Waterlogging risk assessment method and device and storage medium

Abstract

The invention relates to the technical field of risk early warning, and discloses a waterlogging risk assessment method and device and a storage medium. The waterlogging risk assessment method comprises the following steps: determining a waterlogging risk grade division standard of a current waterlogging point, rainfall live parameters obtained based on meteorological grid live data and future rainfall forecast parameters obtained based on intelligent grid forecast data; and according to the waterlogging risk grade division standard and the rainfall live parameter and / or the future rainfall forecast parameter, determining a waterlogging live risk and / or a waterlogging pre-estimation risk of the current waterlogging point. According to the embodiment of the invention, through systematic analysis and research on historical ponding and rainfall data, quantitative parameters such as the rainfall threshold value of the ponding risk of the ponding point and the risk level of the ponding point are obtained, the ponding risk threshold value and the model are constructed, effective data support is provided for issuing and removing ponding risk prompts, and the risk assessment efficiency is improved. And a beneficial decision reference is provided for allocation of urban drainage disposal resources.

Description

Technical Field

[0001] The present invention relates to the technical field of risk early warning, and particularly relates to a method, device and storage medium for waterlogging risk assessment. Background Art

[0002] Urban waterlogging refers to the phenomenon that due to heavy precipitation or continuous precipitation exceeding the urban drainage capacity, waterlogging disasters occur within the city, which not only affects the daily life of residents, but also may cause serious impacts on the economy, transportation and environment of the city. There are many factors causing the risk of waterlogging, involving natural conditions (rainfall, rainfall intensity, topography, soil type, etc.), urban planning (ground hardening rate, green space, water area, etc.), infrastructure construction (infrastructure layout, pipeline design, construction, maintenance, etc.) and many other factors.

[0003] In the aspect of risk judgment of urban waterlogging, it is necessary to focus on solving the problems of establishing an urban waterlogging risk model, that is, determining the waterlogging risk level under different magnitudes of precipitation, and how to take differential countermeasures according to different waterlogging risks. In this regard, meteorological and water conservancy departments in various provinces and cities have carried out different attempts. For example, the Guangdong Climate Center mainly uses hydrodynamic models to carry out urban waterlogging risk assessment, and uses statistical models + critical threshold models to carry out waterlogging risk early warning; the Guangdong Meteorological Observatory conducts waterlogging simulation in key areas based on QPE and high-performance ensemble hydrodynamic models; Guangzhou realizes real-time data sharing through cooperation with departments such as emergency, transportation and water conservancy, and constructs multiple waterlogging meteorological risk assessment models for different objects; the meteorological and water conservancy departments in Dongguan jointly build an urban waterlogging monitoring system to achieve rapid and accurate early warning of waterlogging risks through multi-data fusion; the Meteorological Bureau of Maoming City has established a progressive information notification mechanism that gradually strengthens according to different rainfall thresholds.

[0004] However, the waterlogging risk assessment using hydrodynamic models has problems such as incomplete mastery of drainage network information, inability to master the actual pipeline conditions (blocked or unblocked), and large computational workload, and the accuracy and timeliness are difficult to meet the needs of real-time services. At the same time, for the waterlogging monitoring system completely based on precipitation thresholds, the establishment of thresholds has strong subjective factors, and the ability to estimate future waterlogging risks is weak. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, device and storage medium for waterlogging risk assessment to improve the accuracy of waterlogging risk assessment.

[0006] To achieve this purpose, the present invention adopts the following technical solutions: A method for waterlogging risk assessment includes: Determining the waterlogging risk level division standard of the current waterlogging point, the rainfall actual situation parameters obtained based on meteorological grid actual situation data, and the future rainfall forecast parameters obtained based on intelligent grid forecast data; Determine the actual waterlogging risk and / or the estimated waterlogging risk of the current waterlogging point according to the waterlogging risk level division standard, the actual rainfall parameters and / or the future rainfall forecast parameters.

[0007] Optionally, the determining the actual waterlogging risk and / or the estimated waterlogging risk of the current waterlogging point according to the waterlogging risk level division standard, the actual rainfall parameters and / or the future rainfall forecast parameters includes: Match the actual rainfall parameter q tm with the waterlogging risk rainfall parameter threshold T h in the waterlogging risk level division standard to determine the current waterlogging risk level and issue a corresponding-level actual waterlogging risk warning according to this current waterlogging risk level; Match the future rainfall forecast parameter with the waterlogging risk rainfall parameter threshold T h in the waterlogging risk level division standard to judge the continuation, upgrade or downgrade of the current waterlogging risk level, and issue an estimated waterlogging risk accordingly.

[0008] Optionally, the actual rainfall parameter includes the 30-minute sliding rainfall q t30min and / or the 60-minute sliding rainfall q t60min ; the future rainfall forecast parameter includes the 30-minute sliding estimated rainfall and / or the 60-minute sliding estimated rainfall.

[0009] Optionally, the waterlogging risk rainfall parameter threshold T h is determined according to the average rainfall and the extreme rainfall average of the 30-minute sliding rainfall q t30min and the 60-minute sliding rainfall q t60min at the current waterlogging point.

[0010] Optionally, it further includes: regularly updating the waterlogging risk rainfall parameter threshold T h according to the waterlogging and precipitation data.

[0011] Optionally, in the waterlogging risk level division standard, the relationship between the waterlogging risk level and the waterlogging risk rainfall parameter threshold T h includes: Level 5 risk, corresponding to 90% - 120% of the rainfall reaching the waterlogging risk rainfall parameter threshold T h ; Level 4 risk, corresponding to 120% - 150% of the rainfall reaching the waterlogging risk rainfall parameter threshold T h ; Level 3 risk, corresponding to 150% - 200% of the rainfall reaching the waterlogging risk rainfall parameter threshold T h ; Level 2 risk, with rainfall reaching the threshold T of rainfall parameter for waterlogging risk h 200%~300% of the corresponding; Level 1 risk, with rainfall > waterlogging risk rainfall parameter threshold T h 300% of the corresponding.

[0012] Optionally, it also includes: when the rainfall data reaches the risk elimination time condition, the waterlogging risk is eliminated.

[0013] Optionally, the risk elimination time condition includes: the rain stops at the current waterlogging point for 23 minutes.

[0014] A waterlogging risk assessment device, comprising a memory and a processor; The memory is used to store instructions; The processor is used to execute the instructions in the memory to implement any one of the above waterlogging risk assessment methods.

[0015] A computer-readable storage medium includes instructions, which, when executed on a computer, enable the computer to execute the above-mentioned waterlogging risk assessment method.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The embodiment of the present invention systematically analyzes and studies historical waterlogging and rainfall data, obtains quantitative parameters such as the rainfall threshold for waterlogging risk at waterlogging points and the risk level of waterlogging points, constructs waterlogging risk thresholds and models, provides effective data support for the issuance and removal of waterlogging risk warnings, and provides a useful decision-making reference for the allocation of urban drainage disposal resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of waterlogging risk periods under precipitation of different durations provided in an embodiment of the present invention.

[0019] Figure 2 A distribution map of a waterlogging point in a certain process and three rainfall stations within 2 km of its surrounding area is provided in an embodiment of the present invention.

[0020] Figure 3 1-minute sliding rainfall and rain shape analysis diagrams for three stations provided in an embodiment of the present invention.

[0021] Figure 4 These are the 5-, 10-, and 30-minute sliding rainfall and rainfall pattern analysis charts provided by the embodiments of the present invention for three stations.

[0022] Figure 5 These are the 1-hour sliding rainfall and rainfall pattern analysis charts provided by the embodiments of the present invention for three stations.

[0023] Figure 6 These are the 3-hour sliding rainfall and rainfall pattern analysis charts provided by the embodiments of the present invention for three stations.

[0024] Figure 7 This is the relationship chart between the 1-minute sliding rainfall and the accumulated water depth provided by the embodiments of the present invention.

[0025] Figure 8 These are the relationship charts between the 5- and 10-minute sliding rainfall and the accumulated water depth provided by the embodiments of the present invention.

[0026] Figure 9 These are the relationship charts between the 30- and 60-minute sliding rainfall and the accumulated water depth provided by the embodiments of the present invention.

[0027] Figure 10 This is the relationship chart between the 3-hour sliding rainfall and the accumulated water depth provided by the embodiments of the present invention.

[0028] Figure 11 This is the flowchart of the urban waterlogging risk assessment method provided by the embodiments of the present invention. Detailed implementation manners

[0029] To make the objectives, features, and advantages of the present invention more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] To overcome the defects of the prior art, the embodiments of the present invention establish a simple and practical model that can meet the real-time monitoring and prediction business applications of waterlogging risk through historical data analysis and based on meteorological grid real-time conditions and intelligent grid forecast data.

[0031] Assuming that the drainage capacity of a typical waterlogging point in a city is static and stable in the short term (drainage facilities such as pipes and the condition of the pipes remain unchanged), this application can establish a waterlogging risk model for the city based on waterlogging and precipitation data. In actual work, a risk model can be established for each waterlogging point, and the waterlogging risk threshold can be regularly updated based on waterlogging and precipitation data to match the drainage capacity around the waterlogging point to ensure the rationality of the waterlogging threshold and model. The following is a detailed description of this application in conjunction with the accompanying drawings.

[0032] (1) Waterlogging risk model According to the definition of waterlogging by the water affairs department, waterlogging is defined as waterlogging when the depth exceeds 0.15 meters. That is, when the total rainfall (volume) over a certain period of time exceeds the drainage capacity (volume) of the area, and the waterlogging exceeds 0.15 meters, waterlogging occurs in that area. Based on the three stages before, during, and after waterlogging, the generation, development, and disappearance of waterlogging risk are decomposed into three parameters to establish a waterlogging risk model. See the schematic diagram of waterlogging risk under different durations of precipitation for details. Figure 1 .

[0033] Before waterlogging occurs, it is defined as the risk accumulation period. When the rainfall begins, water accumulates in a certain area, but it does not reach the stage of waterlogging. In this stage, if the total amount of rainfall in a certain period of time is less than the drainage volume of a certain area during that period, there will be no risk of waterlogging. If the total amount of rainfall in a certain period of time exceeds the drainage capacity (amount) of the area, water will begin to accumulate, but it will not reach the level of waterlogging.

[0034] When waterlogging occurs, the waterlogging risk period is defined. During this stage, the depth of waterlogging exceeds 0.15 meters, and waterlogging occurs, which affects urban transportation, houses, public facilities, and urban ecosystems.

[0035] After the waterlogging subsides, it is defined as the risk-free period. During this stage, the waterlogging in the area subsides and the waterlogging risk is eliminated.

[0036] Regarding the classification of waterlogging risk, this application mainly considers the impact of waterlogging of different depths on traffic and other daily lives of citizens to classify the risks, as follows: The fifth level of risk is defined as the possibility of 0-15cm water depth, which will affect citizens' walking travel; Level 4 risk is defined as the possibility of 15-30cm of water accumulation, which may reach the height of the car exhaust port, affecting pedestrians and possibly affecting car driving; The third level risk is defined as the possibility of 30-50cm of water accumulation, which is as high as the air intake of the car engine and may cause the car to break down and cause traffic congestion; Level 2 risk is defined as the possibility of 50-100cm of water accumulation, which is higher than the height of the car engine air intake, causing the car to break down, submerge the car and trap people; The definition of level one risk is the possibility of water accumulation depth exceeding 1m, which will cause risks such as car submersion, people drowning, and damage to houses and public facilities.

[0037] Furthermore, the embodiment of the present application analyzes the water accumulation depth and waterlogging rainfall threshold data of individual waterlogging points (samples) to establish a relationship between the risk level and the waterlogging rainfall threshold.

[0038] To establish a waterlogging risk model, we need to obtain two key parameters: The first is the rainfall threshold for waterlogging risk. This parameter is used to describe the rainfall threshold at which waterlogging risk will occur. If the rainfall is less than the threshold, the area is in the risk accumulation period, with no waterlogging or with waterlogging but not waterlogging. If the rainfall is greater than the threshold after the risk accumulation period, waterlogging risk occurs. This application determines the rainfall threshold for waterlogging risk by analyzing the rainfall values ​​and waterlogging data of different duration precipitation parameters (such as 1 minute, 5 minutes, 10 minutes, 30 minutes, 1 hour and 3 hours sliding rainfall). When a region reaches the rainfall threshold for waterlogging risk, it means that the risk of waterlogging is relatively high.

[0039] The second is the risk elimination time. This parameter is used to describe when the risk of waterlogging will be eliminated after it occurs. This application obtains this parameter by analyzing the relationship between the sliding rainfall at the time when the rain stops (rain is light) and the risk elimination.

[0040] (2) Sample selection This application selects rainstorm process cases and valid samples based on typical waterlogging point cases during rainstorms in Shenzhen in the past three years. In the samples, waterlogging data selects two observation data, waterlogging depth and waterlogging time; precipitation selects data from rain gauges near the waterlogging point at the time of waterlogging. Rainfall data includes parameters such as cumulative rainfall, hourly rainfall, hourly rainfall, and sliding rainfall. Because the existence of cumulative rainfall describes particles that are too coarse and hourly rainfall has the problem of artificial truncation across hours, this application uses sliding rainfall q according to the rainfall-waterlogging model. tm The parameter describes the threshold of waterlogging risk. Sliding rainfall refers to the cumulative rainfall in the past period of time counted minute by minute. For example, the sliding 3-hour rainfall refers to the cumulative rainfall pushed back 3 hours from the current time, while the sliding 60-minute rainfall selects a certain time point and then pushes back 1 hour to count the cumulative rainfall in this continuous 1 hour. Sliding rainfall q tm The calculation formula is as follows: , Among them, t refers to the sliding rainfall at a certain moment, and m refers to the sliding rainfall scale.

[0041] When analyzing the sliding rainfall in this application, the sliding scales m are taken as 1 minute, 5 minutes, 10 minutes, 30 minutes, 1 hour, and 3 hours respectively, and the parameters of the optimal sliding scale are selected from them.

[0042] (3)Determination of the rainfall threshold Th for waterlogging risk A. Selection of rainfall at waterlogging points (analysis of the validity of rainfall data) For the establishment of the waterlogging risk model, the most perfect solution is to have one rainfall station for each waterlogging point. In practice, the number of rainfall stations is limited. Generally, the observation stations are distributed around the waterlogging points and cannot meet the requirement of having one rainfall station for each waterlogging point. To solve the problem of selecting the rainfall at waterlogging points, this application analyzes the validity of the rainfall data at waterlogging points (by selecting the data of rainfall stations near the waterlogging points and the waterlogging data to check whether it is reasonable) to solve the problem of how to select the rainfall data at waterlogging points.

[0043] Select the data of rainfall stations within 2 kilometers of the waterlogging points for analysis. After research, for the rainfall stations near the waterlogging points, the rainfall patterns of the same magnitude of precipitation are relatively stable, and the data has a good correlation of validity. Taking the waterlogging point at the intersection of Shangshijia and Tongxing Road in Guangming District as an example, as Figure 2 shown, there are three rainfall stations, namely Gongming, Hengkeng Reservoir, and Houdikeng Reservoir, near the waterlogging point. The 24-hour cumulative rainfall amounts of a certain process at these three stations are 69.6, 66.9, and 62.5 millimeters respectively. After analysis, the rainfall patterns of their 1-minute, 5-minute, 10-minute, 30-minute, 1-hour, and 3-hour sliding rainfall are basically the same, and the correlation is high, as Figures 3 to 6 shown. Therefore, we can select the rainfall data of any one of the three rainfall stations for analysis with the waterlogging point data.

[0044] Therefore, in actual work, the precipitation observation data with approximately the same magnitude and the closest distance near the waterlogging point can be selected for analysis with the waterlogging data (waterlogging depth) to establish a risk model.

[0045] B. Rainfall parameter q for waterlogging risk tm Select The rainfall amounts represented by the sliding rainfall q of different duration precipitation parameters tm (such as 1-minute, 5-minute, 10-minute, 30-minute, 1-hour, and 3-hour sliding rainfall) have different meanings. This application analyzes the correlation between different duration precipitation parameters and the waterlogging depth data to determine the optimal rainfall parameter.

[0046] The research conclusions are as follows: The correlation between the 1-minute sliding rainfall and the waterlogging depth value is poor, and the lag is large. That is, after the 1-minute sliding rainfall reaches the peak, the waterlogging reaches the peak after a period of time, with a large lag, as Figure 7 .

[0047] The correlation between the 5 - minute and 10 - minute moving rainfall and the accumulated water depth value is average, with a certain lag. That is, after the 5 - minute and 10 - minute moving rainfall reaches its peak, the accumulated water takes some time to reach its peak, showing a certain lag compared with the accumulated water data. For example, Figure 8 。

[0048] The correlation between the 30 - minute and 60 - minute moving rainfall and the accumulated water depth value is the highest, that is, the peak time of the moving rainfall is basically the same as the peak time of the accumulated water. For example, Figure 9 。

[0049] The correlation between the 3 - hour moving rainfall and the accumulated water depth value is poor. For example, Figure 10 。

[0050] Table 1 shows the correlation coefficients between the accumulated water depth and the 5 - minute, 10 - minute, 30 - minute, and 60 - minute moving rainfall. The calculation formula for the correlation coefficient r is as follows: , where x and y are two random variables, Cov(x,y) is the covariance between x and y, Var[x] is the variance of x, and Var[y] is the variance of y. The larger the absolute value of the correlation coefficient r (the closer it is to 1), the higher the degree of linear correlation between the variables; the smaller the absolute value of the correlation coefficient, the lower the degree of linear correlation between the variables.

[0051] Table 1 is a statistical table of the correlation coefficients between the accumulated water depth and rainfall parameters (q tm ) with different durations. It can be seen that the 30 - minute and 60 - minute moving rainfall are highly linearly correlated with the accumulated water data, and the correlation is the best (for the correlation coefficient |r|, |r| < 0.4 is low - degree linear correlation; 0.4 ≤ |r| < 0.7 is significant correlation; 0.7 ≤ |r| < 1 is high - degree linear correlation). Therefore, in this application, the 30 - minute moving rainfall (q t30min ) and the 60 - minute moving rainfall (q t60min ) are used as the rainfall parameters for the waterlogging model.

[0052]

[0053] C. Determination of the waterlogging risk rainfall parameter threshold Th For a certain waterlogging point, analyze the 30 - minute moving rainfall (q t30min ) and the 60 - minute moving rainfall (q t60min ) that reach the waterlogging standard (accumulated water depth exceeds 0.15 meters) to obtain the rainfall parameter threshold Th for this waterlogging point.

[0054] Table 2 shows the 30 - minute moving rainfall (q t30min ) and the 60 - minute moving rainfall (q t60minThe average rainfall and the average extreme rainfall of ( ) are considered. Considering that the rainfall and the accumulated waterlogging are not simply in one-to-one correspondence, these two indicators are synthesized to determine the threshold Th of the rainfall parameter for waterlogging risk: the 30-minute sliding rainfall (q t30min ) is 40 mm or the 60-minute sliding rainfall (q t60min ) is 43 mm. The 30-minute sliding rainfall (q t30min ) and the 60-minute sliding rainfall (q t60min ) are in an "either-or" relationship. As long as one of the rainfall parameters q tm meets the standard, it is considered that there is a waterlogging risk at this waterlogging point.

[0055] Due to the different drainage capacities of each waterlogging point, different waterlogging points have their own sliding rainfall thresholds Th, that is, a one-threshold system for each waterlogging point. The waterlogging risk threshold is updated regularly according to the waterlogging and precipitation data to match the drainage capacity around the waterlogging point and ensure the rationality of the waterlogging parameter threshold and the model.

[0056]

[0057] D. Determination of the waterlogging risk level According to the risk level classification of the waterlogging risk model (corresponding waterlogging depth), the waterlogging depth and waterlogging risk rainfall parameter threshold data of a waterlogging point case (sample) are analyzed to obtain the relationship between the percentage of rainfall reaching the waterlogging risk rainfall parameter threshold and the risk level (corresponding waterlogging depth). For example, the risk level of a waterlogging point is shown in Table 3. From the statistical data, it can be seen that the fifth-level risk of the waterlogging point corresponds to 90%~120% of the rainfall reaching the waterlogging risk rainfall parameter threshold; the fourth-level risk corresponds to 120%~150% of the rainfall reaching the waterlogging risk rainfall parameter threshold; the third-level risk corresponds to 150%~200% of the rainfall reaching the waterlogging risk rainfall parameter threshold; the second-level risk corresponds to 200%~300% of the rainfall reaching the waterlogging risk rainfall parameter threshold; the first-level risk corresponds to rainfall > 300% of the waterlogging risk rainfall parameter threshold. If the threshold of the 60-minute rainfall at the waterlogging point is 100 mm (if the rainfall is 100 mm in 60 minutes, there is a high probability that the waterlogging point will have waterlogging), then if the 60-minute rainfall is 90-120 mm, there may be a fifth-level waterlogging risk (waterlogging below 15 cm); if the 60-minute rainfall is 120-150 mm, there may be a fourth-level waterlogging risk (waterlogging with a depth of 15-30 cm); if the 60-minute rainfall is 150-200 mm, there may be a third-level waterlogging risk (waterlogging with a depth of 30-50 cm); if the 60-minute rainfall is 200-300 mm, there may be a second-level waterlogging risk (waterlogging with a depth of 50-100 cm); if the 60-minute rainfall is greater than 300 mm, there may be a first-level waterlogging risk (waterlogging with a depth greater than 1 m). The same applies to other areas. For waterlogging risk parameters, the corresponding relationships between different waterlogging points in different regions are inconsistent, and it is necessary to establish a waterlogging point risk and a rainfall threshold system for waterlogging rainfall.

[0058]

[0059] (4) Time Tc when waterlogging risk is eliminated This application analyzes the sample rain stop time and risk elimination time (the time when the sample water accumulation is less than 15cm) of the waterlogging point to obtain the waterlogging risk elimination time (Tc) parameter.

[0060] Table 4 shows the risk elimination time statistics of a certain waterlogging point (partial sample). When the risk elimination time difference (minutes) is positive, it means that the waterlogging risk has been eliminated before the rain stops, and the average risk elimination time is 13 minutes; when the risk elimination time difference is negative, it means that the waterlogging risk is eliminated after the rain stops, and the average time is 23 minutes. Therefore, for this waterlogging point, we choose 23 minutes after the rain stops as the risk elimination time.

[0061] This application estimates the estimated risk of the waterlogging point based on the rainfall data of future weather predicted by the smart grid (future rainfall forecast). When the rainfall data reaches the risk elimination time condition, the waterlogging risk is eliminated.

[0062] Since the drainage capacity of each waterlogging point is different, the waterlogging risk elimination time Tc of different waterlogging points is inconsistent, that is, there is a risk elimination time Tc system for each waterlogging point, and the value of the risk elimination time Tc is updated regularly according to waterlogging and precipitation data to match the drainage capacity around the waterlogging point and ensure the rationality of parameters and models.

[0063]

[0064] (5)Waterlogging risk level According to the determination method of waterlogging risk level, match the rainfall parameter (q tm ) of the waterlogging point with the threshold of the rainfall parameter for waterlogging risk (Th) to determine the waterlogging risk level.

[0065] (6)Input parameters and output results of waterlogging risk For the waterlogging risk model, the input parameters are the rainfall actual parameter (30-minute sliding rainfall q t30min and 60-minute sliding rainfall q t60min ) calculated from the meteorological grid actual data and the future rainfall forecast parameter (30- and 60-minute sliding predicted rainfall) obtained from the intelligent grid forecast data. Match the rainfall actual parameter and the future rainfall forecast parameter with the risk level of the waterlogging point to obtain the waterlogging risk level. The output results are the actual waterlogging risk and the predicted risk of the waterlogging point. The flow chart of the waterlogging risk is shown in Figure 11 .

[0066] Match the actual rainfall parameter (q tm ) with the threshold of the rainfall parameter for waterlogging risk (Th). If a certain waterlogging point reaches the risk threshold of a certain level, issue the actual waterlogging risk of a certain level according to the risk level of the waterlogging point. At the same time, match the future rainfall forecast parameter (30- and 60-minute sliding predicted rainfall) with the risk level of the waterlogging point to judge the continuous, upgraded or downgraded situation of the risk of the waterlogging point; when the rainfall at the waterlogging point reaches the risk elimination time condition (for example, the risk elimination time of a certain waterlogging site is 23 minutes, that is, there is no rainfall at this waterlogging point for 23 minutes), the waterlogging risk is eliminated.

[0067] If the actual rainfall does not reach a certain level threshold, match the future rainfall forecast parameter (30- and 60-minute sliding predicted rainfall) of the waterlogging point with the risk threshold of the waterlogging point to obtain the predicted waterlogging risk of the waterlogging point, and judge the risk release, continuous, upgraded or downgraded situation of the waterlogging point until the risk is eliminated; when the rainfall at the waterlogging point reaches the risk elimination time condition, the waterlogging risk is eliminated.

[0068] If neither the actual rainfall nor the future rainfall reaches the risk threshold of the waterlogging point, it indicates that there is no waterlogging risk at this waterlogging point.

[0069] In summary, based on different historical rainfall amounts and historical waterlogging data, the embodiments of the present invention extract the main precipitation parameters and their risk threshold indicators for waterlogging, establish a quantitative relationship between different-duration rainfall data and waterlogging, and construct a waterlogging risk threshold and model, providing technical support for waterlogging risk. Moreover, by organically integrating real-time waterlogging monitoring data and rainfall forecast data and formulating effective discrimination rules, the prediction of waterlogging risk for the next 1 hour is realized.

[0070] Based on the same concept, the embodiments of the present invention further provide a waterlogging risk assessment device, which includes a memory and a processor. At least one instruction is stored in the memory and is loaded and executed by the processor to implement the waterlogging risk assessment method provided by the embodiments of the present invention.

[0071] Based on the same concept, the embodiments of the present invention provide a computer-readable storage medium, in which at least one instruction is stored, and the instruction is loaded and executed by the processor to implement the waterlogging risk assessment method provided by the embodiments of the present invention.

[0072] Those of ordinary skill in the art can understand that all or part of the steps to implement the above embodiments can be completed by hardware, or can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc.

[0073] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing waterlogging risk, characterized in that: include: Determine the waterlogging risk classification standards for current waterlogging points, the actual rainfall parameters obtained based on the actual data of the meteorological grid, and the future rainfall forecast parameters obtained based on the intelligent grid forecast data; According to the waterlogging risk level classification standard and the actual rainfall parameters and / or the future rainfall forecast parameters, the actual waterlogging risk and / or the estimated waterlogging risk of the current waterlogging point are determined.

2. The waterlogging risk assessment method according to claim 1, characterized in that: Determining the actual risk of waterlogging and / or the estimated risk of waterlogging at the current waterlogging point according to the waterlogging risk level classification standard and the actual rainfall parameter and / or the future rainfall forecast parameter includes: The actual rainfall parameter q tm The waterlogging risk rainfall parameter threshold T in the waterlogging risk level classification standard h Matching to determine the current waterlogging risk level and issuing corresponding level of waterlogging risk warning according to the current waterlogging risk level; The future rainfall forecast parameter is compared with the waterlogging risk rainfall parameter threshold T in the waterlogging risk level classification standard. h To match the current waterlogging risk level, determine whether it will continue, escalate or degrade, and publish the estimated waterlogging risk accordingly.

3. The waterlogging risk assessment method according to claim 2, characterized in that: The rainfall real-time parameters include 30-minute sliding rainfall q t30min and / or 60-minute sliding rainfall t60min ; The future rainfall forecast parameters include 30-minute sliding estimated rainfall and / or 60-minute sliding estimated rainfall.

4. The waterlogging risk assessment method according to claim 3, characterized in that: The waterlogging risk rainfall parameter threshold T h , based on the 30-minute sliding rainfall q at the current waterlogging point t30min and 60-minute sliding rainfall q t60min The rainfall average and the extreme rainfall average are used to determine the rainfall.

5. The waterlogging risk assessment method according to claim 4, characterized in that: Also includes: The waterlogging risk rainfall parameter threshold T is regularly updated according to waterlogging and precipitation data h .

6. The waterlogging risk assessment method according to claim 1, characterized in that: In the waterlogging risk classification standard, the waterlogging risk level and the waterlogging risk rainfall parameter threshold T h The relationships include: Level 5 risk, with rainfall reaching the threshold value T of rainfall parameter for waterlogging risk h 90%~120% of Level 4 risk, with rainfall reaching the threshold value T of rainfall parameter for waterlogging risk h 120%~150% of Level 3 risk, with rainfall reaching the threshold value T of rainfall parameter for waterlogging risk h 150%~200% of Level 2 risk, with rainfall reaching the threshold T of rainfall parameter for waterlogging risk h 200%~300% of the corresponding; Level 1 risk, with rainfall > waterlogging risk rainfall parameter threshold T h 300% of the corresponding.

7. The waterlogging risk assessment method according to claim 2, characterized in that: Also includes: When the rainfall data reaches the risk elimination time conditions, the waterlogging risk will be eliminated.

8. The waterlogging risk assessment method according to claim 7, characterized in that: The risk elimination time conditions include: the rain stops at the current waterlogging point for 23 minutes.

9. A waterlogging risk assessment device, characterized in that: including memory and processor; The memory is used to store instructions; The processor is used to execute the instructions in the memory to implement the waterlogging risk assessment method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The method comprises instructions which, when executed on a computer, enable the computer to execute the waterlogging risk assessment method according to any one of claims 1 to 8.

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