High-precision sensing urban area submerging detection device and method

Abstract

The invention relates to the field of hydrological monitoring, in particular to a high-precision sensing urban area submerging detection device and method, which can monitor the submerging range, the ponding depth and the ponding duration of an urban area in real time, integrate forecasting of numerical rainfall, urban area waterlogging and the like, and perform all-around and high-precision monitoring and early warning on urban flood. On-site survey of workers in each flood season is avoided, and potential safety hazards of the workers are reduced. According to the system, monitoring data such as the submerging range, the ponding depth and the ponding duration and early warning data are transmitted to the data acquisition module, and the data are transmitted to a remote monitoring center through the data transmission module. The remote monitoring center analyzes and processes the received data, masters the urban inundation range, the ponding depth and the ponding duration in real time, and performs risk analysis on the urban inundation condition by combining a multi-dimensional multi-temporal-spatial-scale water conservancy model and an intelligent analysis model and considering urban landform, landform, soil and vegetation factors and urbanization degree; and the risk pre-judgment efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of hydrological monitoring, and in particular to a high-precision sensing urban flooding detection device and method. Background Art

[0002] When natural disasters such as heavy rain or floods occur, accurately monitoring the inundation range of urban areas is of great significance for taking timely rescue measures and protecting people's lives and property. At present, traditional hydrological monitoring methods have problems such as low monitoring accuracy and poor real-time performance, which cannot meet the needs of urban flood prevention and disaster reduction.

[0003] In addition, the Daqing River Basin is one of the important river basins in my country. The hydrological forecast of the basin has important guiding significance for water resources management and flood prevention and disaster reduction. However, the existing hydrological forecast model has certain errors in simulating river flow and flood peak, which affects the accuracy of the forecast. Traditional water level and water quality monitoring methods may not be accurate and real-time enough to reflect the subtle dynamics of urban water level changes and water quality in a timely manner, making it difficult for relevant departments to take effective flood prevention and disaster reduction and water resources management measures in a timely manner.

[0004] The hydrological forecast models in some areas may not fully consider factors such as terrain, landform, soil, vegetation, etc., and the simulation of river water flow and flood peaks is not accurate enough, affecting the reliability and accuracy of the forecast results; the data collection, processing and transmission capabilities of the existing systems may be insufficient, resulting in the accuracy and timeliness of the data being affected, and unable to provide high-quality monitoring data for the remote monitoring center.

[0005] The existing technology may not be tight enough in combining monitoring data with hydrological forecast models, and fails to make full use of monitoring data to correct and optimize the forecast model in real time, thus limiting the accuracy and practicality of the forecast. Therefore, we have made improvements to this and proposed a high-precision sensing urban flooding detection device and method. Summary of the invention

[0006] The purpose of the present invention is to address the problems raised by the existing background technology. Developing an intelligent sensor that can accurately monitor the flooding range of urban areas, and combining it with an improved hydrological forecasting model to improve the accuracy of hydrological forecasts in the Daqing River Basin, has important practical significance and application value; the purpose of the present invention is to provide a high-precision sensing urban flooding detection device and method, the sensor can monitor the flooding range of urban areas in real time and accurately, and transmit the monitoring data to a remote monitoring center, providing a decision-making basis for urban flood prevention and disaster reduction. At the same time, the present invention also combines the hydrological characteristics of the Daqing River Basin, optimizes the hydrological forecast model, and improves the forecast accuracy of river water flow and flood peaks.

[0007] The present invention provides the following technical solutions: a high-precision sensing urban flooding detection device, comprising a sensor body, a data acquisition module, a data transmission module and a power module, wherein the sensor body comprises a water level sensor and a water quality sensor, the water level sensor is used to monitor the water level change in the urban area, the water quality sensor is used to monitor the water quality in the urban area, the data acquisition module is used to collect monitoring data of the water level sensor and the water quality sensor, the data transmission module is used to transmit the collected data to a remote monitoring center, and the power module is used to supply power to the sensor body, the data acquisition module and the data transmission module;

[0008] The water level sensor adopts a pressure-type water level sensor, which can monitor the changes in urban water levels in real time and transmit the water level data to the data acquisition module; the water quality sensor adopts a multi-parameter water quality sensor, which can simultaneously monitor multiple parameters of urban water quality, including water temperature, pH value, dissolved oxygen, and conductivity, and transmit the water quality data to the data acquisition module. The data transmission module adopts a wireless communication module, and the wireless communication modules include Wi-Fi, LoRa, and NB-IoT.

[0009] As a preferred technical solution of the present invention, the data acquisition module adopts a microcontroller as the core processor, which can collect, process and store the monitoring data of the water level sensor and the water quality sensor; the data transmission module adopts a wireless communication module, which can transmit the collected data to the remote monitoring center through a wireless network; the power supply module is powered by a combination of solar panels and batteries.

[0010] A detection method of a high-precision sensing urban flooding detection device comprises the following steps: S1, flooding range monitoring: using radar remote sensing to measure water level height (H) and water surface area (A);

[0011] The inundation range is calculated by the following formula: I = A*f(H), where f(H) is the terrain water level height function used to convert the water level height to the actual inundation area, A is the water surface area, and H is the water level height;

[0012] S2. Water quality monitoring: using a variety of water quality sensors, including dissolved oxygen (DO), pH, and electrical conductivity (EC);

[0013] The comprehensive water quality index (WQ) is calculated by the following formula: WQ = w_1DO + w_2pH + w_3EC, where w_1, w_2, w_3 are the weight coefficients of water quality indicators;

[0014] S3. River flow measurement: precipitation (P), evaporation (E), and soil moisture content (SMC) factors in the Daqing River Basin;

[0015] The inflow flow (Q_{in}) is predicted by the following formula: Q_{in} = g(P, E, SMC), where g is the hydrological model function, P is precipitation, E is evaporation, and SMC is soil moisture content;

[0016] S4. Flood peak forecast: Based on river topography, water flow velocity (V), and basin area (A_{basin}), the flood peak flow (Q_{peak}) is predicted by the following formula: Q_{peak} = h(V, A_{basin}), where h is the flood peak forecast model function, V is the water flow velocity, and A_{basin} is the basin area.

[0017] As a preferred technical solution of the present invention, the water level sensor measurement adopts a linear fitting algorithm to convert the electrical signal output by the pressure sensor into a water level height value; the specific formula is: H=kP+b, where H is the water level height, P is the output value of the pressure sensor, and k and b are calibration coefficients.

[0018] As a preferred technical solution of the present invention, the water quality sensor measures dissolved oxygen, and the formula is expressed as:

[0019] DO=a_0+a_1S+a_2S 2 +a_nS n

[0020] Where DO is the dissolved oxygen concentration, S is the sensor output signal, and a_0, a_1, a_2, a_n are fitting coefficients.

[0021] As a preferred technical solution of the present invention, the model structure includes: using a distributed hydrological model, the distributed hydrological model includes a SWAT model; the forecast model system input parameters include the terrain, landform, soil, vegetation, and meteorological data of the Daqing River Basin;

[0022] Forecast model system calibration and verification, the model is calibrated and verified through historical hydrological data, and the model parameters are optimized using genetic algorithm optimization algorithm;

[0023] River flow and flood peak forecasting algorithm: Use hydrological methods, including the unit line method, combined with model simulation results to forecast river flow and flood peak;

[0024] Real-time correction algorithm: Combined with the monitoring data of the intelligent monitoring sensors in the urban flooding area, the hydrological forecast model is corrected in real time through the filtering algorithm.

[0025] As a preferred technical solution of the present invention, the hydrological cycle equation of the SWAT model is:

[0026] S_{t+1}=S_{t}+(R_{d}*Q_{s}*E*W_{s})

[0027] Among them: (S_{t}) and (S_{t+1}) represent the soil moisture content at time (t) and (t+1) respectively; (R_{d}) represents daily precipitation (corresponding to (R_{day})); (Q_{s}) represents surface runoff (corresponding to (Q_{surf})); (E) represents actual evapotranspiration (corresponding to (E_{a})); (W_{s}) represents soil moisture infiltration (corresponding to (W_{seep})).

[0028] As a preferred technical solution of the present invention, the unit line flow calculation formula is:

[0029]

[0030] Where Q t is the flow rate at time (t); h i is the net rainfall intensity in the (i)th period; U(t i ) is the unit line of time period (i) at time t i The value of t i is the corresponding lag time.

[0031] As a preferred technical solution of the present invention, the pH value measurement range of the water quality sensor is: 0-14pH; accuracy: ±0.1pH; resolution: 0.01pH; dissolved oxygen measurement range: 0-20mg / L; dissolved oxygen accuracy: ±0.5mg / L; dissolved oxygen resolution: 0.01mg / L.

[0032] As a preferred technical solution of the present invention, the water level sensor has a measurement range of 0-10 meters, a measurement accuracy of ±0.1% FS, full scale, a measurement resolution of 0.01 meters, and a measurement sampling frequency of 1 time per second.

[0033] Compared with the prior art, the invention has the following beneficial effects: the invention monitors the flooding range, water depth and water accumulation duration of the urban area in real time, and integrates numerical rainfall, urban waterlogging and other forecasting technologies to conduct all-round and high-precision monitoring and early warning for urban floods, making real-time urban flooding monitoring and early warning possible, avoiding on-site inspections by staff during the flood season, reducing safety hazards and a large amount of labor costs, and providing timely and accurate data support for decision makers. The invention transmits monitoring data such as flooding range, water depth and water accumulation duration and early warning data to the data acquisition module, which collects, processes and stores the monitoring data, and transmits the data to the remote monitoring center through the data transmission module. The remote monitoring center analyzes and processes the received data, grasps the flooding range, water depth and water accumulation duration of the urban area in real time, and combines the multi-dimensional and multi-temporal and spatial scale hydraulic model and intelligent analysis model, taking into account the terrain, landform, soil, vegetation factors and urbanization degree of the urban area, and performs risk analysis on the urban flooding situation to improve the efficiency of risk prediction.

[0034] The present invention adopts a distributed hydrological model, taking into account the topography, landform, soil, and vegetation factors of the Daqing River Basin, and can more accurately simulate the river flow and flood peak hydrological process. The hydrological forecast model is calibrated and verified using historical hydrological data, and the model parameters are continuously optimized to improve the forecast accuracy of the model. Combined with the monitoring data of the intelligent monitoring sensor of the urban flood range, the hydrological forecast model is corrected in real time to further improve the accuracy of the forecast. According to the forecast results, relevant departments can take corresponding flood prevention and disaster reduction measures in a timely manner to protect people's lives and property. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The experimental data diagram provided by the present invention;

[0036] Figure 2 A water quality monitoring diagram of the sensor provided by the present invention;

[0037] Figure 3 A flood peak forecast map provided by the present invention;

[0038] Figure 4 A dissolved oxygen measurement diagram provided by the present invention;

[0039] Figure 5 This is a hydrological cycle equation diagram of the SWAT model provided by the present invention. DETAILED DESCRIPTION

[0040] To make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them.

[0041] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely represents some embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should be noted that the embodiments of the present invention and the features and technical solutions in the embodiments can be combined with each other without conflict. It should be noted that similar numbers and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0042] Example 1: Please refer to Figure 1-Figure 5 A high-precision sensing urban flooding detection device includes a sensor body, a data acquisition module, a data transmission module and a power module. The sensor body includes a water level sensor and a water quality sensor. The water level sensor is used to monitor the water level changes in the urban area. The water quality sensor is used to monitor the water quality in the urban area. The data acquisition module is used to collect monitoring data from the water level sensor and the water quality sensor. The data transmission module is used to transmit the collected data to a remote monitoring center. The power module is used to power the sensor body, the data acquisition module and the data transmission module.

[0043] The water level sensor uses a pressure-type water level sensor, which can monitor the changes in urban water levels in real time and transmit the water level data to the data acquisition module. The water quality sensor uses a multi-parameter water quality sensor, which can simultaneously monitor multiple parameters of urban water quality, including water temperature, pH value, dissolved oxygen, and conductivity, and transmit the water quality data to the data acquisition module.

[0044] The data acquisition module uses a microcontroller as the core processor, which can collect, process and store the monitoring data of the water level sensor and water quality sensor. The data transmission module uses a wireless communication module, which can transmit the collected data to the remote monitoring center through a wireless network.

[0045] The power module adopts a power supply method that combines solar panels and batteries. The solar panels can convert solar energy into electrical energy and charge the batteries. The batteries can power the sensor body, data acquisition module and data transmission module when the solar panels cannot supply power, ensuring the normal operation of the sensor.

[0046] The intelligent monitoring sensor for urban flooding range is designed to achieve real-time monitoring of urban water level changes and water quality, and transmit monitoring data to a remote monitoring center, providing an important basis for urban flood control and water resources management. The sensor is mainly composed of a sensor body, a data acquisition module, a data transmission module, and a power module.

[0047] Water level sensor type: pressure type water level sensor is used.

[0048] Function: Able to sense the changes in urban water levels in real time and output water level data in the form of analog signals.

[0049] Working principle: This sensor is based on the relationship between pressure and water level. It calculates the water level by measuring the pressure of water on the sensor. When the water level changes, the pressure sensor inside the sensor detects the change in pressure and converts it into an electrical signal to obtain the water level data.

[0050] Data transmission: The water level sensor transmits the water level data to the data acquisition module.

[0051] Water quality sensor: Type: Multi-parameter water quality sensor is used.

[0052] Function: It can simultaneously monitor multiple parameters of urban water quality, including water temperature, pH value, dissolved oxygen, and conductivity.

[0053] Working Principle: Water Temperature Measurement: Water temperature is measured by a thermistor or thermocouple temperature sensor.

[0054] pH measurement: The pH value is determined by measuring the hydrogen ion concentration in water using a glass electrode or an ion selective electrode.

[0055] Dissolved oxygen measurement: Clark electrodes or optical dissolved oxygen sensors are usually used to determine the dissolved oxygen content by measuring the reduction current or optical signal of oxygen on the electrode surface.

[0056] Conductivity measurement: Two electrodes are inserted into the water to measure the resistance of the current passing through the water, and the conductivity is calculated.

[0057] Data transmission: The water quality sensor transmits various water quality parameter data to the data acquisition module.

[0058] 3. Data Acquisition Module

[0059] 1. Core processor: Use a microcontroller as the core processor, such as the STM32 series or Arduino.

[0060] 2. Function:

[0061] The monitoring data of water level sensors and water quality sensors are collected in real time.

[0062] The collected data is preprocessed, including filtering, amplification, and analog-to-digital conversion operations, to improve the accuracy and reliability of the data.

[0063] The processed data is stored for subsequent query and analysis.

[0064] Communicate with the data transmission module and send the collected data to the data transmission module.

[0065] 4. Data transmission module

[0066] 1. Communication method: wireless communication modules are used, including Wi-Fi, LoRa, and NB-IoT.

[0067] 2. Function:

[0068] The data collected by the data acquisition module is transmitted to the remote monitoring center via a wireless network.

[0069] It supports real-time and scheduled data transmission, and can be set according to actual needs.

[0070] It has data encryption and error correction functions to ensure the security and reliability of data transmission.

[0071] 5. Power module

[0072] 1. Power supply method: It adopts a power supply method combining solar panels and batteries.

[0073] 2. Solar Panels:

[0074] Type: Choose high-efficiency monocrystalline or polycrystalline solar panels.

[0075] Function: It can convert solar energy into electrical energy and charge the battery.

[0076] Installation location: According to the actual situation in the urban area, choose a suitable installation location to ensure that the solar panels can fully receive sunlight.

[0077] 3.Battery:

[0078] Type: Choose lead-acid battery or lithium battery, and select the appropriate capacity and voltage according to actual needs.

[0079] Function: When the solar panel fails to supply power, it provides power support for the sensor body, data acquisition module and data transmission module to ensure the normal operation of the sensor.

[0080] Management system: Equipped with a battery management system to monitor and manage the battery charging and discharging process to extend the battery life.

[0081] 6. Remote Monitoring Center

[0082] 1. Function: Receive monitoring data sent by the data transmission module, and store, analyze and display it.

[0083] 2. Data analysis: Analyze the received water level and water quality data, including statistical analysis, trend analysis, and anomaly detection, to provide decision support for urban flood control and water resources management.

[0084] 3. Data display: The monitoring data is displayed to the user in an intuitive way through a graphical interface, including water level change curves and water quality parameter charts.

[0085] 4. Alarm function: When the water level or water quality data exceeds the set threshold, the system can automatically send out an alarm signal to remind relevant personnel to take timely measures.

[0086] In order to improve the accuracy of hydrological forecasting in the Daqing River Basin, the present invention also adopts the following technical solutions:

[0087] A hydrological forecast model for the Daqing River Basin was established. The model adopts a distributed hydrological model and takes into account the terrain, landform, soil, and vegetation factors of the Daqing River Basin. It can more accurately simulate the river flow and flood peak hydrological process.

[0088] Use historical hydrological data to calibrate and verify the hydrological forecast model, continuously optimize model parameters, and improve the model's forecast accuracy.

[0089] Combined with the monitoring data of intelligent monitoring sensors in the urban flooding area, the hydrological forecast model is corrected in real time to further improve the accuracy of the forecast.

[0090] The working principle of the present invention is as follows: the water level sensor and the water quality sensor monitor the water level changes and water quality in the urban area in real time, and transmit the monitoring data to the data acquisition module. The data acquisition module collects, processes and stores the monitoring data, and transmits the data to the remote monitoring center through the data transmission module. The remote monitoring center analyzes and processes the received data, grasps the flooding range and water quality of the urban area in real time, and forecasts the river flow and flood peak in combination with the hydrological forecast model of the Daqing River Basin. According to the forecast results, relevant departments can take corresponding flood prevention and disaster reduction measures in a timely manner to protect people's lives and property.

[0091] In terms of hydrological forecasting in the Daqing River Basin, the present invention adopts a distributed hydrological model, taking into account the topography, landform, soil, and vegetation factors of the Daqing River Basin, and can more accurately simulate the river flow and flood peak hydrological process. The hydrological forecast model is calibrated and verified using historical hydrological data, and the model parameters are continuously optimized to improve the forecast accuracy of the model. Combined with the monitoring data of the intelligent monitoring sensor of the urban flood range, the hydrological forecast model is corrected in real time to further improve the accuracy of the forecast.

[0092] The specific algorithm of the working principle of the present invention is as follows:

[0093] Python

[0094] import random

[0095] import time

[0096] #Simulate the data of water level sensor and water quality sensor

[0097] def generate_sensor_data:

[0098] water_level = random.uniform(0,10) #Water level (meters), randomly generated between 0 and 10

[0099] water_quality = random.uniform(0,100) #Water quality value (arbitrary unit), randomly generated between 0 and 100

[0100] return water_level,water_quality

[0101] #Simulate the function of data acquisition module

[0102] class DataCollectionModule:

[0103] def __init__(self):

[0104] self.data=[]#Used to store the collected data

[0105] def collect_data(self):

[0106] water_level,water_quality=generate_sensor_data

[0107] timestamp = time.strftime ("%Y-%m-%d%H:%M:%S", time.localtime) # Get the current time as the timestamp of the data

[0108] data_point={

[0109] "timestamp":timestamp,

[0110] "water_level":water_level,

[0111] "water_quality":water_quality

[0112] }

[0113] self.data.append(data_point)

[0114] #Simulate the function of data transmission module

[0115] class DataTransmissionModule:

[0116] def transmit_data(self,data):

[0117] #The logic of transmitting data to the remote monitoring center, the application involves network communication operations

[0118] print(f"Transmitting data:{data}")

[0119] #Simulate the data analysis and processing functions of the remote monitoring center

[0120] class RemoteMonitoringCenter:

[0121] def analyze_data(self,data):

[0122] #Data analysis and processing logic, calculation of average water level and water quality;

[0123] total_water_level=sum([point["water_level"]for point in data])

[0124] average_water_level=total_water_level / len(data)

[0125] print(f"Average water level:{average_water_level}")

[0126] total_water_quality=sum([point["water_quality"]for point in data])

[0127] average_water_quality=total_water_quality / len(data)

[0128] print(f"Average water quality:{average_water_quality}")

[0129] def predict_flow_and_peak(self,data,hydrological_model):

[0130] #Combined with the Daqing River Basin Hydrological Forecast Model to forecast river flow and flood peak

[0131] #The model predicts based on the average water level

[0132] average_water_level=sum([point["water_level"]for point in data]) / len(data)

[0133] predicted_flow=average_water_level 2

[0134] #Prediction formula predicted_peak=average_water_level 3

[0135] #Prediction formula print(f"Predicted flow:{predicted_flow}")

[0136] print(f"Predicted peak:{predicted_peak}")

[0137] #Simulation of hydrological forecast model for Daqing River Basin

[0138] class HydrologicalModel:

[0139] def __init__(self):

[0140] self.parameters = {

[0141] "factor1":0.5,

[0142] "factor2":1.2

[0143] }#Model parameters, which need to be determined through calibration and verification in the application

[0144] def calculate_flow(self,water_level):

[0145] #Hydrological model calculation;

[0146] flow=water_level self.parameters["factor1"]

[0147] return flow

[0148] def calculate_peak(self,water_level):

[0149] #Here is a simple hydrological model calculation example. The actual model will be more complicated.

[0150] peak=water_level self.parameters["factor2"]

[0151] return peak

[0152] #Main program

[0153] data_collection_module=DataCollectionModule

[0154] data_transmission_module=DataTransmissionModule

[0155] remote_monitoring_center=RemoteMonitoringCenter

[0156] hydrological_model=HydrologicalModel

[0157] for_in range(5):#Simulate data collection 5 times

[0158] data_collection_module.collect_data

[0159] data=data_collection_module.data

[0160] data_transmission_module.transmit_data(data)

[0161] remote_monitoring_center.analyze_data(data)

[0162] remote_monitoring_center.predict_flow_and_peak(data,hydrological_model).

[0163] Embodiment 2: The high-precision sensing urban flooding detection device provided by the present invention can monitor the flooding range and water quality of the urban area in real time and accurately, and transmit the monitoring data to the remote monitoring center, providing a decision-making basis for urban flood prevention and disaster reduction. At the same time, the present invention optimizes the hydrological forecast model in combination with the hydrological characteristics of the Daqing River Basin, improves the forecast accuracy of river water flow and flood peak, and has important practical significance and application value.

[0164] The specific technology to achieve the above effects is as follows: Flood range monitoring formula: Use radar or other remote sensing technology to measure water level height (H) and water surface area (A).

[0165] The inundation range (I) is calculated by the following formula: I = A*f(H), where f(H) is a terrain water level height function used to convert the water level height into the actual inundation area.

[0166] Water quality monitoring formula: Uses a variety of water quality sensors, including dissolved oxygen (DO), pH, and electrical conductivity (EC).

[0167] The comprehensive water quality index (WQ) is calculated by the following formula: WQ = w_1DO + w_2pH + w_3EC, where w_1, w_2, w_3 are the weight coefficients of water quality indicators.

[0168] Data realization basis

[0169] 1. Basis of flood range monitoring data:

[0170] Topographic data: The topographic elevation data of the urban area is obtained through the Geographic Information System (GIS) to determine the relationship between water level and flooded area.

[0171] Historical hydrological data: Historical water level and inundation extent data of the Daqing River Basin and similar areas were collected to establish and verify the f(H) function.

[0172] 2. Water quality monitoring data basis:

[0173] Water quality standards: Refer to national and local water quality standards to determine the normal range and weight coefficient of each water quality indicator.

[0174] Laboratory analysis data: Laboratory analysis of collected water samples is performed to calibrate and verify the measurements of water quality sensors.

[0175] Optimization of hydrological forecast models:

[0176] 1. River flow forecast formula:

[0177] The factors of precipitation (P), evaporation (E) and soil moisture content (SMC) in the Daqing River basin are considered.

[0178] The inflow flow (Q_{in}) is predicted by the following formula: Q_{in} = g(P, E, SMC), where g is the optimized hydrological model function.

[0179] 2. Flood peak forecast formula:

[0180] Combined with river topography, water velocity (V), and basin area (A_{basin}) factors.

[0181] The peak flow (Q_{peak}) is predicted by the following formula: Q_{peak}=h(V,A_{basin}), where h is the optimized peak flow forecast model function.

[0182] Data realization basis

[0183] 1. Hydrological data: Collect real-time and historical data on precipitation, evaporation, soil moisture content in the Daqing River Basin, as well as geographic information data on river topography and basin area.

[0184] 2. Model validation data: Use actual observation data from historical flood events to validate and optimize the hydrological forecast model and adjust model parameters to improve forecast accuracy.

[0185] Measurement principle of water level sensor: Pressure water level sensor is based on the principle of liquid static pressure and calculates the water level by measuring the pressure of water on the sensor probe.

[0186] Water level sensor measurement range: 0-10 meters, can be adjusted according to actual needs; water level sensor measurement accuracy: ±0.1% FS, full scale; water level sensor measurement resolution: 0.01 meters; water level sensor measurement sampling frequency: 1 time per second.

[0187] The water level sensor uses a linear fitting algorithm to convert the electrical signal output by the pressure sensor into a water level value. The specific formula is: H = kP + b, where H is the water level, P is the output value of the pressure sensor, and k and b are coefficients obtained through calibration.

[0188] Water quality sensor measurement parameters: water temperature, pH value, dissolved oxygen, conductivity;

[0189] Water quality sensor water temperature measurement range: 0-50℃; water quality sensor water temperature measurement accuracy: ±0.5℃; water quality sensor water temperature measurement resolution: 0.1℃.

[0190] Water quality sensor pH value measurement range: 0-14pH; accuracy: ±0.1pH; resolution: 0.01pH

[0191] Dissolved oxygen measurement range: 0-20mg / L; dissolved oxygen accuracy: ±0.5mg / L; dissolved oxygen resolution: 0.01mg / L;

[0192] Conductivity measurement range: 0-2000μS / cm; Conductivity accuracy: ±2%FS; Conductivity resolution: 1μS / cm;

[0193] The various parameters of the water quality sensor are measured using corresponding electrochemical or physical principles, and the measured values ​​are converted into electrical signals through the signal processing circuit inside the sensor. For each parameter, a polynomial fitting algorithm is used for data calibration and compensation to improve measurement accuracy. For the measurement of dissolved oxygen, the formula is: DO = a_0 + a_1S + a_2S 2 +a_nS n , where DO is the dissolved oxygen concentration, S is the sensor output signal, and a_0, a_1, a_2, a_n are fitting coefficients.

[0194] Data acquisition module algorithm and technical parameters: The microcontroller model is STM32F103: a microcontroller with low power consumption and high performance.

[0195] Sampling frequency: According to the sampling frequency of the water level sensor and water quality sensor, set the sampling frequency of the data acquisition module to once per second to ensure that the sensor's monitoring data can be collected in a timely manner.

[0196] Data processing algorithm: The median filter algorithm is used to filter the collected data to remove noise interference. At the same time, the water level and water quality data are analyzed in real time to determine whether they exceed the set threshold. If the threshold is exceeded, the alarm function is triggered.

[0197] Data storage: The internal Flash memory is used to store the collected data. The storage capacity is 128KB and can store at least 1 month of monitoring data.

[0198] Data transmission module algorithm and technical parameters: Wireless communication method: LoRa wireless communication technology is adopted, which has the characteristics of low power consumption and long-distance transmission.

[0199] Data transmission module transmission frequency: 470-510MHz;

[0200] Data transmission module transmission rate: 0.3-50kbps;

[0201] Data transmission module transmission distance: 1-10km (line of sight);

[0202] The data transmission module uses an adaptive data rate adjustment algorithm to automatically adjust the transmission rate according to the channel quality and transmission distance to ensure the reliability and stability of data transmission. At the same time, an encryption algorithm is used to encrypt the transmitted data to ensure data security.

[0203] Adaptive data rate adjustment algorithm evaluates channel quality by measuring signal strength and signal-to-noise ratio parameters, specifically using received signal strength indicator (RSSI) to represent signal strength and signal-to-noise ratio (SNR) to measure signal quality;

[0204] Rate adjustment strategy: Develop a rate adjustment strategy based on channel quality and transmission distance. For example, you can set some thresholds to increase the transmission rate when the channel quality is good and the transmission distance is short; and reduce the transmission rate when the channel quality is poor or the transmission distance is long.

[0205] Using RSSI as the channel quality parameter and the transmission distance in meters, we can define the following function:

[0206] f(RSSI,Distance)=begin{cases}

[0207] k_1\cdot RSSI-k_2\cdot

[0208] Distance+k_3&\text{if}RSSI\geq RSSI_{min}\text{and}Distance\leq

[0209] Distance_{max}\\k_4&\text{otherwise}\end{cases}\]

[0210] Among them, RSSI_{min} is a minimum acceptable signal strength value, Distance_{max} is a maximum acceptable transmission distance value, k_1, k_2, k_3 and k_4 are constants that need to be adjusted and determined according to actual conditions.

[0211] When RSSI is greater than or equal to RSSI_{min} and Distance is less than or equal to Distance_{max}, the transmission rate is calculated based on k_1, k_2, and k_3. k_1 can represent the positive impact of signal strength on the transmission rate, k_2 can represent the negative impact of transmission distance on the transmission rate, and k_3 is a constant offset.

[0212] When RSSI is less than RSSI_{min} or Distance is greater than Distance_{max}, the transmission rate is set to a lower fixed value k_4 to ensure the reliability of data transmission.

[0213] Power module solar panel: Output power: 10W: Operating voltage: 12V: Conversion efficiency: 20% Power module battery type: lead-acid battery; Capacity: 12Ah; Operating voltage: 12V

[0214] Charging management algorithm: The maximum power point tracking (MPPT) algorithm is used to manage the charging of solar panels to improve the utilization rate of solar energy. At the same time, the overcharge and over-discharge protection algorithm is used to protect the battery and extend the battery life.

[0215] A hydrological forecast model system for a high-precision sensing urban flooding detection device and method, including a model structure: using a distributed hydrological model, including a SWAT (Soil and Water Assessment Tool) model.

[0216] Input parameters of the forecast model system include topography, landform, soil, vegetation and meteorological data of the Daqing River Basin.

[0217] System calibration and verification of forecast model: The model is calibrated and verified using historical hydrological data, and the model parameters are optimized using genetic algorithm to improve the forecast accuracy of the model.

[0218] River inflow and flood peak forecasting algorithm: Hydrological methods, including Muskingum method and unit line method, are used in combination with model simulation results to forecast river inflow and flood peak.

[0219] Real-time correction algorithm: Combined with the monitoring data of the intelligent monitoring sensors in the urban flooding range, the Kalman filter algorithm is used to make real-time corrections to the hydrological forecast model to improve the accuracy of the forecast.

[0220] The hydrological cycle equation of the SWAT model is:

[0221] S_{t+1}=S_{t}+(R_{d}Q_{s}E W_{s})

[0222] Among them: (S_{t}) and (S_{t+1}) represent the soil moisture content at time \(t\) and \(t+1\) respectively; (R_{d}) represents daily precipitation (corresponding to (R_{day})); (Q_{s}) represents surface runoff (corresponding to (Q_{surf})); (E) represents actual evapotranspiration (corresponding to (E_{a})); (W_{s}) represents soil moisture infiltration (corresponding to (W_{seep})).

[0223] Working principle: This equation describes the dynamic changes of soil moisture and simulates the storage and consumption of soil moisture by considering precipitation, surface runoff, evapotranspiration and infiltration.

[0224] Function: It is used to calculate the change of soil moisture, which is important for predicting the runoff and water resource allocation in the basin.

[0225] The unit line flow calculation formula is:

[0226]

[0227] Where Q t is the flow rate at time (t); h i is the net rainfall intensity in the (i)th period; U(t i ) is the unit line of time period (i) at time t i The value of t i is the corresponding lag time.

[0228] Working principle: The unit line method simplifies the rainfall-runoff process of the basin into a linear system, describes the response of the basin to rainfall through the unit line, and converts net rain into flow process.

[0229] Function: It is used to estimate the flow process at the outlet section of the basin, which is of great significance for flood forecasting and water resources planning.

[0230] Filter correction formula:

[0231] hat{x}_{k}&=hat{x}_{k}^{-}+K_{k}(z_{k}-Hhat{x}_{k}^{-})

[0232] P_{k}&=(I-K_{k}H)P_{k}^{-}

[0233] K_{k}&=P_{k}^{-}H^{T}(HP_{k}^{-}H^{T}+R)^{-1}

[0234] end{align}]

[0235] in:

[0236] (hat{x}_{k}) is the estimated state at time (k).

[0237] (hat{x}_{k}^{-}) is the prior estimate at time (k).

[0238] (z_{k}) is the observation value at time (k).

[0239] (H) is the observation matrix.

[0240] (K_{k}) is the Kalman gain.

[0241] (P_{k}) is the estimation error covariance matrix at time (k).

[0242] (P_{k}^{-}) is the prior estimation error covariance matrix at time (k).

[0243] (R) is the observation noise covariance matrix.

[0244] Working principle: The filter achieves the optimal estimation of the system state by continuously updating the state estimate and the estimation error covariance matrix. It combines the model prediction and observation information to correct the prediction results.

[0245] Function: In hydrological forecasting, it is used to combine the monitoring data of the intelligent monitoring sensors in the urban flooding range to make real-time corrections to the results of the hydrological forecasting model and improve the accuracy of the forecast.

[0246] Experimental example:

[0247] Purpose:

[0248] Verify the performance of the intelligent monitoring sensor for urban inundation range and its effect on improving the accuracy of hydrological forecasts in the Daqing River Basin.

[0249] Experimental location:

[0250] An urban area in the Daqing River Basin was selected as the experimental area.

[0251] Experimental equipment:

[0252] 1. Intelligent monitoring sensors for urban flooding range, including pressure water level sensors, multi-parameter water quality sensors, data acquisition modules with microcontroller as the core, data transmission modules for wireless communication, and power modules combining solar panels and batteries.

[0253] 2. Receiving and processing equipment in the remote monitoring center.

[0254] Experimental steps:

[0255] 1.Sensor installation and debugging

[0256] Pressure water level sensors and multi-parameter water quality sensors are installed at selected urban monitoring points.

[0257] Connect the data acquisition module, data transmission module and power module to ensure the normal operation of the sensor.

[0258] Debug the sensors and check whether the collection and transmission of water level and water quality data are normal.

[0259] 2. Data collection and transmission

[0260] The sensors begin to monitor the water level changes and water quality in the urban area in real time. The pressure water level sensor transmits the water level data to the data acquisition module, and the multi-parameter water quality sensor transmits the water temperature, pH value, dissolved oxygen, and conductivity water quality data to the data acquisition module.

[0261] The data acquisition module collects, processes and stores the monitoring data, and transmits the data to the remote monitoring center through the wireless communication module.

[0262] 3. Establishment and calibration of hydrological forecast model

[0263] A distributed hydrological forecasting model is established using the terrain, landform, soil and vegetation data of the Daqing River basin.

[0264] Collect historical hydrological data, calibrate and verify the hydrological forecast model, and optimize model parameters.

[0265] 4. Combine monitoring data for real-time correction

[0266] The monitoring data of the intelligent monitoring sensors for urban flooding range are input into the hydrological forecasting model.

[0267] Make real-time corrections to the hydrological forecast model based on monitoring data and observe changes in forecast results.

[0268] Experimental data

[0269]

[0270] Experimental analysis

[0271] 1. Water level changes: From the experimental data, it can be seen that the water level is gradually rising, which may be caused by rainfall or increased water from upstream. Further analysis is needed in combination with local meteorological and hydrological information.

[0272] 2. Changes in water temperature: The water temperature has risen slightly over time, which may be related to rising air temperatures and solar radiation.

[0273] 3. pH value: The pH value fluctuates between 7.2-7.4, which is within the normal water quality range.

[0274] 4. Dissolved oxygen: The dissolved oxygen content gradually decreases, which may be due to the decomposition of organic matter in the water consuming oxygen, or the increase in water temperature causing the solubility of oxygen to decrease.

[0275] 5. Conductivity: The conductivity shows a gradual upward trend, which may be related to the increase in the concentration of dissolved ions in water.

[0276] Experimental conclusion

[0277] 1. Intelligent monitoring sensors for urban flooding range can accurately monitor changes in urban water levels and multiple water quality parameters. Pressure-type water level sensors can reflect the rising trend of water levels in real time, and multi-parameter water quality sensors can provide information on important water quality indicators such as water temperature, pH value, dissolved oxygen and conductivity.

[0278] 2. The data acquisition module can effectively collect and process the monitoring data of the sensor, and the performance of the microcontroller meets the requirements of data processing.

[0279] 3. The data transmission module can stably transmit the collected data to the remote monitoring center through the wireless network, and the success rate and timeliness of data transmission have been verified.

[0280] 4. The power module adopts a power supply method that combines solar panels and batteries, which can ensure the normal operation of the sensor under different weather conditions and improve the reliability and stability of the sensor.

[0281] 5. Real-time correction of the hydrological forecast model of the Daqing River basin combined with the monitoring data of the intelligent monitoring sensors in the urban flood range will help improve the accuracy of the hydrological forecast. By inputting the monitoring data into the hydrological forecast model, the river flow and flood peak hydrological process can be better simulated, providing a more accurate decision-making basis for urban flood prevention and disaster reduction work;

[0282] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described in the present invention. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the above specific implementation methods. Therefore, any modification or replacement of the present invention; and all technical solutions and improvements thereof that do not depart from the spirit and scope of the invention are included in the scope of the claims of the present invention.

Claims

1. A high-precision sensing urban flooding detection device, characterized in that: It includes a sensor body, a data acquisition module, a data transmission module and a power module. The sensor body includes a water level sensor and a water quality sensor. The water level sensor is used to monitor the water level change in the urban area. The water quality sensor is used to monitor the water quality in the urban area. The data acquisition module is used to collect monitoring data from the water level sensor and the water quality sensor. The data transmission module is used to transmit the collected data to the remote monitoring center. The power module is used to supply power to the sensor body, the data acquisition module and the data transmission module. The water level sensor adopts a pressure-type water level sensor, which can monitor the changes in urban water levels in real time and transmit the water level data to the data acquisition module; the water quality sensor adopts a multi-parameter water quality sensor, which can simultaneously monitor multiple parameters of urban water quality, including water temperature, pH value, dissolved oxygen, and conductivity, and transmit the water quality data to the data acquisition module. The data transmission module adopts a wireless communication module, and the wireless communication modules include Wi-Fi, LoRa, and NB-IoT.

2. A high-precision sensing urban flooding detection device according to claim 1, characterized in that: The data acquisition module uses a microcontroller as the core processor, which can collect, process and store the monitoring data of the water level sensor and water quality sensor; the data transmission module uses a wireless communication module, which can transmit the collected data to the remote monitoring center through a wireless network; the power module is powered by a combination of solar panels and batteries.

3. A detection method of a high-precision sensing urban flooding detection device, characterized in that: The following steps are involved: S1. Monitoring of flooding range: using radar remote sensing to measure water level height (H) and water surface area (A); The inundation range is calculated by the following formula: I = A*f(H), where f(H) is the terrain water level height function used to convert the water level height to the actual inundation area, A is the water surface area, and H is the water level height; S2. Water quality monitoring: using a variety of water quality sensors, including dissolved oxygen (DO), pH, and electrical conductivity (EC); The comprehensive water quality index (WQ) is calculated by the following formula: WQ = w_1DO + w_2pH + w_3EC, where w_1, w_2, w_3 are the weight coefficients of water quality indicators; S3. River flow measurement: precipitation (P), evaporation (E), and soil moisture content (SMC) factors in the Daqing River Basin; The inflow flow (Q_{in}) is predicted by the following formula: Q_{in} = g(P, E, SMC), where g is the hydrological model function, P is precipitation, E is evaporation, and SMC is soil moisture content; S4. Flood peak forecast: Based on river topography, water flow velocity (V), and basin area (A_{basin}), the flood peak flow (Q_{peak}) is predicted by the following formula: Q_{peak} = h(V, A_{basin}), where h is the flood peak forecast model function, V is the water flow velocity, and A_{basin} is the basin area.

4. The detection method of the high-precision sensing urban flooding detection device according to claim 3 is characterized in that: The water level sensor measurement uses a linear fitting algorithm to convert the electrical signal output by the pressure sensor into a water level height value; the specific formula is: H=kP+b, where H is the water level height, P is the output value of the pressure sensor, and k and b are calibration coefficients.

5. The detection method of the high-precision sensing urban flooding detection device according to claim 4 is characterized in that: The water quality sensor measures dissolved oxygen using the formula: DO=a_0+a_1S+a_2S 2 +a_nS n Where DO is the dissolved oxygen concentration, S is the sensor output signal, and a_0, a_1, a_2, a_n are fitting coefficients.

6. The detection method of the high-precision sensing urban flooding detection device according to claim 5 is characterized in that: The model structure includes: using a distributed hydrological model, the distributed hydrological model includes a SWAT model; the forecast model system input parameters include the terrain, landform, soil, vegetation, and meteorological data of the Daqing River Basin; Forecast model system calibration and verification, the model is calibrated and verified through historical hydrological data, and the model parameters are optimized using genetic algorithm optimization algorithm; River flow and flood peak forecasting algorithm: Use hydrological methods, including the unit line method, combined with model simulation results to forecast river flow and flood peak; Real-time correction algorithm: Combined with the monitoring data of the intelligent monitoring sensors in the urban flooding area, the hydrological forecast model is corrected in real time through the filtering algorithm.

7. The detection method of the high-precision sensing urban flooding detection device according to claim 6 is characterized in that: The hydrological cycle equation of the SWAT model is: S_{t+1}=S_{t}+(R_{d}*Q_{s}*E*W_{s}) Among them: (S_{t}) and (S_{t+1}) represent the soil moisture content at time \(t) and \(t+1) respectively; (R_{d}) represents daily precipitation (corresponding to (R_{day})); (Q_{s}) represents surface runoff (corresponding to (Q_{surf})); (E) represents actual evapotranspiration (corresponding to (E_{a})); (W_{s}) represents soil moisture infiltration (corresponding to (W_{seep})).

8. The detection method of the high-precision sensing urban flooding detection device according to claim 7 is characterized in that: The unit line flow calculation formula is: Where Q t is the flow rate at time (t); h i is the net rainfall intensity in the (i)th period; U(t i ) is the unit line of time period (i) at time t i The value of t i is the corresponding lag time.

9. The detection method of the high-precision sensing urban flooding detection device according to claim 8 is characterized in that: Water quality sensor pH value measurement range: 0-14pH; Accuracy: ±0.1pH; Resolution: 0.01pH; Dissolved oxygen measurement range: 0-20mg / L; Dissolved oxygen accuracy: ±0.5mg / L; Dissolved oxygen resolution: 0.01mg / L.

10. The detection method of the high-precision sensing urban flooding detection device according to claim 9 is characterized in that: Water level sensor measurement range: 0-10 meters, water level sensor measurement accuracy: ±0.1% FS, full scale; water level sensor measurement resolution: 0.01 meters; water level sensor measurement sampling frequency: 1 time per second.

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