A method for judging the degree of urban waterlogging based on dynamic adjustment of liquid position signal interval

Abstract

The application discloses a kind of urban waterlogging degree of judgment method based on dynamic adjustment liquid position confidence interval, belong to urban waterlogging monitoring technical field.To solve the problem of dynamic accurate determination of waterlogging degree.The present application includes collecting multi-source real-time liquid level data;Establish the trigger condition of waterlogging judgment, including sliding window trigger condition, event trigger condition, spatial partition trigger condition, and consider basic auxiliary trigger information;Build liquid position confidence interval basic model, calculate liquid position confidence interval;Calculate liquid position confidence interval, get adjusted dynamic liquid position confidence interval, execute according to the order of event trigger condition→sliding window trigger condition→spatial partition trigger condition;Carry out waterlogging degree judgment driven by dynamic confidence interval, obtain waterlogging degree judgment result;Output the judgment result of urban waterlogging degree of dynamic adjustment liquid position confidence interval after correction.The present application improves the accuracy, real-time and pertinence of urban waterlogging degree judgment.

Description

Technical Field

[0001] This invention belongs to the field of urban waterlogging monitoring technology, specifically relating to a method for judging the degree of urban waterlogging based on dynamically adjusted liquid position information intervals. Background Technology

[0002] With the acceleration of urbanization, the rate of hardening of urban underlying surfaces has increased, and extreme rainfall events have become more frequent, making urban flooding a prominent problem affecting residents' lives, traffic, and the safety of municipal facilities. Currently, the assessment of urban flooding levels mainly relies on three traditional methods, which have significant technical shortcomings:

[0003] 1. Static parameter modeling and judgment: The liquid level calculation model is constructed using fixed error parameters, which does not take into account the fluctuation characteristics of "short-term sudden rise and fall" of the water level in urban flooding. This results in a large deviation in the calculation of the confidence interval (the error usually exceeds 15%), and it cannot adapt to real-time liquid level changes.

[0004] 2. Single-trigger adjustment method: Adjustments are made based solely on single data fluctuations or events, without taking into account the spatial heterogeneity of urban flooding (such as the difference in drainage between low-lying areas and high-lying areas). This results in problems such as "lagging adjustments in low-lying areas and over-adjustments in high-lying areas," indicating insufficient targeting in the assessment.

[0005] 3. Lack of closed-loop verification mechanism: After adjustment, the rationality of the confidence interval is not specifically verified, or it is only corrected by simple data comparison, which makes it difficult to find abnormal values ​​of physical laws (such as the upper confidence limit being higher than the terrain elevation), resulting in low reliability of early warning information;

[0006] 4. High reliance on manual assistance: Some key parameters require manual intervention for adjustment, with a response delay of more than 30 minutes, which cannot meet the actual needs of "minute-level early warning" in the event of sudden urban flooding.

[0007] Therefore, there is an urgent need for a solution to the shortcomings of traditional methods, such as fixed parameters, one-sided adjustments, lack of verification, and delayed response, so as to provide precise and efficient technical support for urban flood control. Summary of the Invention

[0008] The problem this invention aims to solve is to achieve dynamic and accurate determination of the degree of urban flooding, and proposes a method for determining the degree of urban flooding based on dynamically adjusting the liquid position information interval.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A method for determining the degree of urban flooding based on dynamically adjusted liquid position information intervals includes the following steps:

[0011] S1. Collect multi-source real-time liquid level data, including real-time liquid level data and historical liquid level data from multiple stations within the urban flood monitoring area, and preprocess the data to obtain preprocessed multi-source real-time liquid level data;

[0012] S2. Establish triggering conditions for waterlogging judgment, including sliding window triggering conditions, event triggering conditions, and spatial partitioning triggering conditions, and consider basic auxiliary triggering information;

[0013] S3. Construct a basic model of the liquid position confidence interval and calculate the liquid position confidence interval;

[0014] S4. Based on the preprocessed multi-source real-time liquid level data obtained in step S1, calculate the liquid level information interval for different triggering conditions of the waterlogging judgment obtained in step S2, and obtain the adjusted dynamic liquid level information interval. Execute the following sequence: event triggering condition → sliding window triggering condition → spatial partitioning triggering condition.

[0015] S5. For the adjusted dynamic liquid position confidence interval obtained in step S4, perform dynamic confidence interval driven water accumulation degree judgment to obtain water accumulation degree judgment result;

[0016] S6. Adjust, verify, and correct any anomalies in the water accumulation level judgment results obtained in step S5, and output the corrected judgment results of the urban waterlogging level in the dynamically adjusted liquid position information interval.

[0017] Furthermore, the preprocessing method in step S1 includes removing jump values ​​caused by sensor failures using the 3σ principle, using linear interpolation to fill in short-term data gaps, and then uniformly converting the data to the 2000 National Geodetic Coordinate System to obtain preprocessed multi-source real-time liquid level data.

[0018] Furthermore, the specific implementation method of step S2 includes the following steps:

[0019] S2.1. Set the sliding window trigger conditions, including the real-time liquid level change over a preset time exceeds the liquid level fluctuation threshold, and the change rate of the standard deviation of the liquid level error within the preset time scrolling window exceeds the change rate threshold, wherein the standard deviation of the error is calculated based on the measured data of a preset number of groups within the window;

[0020] S2.2. Set the event triggering conditions to obtain emergency scenario information by connecting to the multi-department system, including the rainstorm warning level of the meteorological department, the blockage warning flow rate of the pipeline monitoring system being lower than the preset flow rate threshold, the terrain modification notice of the municipal department, and the slope change caused by construction around the monitoring point exceeding the preset slope threshold.

[0021] S2.3. Set the spatial zoning trigger condition to be based on terrain elevation data and drainage capacity, divide the monitoring area into low-lying water accumulation area, ordinary flat area, and high-lying drainage area. The slope of the low-lying water accumulation area is lower than the preset low slope threshold, the slope of the ordinary flat area is between the preset low slope threshold and the preset high slope threshold, and the slope of the high-lying drainage area is higher than the preset high slope threshold. Set the liquid level rise rate threshold for each zone.

[0022] S2.4. Set basic auxiliary trigger information including synchronously acquiring land use type data and historical waterlogging event records. Land use type data includes green space, roads, and building land. Historical waterlogging event records include water depth, duration, and disaster-bearing body information.

[0023] Furthermore, in step S3, error modeling is first performed, then the basic confidence interval is calculated, and finally the sliding window parameter update port, event parameter mapping call port, and spatial partition threshold adaptation port are set. The specific implementation method includes the following steps:

[0024] S3.1. Error modeling includes systematic errors and random errors;

[0025] System errors are compensated for using a formula established through on-site calibration at preset intervals.

[0026]

[0027] in, This represents the actual liquid level. To measure the liquid level, This is a fixed deviation value;

[0028] Random errors are tested based on historical data to examine distribution characteristics. A preset sample size threshold n0 is used. Large samples with n≥n0 adopt a normal distribution, while small samples with n<n0 adopt a t-distribution.

[0029] The probability density function of a large sample normal distribution is:

[0030]

[0031] Where σ is the standard deviation of the random error. This is random error;

[0032]

[0033] in, This represents the average actual liquid level.

[0034] The random error of a small sample follows a t-distribution with n-1 degrees of freedom, and its probability density function is:

[0035]

[0036] Where Г(·) is the gamma function and n is the sample size;

[0037] S3.2. Based on the random error distribution and combined with the confidence level, construct the basic confidence interval. The formulas are divided into two categories:

[0038] The baseline confidence interval for a large sample is:

[0039]

[0040] in, Based on the basic confidence interval, The average value of the actual liquid level after compensation; α represents the two-sided quantile of the standard normal distribution, and α is the significance level. This is the initial standard deviation;

[0041] The baseline confidence interval for a small sample is:

[0042]

[0043] in, These are the two-tailed quantiles of the t-distribution. To compensate for the sample standard deviation of the actual liquid level;

[0044] ;

[0045] S3.3. Set the sliding window parameter update port to support real-time replacement of standard deviation, the event parameter mapping call port to support preset rule triggering, and the spatial partition threshold adaptation port to support partition differential parameter input.

[0046] Furthermore, the method for updating the dynamic liquid level information interval that satisfies the sliding window triggering condition in step S4 includes first calculating the average value of the actual liquid level within the sliding window, using the following formula:

[0047]

[0048] in, This represents the total number of sliding windows. Let i be the actual liquid level of the i-th sliding window. This represents the average actual liquid level within the sliding window;

[0049] Then calculate the sample standard deviation of the random error within the sliding window. :

[0050] ;

[0051] Then, parameter replacement is performed, replacing the initial standard deviation of the base model with the newly calculated standard deviation, and the confidence interval is updated synchronously.

[0052] Furthermore, the method for updating the dynamic liquid position information interval that meets the event triggering conditions in step S4 includes adjusting the standard deviation and the system error compensation value based on the event-adjustment coefficient mapping table.

[0053] The adjustment formula for standard deviation is:

[0054]

[0055] in, The initial standard deviation, This is the adjustment factor corresponding to the event. The adjusted standard deviation for the event trigger;

[0056] The system error compensation value is adjusted to target pipeline blockage early warning, based on the basic compensation value b. base A fixed compensation amount △b is added to the base.

[0057] Furthermore, the method for updating the dynamic liquid position information interval that meets the spatial partitioning triggering conditions in step S4 includes adjustments for low-lying waterlogged areas, high-lying drainage areas, and ordinary flat areas.

[0058] The standard deviation of low-lying, waterlogged areas is adjusted by multiplying the current standard deviation by the zoning adjustment factor k. low The upper limit of the confidence interval is adjusted to be the current upper limit of the confidence interval plus a preset increment value ΔCI. low ;

[0059] The standard deviation of the highland drainage area is adjusted by multiplying the current standard deviation by the zoning adjustment factor k. high ;

[0060] The standard deviation and confidence interval for the ordinary flat region will continue to be used.

[0061] Furthermore, the dynamic confidence interval-driven method for determining the degree of water accumulation in step S5 is as follows:

[0062] Mild flooding: The confidence interval mean is within the preset mild flooding mean interval and the upper confidence limit is ≤ the preset mild flooding upper limit threshold × the partition weight;

[0063] Moderate flooding: The confidence interval mean is within the preset moderate flooding mean interval and the upper confidence limit is ≤ the preset moderate flooding upper limit threshold × the partition weight;

[0064] Severe flooding: Confidence interval mean > preset severe flooding mean threshold or confidence upper limit > preset severe flooding upper limit threshold × zone weight.

[0065] Furthermore, the specific implementation method of step S6 includes the following steps:

[0066] S6.1. Sliding window adjustment verification: After the sliding window is updated, the probability that the confidence interval covers the measured liquid level value is greater than or equal to the preset coverage probability threshold for ordinary areas / the preset coverage probability threshold for key areas. If the threshold is not met, the window duration is adjusted according to the preset rules and the standard deviation is recalculated.

[0067] S6.2. Event Mapping Adjustment Verification: Compare the judgment results before and after the event is triggered with the actual flooding situation. If the deviation rate is greater than the preset deviation rate threshold, adjust the standard deviation adjustment coefficient in the mapping table.

[0068] S6.3. Spatial zoning adjustment verification: Cross-zoning cross-validation, using the low-lying area data adjustment model to verify the performance of the ordinary area, ensuring the transferability of zoning adjustment rules. If the coverage probability of a certain zoning is less than the preset zoning coverage probability threshold, the liquid level rise rate threshold of that zoning is recalibrated.

[0069] S6.4. Anomaly Correction: For adjustment results that exceed physical laws, replace them with the mean of the confidence interval of a preset number of adjacent monitoring points in the same zone, or re-trigger the adjustment logic of step S4.

[0070] The beneficial effects of this invention are:

[0071] The present invention provides a method for judging the degree of urban flooding based on dynamically adjusted liquid position confidence intervals, which improves the accuracy of judgment. Through the "three-dimensional dynamic adjustment + special verification" mechanism, the confidence interval covers the probability of measured values ​​to meet the preset standard, and the deviation rate of flooding level judgment is controlled within the preset range, which is significantly better than the traditional static model.

[0072] The present invention provides a method for judging the degree of urban flooding based on dynamically adjusting the liquid position information interval, which enhances real-time responsiveness; the response time after triggering adjustment is ≤ preset response time, the judgment result is updated according to a preset update frequency, and the early warning push delay meets the preset delay requirement, thus meeting the emergency needs in the event of sudden flooding.

[0073] The present invention describes a method for judging the degree of urban flooding based on dynamically adjusting the liquid position information interval, which enhances the scenario-specificity; through spatial partitioning adaptation and event mapping rules, it realizes differentiated adjustments for "different regions and different events", solving the adaptation problem of spatial heterogeneity and sudden characteristics of urban flooding.

[0074] The present invention provides a method for judging the degree of urban flooding based on dynamically adjusting liquid position information intervals, ensuring traceability of operation and maintenance; fully recording adjustment logs and verification results, supporting data backtracking for more than a preset period of time, facilitating subsequent optimization and adjustment rules and parameters, and improving the long-term operational stability of the system. Attached Figure Description

[0075] Figure 1 This is a flowchart of a method for determining the degree of urban flooding based on dynamically adjusted liquid position information intervals, as described in this invention. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described specific embodiments are merely a part of the embodiments of the invention, and not all of them. The components of the specific embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations, and the invention may also have other embodiments.

[0077] Therefore, the following detailed description of specific embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected specific embodiments of the invention. All other specific embodiments obtained by those skilled in the art based on these specific embodiments without inventive effort are within the scope of protection of this invention.

[0078] To further understand the invention's content, features, and effects, the following specific embodiments are provided, along with accompanying drawings. Figure 1 Detailed explanation is as follows:

[0079] Example 1:

[0080] A method for determining the degree of urban flooding based on dynamically adjusted liquid position information intervals includes the following steps:

[0081] S1. Collect multi-source real-time liquid level data, including real-time liquid level data and historical liquid level data from multiple stations within the urban flood monitoring area, and preprocess the data to obtain preprocessed multi-source real-time liquid level data;

[0082] Furthermore, the preprocessing method in step S1 includes removing jump values ​​caused by sensor failures using the 3σ principle, using linear interpolation to fill in short-term data gaps, and then uniformly converting the data to the 2000 National Geodetic Coordinate System to obtain preprocessed multi-source real-time liquid level data.

[0083] Furthermore, the 3σ principle outlier removal algorithm identifies and removes liquid level fluctuations caused by sensor malfunctions, with the core function being to define a reasonable range based on the normal distribution characteristics of the data.

[0084] Steps and formulas: ① Calculate the sample mean of the liquid level dataset ( ):

[0085] Suppose the raw liquid level data at a monitoring point over a period of time is y1, y2, …, y m (where m is the total amount of data, and the number of data groups in the statistical period is determined according to a preset sampling frequency), then:

[0086]

[0087] wherein, y i is the liquid level measurement value at the i-th moment, is the average liquid level in this period.

[0088] ② Calculate the sample standard deviation (s y ):

[0089]

[0090] wherein, m−1 is the degree of freedom (sample standard deviation needs to eliminate the deviation of mean estimation), and s y reflects the dispersion degree of liquid level data.

[0091] ③ Delimit the outlier judgment threshold: According to the 3σ principle, if a certain liquid level value y i meets the following conditions, it is determined as an outlier and eliminated:

[0092]

[0093] Application example: The liquid level value at a monitoring point at 10:15 is 28cm, in this period =15cm, s y =4cm, so the threshold range is

[0094] 15±12=[3,27]cm, and 28cm is out of the range, so it is determined as an outlier.

[0095] Further, the function of the linear interpolation missing value completion algorithm is: complete short-term data missing (such as sensor signal interruption), and construct a linear relationship based on valid data points before and after the missing period. Steps and formulas: Let the moment corresponding to the missing value be t0, the previous valid data point is (t1,y1) (t1<t0), and the next valid data point is (t2,y2) (t2>t0), then the liquid level value y0 at the missing moment is calculated by linear interpolation:

[0096]

[0097] wherein, t1, t0, t2 are moments (unified conversion to a preset time unit for calculation), y1 and y2 are valid liquid level values at corresponding moments, and y0 is the completed missing liquid level value.

[0098] S2. Establish triggering conditions for waterlogging judgment, including sliding window triggering conditions, event triggering conditions, and spatial partitioning triggering conditions, and consider basic auxiliary triggering information;

[0099] Furthermore, the specific implementation method of step S2 includes the following steps:

[0100] S2.1. Set the sliding window trigger conditions, including the real-time liquid level change over a preset time exceeds the liquid level fluctuation threshold, and the change rate of the standard deviation of the liquid level error within the preset time scrolling window exceeds the change rate threshold, wherein the standard deviation of the error is calculated based on the measured data of a preset number of groups within the window;

[0101] S2.2. Set the event triggering conditions to obtain emergency scenario information by connecting to the multi-department system, including the rainstorm warning level of the meteorological department, the blockage warning flow rate of the pipeline monitoring system being lower than the preset flow rate threshold, the terrain modification notice of the municipal department, and the slope change caused by construction around the monitoring point exceeding the preset slope threshold.

[0102] S2.3. Set the spatial zoning trigger condition to be based on terrain elevation data and drainage capacity, divide the monitoring area into low-lying water accumulation area, ordinary flat area, and high-lying drainage area. The slope of the low-lying water accumulation area is lower than the preset low slope threshold, the slope of the ordinary flat area is between the preset low slope threshold and the preset high slope threshold, and the slope of the high-lying drainage area is higher than the preset high slope threshold. Set the liquid level rise rate threshold for each zone.

[0103] S2.4. Set basic auxiliary trigger information including synchronously acquiring land use type data and historical waterlogging event records. Land use type data includes green space, roads, and building land. Historical waterlogging event records include water depth, duration, and disaster-bearing body information.

[0104] S3. Construct a basic model of the liquid position confidence interval and calculate the liquid position confidence interval;

[0105] Furthermore, in step S3, error modeling is first performed, then the basic confidence interval is calculated, and finally the sliding window parameter update port, event parameter mapping call port, and spatial partition threshold adaptation port are set. The specific implementation method includes the following steps:

[0106] S3.1. Error modeling includes systematic errors and random errors;

[0107] System errors are compensated for using a formula established through on-site calibration at preset intervals.

[0108]

[0109] in, This represents the actual liquid level. To measure the liquid level, This is a fixed deviation value;

[0110] Random errors are tested based on historical data to examine distribution characteristics. A preset sample size threshold n0 is used. Large samples with n≥n0 adopt a normal distribution, while small samples with n<n0 adopt a t-distribution.

[0111] The probability density function of a large sample normal distribution is:

[0112]

[0113] Where σ is the standard deviation of the random error. This is random error;

[0114]

[0115] in, This represents the average actual liquid level.

[0116] The random error of a small sample follows a t-distribution with n-1 degrees of freedom, and its probability density function is:

[0117]

[0118] Where Г(·) is the gamma function and n is the sample size;

[0119] S3.2. Based on the random error distribution and combined with the confidence level, construct the basic confidence interval. The formulas are divided into two categories:

[0120] The baseline confidence interval for a large sample is:

[0121]

[0122] in, Based on the basic confidence interval, The average value of the actual liquid level after compensation; α represents the two-sided quantile of the standard normal distribution, and α is the significance level. This is the initial standard deviation;

[0123] The baseline confidence interval for a small sample is:

[0124]

[0125] in, These are the two-tailed quantiles of the t-distribution. To compensate for the sample standard deviation of the actual liquid level;

[0126] ;

[0127] S3.3. Set the sliding window parameter update port to support real-time replacement of standard deviation, the event parameter mapping call port to support preset rule triggering, and the spatial partition threshold adaptation port to support partition differential parameter input.

[0128] S4. Based on the preprocessed multi-source real-time liquid level data obtained in step S1, calculate the liquid level information interval for different triggering conditions of the waterlogging judgment obtained in step S2, and obtain the adjusted dynamic liquid level information interval. Execute the following sequence: event triggering condition → sliding window triggering condition → spatial partitioning triggering condition.

[0129] Furthermore, the method for updating the dynamic liquid level information interval that satisfies the sliding window triggering condition in step S4 includes first calculating the average value of the actual liquid level within the sliding window, using the following formula:

[0130]

[0131] in, This represents the total number of sliding windows. Let i be the actual liquid level of the i-th sliding window. This represents the average actual liquid level within the sliding window;

[0132] Then calculate the sample standard deviation of the random error within the sliding window. :

[0133] ;

[0134] Then, parameter replacement is performed, replacing the initial standard deviation of the base model with the newly calculated standard deviation, and the confidence interval is updated synchronously. For example, using σ... window Replace the current standard deviation of the base model (e.g., event-adjusted σ) event Update the confidence interval:

[0135] ;

[0136] Furthermore, the triggering condition is that any condition of "sliding window trigger information" is met;

[0137] Furthermore, the method for updating the dynamic liquid position information interval that meets the event triggering conditions in step S4 includes adjusting the standard deviation and the system error compensation value based on the event-adjustment coefficient mapping table.

[0138] The adjustment formula for standard deviation is:

[0139]

[0140] in, The initial standard deviation, This is the adjustment factor corresponding to the event. The adjusted standard deviation for the event trigger;

[0141] The system error compensation value is adjusted to target pipeline blockage early warning, based on the basic compensation value b. base By adding a fixed compensation amount Δb to the base, we get:

[0142]

[0143] Where △b is the additional compensation amount corresponding to a specific event, b event This is the system error compensation value after the event is triggered.

[0144] Furthermore, the event-adjustment coefficient mapping table is shown in Table 1:

[0145] Table 1

[0146]

[0147] Furthermore, the method for updating the dynamic liquid position information interval that meets the spatial partitioning triggering conditions in step S4 includes adjustments for low-lying waterlogged areas, high-lying drainage areas, and ordinary flat areas.

[0148] The standard deviation of low-lying, waterlogged areas is adjusted by multiplying the current standard deviation by the zoning adjustment factor k. low The upper limit of the confidence interval is adjusted to be the current upper limit of the confidence interval plus a preset increment value ΔCI. low ;

[0149] The standard deviation of the highland drainage area is adjusted by multiplying the current standard deviation by the zoning adjustment factor k. high ;

[0150] The standard deviation and confidence interval for the ordinary flat region will continue to be used.

[0151] Furthermore, the formula is as follows:

[0152] (1) Low-lying waterlogged areas (slope is lower than the preset low slope threshold, and the liquid level rise rate exceeds the corresponding zone threshold)

[0153] ① Standard deviation adjustment: Multiply the current standard deviation by the zoning adjustment factor k. low :

[0154]

[0155] ② Confidence interval upper limit adjustment: Add a preset increment value ΔCI to the current confidence interval upper limit. low :

[0156]

[0157] (2) Standard deviation adjustment for high-slope drainage areas (slope exceeding the preset high slope threshold, and liquid level rise rate exceeding the corresponding zone threshold): Multiply the current standard deviation by the zone adjustment coefficient k. high :

[0158]

[0159] (3) For the ordinary flat area (slope between the preset low slope threshold and the preset high slope threshold), no additional adjustments are made, and the current standard deviation and confidence interval are used:

[0160]

[0161] ;

[0162] S5. For the adjusted dynamic liquid position confidence interval obtained in step S4, perform dynamic confidence interval driven water accumulation degree judgment to obtain water accumulation degree judgment result;

[0163] Further, dynamic parameter input: obtain the adjusted confidence interval mean, confidence upper limit, confidence level, and monitoring point zoning identifier;

[0164] Furthermore, the zoning weights are set: values ​​are assigned based on regional risk sensitivity, with corresponding zoning weights assigned to low-lying areas, ordinary areas, and high-risk areas. ;

[0165] Furthermore, the dynamic confidence interval-driven method for determining the degree of water accumulation in step S5 is as follows:

[0166] Mild flooding: The confidence interval mean is within the preset mild flooding mean interval and the upper confidence limit is ≤ the preset mild flooding upper limit threshold × the partition weight;

[0167] Moderate flooding: The confidence interval mean is within the preset moderate flooding mean interval and the upper confidence limit is ≤ the preset moderate flooding upper limit threshold × the partition weight;

[0168] Severe flooding: Confidence interval mean > preset severe flooding mean threshold or confidence upper limit > preset severe flooding upper limit threshold × zone weight.

[0169] S6. Adjust, verify, and correct any anomalies in the water accumulation level judgment results obtained in step S5, and output the corrected judgment results of the urban waterlogging level in the dynamically adjusted liquid position information interval.

[0170] Furthermore, the specific implementation method of step S6 includes the following steps:

[0171] S6.1. Sliding window adjustment verification: After the sliding window is updated, the probability that the confidence interval covers the measured liquid level value is greater than or equal to the preset coverage probability threshold for ordinary areas / the preset coverage probability threshold for key areas. If the threshold is not met, the window duration is adjusted according to the preset rules and the standard deviation is recalculated.

[0172] S6.2. Event Mapping Adjustment Verification: Compare the judgment results before and after the event is triggered with the actual flooding situation. If the deviation rate is greater than the preset deviation rate threshold, adjust the standard deviation adjustment coefficient in the mapping table.

[0173] S6.3. Spatial zoning adjustment verification: Cross-zoning cross-validation, using the low-lying area data adjustment model to verify the performance of the ordinary area, ensuring the transferability of zoning adjustment rules. If the coverage probability of a certain zoning is less than the preset zoning coverage probability threshold, the liquid level rise rate threshold of that zoning is recalibrated.

[0174] S6.4. Anomaly Correction: For adjustment results that exceed physical laws, replace them with the mean of the confidence interval of a preset number of adjacent monitoring points in the same zone, or re-trigger the adjustment logic of step S4.

[0175] Furthermore, dynamic visualization: generating "historical process line of event triggering", "corrected monitoring data and early warning diagnosis information", and "visualized water accumulation map and risk perimeter prompts";

[0176] Tiered early warning push: Push appropriate early warning information to different zones, with the push frequency synchronized with the S4 adjustment frequency;

[0177] Standardized Interface: Provides a data interface in JSON format, containing key parameters of the adjustment method and the final judgment result, for use by the urban flood control dispatch platform. The interface update frequency is consistent with the liquid level acquisition frequency.

[0178] Adjustment log storage: Automatically records the triggering conditions, adjustment methods, parameters before and after the adjustment, and verification results for each adjustment, supporting traceability and review.

[0179] Furthermore, in this embodiment, the system is deployed on a municipal flood control cloud platform, with the following hardware configuration: CPU: Intel Xeon Gold 6420 (2 cores), RAM: 128GB, Hard Drive: 4TB SSD; Software environment: Operating system: Linux CentOS 9.0, Model calculation engine: Python (relying on NumPy and Pandas libraries), Visualization: ECharts, Early Warning Push via SMS gateway + APP. The seven modules are integrated through a microservice architecture, and data transfer between modules uses a Kafka message queue to ensure real-time performance and stability.

[0180] The following is a practical application example of this embodiment:

[0181] Real-time data: 30 liquid level sensors (numbered L1-L30) were deployed in the target area, with a sampling frequency of once every 5 minutes, to collect real-time liquid level data from 9:00 to 13:00 on July 20, 2024. Among them, the liquid level at station L15 (low-lying area) rose by 4cm from 9:30 to 9:35. The latitude and longitude, 85 elevation benchmark, and sensor signal strength of each station were recorded simultaneously (all ≥-70dBm, operating normally).

[0182] Historical data: Liquid level data for three hydrological years from 2021 to 2023 were obtained from the Municipal Meteorological Bureau and municipal departments, including scenarios with different rainfall intensities and urban flooding levels. The average initial standard deviation of 1.2 cm for low-lying areas under the rainstorm scenario was extracted.

[0183] Data preprocessing: The outlier at station L8 (10:15, where the liquid level rose by 8cm, exceeding the reasonable range) was removed using the “3σ principle”. The data from stations L7 and L9 were then completed by linear interpolation and uniformly converted to the National 2000 coordinate system to form a standardized dataset.

[0184] Dynamically adjust the acquisition of trigger information;

[0185] Sliding window trigger information: A liquid level change of 4cm (>3cm) was detected at station L15 from 9:30 to 9:35, triggering the sliding window adjustment;

[0186] Event trigger information: At 10:00, a red rainstorm warning was received from the Municipal Meteorological Bureau (the expected 24-hour rainfall is >100mm), and at the same time, the flow velocity at 3 pipeline monitoring points was <0.2m / s (pipeline blockage warning).

[0187] Spatial zoning trigger information: Based on DEM data from UAV aerial surveys in January 2024 (accuracy 1:2000), the target area is divided into low-lying waterlogged areas (including 10 stations such as L15 and L16), ordinary flat areas (including 12 stations such as L5 and L6), and high-lying drainage areas (including 8 stations such as L25 and L26). The threshold for the liquid level rise rate in low-lying areas is set to 2cm / 10 minutes.

[0188] Basic auxiliary information: Extracted 2024 land use planning data, with roads accounting for 32%, building land 38%, green space 25%, and water bodies 5% in the target area. Simultaneously, records of rainstorm and waterlogging events in the same period of 2023 were obtained (maximum water depth in low-lying areas was 42cm).

[0189] Construction of basic model for liquid position information interval;

[0190] Error modeling: Through on-site calibration on July 1, 2024, a systematic error compensation formula was established (actual liquid level = measured liquid level - 0.3cm); based on historical data verification, the random error conforms to a normal distribution (n=120>30, large sample).

[0191] Basic confidence interval calculation: Set the confidence level at 90% for ordinary areas and 95% for key areas (including 3 schools and 2 hospitals). Initial standard deviation: 1.0 cm for ordinary areas, 1.2 cm for low-lying areas, and 0.8 cm for high-lying areas. The initial formula is: Confidence interval = mean liquid level ± 1.96 × initial standard deviation (Zα / 2 = 1.96, corresponding to 95% confidence level).

[0192] Adjust the interface reservation: enable the sliding window parameter update port, event parameter mapping call port, and spatial partition threshold adaptation port to ensure seamless integration with module 4.

[0193] Dynamic adjustment;

[0194] Event Trigger Adjustment: At 10:00, a red rainstorm warning was received. Adjustments were made according to the mapping table rules: initial standard deviation × 1.8 (1.2cm × 1.8 = 2.16cm in low-lying areas), increasing the confidence level for the entire area to 95%; simultaneously, a pipeline blockage warning was received, and the system error compensation coefficient was increased by 0.5cm (adjusted compensation formula: actual liquid level = measured liquid level - 0.3cm + 0.5cm = measured liquid level + 0.2cm).

[0195] Sliding window adjustment: The sliding window condition is triggered at station L15, and a 30-minute rolling window (containing 6 sets of data) from 9:35 to 10:05 is constructed. The standard deviation of the liquid level error within the window is calculated as σ = 1.6 cm. The event-adjusted standard deviation (2.16 cm → 1.6 cm) is replaced, and the confidence interval is updated.

[0196] Spatial zoning adaptation: Station L15 is located in a low-lying area, with a liquid level rise rate of 4cm / 10 minutes (>2cm / 10 minutes), triggering zoning adjustment: standard deviation 1.6cm×1.1=1.76cm, confidence interval upper limit +1.0cm;

[0197] Collaborative logic: Executed in the order of "event triggering → sliding window → spatial partitioning", the final adjusted standard deviation of L15 station is 1.76cm, with a confidence level of 95% and a system error compensation coefficient of +0.2cm.

[0198] Determination of water accumulation level;

[0199] Dynamic parameter input: After adjustment, the mean confidence interval of station L15 is 32cm, the upper confidence limit is 38cm, and the zone is identified as a low-lying area (weight 1.2).

[0200] Judgment process: 31.2cm≤45×1.2=54cm, and the average value of 32cm is within the range of 15-35cm, so it is judged as moderate waterlogging.

[0201] Multiple methods were used to adjust, verify, and correct the effects.

[0202] Validation of sliding window adjustment: After updating the sliding window at station L15, the probability of the confidence interval covering the measured value was 93% (≥95% key area standard, not met). When the window was reduced to 20 minutes (4 sets of data) and recalculated, the standard deviation was 1.8cm, and the coverage probability increased to 96%.

[0203] Event mapping adjustment verification: Comparing the judgment results of 10 low-lying area stations under the red rainstorm warning with the manual patrol data, the accuracy rate of severe waterlogging judgment is 94% (≥92%, meeting the standard).

[0204] Spatial zoning adjustment verification: The model adjusted using low-lying area data was used to verify the performance of L6 stations in ordinary areas, with a coverage probability of 91% (≥88%, meeting the standard).

[0205] Anomaly correction: No adjustment results that exceed the laws of physics, no additional correction is required.

[0206] Dynamic determination result output and application;

[0207] Dynamic visualization: Generates an "Event Trigger Change Timeline" (weather rainfall changes from light rain to moderate rain to heavy rain), "Corrected Monitoring Data and Early Warning Diagnostic Information" (Early warning information: Flooding warning has been issued under the Hongxing East Road Expressway Bridge. The current water level is 31.2cm, the threshold is 32.5cm, and the surface water is 45mm. This has exceeded the threshold by 10% and will affect the area under the Hongxing East Road Expressway Bridge. Please dispatch personnel to handle the situation promptly), and "Visualized Flooding Map and Risk Surrounding Area Warnings" (The map uses colors to distinguish between flooding, with red indicating flooding, and provides risk area warnings for key users in the surrounding area).

[0208] Tiered early warning push: Send early warning information to businesses and health service centers in low-lying areas: "Roads are flooded, please detour", push frequency 1 time / 5 minutes;

[0209] Standardized Interface: The API interface is now online, returning JSON format data, including the standard deviation of 1.8cm within the window, the event type "Red Rainstorm Warning + Urban Flooding", the zone identifier "Low-lying Area", and the judgment result "Severe Urban Flooding", for navigation apps to use;

[0210] Adjust log storage: Record the triggering conditions, adjustment method (event mapping + sliding window + spatial partitioning), parameters before and after adjustment (standard deviation 1.2cm → 1.8cm) and verification results (coverage probability 96%). The log retention period is set to 3 years.

[0211] In this embodiment, the real-time judgment results on July 29, 2025, were compared with the actual waterlogging situation: the hit rate for mild waterlogging areas was 93%, the hit rate for moderate waterlogging areas was 94%, and the hit rate for severe waterlogging areas was 97%. The time for the early warning information to be pushed to relevant departments was less than 3 minutes, which provided 1.5 hours of preparation time for traffic management and emergency rescue. No major traffic congestion or safety accidents caused by water accumulation occurred.

[0212] Statistics show that the confidence interval determined by the system covers the measured value on average 92.5%, the deviation rate of water accumulation level determination is only 6.8%, and the response delay is ≤5 seconds, which fully meets the actual needs of urban waterlogging prevention and control, and verifies the effectiveness and practicality of this method and system.

[0213] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0214] Although this application has been described above with reference to specific embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of this application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in this application can be combined with each other in any way. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, this application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for determining the degree of urban flooding based on dynamically adjusted liquid position information intervals, characterized in that, Includes the following steps: S1. Collect multi-source real-time liquid level data, including real-time liquid level data and historical liquid level data from multiple stations within the urban flood monitoring area, and preprocess the data to obtain preprocessed multi-source real-time liquid level data; S2. Establish triggering conditions for waterlogging judgment, including sliding window triggering conditions, event triggering conditions, and spatial partitioning triggering conditions, and consider basic auxiliary triggering information; S3. Construct a basic model of the liquid position confidence interval and calculate the liquid position confidence interval; S4. Based on the preprocessed multi-source real-time liquid level data obtained in step S1, calculate the liquid level information interval for different triggering conditions of the waterlogging judgment obtained in step S2, and obtain the adjusted dynamic liquid level information interval. Execute the following sequence: event triggering condition → sliding window triggering condition → spatial partitioning triggering condition. S5. For the adjusted dynamic liquid position confidence interval obtained in step S4, perform dynamic confidence interval driven water accumulation degree judgment to obtain water accumulation degree judgment result; S6. Adjust, verify, and correct any anomalies in the water accumulation level judgment results obtained in step S5, and output the corrected judgment results of the urban waterlogging level in the dynamically adjusted liquid position information interval; The specific implementation method of step S6 includes the following steps: S6.

1. Sliding window adjustment verification: After the sliding window is updated, the probability that the confidence interval covers the measured liquid level value is greater than or equal to the preset coverage probability threshold for ordinary areas / the preset coverage probability threshold for key areas. If the threshold is not met, the window duration is adjusted according to the preset rules and the standard deviation is recalculated. S6.

2. Event Mapping Adjustment Verification: Compare the judgment results before and after the event is triggered with the actual flooding situation. If the deviation rate is greater than the preset deviation rate threshold, adjust the standard deviation adjustment coefficient in the mapping table. S6.

3. Spatial zoning adjustment verification: Cross-zoning cross-validation, using the low-lying area data adjustment model to verify the performance of the ordinary area, ensuring the transferability of zoning adjustment rules. If the coverage probability of a certain zoning is less than the preset zoning coverage probability threshold, the liquid level rise rate threshold of that zoning is recalibrated. S6.

4. Anomaly Correction: For adjustment results that exceed physical laws, replace them with the mean of the confidence interval of a preset number of adjacent monitoring points in the same zone, or re-trigger the adjustment logic of step S4.

2. The method for determining the degree of urban flooding based on dynamically adjusted liquid position information intervals according to claim 1, characterized in that, The preprocessing method in step S1 includes removing jump values ​​caused by sensor failures using the 3σ principle, using linear interpolation to fill in short-term data gaps, and then uniformly converting the data to the 2000 National Geodetic Coordinate System to obtain preprocessed multi-source real-time liquid level data.

3. The method for determining the degree of urban flooding based on dynamically adjusted liquid position information intervals according to claim 2, characterized in that, The specific implementation method of step S2 includes the following steps: S2.

1. Set the sliding window trigger conditions, including the real-time liquid level change over a preset time exceeds the liquid level fluctuation threshold, and the change rate of the standard deviation of the liquid level error within the preset time scrolling window exceeds the change rate threshold, wherein the standard deviation of the error is calculated based on the measured data of a preset number of groups within the window; S2.

2. Set the event triggering conditions to obtain emergency scenario information by connecting to the multi-department system, including the rainstorm warning level of the meteorological department, the blockage warning flow rate of the pipeline monitoring system being lower than the preset flow rate threshold, the terrain modification notice of the municipal department, and the slope change caused by construction around the monitoring point exceeding the preset slope threshold. S2.

3. Set the spatial zoning trigger condition to be based on terrain elevation data and drainage capacity, divide the monitoring area into low-lying water accumulation area, ordinary flat area, and high-lying drainage area. The slope of the low-lying water accumulation area is lower than the preset low slope threshold, the slope of the ordinary flat area is between the preset low slope threshold and the preset high slope threshold, and the slope of the high-lying drainage area is higher than the preset high slope threshold. Set the liquid level rise rate threshold for each zone. S2.

4. Set basic auxiliary trigger information including synchronously acquiring land use type data and historical waterlogging event records. Land use type data includes green space, roads, and building land. Historical waterlogging event records include water depth, duration, and disaster-bearing body information.

4. The method for determining the degree of urban flooding based on dynamically adjusted liquid position information intervals according to claim 3, characterized in that, Step S3 first performs error modeling, then calculates the basic confidence interval, and finally sets the sliding window parameter update port, event parameter mapping call port, and spatial partition threshold adaptation port. The specific implementation method includes the following steps: S3.

1. Error modeling includes systematic errors and random errors; System errors are compensated for using a formula established through on-site calibration at preset intervals. in, This represents the actual liquid level. To measure the liquid level, This is a fixed deviation value; Random errors are tested based on historical data to examine distribution characteristics. A preset sample size threshold n0 is used. Large samples with n≥n0 adopt a normal distribution, while small samples with n<n0 adopt a t-distribution. The probability density function of a large sample normal distribution is: Where σ is the standard deviation of the random error. This is random error; in, This represents the average actual liquid level. The random error of a small sample follows a t-distribution with n-1 degrees of freedom, and its probability density function is: Where Г(·) is the gamma function and n is the sample size; S3.

2. Based on the random error distribution and combined with the confidence level, construct the basic confidence interval. The formulas are divided into two categories: The baseline confidence interval for a large sample is: in, Based on the basic confidence interval, The average value of the actual liquid level after compensation; α represents the two-tailed quantile of the standard normal distribution, and α is the significance level. This is the initial standard deviation; The baseline confidence interval for a small sample is: in, These are the two-tailed quantiles of the t-distribution. To compensate for the sample standard deviation of the actual liquid level; ； S3.

3. Set the sliding window parameter update port to support real-time replacement of standard deviation, the event parameter mapping call port to support preset rule triggering, and the spatial partition threshold adaptation port to support partition differential parameter input.

5. The method for determining the degree of urban flooding based on dynamically adjusted liquid position information intervals according to claim 4, characterized in that, The method for updating the dynamic liquid level information interval that meets the sliding window triggering condition in step S4 includes first calculating the average value of the actual liquid level within the sliding window, using the following formula: in, This represents the total number of sliding windows. Let i be the actual liquid level of the i-th sliding window. This represents the average actual liquid level within the sliding window; Then calculate the sample standard deviation of the random error within the sliding window. : ； Then, parameter replacement is performed, replacing the initial standard deviation of the base model with the newly calculated standard deviation, and the confidence interval is updated synchronously.

6. The method for determining the degree of urban flooding based on dynamically adjusted liquid position information intervals according to claim 5, characterized in that, The method for updating the dynamic liquid position information interval that meets the event triggering conditions in step S4 includes adjusting the standard deviation and the system error compensation value based on the event-adjustment coefficient mapping table. The adjustment formula for standard deviation is: in, The initial standard deviation, This is the adjustment factor corresponding to the event. The adjusted standard deviation for the event trigger; The system error compensation value is adjusted to target pipeline blockage early warning, based on the basic compensation value b. base A fixed compensation amount △b is added to the base.

7. The method for determining the degree of urban flooding based on dynamically adjusted liquid position information intervals according to claim 6, characterized in that, The update method for the dynamic liquid position information interval that meets the spatial partition triggering conditions in step S4 includes the adjustment of low-lying water accumulation area, high-lying drainage area, and ordinary flat area; The standard deviation of low-lying, waterlogged areas is adjusted by multiplying the current standard deviation by the zoning adjustment factor k. low The upper limit of the confidence interval is adjusted to be the current upper limit of the confidence interval plus a preset increment value ΔCI. low ; The standard deviation of the highland drainage area is adjusted by multiplying the current standard deviation by the zoning adjustment factor k. high ; The standard deviation and confidence interval for the ordinary flat region will continue to be used.

8. The method for determining the degree of urban flooding based on dynamically adjusted liquid position information intervals according to claim 7, characterized in that, The dynamic confidence interval-driven method for determining the degree of water accumulation in step S5 is as follows: Mild flooding: The confidence interval mean is within the preset mild flooding mean interval and the upper confidence limit is ≤ the preset mild flooding upper limit threshold × the partition weight; Moderate flooding: The confidence interval mean is within the preset moderate flooding mean interval and the upper confidence limit is ≤ the preset moderate flooding upper limit threshold × the partition weight; Severe flooding: Confidence interval mean > preset severe flooding mean threshold or confidence upper limit > preset severe flooding upper limit threshold × zone weight.

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