Rainwater online monitoring and early warning system based on multi-source data fusion analysis

Abstract

The invention discloses a rainwater on-line monitoring and early warning system based on multi-source data fusion analysis, relates to the technical field of rainwater on-line monitoring and early warning, and solves the technical problem that in the prior art, environmental standards for evaluating rainfall capacity in a rainfall stage are continuously variable, so that data processing cannot be carried out on collected data. Specifically, the rainfall online monitoring unit performs sensor network construction on a rainfall area; monitoring point positions are set in all areas of the rainfall area, sensors are set in the monitoring point positions, and all the areas where the monitoring point positions are set are a flat area, a rainwater flowing area and a rainwater converging area; after the construction of the sensor network is completed, carrying out data acquisition on a rainfall area; the rainfall data processing unit is used for carrying out data processing on the collected data of the sensor network; the rainfall prediction unit is used for performing rainfall prediction on the rainfall area; and the waterlogging risk assessment unit is used for performing waterlogging risk assessment on the rainfall area.

Description

Technical Field

[0001] The present invention relates to the technical field of rainwater online monitoring and early warning, and in particular to a rainwater online monitoring and early warning system based on multi-source data fusion analysis. Background Art

[0002] The rainwater online monitoring and early warning system is an intelligent system that uses modern information technology to monitor, analyze and warn rainfall conditions in real time. It plays an important role in urban flood control, water conservancy management, environmental protection and other fields.

[0003] However, in existing technologies, the environmental standards for assessing rainfall during a rainfall phase are constantly changing, making it impossible to process the collected data, reducing the accuracy of data monitoring. At the same time, it is impossible to predict real-time rainfall trends based on real-time sensor networks. In addition, it is impossible to conduct online monitoring based on the data collected in the rainfall area, and it is impossible to effectively conduct urban flooding risk assessment.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose an online rainwater monitoring and early warning system based on multi-source data fusion analysis.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The rainwater online monitoring and early warning system based on multi-source data fusion analysis includes a rainwater detection and early warning platform, wherein the rainwater detection and early warning platform is communicatively connected to a rainfall online monitoring unit, a rainfall data processing unit, a rainfall prediction unit, and a waterlogging risk assessment unit; Rainfall online monitoring unit, building a sensor network for rainfall areas; Rainfall data processing unit, which processes the collected data from the sensor network; Rainfall prediction unit, which makes rainfall prediction for rainfall areas; The waterlogging risk assessment unit conducts waterlogging risk assessment in rainfall areas.

[0007] As a preferred embodiment of the present invention, a sensor network is constructed for the rainfall area, monitoring points are set in various areas of the rainfall area, and sensors are set at the monitoring points, wherein the various areas where the monitoring points are set are respectively a flat area, a rainwater flow area, and a rainwater gathering area; after the sensor network is constructed, data is collected for the rainfall area, and a rainfall data processing signal is generated and sent to a rainfall data processing unit.

[0008] As a preferred embodiment of the present invention, the process of the rainfall data processing unit is as follows: According to the distribution of the sensor network points, the monitoring points are randomly selected and the rainfall data of the current period is statistically analyzed for the monitoring points. There are i sub-times in the current period, i=1,…,n; The rainfall at each moment of the selected monitoring point is counted, and the rainfall sequence X={x1, x2, ..., x n}, calculate the mean and standard deviation based on the rainfall series; The mean calculation formula is: ;in, is the mean rainfall; The formula for calculating standard deviation is: ;in, is the standard deviation of rainfall; After obtaining the mean and standard deviation, real-time anomaly detection is performed on the rainfall data corresponding to each sub-moment; Among them, the abnormal judgment conditions are: , then the current rainfall X i is an outlier.

[0009] As a preferred embodiment of the present invention, when an abnormal value is detected, a correction simulation calculation is performed on the current rainfall value to obtain an actual preset value; wherein the correction simulation calculation is performed in the following manner: ; And mark the modified simulated rainfall value as the preset value; The output value is calculated based on the preset value and the current abnormal value, and the difference is marked as the abnormal deviation. The continuously occurring abnormal values ​​are counted, and the floating trend of the abnormal deviation corresponding to the abnormal value is inferred. If the floating trend of the abnormal deviation is a decreasing trend, it indicates that the current sensor network's collected data is in the automatic compensation stage and the compensation effect is obvious, that is, the current abnormal value is marked and counted. If the floating trend of the abnormal deviation is an increasing trend, it indicates that the collected data of the current sensor network is in a numerical discrete state. The current abnormal value is marked and not counted, and the corresponding timestamp of the marked abnormal value is traced to find out the cause of the abnormality according to the traceability result. Based on this standard, the point is reset and data statistics are performed in time; the real-time collected data is sent to the rain detection and early warning platform.

[0010] As a preferred embodiment of the present invention, the process of the rainfall prediction unit is as follows: Points were screened based on the sensor network constructed within the rainfall area. The rainwater flow trajectory was used as the point screening criterion, and data were collected at points within the same trajectory. The collected points were located in the flat area, rainwater flow area, and rainwater convergence area, respectively. The collected points were marked as rainwater contact points, rainwater flow points, and rainwater convergence points. During the reciprocating floating stage of real-time rainfall collection at rainwater contact points, the floating amount of the mean rainwater flow collected in real time in the rainfall sequence corresponding to the rainwater flow points is obtained; during the non-floating stage of real-time rainwater flow collection at rainwater flow points, the decreasing speed of the rainwater to be discharged corresponding to the rainwater convergence points is obtained.

[0011] As a preferred embodiment of the present invention, a threshold comparison is performed between the floating amount of the mean value of the real-time collected rainwater flow in the rainfall sequence corresponding to the rainwater flow point and the decreasing speed of the rainwater to be discharged corresponding to the rainwater convergence point: If the real-time collected mean fluctuation of rainwater flow exceeds the mean fluctuation threshold, or the decline rate of the amount of rainwater to be discharged corresponding to the rainwater convergence point exceeds the decline rate threshold, a continuous rainfall signal is generated and sent to the rainwater detection and early warning platform; if the real-time collected mean fluctuation of rainwater flow does not exceed the mean fluctuation threshold, and the decline rate of the amount of rainwater to be discharged corresponding to the rainwater convergence point does not exceed the decline rate threshold, an intermittent rainfall signal is generated and sent to the rainwater detection and early warning platform.

[0012] As a preferred embodiment of the present invention, the process of the waterlogging risk assessment unit is as follows: Based on the sensor network in the rainfall area, the water depth in the rainfall area is collected. The specific water balance formula is:

[0013] Among them, h is the depth of water, q x and q y is the single-width flow in the x and y directions; i is the rainfall intensity, specifically the rainfall input per unit time and unit area; o is the drainage intensity, specifically the water intensity discharged through the drainage network and pumping station; f is the infiltration rate, specifically the water intensity of rainwater infiltrating into the soil; It is expressed as the rate of change of water depth over time, and its physical meaning is the dynamic change of water depth over time; and Expressed as the spatial rate of change of single-width flow; partial derivative of spatial coordinates x and y, unit: m; q x and q y They are the single width flow in the x and y directions, unit: m 2 / s, which is the water flow rate per unit width.

[0014] As a preferred embodiment of the present invention, when i is much larger than o+f, then iof>0 is equal to the right side of the balance formula. >0, if the drainage system is fully loaded, ≈0, the flow rate no longer increases, then >0, the depth of water accumulation increases over time, indicating that the rainfall intensity is greater than the drainage + infiltration capacity, and an intensity waterlogging signal is generated and sent to the rain detection and early warning platform; When the drainage network is blocked, the rainwater that originally flowed out will accumulate. <0 or <0; then When the rainfall intensity and drainage intensity are equal to the corresponding infiltration rate, >0, it indicates that rainwater drainage is abnormal, and a drainage waterlogging signal is generated and sent to the rainwater detection and early warning platform; the rainwater detection and early warning platform carries out targeted rainfall protection according to various types of waterlogging signals.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, the corresponding environmental standards at each moment in the rainfall scene are variable. By processing the collected data, the collected abnormal values ​​can be marked, thereby improving the accuracy of rainfall data monitoring, avoiding the single evaluation standard for real-time rainfall collection in dynamic environmental scenes, and failing to perform targeted abnormal value detection, thereby reducing the early warning availability of rainfall monitoring data.

[0016] 2. In the present invention, rainfall forecasts are made for rainfall areas, and future rainfall trends in the current rainfall area are inferred through short-term rainfall forecasts. Not only the rainfall amount is counted, but the rainfall trend is inferred based on real-time scenarios to improve the accuracy of rainfall forecasts, so as to improve the accuracy of reference decisions for subsequent waterlogging risk assessments in rainfall areas.

[0017] 3. In the present invention, waterlogging risk assessment is conducted on the rainfall area. Waterlogging risk assessment is conducted based on data collected in the rainfall area. Rainfall in the rainfall area is monitored online in real time. Early warning can be issued in time when monitoring anomalies occur, effectively improving the timeliness of early warning of waterlogging risk. When the waterlogging risk intensifies, timely guidance can be provided to minimize the impact of waterlogging risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0019] Figure 1 This is a system principle block diagram of the present invention; Figure 2 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0022] See also Figure 1-2 As shown in FIG, a rainwater online monitoring and early warning system based on multi-source data fusion analysis includes a rainwater detection and early warning platform, wherein the rainwater detection and early warning platform is communicatively connected to a rainfall online monitoring unit, a rainfall data processing unit, a rainfall prediction unit, and a waterlogging risk assessment unit; The rain detection and early warning platform generates a rainfall online monitoring signal and sends it to the rainfall online monitoring unit; After receiving the rainfall, the online monitoring unit constructs a sensor network for the rainfall area, sets monitoring points in various areas of the rainfall area, and sets sensors at the monitoring points. The areas where the monitoring points are set are flat areas, rainwater flow areas, and rainwater gathering areas. After the sensor network is built, data is collected from the precipitation area, and a rainfall data processing signal is generated and sent to the rainfall data processing unit; The rainfall data processing unit is used to process the data collected by the sensor network. In rainfall scenarios, the corresponding environmental standards at different times are variable. By processing the collected data, the collected outliers can be marked, thereby improving the accuracy of rainfall data monitoring. This avoids the single evaluation standard for real-time rainfall collection in dynamic environmental scenarios, which makes it impossible to perform targeted outlier detection and reduces the early warning availability of rainfall monitoring data. In this way, outliers can be marked on the collected data to accurately infer the impact of rainfall at the current stage; According to the distribution of the sensor network points, the monitoring points are randomly selected and the rainfall data of the current period is statistically analyzed for the monitoring points. There are i sub-times in the current period, i=1,…,n; The rainfall at each moment of the selected monitoring point is counted, and the rainfall sequence X={x1, x2, ..., xn}, calculate the mean and standard deviation based on the rainfall series; The mean calculation formula is: ;in, is the mean rainfall; The formula for calculating standard deviation is: ;in, is the standard deviation of rainfall; After obtaining the mean and standard deviation, real-time anomaly detection is performed on the rainfall data corresponding to each sub-moment; Among them, the abnormal judgment conditions are: , then the current rainfall X i is an outlier; When an abnormal value is detected, the current rainfall value is corrected and simulated to obtain the actual preset value; the correction simulation calculation method is: It should be explained that i is a natural number greater than 1; and the rainfall value after the modified simulation calculation is marked as the preset value; The output value is calculated based on the preset value and the current abnormal value, and the difference is marked as the abnormal deviation; the continuously occurring abnormal values ​​are counted, and the floating trend of the abnormal deviation corresponding to the abnormal value is inferred. If the floating trend of the abnormal deviation is a decreasing trend, it indicates that the current sensor network's collected data is in the automatic compensation stage and the compensation effect is obvious, that is, the current abnormal value is marked and counted. It should be explained that in this scenario, the statistics of the abnormal value can infer the floating trend to reflect the compensation characteristics of the abnormal value; If the floating trend of the abnormal deviation is an increasing trend, it indicates that the data collected by the current sensor network is in a discrete state. The current abnormal value is marked and not counted. The timestamp corresponding to the marked abnormal value is traced to find the cause of the abnormality based on the traceability result. Based on this, the point is reset and data statistics are performed in a timely manner. Send real-time collected data to the rain detection and early warning platform; After receiving the real-time collected data, the rain detection and warning platform generates a rainfall prediction signal and sends it to the rainfall prediction unit; After receiving the rainfall forecast signal, the rainfall forecast unit conducts rainfall forecasts for the rainfall area. It infers the future rainfall trend of the current rainfall area through short-term rainfall forecasts. It not only counts the rainfall amount, but also infers the rainfall trend based on the real-time scenario, thereby improving the accuracy of rainfall forecasts. This will help improve the accuracy of reference decisions for subsequent waterlogging risk assessments in the rainfall area. Points were screened based on the sensor network constructed within the rainfall area. The rainwater flow trajectory was used as the point screening criterion, and data were collected at points within the same trajectory. The collected points were located in the flat area, rainwater flow area, and rainwater convergence area, respectively. The collected points were marked as rainwater contact points, rainwater flow points, and rainwater convergence points. During the reciprocating floating stage of rainfall collected in real time at the rainwater contact points, the real-time floating amount of the mean rainfall flow collected in the rainfall sequence corresponding to the rainwater flow point is obtained. It should be explained that the mean flow statistics in this scenario are when the mean flow reaches a certain threshold. For example, if the current flow is less than half of the supply flow in the actual flow area, the data is not collected at this time and has no reference value. It is necessary to understand the impact of rainfall fluctuation on the mean flow fluctuation to determine the fluctuation of the mean rainfall flow when rainfall fluctuates back and forth, so as to more accurately infer rainfall forecasts. If the mean flow does not change even though the rainfall fluctuates, it means that the continuous fluctuation of rainfall is still in the high rainfall scenario. During the period of no fluctuation in rainwater flow rate collected in real time at the rainwater flow point, the falling speed of the amount of rainwater to be discharged corresponding to the rainwater gathering point is obtained; The threshold value is compared between the floating amount of the mean rainfall flow collected in real time in the rainfall sequence corresponding to the rainwater flow point and the decreasing speed of the amount of rainwater to be discharged corresponding to the rainwater convergence point: If the real-time collected mean fluctuation of rainwater flow exceeds the mean fluctuation threshold, or the decreasing speed of the amount of rainwater to be discharged corresponding to the rainwater convergence point exceeds the decreasing speed threshold, it is inferred that the rainfall trend in the rainfall area during the current period is a continuous trend, and a continuous rainfall signal is generated and sent to the rainwater detection and early warning platform; If the mean fluctuation of the real-time collected rainwater flow does not exceed the mean fluctuation threshold, and the decreasing speed of the amount of rainwater to be discharged corresponding to the rainwater convergence point does not exceed the decreasing speed threshold, it is inferred that the rainfall trend in the rainfall area during the current period is intermittent, and an intermittent rainfall signal is generated and sent to the rainwater detection and early warning platform; The rain detection and early warning platform sets a targeted monitoring cycle for the sensor network based on the intermittent rainfall signal or continuous rainfall signal received; At the same time, a waterlogging risk assessment signal is generated and sent to the waterlogging risk assessment unit; After receiving the waterlogging risk assessment signal, the waterlogging risk assessment unit conducts a waterlogging risk assessment on the rainfall area. Based on the data collected in the rainfall area, the unit conducts real-time online monitoring of rainfall in the rainfall area and can issue early warnings in the event of abnormal monitoring. This effectively improves the timeliness of waterlogging risk warnings and enables timely drainage when waterlogging risk intensifies, minimizing the impact of waterlogging risk. Based on the sensor network in the rainfall area, the water depth in the rainfall area is collected. The specific water balance formula is:

[0023] Among them, h is the depth of water, q x and q y is the single-width flow in the x and y directions; i is the rainfall intensity, specifically the rainfall input per unit time and unit area; o is the drainage intensity, specifically the water intensity discharged through the drainage network and pumping station; f is the infiltration rate, specifically the water intensity of rainwater infiltrating into the soil; It is expressed as the rate of change of water depth over time, and its physical meaning is the dynamic change of water depth over time; and Expressed as the spatial rate of change of single-width flow; it is the partial derivative of the spatial coordinates x and y (unit: m, two-dimensional coordinates of the plane), reflecting the "changing trend of flow along the x and y directions"; q x and q y They are the single width flow in the x and y directions (unit: m 2 / s, that is, the water flow rate per unit width, which can be understood as "discharge per meter width"); physical meaning: if > 0, indicating that along the x direction, the downstream flow is larger than the upstream (possibly due to the increase in flow caused by water confluence); if <0, the flow rate decreases along the x direction (possibly due to infiltration or drainage); When i is much larger than o+f, then iof>0 on the right side of the equilibrium formula. >0, if the drainage system is fully loaded, ≈0, the flow rate no longer increases, then >0, the depth of water accumulation increases over time, indicating that the rainfall intensity is greater than the drainage + infiltration capacity, and an intensity waterlogging signal is generated and sent to the rain detection and early warning platform; When the drainage network is blocked, the rainwater that originally flowed out will accumulate. <0 or <0; then When the rainfall intensity and drainage intensity are equal to the corresponding infiltration rate, >0, it indicates that the rainwater drainage is abnormal, and a drainage waterlogging signal is generated and sent to the rainwater detection and early warning platform; The rain detection and early warning platform provides targeted rainfall protection based on various types of waterlogging signals.

[0024] When the present invention is in use, the rainfall online monitoring unit constructs a sensor network for the rainfall area; the rainfall data processing unit processes the collected data of the sensor network; the rainfall prediction unit predicts rainfall for the rainfall area; and the waterlogging risk assessment unit assesses waterlogging risk for the rainfall area.

[0025] Thresholds, preset values, and preset ranges are set for comparative analysis of results to determine whether they are good or bad. The values ​​are set based on a combination of large-scale model analysis of sample data and manual experience, and can also be adjusted appropriately based on seasonal or common-sense factors. The settings of weight ratio coefficients, influencing factors, etc. are assigned specific values ​​according to the influence of each parameter on the result, which ultimately reflects the impact on the result. They are also set and entered into storage through a combination of large-scale model analysis of sample data and manual experience. Appropriate adjustments can also be made based on seasonal or common-sense influencing conditions.

[0026] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Rainwater online monitoring and early warning system based on multi-source data fusion analysis, characterized by: It includes a rain detection and early warning platform, wherein the rain detection and early warning platform is communicatively connected with a rainfall online monitoring unit, a rainfall data processing unit, a rainfall prediction unit, and a waterlogging risk assessment unit; Rainfall online monitoring unit, building a sensor network for rainfall areas; Rainfall data processing unit, which processes the collected data from the sensor network; Rainfall prediction unit, which makes rainfall prediction for rainfall areas; The waterlogging risk assessment unit conducts waterlogging risk assessment in rainfall areas.

2. The rainwater online monitoring and early warning system based on multi-source data fusion analysis according to claim 1 is characterized in that: A sensor network is constructed for the rainfall area, monitoring points are set in various areas of the rainfall area, and sensors are set at the monitoring points. Among them, the areas where the monitoring points are set are flat areas, rainwater flow areas, and rainwater gathering areas. After the sensor network is constructed, data is collected in the precipitation area, and a rainfall data processing signal is generated and sent to the rainfall data processing unit.

3. The rainwater online monitoring and early warning system based on multi-source data fusion analysis according to claim 2 is characterized in that: The process of the rainfall data processing unit is as follows: According to the distribution of the sensor network points, the monitoring points are randomly selected and the rainfall data of the current period is statistically analyzed for the monitoring points. There are i sub-times in the current period, i=1,…,n; The rainfall at each moment of the selected monitoring point is counted, and the rainfall sequence X={x1, x2, ..., x n }, calculate the mean and standard deviation based on the rainfall series; The mean calculation formula is: ;in, is the mean rainfall; The formula for calculating standard deviation is: ;in, is the standard deviation of rainfall; After obtaining the mean and standard deviation, real-time anomaly detection is performed on the rainfall data corresponding to each sub-moment; Among them, the abnormal judgment conditions are: , then the current rainfall X i is an outlier.

4. The rainwater online monitoring and early warning system based on multi-source data fusion analysis according to claim 3 is characterized in that: When an abnormal value is detected, the current rainfall value is corrected and simulated to obtain the actual preset value; the correction simulation calculation method is: ; And mark the modified simulated rainfall value as the preset value; Calculate the output value based on the preset value and the current abnormal value and mark the difference as the abnormal deviation; Count the continuously occurring abnormal values ​​and infer the floating trend of the abnormal deviation corresponding to the abnormal value. If the floating trend of the abnormal deviation is a decreasing trend, it indicates that the current sensor network's collected data is in the automatic compensation stage and the compensation effect is obvious, that is, the current abnormal value is marked and counted; If the floating trend of the abnormal deviation is an increasing trend, it indicates that the data collected by the current sensor network is in a discrete state. The current abnormal value is marked and not counted. The timestamp corresponding to the marked abnormal value is traced to find the cause of the abnormality based on the traceability result. Based on this, the point is reset and data statistics are performed in a timely manner. The real-time collected data is sent to the rain detection and early warning platform.

5. The rainwater online monitoring and early warning system based on multi-source data fusion analysis according to claim 4 is characterized in that: The process of the rainfall prediction unit is as follows: Points were screened based on the sensor network constructed within the rainfall area. The rainwater flow trajectory was used as the point screening criterion, and data were collected at points within the same trajectory. The collected points were located in the flat area, rainwater flow area, and rainwater convergence area, respectively. The collected points were marked as rainwater contact points, rainwater flow points, and rainwater convergence points. During the reciprocating floating stage of real-time rainfall collection at rainwater contact points, the floating amount of the mean rainwater flow collected in real time in the rainfall sequence corresponding to the rainwater flow points is obtained; during the non-floating stage of real-time rainwater flow collection at rainwater flow points, the decreasing speed of the rainwater to be discharged corresponding to the rainwater convergence points is obtained.

6. The rainwater online monitoring and early warning system based on multi-source data fusion analysis according to claim 5 is characterized in that: The threshold value is compared between the floating amount of the mean rainfall flow collected in real time in the rainfall sequence corresponding to the rainwater flow point and the decreasing speed of the amount of rainwater to be discharged corresponding to the rainwater convergence point: If the real-time collected mean fluctuation of rainwater flow exceeds the mean fluctuation threshold, or the decreasing speed of the amount of rainwater to be discharged corresponding to the rainwater convergence point exceeds the decreasing speed threshold, a continuous rainfall signal is generated and sent to the rainwater detection and early warning platform; If the real-time collected mean fluctuation of rainwater flow does not exceed the mean fluctuation threshold, and the decreasing speed of the amount of rainwater to be discharged corresponding to the rainwater convergence point does not exceed the decreasing speed threshold, an intermittent rainfall signal is generated and sent to the rainwater detection and early warning platform.

7. The rainwater online monitoring and early warning system based on multi-source data fusion analysis according to claim 6 is characterized in that: The process of the waterlogging risk assessment unit is as follows: Based on the sensor network in the rainfall area, the water depth in the rainfall area is collected. The specific water balance formula is: ； Among them, h is the depth of water, q x and q y is the single-width flow in the x and y directions; i is the rainfall intensity, specifically the rainfall input per unit time and unit area; o is the drainage intensity, specifically the water intensity discharged through the drainage network and pumping station; f is the infiltration rate, specifically the water intensity of rainwater infiltrating into the soil; It is expressed as the rate of change of water depth over time, and its physical meaning is the dynamic change of water depth over time; and Expressed as the spatial rate of change of single-width flow; partial derivative of spatial coordinates x and y, unit: m; q x and q y They are the single width flow in the x and y directions, unit: m 2 / s, which is the water flow rate per unit width.

8. The rainwater online monitoring and early warning system based on multi-source data fusion analysis according to claim 7 is characterized in that: When i is much larger than o+f, then iof>0 on the right side of the equilibrium formula. >0, if the drainage system is fully loaded, ≈0, the flow rate no longer increases, then >0, the depth of water accumulation increases over time, indicating that the rainfall intensity is greater than the drainage + infiltration capacity, and an intensity waterlogging signal is generated and sent to the rain detection and early warning platform; When the drainage network is blocked, the rainwater that originally flowed out will accumulate. <0 or <0; then When the rainfall intensity and drainage intensity are equal to the corresponding infiltration rate, >0, it indicates that rainwater drainage is abnormal, and a drainage waterlogging signal is generated and sent to the rainwater detection and early warning platform; the rainwater detection and early warning platform carries out targeted rainfall protection according to various types of waterlogging signals.

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