level prediction function, so that the predicted water level of each monitoring point at a future preset time is predicted, and a water level prediction value is obtained.

[0064] As shown in Figure 4 As a preferred embodiment of the present application, the waterlogging research and judgment module 400 includes:

[0065] The simulation unit 401 is configured to obtain water level prediction values of all monitoring points, perform fluid simulation in a city drainage system model according to the water level prediction values, and obtain a theoretical water level prediction value of each monitoring point.

[0066] In the module, the simulation unit 401 retrieves the water level prediction values of all monitoring points at the same time, imports the city drainage system model into a fluid simulation software, initializes the city drainage system model according to the predicted water level prediction values of the monitoring points, determines the water level distribution in each flow channel, and performs simulation. During the simulation, the rainfall gain is determined according to the real-time rainfall, that is, the water flow of each position is increased in real time. The simulation time is a preset value, for example, one minute. The water level of each monitoring point after one minute is simulated to obtain a theoretical water level prediction value. The prediction time corresponding to the theoretical water level prediction value is recorded.

[0067] The data comparison unit 402 is configured to obtain real-time monitoring point water level data of each monitoring point, compare the real-time monitoring point water level data with the theoretical water level prediction value, and determine that the monitoring point is abnormal if the difference is greater than a preset value.

[0068] In the module, the data comparison unit 402 extracts the real-time monitoring point water level data corresponding to the prediction time when the prediction time arrives, extracts the actual water level of each monitoring point therefrom, and determines that there is an anomaly at the monitoring point if the difference between the two is greater than a preset value, which indicates that the flow channel connected with the monitoring point has an anomaly, the monitoring point where the anomaly occurs is defined as an abnormal monitoring point, and other normal monitoring points are defined as normal monitoring points.

[0069] The waterlogging positioning unit 403 is configured to determine an abnormal interval based on the abnormal monitoring points, perform sampling simulation in the abnormal interval, determine the position of the waterlogging based on the simulation result, and generate a research and judgment result.

[0070] In the module, as shown in FIG. Figure 5 The waterlogging positioning unit 403 determines the abnormal interval based on the abnormal monitoring points, positions the abnormal monitoring points according to the flow direction of the water flow, determines the flow channel between the abnormal monitoring points and the normal monitoring points upstream in the same flow path as the abnormal interval, selects a plurality of sampling points in the abnormal interval at a preset length interval, regards each sampling point as a waterlogging position, sets a simulated drainage position in the city drainage system model, simulates based on the initialized data of this prediction, stores all simulation results, each simulation result contains the theoretical water level prediction value of each monitoring point, compares the theoretical water level prediction value in each simulation result with the actual monitoring point water level data, specifically, calculates the difference between the theoretical water level prediction value and the actual water level of all abnormal monitoring points, regards the abnormal monitoring point as successfully simulated if the difference is less than a preset value, calculates the proportion of the successfully simulated abnormal monitoring points, and determines that the sampling point corresponding to the current simulation scheme is the abnormal point if the proportion is greater than a preset value, for example, the number of successfully simulated abnormal monitoring points accounts for 90% of the total number of abnormal monitoring points, which indicates that there is water flow leakage at the position, and a research and judgment result is generated.

[0071] The application also provides a city waterlogging monitoring and safety research and judgment method based on the Internet of Things, which comprises the following steps:

[0072] S100, collecting city drainage system monitoring point water level data and monitoring point elevation data.

[0073] In this step, the water level value monitored by the Internet of Things device arranged at each monitoring point is acquired in real time to obtain the monitoring point water level data. Specifically, the water level of the monitoring point is measured by a water level gauge, which can be a float type water level gauge, a pressure type water level gauge or an ultrasonic water level gauge. The monitoring point is arranged at the intersection of the flow channel, which includes a river channel and a drainage pipeline and the like channel for water flow. All the flow channels connected to each other in the monitored area are divided into a drainage system. When abnormal weather occurs, such as when the rainfall reaches the preset value, data acquisition is performed by 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 arranged at the intersection of the river channel, the elevation of the river bottom of the monitoring point is the monitoring point elevation data.

[0074] S200, acquire a city drainage map, and construct a city drainage system model based on the monitoring point elevation data.

[0075] In this step, the city drainage map is acquired. The positions of various flow channels are recorded in the city drainage map, and the widths of the flow channels at various places and the flow directions in the flow channels are recorded. The edges of the flow channels are extracted according to the city drainage map. The city drainage system model is constructed in a three-dimensional coordinate system according to the edge data of the flow channels and the corresponding monitoring point elevation data. The city drainage system model is imported into a fluid simulation software. Subsequently, fluid simulation is performed based on the city drainage system model.

[0076] S300, construct a water level prediction function according to the monitoring point water level data, and generate water level prediction values of various monitoring points based on the water level prediction function.

[0077] In this step, the water level prediction function is constructed according to the monitoring point water level data. The water level data of each monitoring point is continuously acquired to determine the water level change of each monitoring point. The water level change of the monitoring point is 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 various monitoring points in a short time in the future.

[0078] S400, traverse each monitoring point, and locate an abnormal area according to the water level prediction value and the monitoring point water level data acquired in real time to generate a research and judgment result.

[0079] In this step, each monitoring point is traversed, and one monitoring point is selected each time. The city drainage system model is imported into a fluid simulation software such as MIKE FLOOD, the water level of the monitoring point at this time is set according to the water level prediction value of each monitoring point, and the water levels between adjacent monitoring points are determined in a gradient increasing manner. For example, the A monitoring point is located upstream of the B monitoring point, the water level prediction value of the A monitoring point is a, the water level prediction value of the B monitoring point is b, the length of the flow channel between the A monitoring point and the B monitoring point is L0, a point between the A monitoring point and the B monitoring point is defined as C, the distance between C and A is L, and the water level prediction value at C is set to , according to which the water level of any point in any flow channel in the city 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 time. The theoretical water level prediction value is stored, and when the real-time monitoring point water level data at this time is measured, it is compared with the theoretical water level prediction value. If the difference between the two is greater than a preset value, it is determined that there is a flow channel anomaly near the monitoring point, and a research and judgment result is generated to notify the staff to check.

[0080] In an embodiment of the present application, the step of obtaining a city drainage map and constructing a city drainage system model based on monitoring point elevation data comprises:

[0081] S201, obtaining a city drainage map, and extracting a drainage system plane model based on the city drainage map.

[0082] In this step, a city drainage map is obtained, which records the positions and width information of each flow channel in the city drainage system, and contains the water flow direction of the flow channel. The drainage system plane model is extracted from the city drainage map, which only contains the area of the flow channel and discards other areas.

[0083] S202, call monitoring point elevation data, and determine the position of each monitoring point in the drainage system plane model according to the monitoring point elevation data.

[0084] In this step, the monitoring point elevation data is called, which at least includes the positioning data of the monitoring point and the height of the flow channel at the monitoring point. According to the positioning data of the monitoring point, it is matched with the position in the drainage system plane model.

[0085] S203, height lifting of riverbed in drainage system plane model according to water flow direction, get city drainage system model.

[0086] In this step, based on the drainage system plane model, the edge data of each flow channel is extracted, and a three-dimensional model of each flow channel is constructed in a three-dimensional coordinate system. The bottom elevation of the flow channel is determined according to the monitoring point elevation data, and the wall height of the flow channel is determined according to the flow channel height at the monitoring point.

[0087] In an embodiment of the present application, the step of constructing a water level prediction function according to the monitoring point water level data, generating water level prediction values of each monitoring point based on the water level prediction function, comprises:

[0088] S301, according to the preset time interval, the monitoring point water level data is called, and the monitoring point water level data is pretreated.

[0089] In this step, the monitoring point water level data is called according to the preset time interval. Specifically, the time interval is determined according to 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 monitoring point water level data is obtained, the city river water level is monitored, and data cleaning is performed: first, the original data needs to be checked for errors and abnormal values, 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 method can be used to fill in the data before and after the time period; time synchronization: since the data comes from multiple different devices, ensure that the time stamps of all data are consistent.

[0090] S302, generating monitoring point water level coordinates according to the pretreated monitoring point water level data, and fitting a function based on the monitoring point water level coordinates to obtain a water level prediction function.

[0091] In this step, the monitoring point water level data corresponding to each monitoring point is extracted, the generation time of the monitoring point water level data and the corresponding water level height are called, and the monitoring point water level coordinates (t, h) are constructed, wherein t is the generation time of the monitoring point water level data, h is the monitoring water level height, the monitoring point water level coordinates (t, h) are imported into the corresponding function fitting tool by using the preset function fitting tool, and the corresponding water level prediction function is fitted. The function fitting tool can use matlab.

[0092] S303, predicting the water level in the preset time through the water level prediction function to generate water level prediction values of each monitoring point.

[0093] In this step, the water level in the preset time is predicted through the water level prediction function, and the water level prediction values of each monitoring point are obtained. According to the preset time gradient, for example, one minute, the time value is substituted into each water level prediction function, so that the predicted water level of each monitoring point at the future preset time is predicted.

[0094] In an embodiment of the present application, the step of traversing each monitoring point, locating an abnormal area according to the water level prediction value and the real-time obtained monitoring point water level data, and generating a research and judgment result comprises:

[0095] S401, obtain the water level prediction value of all monitoring points, and perform fluid simulation simulation in the urban drainage system model according to the water level prediction value to obtain the theoretical water level prediction value of each monitoring point.

[0096] In this step, the water level prediction values of all monitoring points at the same time are called, 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, the water level distribution in each flow channel is determined, and simulation is performed. During the simulation, the rainfall gain is determined according to the real-time rainfall, that is, the water flow at each position is increased in real time. The simulation time is a preset value, such as one minute. The water level of each monitoring point after one minute is simulated to obtain the theoretical water level prediction value. The prediction time corresponding to the theoretical water level prediction value is recorded.

[0097] The theoretical water level prediction value is called and compared with the theoretical water level prediction value. If the difference is greater than a preset value, it is determined that the monitoring point is abnormal.

[0098] In this step, when the prediction time arrives, the real-time monitoring point water level data corresponding to the prediction time is extracted, the actual water level of each monitoring point is extracted therefrom, and if the difference between the two is greater than a preset value, it is determined that there is an abnormal monitoring point, which indicates that the flow channel connected with the monitoring point is abnormal. The monitoring point where the anomaly occurs is defined as an abnormal monitoring point, and the other normal monitoring points are defined as normal monitoring points.

[0099] Based on the abnormal monitoring points, the abnormal interval is determined, sampling simulation is performed in the abnormal interval, the position of waterlogging is determined according to the simulation result, and the research and judgment result is generated.

[0100] In the present step, the abnormal interval is determined based on the abnormal monitoring point, the abnormal monitoring point is positioned according to the flow direction of the water flow, in the same flow path, the flow channel between the abnormal monitoring point belonging to the adjacent relationship and the normal monitoring point upstream is determined as the abnormal interval, a plurality of sampling points are selected in the abnormal interval according to the preset length interval, each sampling point is regarded as a waterlogging position, the simulation drainage position is set in the city drainage system model, and simulation is performed based on the initialization data of the present prediction, all simulation results are stored, each simulation result contains 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 monitoring point water level data, 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 regarded as successful simulation, the proportion of the abnormal monitoring points in the successful simulation is calculated, when the proportion is greater than a preset value, for example, the number of the abnormal monitoring points in the successful simulation accounts for 90% of the total number of the abnormal monitoring points, it is determined that the sampling point corresponding to the present simulation scheme is the abnormal point, the position exists water flow leakage, and a research and judgment result is generated.

[0101] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An Internet of Things-based urban flood monitoring and safety assessment system, characterized in that, The system includes: The Internet of Things (IoT) data acquisition module is used to collect water level data and elevation data of monitoring points in the urban drainage system. The drainage system model building module is 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 urban flooding assessment module is used to traverse each monitoring point, locate abnormal areas based on the predicted water level value and the real-time water level data of the monitoring points, and generate assessment results. The urban flooding assessment module includes: The simulation unit is used to obtain the predicted water level values ​​of all monitoring points, and to perform fluid simulation in the urban drainage system model based on the predicted water level values ​​to obtain the theoretical predicted water level value of each monitoring point. The data comparison unit is used to acquire 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 the preset value, it is determined that there is an anomaly at the monitoring point. The waterlogging location unit is used to determine the abnormal range based on the monitoring points where there are abnormalities, perform sampling simulation in the abnormal range, determine the location of waterlogging based on the simulation results, and generate an assessment result. Extract the real-time water level data of the monitoring points corresponding to the predicted time, and extract the actual water level of each monitoring point. If the difference between the two is greater than the preset value, it is determined that there is an anomaly at the monitoring point. The monitoring point with the anomaly is defined as an abnormal monitoring point, and other normal monitoring points are defined as normal monitoring points. Anomaly monitoring points are located based on the direction of water flow. Within the same flow path, the flow channel between adjacent anomaly monitoring points and upstream normal monitoring points is identified as an anomaly interval. Multiple sampling points are selected within the anomaly interval at preset length intervals, and each sampling point is considered a location of waterlogging. Simulated drainage locations are set in the urban drainage system model, and simulations are performed based on the initial data of this prediction. All simulation results are stored, and each simulation result contains the 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 points, and the difference between the theoretical water level prediction value and the actual water level for all anomaly monitoring points is calculated. If the difference is less than a preset value, the anomaly monitoring point is considered to have been successfully simulated. The proportion of successfully simulated anomaly monitoring points is calculated. When the proportion is greater than a preset value, the sampling point corresponding to the current simulation scheme is determined to be an anomaly point, and an assessment result is generated.

2. The urban flooding monitoring and safety assessment system based on the Internet of Things as described in claim 1, characterized in that, The drainage system model building module includes: The data acquisition unit is used to acquire an urban drainage map and extract a planar model of the drainage system based on the urban drainage map. A data retrieval unit is used to retrieve the elevation data of monitoring points and determine the position of each monitoring point in the drainage system plane model based on the elevation data. A 3D model building unit is used to raise the height of the riverbed in the planar model of the drainage system according to the direction of water flow, so as to obtain the urban drainage system model.

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

4. The urban flooding monitoring and safety assessment system based on the Internet of Things as described in claim 1, characterized in that, The monitoring point is located at the confluence of water flows.

5. The urban flooding monitoring and safety assessment system based on the Internet of Things as described in claim 1, characterized in that, The elevation data of the monitoring points is the location data of the monitoring points.

6. The urban flooding monitoring and safety assessment system based on the Internet of Things as described in claim 1, characterized in that, Urban drainage maps should include at least the width of water channels and the direction of water flow.

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