Regional waterlogging prevention early warning system and method based on big data

Abstract

The invention relates to the technical field of regional waterlogging prevention, in particular to a regional waterlogging prevention early warning system and method based on big data, and the system comprises a data collection module which is used for collecting data, and the data comprise meteorological data, hydrological data and geographic data; the data analysis module is used for storing the data and establishing a database based on the collected data, and analyzing the waterlogging risk of the future rainfall region according to the database and the future rainfall region; the early warning module is used for outputting early warning information based on an analysis result of the data analysis module, the drainage capacity of a drainage pipe can be conveniently detected subsequently by selecting a proper drainage position and the density of a drainage pipe network according to the distance from a drainage point, the data are stored, a database is established, and the subsequent analysis of the data analysis module is facilitated; and early warning is carried out according to the early warning module, so that early warning information can be conveniently and timely made, and the response time is shorter.

Description

Technical Field

[0001] The present invention relates to the technical field of regional flood prevention, and in particular to a regional flood prevention early warning system and method based on big data. Background Art

[0002] Waterlogging refers to the phenomenon of waterlogging disasters in cities due to heavy or continuous rainfall that exceeds the city's drainage capacity. If the waterlogging is serious, the water storage capacity of lakes will be reduced, and the water level of lakes will rise during the flood season. When the depth of accumulated water reaches 15 cm or higher, it can be regarded as waterlogging. On busy main roads, waterlogging depth of 10 cm may affect driving. At present, there is a need for flood prevention warnings in the area.

[0003] At present, a solution has been proposed in the existing technology for regional flood prevention and early warning. For example, a Chinese patent application with announcement number CN212510528U discloses an online monitoring system for drainage network based on the Internet of Things, including a field detection device, an online collection gateway and an early warning processing device. The field monitoring device is connected to the online collection gateway to be used for flood-prone points in the drainage network. By deploying field detection devices, online collection gateways and subsequent early warning processing devices at the flood-prone points, real-time data collection and transmission are strengthened to meet the needs of daily management, operation scheduling, disaster prediction, early warning and forecasting, and auxiliary decision-making, thereby improving the level of urban drainage and flood prevention operation management.

[0004] Although the above technical solutions have improved the level of urban drainage and flood control operation and management, with the acceleration of urbanization, urban waterlogging problems are becoming increasingly serious. Traditional flood control early warning systems often rely on limited monitoring points and empirical models, making it difficult to achieve comprehensive and accurate early warnings. Summary of the invention

[0005] The purpose of the present invention can be achieved through the following technical solutions: a regional flood prevention early warning system based on big data,

[0006] Data collection module: The data collection module is used to collect data, including meteorological data, hydrological data and geographical data; meteorological data includes rainfall intensity and rainfall duration, hydrological data includes water level and leaf-falling area of ​​drainage points, and geographical data includes terrain height, that is, whether the terrain is a depression or non-depression;

[0007] Data analysis module: based on the collected data, the data analysis module stores the data and establishes a database, and analyzes the risk of waterlogging in the future rainfall area according to the database and the area where rainfall is expected in the future;

[0008] Early warning module: The early warning module outputs early warning information based on the analysis results of the data analysis module.

[0009] Preferably, the data processing module is used to analyze whether a region has a risk of waterlogging:

[0010] Establish a database, the database includes historical rainfall prone waterlogging areas, and establish a set of prone waterlogging areas M1 = [X1, X 11 , ..., X 1n ];

[0011] Then, the future rainfall area is obtained according to the meteorological data in the data acquisition module, and a future rainfall area set [Y1, Y2, ..., Y n ], based on historical rainfall prone waterlogging area X i and future rain area Y i By contrast, if the historical rainfall prone waterlogging set M1 and the future rainy area have the same area, that is, X i =Y i ;

[0012] Calculate the same future rainfall area Y i The water storage volume ΔV in the area prone to waterlogging in one hour. If the water storage volume ΔV is greater than the regional water storage volume threshold γ1, the early warning module outputs an early warning flood prevention signal; (wherein, the regional water storage volume threshold γ1 is set based on whether the degree of waterlogging affects the normal activities of pedestrians).

[0013] Preferably, the specific method for establishing the historical rainfall prone waterlogging area is:

[0014] S1, dividing the area into depression area and non-depression area based on the geographic information collected by the data collection module;

[0015] S2, based on the hydrological information collected by the data collection module, the depression area and the non-depression area are divided into a depression prone to leaf litter accumulation area F1, a depression non-prone to leaf litter accumulation area F2, a non-depression prone to leaf litter accumulation area ω3 and a non-depression non-prone to leaf litter accumulation area F4; wherein the prone to leaf litter accumulation area and the non-prone to leaf litter accumulation area are set based on whether there are trees nearby. For example, if there are trees nearby and it is a depression, then this area is the depression prone to leaf litter accumulation area F1;

[0016] Among them, the degree of easy accumulation of water: the area of ​​fallen leaves in the depression that is easy to accumulate F1 is greater than the area of ​​fallen leaves that is not easy to accumulate F2 in the depression that is greater than the area of ​​fallen leaves that is easy to accumulate F3 in the non-depression that is greater than the area of ​​fallen leaves that is not easy to accumulate F4 in the non-depression;

[0017] S3, based on the historical rainfall intensity, historical rainfall duration and the flow rate Q of the drainage system in the F1, F2, F3 and F4 areas per unit time, calculate the water storage capacity ΔV of these areas. If the water storage capacity ΔV is greater than the regional water storage capacity threshold γ1, the corresponding area is marked as a historically prone to waterlogging area X i ;

[0018] A mapping relationship is established between the F1, F2, F3 and F4 areas and the early warning module, and different early warning signals are output through the early warning module. The early warning signals include Class I early warning signals, Class II early warning signals and Class III early warning signals.

[0019] Preferably, the severity of the warning signal: Class I warning signal is greater than Class II warning signal, which is greater than Class III warning signal;

[0020] Based on the future rainfall intensity and future rainfall duration, and the mutual mapping relationship between F1, F2, F3 and F4 and the early warning module;

[0021] If the future rainfall intensity and future rainfall duration are greater than the preset rainfall intensity threshold -ω1 and rainfall duration threshold δ1, the warning module outputs a Class I warning signal for the F1, F2, F3 and F4 areas;

[0022] If the future rainfall intensity is between the preset rainfall intensity threshold ω2 and the rainfall intensity threshold ω1, the warning module outputs a Class I warning signal for the F1 region, a Class II warning signal for the F2 and F3 regions, and a Class III warning signal for the F4 region.

[0023] Preferably, the water storage capacity ΔV of the area is specifically calculated as follows:

[0024] The data acquisition module is used to obtain different historical data, including historical rainfall intensity P X (mm / hour), rainfall duration t1 (hours) and catchment area A1 of rainfall depression area;

[0025] If the product of rainfall intensity and rainfall time minus the amount of water flowing out is greater than 0, that is, ΔV>0, it indicates that water will accumulate in the area; Where ΔV is the water storage capacity in the area to be rained, Q is the flow rate of the drainage network (mm / s), that is, the flow rate passing through the drainage system per unit time; is the runoff coefficient;

[0026] Among them, the historical rainfall intensity P X (mm / hour), rainfall duration t1 (hours), and catchment area A1, P X *t1 gets rainfall, P X *A*t1 is the water catchment in the rainfall depression area, and the runoff coefficient is the ratio of rainfall converted into surface runoff. It is the actual amount of water collected in the rainfall depression area; this makes it convenient to issue different early warning signals based on the water-prone locations in each area.

[0027] Preferably, the regional flood prevention warning system further includes a control unit, which is used to control the road condition repair in the area prone to waterlogging and the correction of the drainage network flow, specifically:

[0028] Based on the set of waterlogging-prone areas in historical data, the rainfall period of the waterlogging-prone areas is obtained, and a rainfall period set is established: [T1, T2, …, T n ], where T1 is the first rainfall time in the historical data, T2 is the next rainfall time in the historical data, and T n is the n rainfall periods in the historical data;

[0029] Among them, the correction of drainage network flow Q before each rainfall cycle is mainly as follows:

[0030] Get the initial rainfall time T in the future m , the initial rainfall time T m The difference calculation is performed with the nearest adjacent cycle in the rainfall cycle set. If the cycle time difference is greater than the preset time threshold, the control unit outputs a signal to correct the drainage network flow Q; (wherein the preset time threshold is set based on the degree of leaf fall in the waterlogged area. For example, after a rainfall, leaves will fall five days later, and the fallen leaves affect the water flow, so the time threshold can be set to five days). Otherwise, the drainage network flow Q is calculated normally; (Since these areas prone to waterlogging will have fallen leaves within this cycle time difference, the fallen leaves increase the roughness of the surface and reduce the water flow rate, resulting in a large error in the calculation of the storage force ΔV in these areas, so the drainage network flow Q needs to be corrected).

[0031] The corrected drainage network flow Q C Specifically, the difference between two adjacent periods in the set is calculated according to the formula: Where Q is the initial drainage network flow, T i is one of the cycles in the rainfall cycle set, is the correction factor.

[0032] Preferably, the runoff coefficient The specific calculation method is as follows:

[0033]

[0034] Where C1 is the average water layer thickness obtained by evenly distributing the total amount of runoff generated in the basin or catchment area over the entire basin area within a certain period of time;

[0035] C2 is the amount of precipitation that falls on the basin or catchment area during the same period of time;

[0036] f e +[f0-f e ]e -βt is the ground infiltration rate;

[0037] f0 is the initial maximum infiltration rate (m / s), f e is the steady infiltration rate, β attenuation coefficient (s -1 ), t is the time.

[0038] Preferably, the regional flood prevention warning system further comprises an emergency allocation unit, which makes different emergency measures based on the warning signals received in the F1, F2, F3 and F4 areas;

[0039] Under the Class I and Class II warning signals, drainage points near the F1, F2, F3 and F4 areas are obtained, and the drainage points include the river E1, the lake E2, and the flood drainage ditch E3;

[0040] The drainage capacity of each drainage point is calculated respectively. If the drainage point has the drainage capacity, the emergency allocation unit outputs a drainage signal to the corresponding drainage point.

[0041] Under a Class III warning signal, the emergency distribution unit outputs real-time monitoring of the blockage situation in the fallen leaf area.

[0042] Preferably, the specific calculation method of the drainage capacity of each drainage point is as follows:

[0043] The maximum water storage capacity and current water storage capacity of the drainage point are obtained, and the amount of water falling into the drainage point is calculated according to the product of rainfall intensity and rainfall duration. Then, the maximum water storage capacity of the drainage point is subtracted from the sum of the current water storage capacity and the amount of water falling into the drainage point to obtain the drainage capacity of each drainage point.

[0044] Preferably, a regional flood prevention early warning method based on big data comprises the following steps:

[0045] S1, collect meteorological data, hydrological data and geographic information through the data acquisition module;

[0046] S2, analyzing the risk of regional waterlogging based on the collected meteorological data, hydrological data and geographic information through the data analysis module;

[0047] S3. The warning module outputs warning information based on the analysis results of the data analysis module.

[0048] By ranking the severity of the flood risk levels in areas prone to waterlogging, it is beneficial for different staff to make different maintenance measures based on the ranking of different risk levels, and it is convenient to make different emergency measures according to the rainfall level and the level of flood susceptibility.

[0049] By correcting the drainage network flow Q, the water storage capacity ΔV in the future rainfall area can be calculated more accurately, and different early warning signals can be outputted conveniently according to the water storage capacity ΔV in the future rainfall area. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below in conjunction with the accompanying drawings;

[0051] Figure 1 It is a schematic diagram of the system flow in the present invention;

[0052] Figure 2 It is a flow chart of the method in the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0054] See also Figure 1 As shown, the present invention is a regional flood prevention early warning system based on big data, including a data acquisition module, a data analysis module and an early warning module;

[0055] Data collection module: The data collection module is used to collect data, including meteorological data, hydrological data and geographical data; meteorological data includes rainfall intensity and rainfall duration, hydrological data includes water level and leaf-falling area of ​​drainage points, and geographical data includes terrain height, that is, whether the terrain is a depression or non-depression;

[0056] Data analysis module: based on the collected data, the data analysis module stores the data and establishes a database, and analyzes the risk of waterlogging in the future rainfall area according to the database and the area where rainfall is expected in the future;

[0057] Early warning module: The early warning module outputs early warning information based on the analysis results of the data analysis module.

[0058] Specifically, the data collection module periodically collects meteorological data and hydrological data, and obtains rainfall intensity, rainfall duration, elevation, slope, distance from drainage point and density of drainage pipe network based on the meteorological data and hydrological data. The elevation and slope can divide the area into depressions and non-depressions. The distance from the drainage point can be used to select a suitable drainage location. The density of the drainage pipe network can facilitate the subsequent detection of the drainage capacity of the drainage pipe. These data are stored and a database is established to facilitate the analysis of the subsequent data analysis module, and the early warning module is used to issue an early warning, so as to facilitate timely early warning information and faster response time.

[0059] It should be noted that the data analysis module includes the use of statistical methods to perform descriptive statistical analysis on the data: such as mean, maximum, minimum standard deviation, etc.; analysis of data changing trends over time, such as water level and flow rate; correlation analysis: analysis of the correlation between different data, such as the relationship between rainfall and water level.

[0060] The data processing module is used to analyze whether the area has the risk of waterlogging:

[0061] Establish a database, the database includes historical rainfall prone waterlogging areas, and establish a set of prone waterlogging areas M1 = [X1, X 11 , …, X 1n ];

[0062] Then, the future rainfall area is obtained according to the meteorological data in the data acquisition module, and a future rainfall area set [Y1, Y2, ..., Y n ], based on historical rainfall prone waterlogging area X i and future rain area Y i By contrast, if the historical rainfall prone waterlogging set M1 and the future rainy area have the same area, that is, X i =Y i ;

[0063] Calculate the same future rainfall area Y i The water storage volume ΔV in the area prone to waterlogging in one hour. If the water storage volume ΔV is greater than the regional water storage volume threshold γ1, the early warning module outputs an early warning flood prevention signal; (wherein, the regional water storage volume threshold γ1 is set based on whether the degree of waterlogging affects the normal activities of pedestrians);

[0064] Specifically, remote sensing technologies such as meteorological satellites and radars are used to monitor rainfall conditions in a large area, obtain rainfall distribution maps, and establish areas prone to waterlogging during historical rainfall based on the rainfall distribution maps and rainfall levels. These areas are then marked as a set, and the areas that will rain in the future are collected through the data collection module and compared with the historical rainfall areas. i If the water storage volume ΔV in the area prone to waterlogging in one hour is greater than the regional water storage threshold γ1, it indicates that the possibility of waterlogging in the area is very high. Subsequently, the alarm information is issued through the early warning module, which can improve the early warning accuracy and faster response time.

[0065] The specific method for establishing the historical rainfall prone waterlogging area is as follows:

[0066] S1, dividing the area into depression area and non-depression area based on the geographic information collected by the data collection module;

[0067] S2, based on the hydrological information collected by the data collection module, the depression area and the non-depression area are divided into a depression prone to leaf litter accumulation area F1, a depression non-prone to leaf litter accumulation area F2, a non-depression prone to leaf litter accumulation area F3 and a non-depression non-prone to leaf litter accumulation area F4; wherein the prone to leaf litter accumulation area and the non-prone to leaf litter accumulation area are set based on whether there are trees nearby. For example, if there are trees nearby and it is a depression, then this area is the depression prone to leaf litter accumulation area F1;

[0068] Among them, the degree of easy accumulation of water: the area of ​​fallen leaves in the depression that is easy to accumulate F1 is greater than the area of ​​fallen leaves that is not easy to accumulate F2 in the depression that is greater than the area of ​​fallen leaves that is easy to accumulate F3 in the non-depression that is greater than the area of ​​fallen leaves that is not easy to accumulate F4 in the non-depression;

[0069] S3, based on the historical rainfall intensity, historical rainfall duration and the flow rate Q of the drainage system in the F1, F2, F3 and F4 areas per unit time, calculate the water storage capacity ΔV of these areas. If the water storage capacity ΔV is greater than the regional water storage capacity threshold γ1, the corresponding area is marked as a historically prone to waterlogging area X i ;

[0070] Establishing a mapping relationship between the F1, F2, F3 and F4 areas and the early warning module, and outputting different early warning signals through the early warning module, the early warning signals including Class I early warning signals, Class II early warning signals and Class III early warning signals;

[0071] Specifically, since different terrains will cause different degrees of water accumulation, the area is divided into depression areas and non-depression areas according to the terrain, and whether there are fallen leaves in the depression areas and non-depression areas will also affect whether the area will accumulate water, so these areas are further divided into depression areas F1 where fallen leaves are prone to accumulation, depression areas F2 where fallen leaves are not prone to accumulation, non-depression areas F3 where fallen leaves are prone to accumulation, and non-depression areas F4 where fallen leaves are not prone to accumulation. By dividing these areas, the regional flood prevention and early warning system can make different early warning signals for different areas, which is highly targeted and convenient for making early warning measures and signals.

[0072] The severity of the warning signal: Class I warning signal is greater than Class II warning signal, which is greater than Class III warning signal;

[0073] Based on the future rainfall intensity and future rainfall duration, and the mutual mapping relationship between F1, F2, F3 and F4 and the early warning module;

[0074] If the future rainfall intensity and future rainfall duration are greater than the preset rainfall intensity threshold -ω1 and rainfall duration threshold δ1, the warning module outputs a Class I warning signal for the F1, F2, F3 and F4 areas;

[0075] If the future rainfall intensity is between the preset rainfall intensity threshold ω2 and the rainfall intensity threshold ω1, the warning module outputs a Class I warning signal for the F1 region, a Class II warning signal for the F2 and F3 regions, and a Class III warning signal for the F4 region;

[0076] If the future rainfall intensity and the future rainfall duration are less than the preset rainfall intensity threshold ω2, the warning module outputs a Class II warning signal for the ω1 area and a Class III warning signal for the F2, F3 and ω4 areas; it should be noted that the rainfall intensity threshold ω1 and the rainfall intensity threshold ω2 are set based on the terrain type of the rainfall area, that is, the degree of water accumulation in the depression area and the non-depression area, and the rainfall intensity threshold ω1 is greater than the rainfall intensity threshold ω2;

[0077] It should be noted that when the rainfall is heavy, no matter in which area, the warning module needs to output a Class I warning signal. When the rainfall is between the highest and lowest rainfall thresholds, in different leaf-falling areas, when the rainfall is small, the fallen leaves may hinder the penetration and flow of water, resulting in increased water accumulation, while when the rainfall is heavy, the impact of the rain will wash away the fallen leaves, reducing the impact of the fallen leaves on the water accumulation. Therefore, a Class II warning signal can be output at this time, and a Class III warning signal can be output in other cases.

[0078] Specifically, ranking the severity of waterlogging risk levels in areas prone to waterlogging is conducive to different staff taking different maintenance measures according to the ranking of different risk levels, and is convenient for taking different emergency measures according to rainfall levels and waterlogging susceptibility levels;

[0079] The specific calculation method of the water storage capacity ΔV in the area is:

[0080] The data acquisition module is used to obtain different historical data, including historical rainfall intensity P X (mm / hour), rainfall duration t1 (hours) and catchment area A1 of rainfall depression area;

[0081] If the product of rainfall intensity and rainfall time minus the amount of water flowing out is greater than 0, that is, ΔV>0, it indicates that water will accumulate in the area; Where ΔV is the water storage capacity of the area to be rained, Q is the flow rate of the drainage network (cubic meters per second), that is, the flow rate passing through the drainage system per unit time; is the runoff coefficient;

[0082] Among them, the historical rainfall intensity P X (mm / hour), rainfall duration t1 (hours), and catchment area A1, P X *t1 gets rainfall, P X *A*t1 is the water catchment in the rainfall depression area, and the runoff coefficient is the ratio of rainfall converted into surface runoff. The actual amount of water collected in the rainfall depression area; this makes it easy to issue different warning signals based on the water-prone locations in each area;

[0083] It should be noted that the measures that need to be taken at different risk levels are as follows: For Class I warning signals, measures should be taken to control risks, reduce the level of waterlogging disasters, raise anti-flooding baffles or high entry and exit steps at entrances and exits, and remind relevant departments and local personnel to organize all forces to prepare for emergency rescue and urgently evacuate people affected by waterlogging disasters according to the situation;

[0084] Class II warning signals: Improve the water-blocking capacity of the assessed object, improve the drainage capacity of the urban system, and build intelligent monitoring systems to remind relevant departments and local personnel to pay close attention to changes in rainfall, report potential hazards in a timely manner, implement emergency measures such as materials and machinery, and make preparations for the evacuation of the masses;

[0085] Category III warning signal: the water inflow risk is generally controllable and no risk control measures are mandatory. Relevant departments and local personnel are reminded to pay close attention to rainfall conditions, strengthen inspections and monitoring of waterlogging risk points, and promptly report and handle waterlogging when it is discovered.

[0086] The general public: including urban and rural residents, who need to understand the warning level, impact range and recommended measures so as to take corresponding protective measures; Government and related departments: including emergency management departments, urban management departments, transportation departments, etc., who need to take emergency response measures such as traffic control and rescue preparation according to the warning information; Enterprises and organizations: including schools, hospitals, factories, etc., who need to adjust their work plans according to the warning information to ensure the safety of personnel; Media and information dissemination platforms: including TV stations, radio stations, Internet platforms, etc., who are responsible for disseminating the warning information to a wider audience;

[0087] Ranking the risk levels of areas where flooding may occur will help different types of personnel take different measures, reduce workload to a certain extent, and improve the level of early warning.

[0088] The regional flood prevention early warning system also includes a control unit, which is used to control the road condition repair in the area prone to waterlogging and the correction of the drainage network flow, specifically:

[0089] Based on the set of waterlogging-prone areas in historical data, the rainfall period of waterlogging-prone areas is obtained, and a set of rainfall periods for waterlogging is established: [T1, T2, ..., T n ], where T1 is the rainfall time of the first waterlogging in the historical data, T2 is the rainfall time of the next waterlogging in the historical data, and T n is the rainfall time of waterlogging in the n-th period of the historical data,

[0090] Among them, the correction of drainage network flow Q before each rainfall cycle is mainly as follows:

[0091] Get the initial rainfall time T in the future m , the initial rainfall time T m The difference calculation is performed with the next time of waterlogging that occurs in the adjacent rainfall cycle set. If the cycle time difference is greater than the preset time threshold, the control unit outputs a signal to correct the drainage network flow Q; (where the preset time threshold is set based on the degree of leaf fall in the waterlogged area. For example, after a rainfall, leaves will fall five days later, and the fallen leaves affect the water flow, so the time threshold can be set to five days). Otherwise, the drainage network flow Q is calculated normally; (Since these areas prone to waterlogging will have fallen leaves within this cycle time difference, the fallen leaves increase the roughness of the surface and reduce the water flow rate, resulting in a large error in the calculation of the storage force ΔV in these areas, so the drainage network flow Q needs to be corrected).

[0092] The corrected drainage network flow Q C Specifically, the difference between two adjacent periods in the set is calculated according to the formula: Where Q is the initial drainage network flow, T i is one of the cycles in the rainfall cycle set, is the correction factor;

[0093] It should be noted that during the rainfall cycle in areas prone to waterlogging, some of the drainage networks will have their own flow affected by the presence of fallen leaves or silt. However, when it rains in these areas prone to waterlogging, the fallen leaves or silt will be washed away by the water flow. Therefore, after a rainfall, the flow Q of the drainage network will return to normal. By establishing a set based on the rainfall cycles in these areas prone to waterlogging, it is possible to know the time between one rainfall cycle and the next rainfall cycle. For example, T1 is January 5 of a certain year, T2 is the following February, and T n for December;

[0094] It should also be noted that when the meteorological data obtains the initial rainfall time T in the future m When the rainfall in these areas is larger than the previous period, the time difference is greater than the set time threshold, which will cause fallen leaves in the area. The fallen leaves increase the roughness of the surface and reduce the water flow rate, resulting in a large error in the calculation of the storage capacity ΔV in these areas. Therefore, the drainage network flow Q needs to be corrected. Therefore, the future initial rainfall time T m The difference between the next time when waterlogging occurs in the set is calculated, and the correction coefficient is obtained based on the difference between two adjacent cycles in the set. Therefore, the drainage network flow Q is corrected according to the correction coefficient;

[0095] In this way, the water storage volume ΔV of the future rainfall area can be calculated more accurately, and different warning signals can be outputted conveniently according to the water storage volume ΔV of the future rainfall area;

[0096] Based on the above-mentioned modified drainage network flow Q, the following example is given: For example, a set of rainfall time for waterlogging in a waterlogged area is established within a year, where the set of rainfall time for waterlogging can be:

[0097] January 3, February 4, March 10, April 3, May 8, June 10,…, December 3

[0098] In the rainfall time set, for example, after the first rainfall on February 4, the fallen leaves or silt near the drainage network will be washed away by the scouring effect of the water flow. At this moment, the drainage network flow Q is normal. However, during the period from February 4 to March 10, the presence of fallen leaves will cause the drainage network flow Q to change, so the drainage network flow Q needs to be corrected.

[0099] If the current month is April, and the meteorological data shows that the next rainy day is April 13, the difference between this date and the corresponding next time in the collection, May 8, is 25 days. 25 days is greater than the preset time threshold (five days), indicating that there will be fallen leaves in the waterlogged area within these 10 days, and the fallen leaves will affect the subsequent water flow rate. Therefore, it is necessary to calculate the difference between this date and the most recent date, April 3, and then calculate the difference between the two adjacent periods in the collection (i.e., April 3 to May 8, the difference is 35 days, and the default month is thirty days). The correction coefficient can be obtained by calculating the ratio of the two differences. According to the correction coefficient and the normal drainage network flow Q, the product calculation can be performed to obtain the corrected drainage network flow Q C , set the normal drainage network flow Q to 10 cubic meters per second, Then 0.71 cubic meters per second is the corrected drainage network flow rate, which can be used to more accurately calculate the water storage capacity ΔV in the future rainfall area and facilitate regional waterlogging warning based on the water storage capacity ΔV.

[0100] That is, the repair signal output by the control module also includes: leveling the depression area, reducing the slope of the depression area, facilitating the discharge of water flow, and repairing the trees near the depression area to reduce the deposition of fallen leaves in the depression area or non-depression area, facilitating the drainage of the drainage network;

[0101] It is also possible to drain the accumulated water in low-lying and flood-prone areas in a timely manner by constructing agricultural water conservancy projects; increase the construction of drainage pipelines, gradually eliminate blank areas in the pipeline network, transform rainwater and sewage pipelines that are prone to waterlogging and mis-mixing, and repair damaged and dysfunctional drainage and flood prevention facilities; reasonably carry out river, lake, drainage ditch, roadside ditch and other improvement projects to improve flood discharge and drainage capabilities and ensure that the drainage capacity of the urban pipeline system matches.

[0102] The runoff coefficient The specific calculation method is as follows:

[0103]

[0104] Where C1 is the average water layer thickness obtained by evenly distributing the total amount of runoff generated in the basin or catchment area over the entire basin area within a certain period of time;

[0105] C2 is the amount of precipitation that falls on the basin or catchment area during the same period of time;

[0106] f e +[f0-f e ]e -βt is the ground infiltration rate;

[0107] f0 is the initial maximum infiltration rate (m / s), f e is the steady infiltration rate, β attenuation coefficient (s -1 ), t is the time.

[0108] Specifically, ground permeability directly affects the infiltration of rainfall and surface runoff. High permeability means that more rainfall can infiltrate into the ground, reducing surface runoff and thus reducing the risk of waterlogging. On the contrary, low permeability leads to increased surface runoff, increasing the possibility of waterlogging. By obtaining the initial maximum infiltration rate f0 and the stable infiltration rate f e , the initial maximum infiltration rate f0 and the stable infiltration rate f e Perform difference calculation to obtain the infiltration rate during rainfall, and finally calculate the ground infiltration rate f k , so it is convenient to calculate the water storage volume ΔV, so as to determine and calculate the risk of waterlogging;

[0109] The runoff coefficient is a value between 0 and 1, which reflects the proportion of rainfall converted into surface runoff. The runoff coefficient will be different for different surface cover types and conditions. For example, for areas prone to leaf fall, the runoff coefficient may be different because fallen leaves may affect water infiltration and evaporation.

[0110] For areas prone to leaf accumulation, fallen leaves may increase the roughness of the surface, reduce water flow velocity, and thus increase water storage capacity. This impact can be taken into account by adjusting the runoff coefficient. By comprehensively considering the impact of depressions and fallen leaves, the runoff coefficient can be adjusted to more accurately estimate the water storage capacity and water accumulation depth, and adaptively adjust the drainage network flow Q. For non-depression areas, the impact of fallen leaves also needs to be considered.

[0111] The regional flood prevention warning system also includes an emergency allocation unit, which makes different emergency measures based on the warning signals received in the F1, F2, F3 and F4 areas;

[0112] Under the Class I and Class II warning signals, drainage points near the F1, F2, F3 and F4 areas are obtained, and the drainage points include the river E1, the lake E2, and the flood drainage ditch E3;

[0113] The drainage capacity of each drainage point is calculated respectively. If the drainage point has the drainage capacity, the emergency allocation unit outputs a drainage signal to the corresponding drainage point.

[0114] Under the Class III warning signal, the emergency distribution unit outputs real-time monitoring of the blockage situation in the fallen leaf area;

[0115] It should be noted that when the data collection module collects the area where it will rain in the future, and the intensity of the future rainfall is relatively large, that is, when it rains under the Class I warning signal and the Class II warning signal, the emergency measures assigned by the emergency allocation unit include alleviating waterlogging through different drainage points, carrying out river, lake, flood drainage ditch, road ditch and other improvement projects, and improving the flood discharge and drainage capacity; at the same time, it is also necessary to reserve sufficient emergency materials in advance, including drainage equipment, sandbags, temporary defense materials, etc., to ensure that the emergency materials can be quickly put into use when needed, increase the construction of drainage pipe network, gradually eliminate the blank areas of pipe network, and in principle, the newly built drainage pipes should be able to meet the upper limit requirements of national construction standards;

[0116] When rainfall occurs under a Class III warning signal, the emergency measures assigned by the emergency allocation unit include but are not limited to detecting whether the drainage network is blocked and identifying whether there are obstacles affecting the water retention path.

[0117] The specific calculation method of the drainage capacity of each drainage point is as follows:

[0118] The maximum water storage capacity and current water storage capacity of the drainage point are obtained, and the amount of water falling into the drainage point is calculated according to the product of rainfall intensity and rainfall duration. Then, the maximum water storage capacity of the drainage point is subtracted from the sum of the current water storage capacity and the amount of water falling into the drainage point to obtain the drainage capacity of each drainage point.

[0119] like Figure 2 As shown, a regional flood prevention early warning method based on big data includes the following steps:

[0120] S1, collect meteorological data, hydrological data and geographic information through the data acquisition module;

[0121] S2, analyzing the risk of regional waterlogging based on the collected meteorological data, hydrological data and geographic information through the data analysis module;

[0122] S3. The warning module outputs warning information based on the analysis results of the data analysis module.

[0123] The proportional factor coefficient is used to correct the deviation of various parameters in the process of formula calculation, so as to make the calculation result more accurate; the setting of the threshold value is for the convenience of comparison. The size of the threshold value depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value;

[0124] The size of the coefficient is to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. The size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding operating coefficient for each set of sample data by technicians in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value;

[0125] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A regional flood prevention early warning system based on big data, characterized in that: include Data acquisition module: The data acquisition module is used to collect data, including meteorological data, hydrological data and geographical data; Data analysis module: based on the collected data, the data analysis module stores the data and establishes a database, and analyzes the risk of waterlogging in the future rainfall area according to the database and the area where rainfall is expected in the future; Early warning module: The early warning module outputs early warning information based on the analysis results of the data analysis module.

2. According to the big data-based regional flood prevention early warning system of claim 1, it is characterized in that: The data processing module is used to analyze whether the area has the risk of waterlogging: Establish a database, the database includes historical rainfall prone waterlogging areas, and establish a set of prone waterlogging areas M1 = [X1, X 11 , …, X 1n ]; Then, the future rainfall area is obtained according to the meteorological data in the data acquisition module, and a future rainfall area set [Y1, Y2, ..., Y n ], based on historical rainfall prone waterlogging area X i and future rain area Y i By contrast, if the historical rainfall prone waterlogging set M1 and the future rainy area have the same area, that is, X i =Y i ; Calculate the same future rainfall area Y i The water storage volume ΔV in the area prone to waterlogging in one hour. If the water storage volume ΔV is greater than the regional water storage volume threshold γ1, the early warning module outputs an early warning flood prevention signal.

3. The regional flood prevention early warning system based on big data according to claim 2 is characterized in that: The specific method for establishing the historical rainfall prone waterlogging area is as follows: S1, dividing the area into depression area and non-depression area based on the geographic information collected by the data collection module; S2, based on the hydrological information collected by the data collection module, the depression area and the non-depression area are divided into a depression prone to leaf litter accumulation area F1, a depression non-prone to leaf litter accumulation area F2, a non-depression prone to leaf litter accumulation area F3 and a non-depression non-prone to leaf litter accumulation area F4; wherein the prone to leaf litter accumulation area and the non-prone to leaf litter accumulation area are set based on whether there are trees nearby. For example, if there are trees nearby and it is a depression, then this area is the depression prone to leaf litter accumulation area F1; Among them, the degree of easy accumulation of water: the area of ​​fallen leaves in the depression that is easy to accumulate F1 is greater than the area of ​​fallen leaves that is not easy to accumulate F2 in the depression that is greater than the area of ​​fallen leaves that is easy to accumulate F3 in the non-depression that is greater than the area of ​​fallen leaves that is not easy to accumulate F4 in the non-depression; S3, based on the historical rainfall intensity, historical rainfall duration and the flow rate Q of the drainage system in the F1, F2, F3 and F4 areas per unit time, calculate the water storage capacity ΔV of these areas. If the water storage capacity ΔV is greater than the regional water storage capacity threshold γ1, the corresponding area is marked as a historically prone to waterlogging area X i ; A mapping relationship is established between the F1, F2, F3 and F4 areas and the early warning module, and different early warning signals are output through the early warning module. The early warning signals include Class I early warning signals, Class II early warning signals and Class III early warning signals.

4. The regional flood prevention early warning system based on big data according to claim 3 is characterized in that: The severity of the warning signal: Class I warning signal is greater than Class II warning signal, which is greater than Class III warning signal; Based on the future rainfall intensity and future rainfall duration, and the mutual mapping relationship between F1, F2, F3 and F4 and the early warning module; If the future rainfall intensity and future rainfall duration are greater than the preset rainfall intensity threshold -ω1 and rainfall duration threshold δ1, the warning module outputs a Class I warning signal for the F1, F2, F3 and F4 areas; If the future rainfall intensity is between the preset rainfall intensity threshold ω2 and the rainfall intensity threshold ω1, the warning module outputs a Class I warning signal for the F1 region, a Class II warning signal for the F2 and F3 regions, and a Class III warning signal for the F4 region; If the future rainfall intensity and future rainfall duration are less than the preset rainfall intensity threshold value ω2, the warning module will output a Class II warning signal for the F1 area and a Class III warning signal for the F2, F3 and F4 areas.

5. The regional flood prevention early warning system based on big data according to claim 4 is characterized in that: The specific calculation method of the water storage capacity ΔV in the area is: The data acquisition module is used to obtain different historical data, including historical rainfall intensity P X , rainfall duration t1 and catchment area A1 of rainfall depression area; If the product of rainfall intensity and rainfall time minus the amount of water flowing out is greater than 0, that is, ΔV>0, it indicates that water will accumulate in the area; Among them, ΔV is the water storage capacity in the area to be rained, and Q is the flow rate of the drainage network, that is, the flow rate passing through the drainage system per unit time; is the runoff coefficient.

6. The regional flood prevention early warning system based on big data according to claim 5 is characterized in that: The regional flood prevention early warning system also includes a control unit, which is used to control the road condition repair in the area prone to waterlogging and the correction of the drainage network flow, specifically: Based on the set of waterlogging-prone areas in historical data, the rainfall period of waterlogging-prone areas is obtained, and a set of rainfall periods for waterlogging is established: [T1, T2, …, T n ], where T1 is the rainfall time of the first waterlogging in the historical data, T2 is the rainfall time of the next waterlogging in the historical data, and T n is the rainfall time of waterlogging in the n-th period of the historical data, Among them, the correction of drainage network flow Q before each rainfall cycle is mainly as follows: Get the initial rainfall time T in the future m , the initial rainfall time T m The difference between the time when waterlogging occurs in the next adjacent period in the rainfall cycle set is calculated. If the cycle time difference is greater than the preset time threshold, the control unit outputs a signal to correct the drainage network flow Q. Otherwise, the drainage network flow Q is calculated normally. The corrected drainage network flow Q C Specifically, the difference between two adjacent periods in the set is calculated according to the formula: Where Q is the initial drainage network flow, T i is one of the time periods in the rainfall cycle set, is the correction factor.

7. The regional flood prevention early warning system based on big data according to claim 5 is characterized in that: The runoff coefficient The specific calculation method is as follows: Where C1 is the average water layer thickness obtained by evenly distributing the total amount of runoff generated in the basin or catchment area over the entire basin area within a certain period of time; C2 is the amount of precipitation that falls on the basin or catchment area during the same period of time; f e +[f0-f e ]e -βt is the ground infiltration rate; f0 is the initial maximum infiltration rate, f e is the steady infiltration rate, β is the decay coefficient, and t is the time.

8. The regional flood prevention early warning system based on big data according to claim 6 is characterized in that: The regional flood prevention warning system also includes an emergency allocation unit, which makes different emergency measures based on the warning signals received in the F1, F2, F3 and F4 areas; Under the Class I and Class II warning signals, drainage points near the F1, F2, F3 and F4 areas are obtained, and the drainage points include the river E1, the lake E2, and the flood drainage ditch E3; The drainage capacity of each drainage point is calculated respectively. If the drainage point has the drainage capacity, the emergency allocation unit outputs a drainage signal to the corresponding drainage point. Under a Class III warning signal, the emergency distribution unit outputs real-time monitoring of the blockage situation in the fallen leaf area.

9. The regional flood prevention early warning system based on big data according to claim 8 is characterized in that: The specific calculation method of the drainage capacity of each drainage point is as follows: The maximum water storage capacity and current water storage capacity of the drainage point are obtained, and the amount of water falling into the drainage point is calculated according to the product of rainfall intensity and rainfall duration. Then, the maximum water storage capacity of the drainage point is subtracted from the sum of the current water storage capacity and the amount of water falling into the drainage point to obtain the drainage capacity of each drainage point.

10. A regional flood prevention early warning method based on big data, characterized in that: The following steps are involved: S1, collect meteorological data, hydrological data and geographic information through the data acquisition module; S2, analyzing the risk of regional waterlogging based on the collected meteorological data, hydrological data and geographic information through the data analysis module; S3. The warning module outputs warning information based on the analysis results of the data analysis module.

Images