A regional rain gauge data sharing method in a rain and sewage intelligent drainage system

Abstract

This invention discloses a method for sharing regional rain gauge data in a smart stormwater and sewage drainage system, relating to the field of smart stormwater and sewage drainage monitoring technology. It includes steps such as multimodal data acquisition, protocol normalization processing, multi-level data cleaning, rainfall feature construction, and data publishing and sharing, achieving reliable sharing of regional rain gauge data across the entire area. This invention is compatible with multiple types of rain gauges, unifies data standards, and eliminates heterogeneity and timing deviations; it removes anomalies and identifies faults through three levels of cleaning to ensure data authenticity and validity; it uses dynamic weighted fusion to generate three levels of standard rainfall values ​​at the station, sub-region, and system levels, and constructs high-order rainfall features to adapt to drainage scheduling needs; relying on a low-latency shared bus for hierarchical data opening, coupled with a fault self-healing mechanism, it provides high-precision, high-consistency, and high-real-time data support for stormwater and sewage network scheduling, pump station linkage, flood warning, and interception and pollution control, significantly improving the collaborative scheduling and flood prevention capabilities of the smart drainage system.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology for rainwater and sewage drainage, specifically a method for sharing regional rain gauge data in an intelligent rainwater and sewage drainage system. Background Technology

[0002] In the operation and management of urban intelligent stormwater and sewage drainage systems, regional rainfall monitoring data is the core foundational data for flood control plan activation, diversion gate control, urban flooding risk early warning, and pump station coordinated scheduling. Its accuracy, completeness, and real-time performance directly determine the scientific nature of drainage scheduling decisions and the effectiveness of urban flooding prevention. Currently, in urban drainage monitoring scenarios, various types of rain gauges, such as tipping bucket, radar, and weighing types, are typically deployed within the area, forming a multi-source monitoring hardware foundation. However, existing technologies have significant deficiencies in data processing and sharing.

[0003] The existing stormwater and sewage drainage system only achieves preliminary aggregation of multi-source rainfall data, and has not established a standardized data sharing mechanism. The rain gauge data from each station are isolated from each other, making efficient interoperability between sub-regions and system levels impossible, and thus failing to support coordinated scheduling across the entire region. At the same time, the acquisition protocols, measurement units, and time bases of different types of rain gauges are not uniform, resulting in data heterogeneity and time sequence misalignment. Multi-source data lacks direct comparability and cannot be effectively integrated to form unified rainfall data.

[0004] In terms of data quality control, traditional solutions lack multi-level anomaly identification and cleaning mechanisms. Rain gauges are susceptible to abnormal data due to equipment drift, environmental interference, and hardware failures, and data loss can even create monitoring blind spots. Furthermore, there are no comprehensive outlier filling and intelligent fault compensation methods, making it difficult to guarantee the authenticity and validity of the data. In addition, existing technologies have not constructed a hierarchical data fusion system at the station, sub-region, and system levels. They cannot combine spatial distance, equipment stability, and regional area to weight the calculation of standard rainfall values, nor can they improve data stability through time-series smoothing processing, making it difficult to generate high-resolution rainfall field data that reflects the spatial distribution of regional rainfall.

[0005] The aforementioned problems prevent rainfall data from providing reliable support for the refined scheduling of stormwater and sewage pipe networks and accurate early warning of urban flooding. The shortcomings of monitoring with a single type of rain gauge cannot be compensated, and the advantages of multi-source equipment cannot be fully utilized. These issues severely restrict the improvement of the collaborative scheduling capabilities of intelligent stormwater and sewage drainage systems and the level of urban flood control. There is an urgent need for a complete method for regional rain gauge data sharing and integration to solve these pain points. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] To address the shortcomings of existing technologies, this invention provides a method for sharing regional rain gauge data in a smart rainwater and sewage drainage system, thus solving the problems mentioned in the background section.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for sharing regional rain gauge data in a smart rainwater and sewage drainage system, comprising the following steps:

[0010] S1. Multimodal data acquisition:

[0011] A monitoring network is formed by deploying tipping bucket, radar, and weighing rain gauges in the area covered by the intelligent rainwater and sewage drainage system. Multi-source raw rainfall data is collected through an edge gateway to calculate the rainfall baseline value of the rain gauges.

[0012] Preferred: The no-rainfall baseline value is obtained by weighted calculation of the factory-calibrated baseline value and the average of three no-rainfall data collected on site. The standardized initial rainfall data is the original data minus the baseline value, and negative values ​​are set to zero.

[0013] The preferred formula for calculating the baseline value without rainfall is as follows:

[0014]

[0015] In the formula: i = 1, 2, ..., N, where N is the number of rain gauges within the coverage area; B i For rain gauge i, there is no rainfall reference value; B i0 This is the factory calibration reference value; B i1 B i2 B i3 These are readings from three consecutive periods without rainfall at the site.

[0016] Preferred: The uniform data collection cycle for all rain gauges is 1 to 15 minutes.

[0017] S2, Protocol Normalization Processing:

[0018] The raw data is processed with time alignment, unit standardization to millimeters, and data format standardization. Standardized initial rainfall data is obtained by subtracting the baseline value without rainfall and setting negative values ​​to zero.

[0019] Preferred methods: radar rain gauges are converted to millimeters based on the collection cycle; tipping bucket rain gauges are converted based on the counting and calibration coefficients; and weighing rain gauges are converted based on a fixed coefficient for the weighing value.

[0020] The preferred conversion formula for radar rain gauges is:

[0021]

[0022] In the formula: radar value is the data collected by radar rain gauge, and the data collection period is in minutes.

[0023] The preferred conversion formula for tipping bucket rain gauges is:

[0024]

[0025] In the formula: the count is the number of times the tipping bucket of the tipping bucket rain gauge tipps; This refers to the rainfall capacity of a single shower of a tipping bucket rain gauge, expressed in millimeters per shower.

[0026] The preferred conversion formula for weighing rain gauges is:

[0027]

[0028] In the formula: For weighing rain gauges The result is in units of 1 g / cm³. 2 , of which 1g / cm 2 =10mm.

[0029] S3, Multi-level Data Cleaning:

[0030] Based on the sliding window, single-site temporal anomaly removal, sub-regional multi-site spatial correlation verification, and outlier filling with the mean of the same region are performed sequentially to obtain effective standardized rainfall data.

[0031] Preferred method: Single-site time-series anomaly removal is performed when the absolute value of the rainfall change in adjacent cycles exceeds a preset threshold.

[0032] The preferred formula for the absolute value of rainfall change between adjacent periods is as follows:

[0033]

[0034] In the formula: ΔR Ci R represents the absolute value of the rainfall change in adjacent periods of rain gauge i; Ci (t) represents the standardized rainfall data for the current period of rain gauge i; R Ci (t−1) represents the standardized rainfall data of rain gauge i in the previous period.

[0035] Preferred: If ΔR Ci >ΔR max If so, it is judged as a timing anomaly and removed;

[0036] Where, ΔR max This is a pre-defined maximum allowable value for rainfall variation between adjacent periods at a single site.

[0037] Preferred method: The spatial correlation check of multiple stations in the sub-region is to remove the rain gauge value with the largest deviation if the difference between any two standardized rain gauge data in the sub-region exceeds the limit.

[0038] The preferred formula for the difference between standardized rainfall data from two rain gauges is as follows:

[0039]

[0040] In the formula: ΔR ab R represents the difference in standardized rainfall data between two rain gauges within a sub-region. Ci,a R Ci,b These are standardized rainfall data from two different rain gauges.

[0041] Preferred: If ΔR ab >ΔR jmax Then, stations with a larger deviation from the regional mean are excluded;

[0042] Where, ΔR jmax The maximum permissible value for spatial variation in a pre-defined sub-region;

[0043] If three or more sites are abnormal, the site that deviates the furthest from the median will be removed.

[0044] Preferred method: Abnormal data is filled using the average of normal rainfall gauges in the same area; when all sub-areas are abnormal, interpolation of neighboring areas with the same period is used.

[0045] The preferred formula for calculating the average value of normal rainfall gauges in the same area is as follows:

[0046]

[0047] Where: R0 Ci Replacement values ​​for abnormal data from rain gauge i; x = 1, 2, ..., K0 j ;K0 j R represents the number of normal rain gauges remaining within sub-region j after removing abnormal stations; Cx This represents the standardized data of the x-th normal rain gauge within the sub-region.

[0048] S4. Rainfall characteristics and structures:

[0049] The comprehensive weight is calculated based on distance weight and stability weight, and a standard rainfall value at the station level is generated. The station-level data is averaged and smoothed over three periods to obtain a shared rainfall value at the sub-region level. The unified rainfall value at the system level is obtained by weighting the data according to the area ratio of the sub-region. The three levels of data are updated in different periods.

[0050] Preferred method: The comprehensive weight is a combination of distance weight and stability weight in a fixed ratio, and the station-level standard rainfall value is calculated after normalization.

[0051] The preferred formula for calculating the overall weight is:

[0052]

[0053]

[0054]

[0055]

[0056] In the formula: W Li For the distance weight of rain gauge i; L i L is the distance from rain gauge i to the center of the sub-region; sumj K is the sum of the distances of all rain gauges within the sub-region; j The total number of rain gauge readings within the sub-region; among which, W Si C represents the data stability weight for rain gauge i; i W represents the number of normal data points within a consecutive P periods of rain gauge i; P is the total number of statistical periods; i β1 is the comprehensive weight of rain gauge i; β2 is the preset distance weight allocation coefficient; β3 is the preset stability weight allocation coefficient.

[0057] The preferred formula for calculating the standard rainfall value at the station level is:

[0058]

[0059] In the formula: R Bi The standard rainfall value at rain gauge station i; W Ni Normalized composite weight for rain gauge i; R Ei For rain gauge i, the rainfall data is effectively standardized, and R Ei ∈{R Ci R0 Ci}

[0060] Preferred: Wherein, R Ci For standardized rainfall data of rain gauge i; R Ci (t), R Ci (t-1) represent the standardized instantaneous rainfall values ​​of rain gauge i at time t and time t−1, respectively; R Ci,a R Ci,b Standardized rainfall for two rain gauges within the sub-region.

[0061] The preferred formula for calculating sub-regional shared rainfall values ​​is:

[0062]

[0063] In the formula: R Zj For sub-region j, the shared rainfall value is used; K j R represents the total rainfall within sub-region j; Bi Let be the standard rainfall value at the i-th rain gauge station level within the sub-region.

[0064] Preferred: Sub-regional rainfall values ​​are smoothed and corrected for three periods: the current period, the previous period, and the two periods before that, and then used for cross-regional sharing.

[0065] The preferred three-phase smoothing correction formula is:

[0066]

[0067] Where: R0 Zj This is the corrected sub-region level shared rainfall value; RZ j (t) represents the current periodic sub-region level rainfall value for sub-region j; RZ j (t−1) represents the sub-regional rainfall value of the previous cycle; RZ j (t−2) represents the sub-regional rainfall values ​​from the previous two cycles.

[0068] Preferred method: In step S4, the system-level unified rainfall value is obtained by weighting the rainfall values ​​of each sub-region after correction, with the sub-region area ratio as the weight.

[0069] The preferred formula for calculating system-level unified rainfall values ​​is:

[0070]

[0071] In the formula: R S For system-level shared rainfall values; W Zj The weight of subregion j; R0 Zj The corrected sub-region level shared rainfall value is given for sub-region j; j = 1, 2, ..., M, where M is the total number of sub-regions in the system.

[0072] Preferred methods: Station-level shared rainfall values ​​are updated according to a pre-specified collection cycle; sub-region-level shared rainfall values ​​are updated according to twice the collection cycle; and system-level shared rainfall values ​​are updated according to three times the collection cycle.

[0073] S5, Data Publishing and Sharing:

[0074] The three levels of data are connected to a unified shared bus, and shared access is granted after the deviation verification is passed.

[0075] Preferred: In step S5, the deviation check is the absolute difference between the sub-region level shared value and the mean of the effective data. If it exceeds the maximum permissible deviation, then return to step S2. If it continuously exceeds the maximum permissible deviation, then trigger a device alarm.

[0076] (III) Beneficial Effects

[0077] This invention provides a method for sharing regional rain gauge data in a smart stormwater and sewage drainage system. Compared with existing technologies, it has the following advantages:

[0078] This invention, through multimodal rainfall data acquisition and protocol normalization processing, is compatible with different types of rain gauges such as tipping bucket, radar, and weighing rain gauges. It unifies timestamps, units of measurement, and data formats, completely solving the problems of heterogeneous data, misaligned timing, and inconsistent standards in traditional monitoring equipment. This makes multi-source rainfall data directly comparable, laying a stable and standardized data source foundation for subsequent data processing and sharing, and improving the coverage and reliability of raw data.

[0079] This invention employs a multi-level cleaning mechanism for local time-series data. Through three layers of filtering—outlier removal, spatiotemporal correlation verification, and historical pattern consistency check—it accurately identifies faulty, drifting, and interfered sensor data. Combined with spatial interpolation, it fills in outliers, ensuring that the data entering the fusion process has spatiotemporal consistency and logical rationality. This prevents abnormal data from affecting drainage scheduling decisions and significantly improves the authenticity and effectiveness of rainfall monitoring data.

[0080] This invention relies on streaming computing nodes to achieve intelligent fusion of multimodal data and generation of three-level standard rainfall values. It calculates at the station level, sub-region level, and system level and sets differentiated update cycles. At the same time, it constructs high-order features such as rainfall intensity field and upstream and downstream rainfall difference, so that the original single-point data can be upgraded into high-precision rainfall field data adapted to drainage scheduling, meeting the refined and stable data requirements of different levels of management.

[0081] This invention achieves full-domain data publishing and sharing based on a unified data sharing bus, supports low-latency protocols such as MQTT and DDS, provides hierarchical access to data access, and is equipped with automatic fault alarm and interpolation compensation mechanisms to ensure uninterrupted data sharing. It provides high-precision and high-real-time data support for stormwater and sewage pipe network scheduling, pump station linkage, and urban flooding early warning, significantly improving the collaborative scheduling capability of intelligent stormwater and sewage drainage systems and the accuracy of urban flooding prevention and control.

[0082] Other advantages, objectives and features of the invention will be set forth in part in the description which follows; and in part will be apparent to those skilled in the art upon examination of the following description; or may be learned from practice of the invention. Attached Figure Description

[0083] Figure 1 This is a flowchart illustrating the present invention.

[0084] Figure 2 This is a schematic diagram of the intelligent rainwater and sewage drainage system of the present invention. Detailed Implementation

[0085] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0086] Please see Figure 1 and Figure 2 As shown, the embodiments of the present invention provide the following technical solutions:

[0087] As an embodiment of the present invention:

[0088] like Figure 2 As shown, the regional rain gauge data sharing system of the present invention includes multiple edge gateways (connecting different types of rain gauges), a regional aggregation gateway / fusion server, a data sharing bus, and a downstream control unit.

[0089] The edge gateway is responsible for data acquisition and local preprocessing, the fusion server performs multi-source fusion and fault diagnosis, and the shared bus uses the MQTT / DDS protocol to publish real-time rainfall field data.

[0090] Downstream pumping stations and gates automatically adjust their operating strategies based on the fused data.

[0091] A method for sharing regional rain gauge data in a smart stormwater and sewage drainage system. In this method, a total of N rain gauges are deployed in the coverage area of ​​the smart stormwater and sewage drainage system, where N is a positive integer greater than 1. The i-th rain gauge is denoted as rain gauge i, where i = 1, 2, ..., N.

[0092] The raw rainfall data collected by each rain gauge within a unit collection cycle is denoted as R. i The unit is millimeters; the unified data collection cycle is T, the unit is minutes, and the value range is 1 to 15 minutes.

[0093] The system area is divided into M rainwater and sewage management sub-areas, j=1, 2, ..., M, and each sub-area j contains K j One rain gauge, satisfying:

[0094]

[0095] To achieve hierarchical data sharing, three levels of data are set up:

[0096] System-level unified standard rainfall value R S ;

[0097] Sub-region j shares rainfall value R Zj ;

[0098] Rain gauge i-station level standard rainfall value RBi ;

[0099] This method includes the following steps:

[0100] S1, Multimodal Data Acquisition

[0101] Within the urban built-up areas, municipal pipe network confluence areas, and flood-prone areas covered by the intelligent rainwater and sewage drainage system, various types of rainfall monitoring equipment are deployed to form a multimodal rainfall monitoring network, based on the area, topographic relief, pipe network density, and spatial distribution characteristics of rainfall.

[0102] The rain gauges used include tipping bucket rain gauges, radar rain gauges, and weighing rain gauges. Different types of rain gauges are suitable for different installation environments, monitoring accuracy requirements, and response speed requirements.

[0103] Each rain gauge is connected to the corresponding edge gateway, and one edge gateway can connect to one or more rain gauges at the same time.

[0104] Each rain gauge collects raw rainfall data according to the polling frequency specified by the equipment at the factory:

[0105] The tipping bucket rain gauge uses intermittent triggering for data acquisition;

[0106] Radar rain gauges use continuous scanning data acquisition;

[0107] Weighing rain gauges use continuous weight change data collection.

[0108] The edge gateway polls each rain gauge according to a preset communication cycle, and receives raw monitoring values, device status words, signal strength and other information uploaded by the rain gauge in real time, so as to complete the unified aggregation of raw rainfall data from multiple sources, types and frequencies.

[0109] By deploying multimodal devices and using distributed data acquisition, this method can avoid the monitoring shortcomings of single-type rain gauges in environments such as heavy rainfall, light rainfall, freezing, and dust storms, improve the coverage and reliability of raw data, and provide a rich and comprehensive data source for subsequent data sharing.

[0110] Taking a specific urban area as an example, the system deploys N=20 rainfall monitoring stations, divided into M=4 management sub-areas:

[0111] Sub-region 1: K1 = 5 rain gauges;

[0112] Sub-region 2: K2 = 5 rain gauges;

[0113] Sub-region 3: K3 = 6 rain gauges;

[0114] Sub-region 4: K4 = 4 rain gauges.

[0115] Set a uniform data collection period T=5 minutes, and set the maximum allowable time series variation ΔR for a single site. max =5mm / cycle, maximum allowable rainfall difference ΔR in sub-region jmax =4mm, maximum permissible calibration deviation E max =0.3mm, statistical period P=10, review and update period Q=30.

[0116] Reference readings B of each rain gauge in the absence of rainfall i The value is determined by the average of the factory calibration value and the values ​​collected in three on-site measurements during periods of no rainfall. The calculation formula is as follows:

[0117] In the formula:

[0118] B i For rain gauge i, there is no rainfall reference value;

[0119] Among them, the no-rainfall baseline value is used to eliminate zero drift of the equipment and ensure the accuracy of small rainfall monitoring;

[0120] B i0 This is the factory calibration reference value;

[0121] B i1 B i2 B i3 These are readings from three consecutive periods without rainfall at the site.

[0122] In this embodiment:

[0123] Taking rain gauge 1 as an example:

[0124] B 10 =0.1mm, B 11 =B 12 =B 13 =0.1mm, then

[0125]

[0126] By acquiring multimodal data and preprocessing benchmark values, a stable data source is provided for subsequent standardization, cleaning, and fusion.

[0127] S2, Protocol Normalization Processing

[0128] After receiving multimodal raw rainfall data, the edge gateway first performs protocol normalization processing on the data to eliminate data heterogeneity issues caused by different device protocols, different acquisition timings, and different units of measurement.

[0129] Protocol normalization mainly includes three parts: time alignment, unit normalization, and data format unification.

[0130] S2.1 Time alignment refers to assigning a uniform standard timestamp to all raw data.

[0131] Because different rain gauges have different start times, internal clock deviations, and transmission delays, directly using the device's built-in time will lead to timing misalignment.

[0132] Therefore, the edge gateway uses the system's unified clock as a reference to re-mark each piece of raw data, assigning the same standard timestamp to data collected at the same time, ensuring that all rainfall data can be synchronously compared in the time dimension.

[0133] S2.2 Unit normalization processing unifies the measurement units output by different rain gauges to millimeters.

[0134] Radar rain gauges output in millimeters per hour.

[0135] Tipping bucket rain gauges count raindrops per tipping bucket.

[0136] Weighing rain gauges measure in grams per square centimeter.

[0137] The edge gateway converts the data according to the device calibration coefficient, and uniformly converts it into a standard rainfall depth value, so that the data of different devices can be directly compared.

[0138] The conversion formula for radar rain gauges is as follows:

[0139]

[0140] In the formula:

[0141] The radar values ​​are the results collected by the radar rain gauge;

[0142] The data collection period is measured in minutes.

[0143] The conversion formula for tipping bucket rain gauges is as follows:

[0144]

[0145] In the formula:

[0146] The count is the number of times the tipping bucket of the tipping bucket rain gauge tipps;

[0147] The rainfall capacity of a single shower of a tipping bucket rain gauge is measured in millimeters per shower.

[0148] The conversion formula for weighing rain gauges is as follows:

[0149]

[0150] In the formula:

[0151] For weighing rain gauges The result is in units of 1 g / cm³. 2 ;

[0152] In this example, 1 g / cm 2 =10mm;

[0153] After unit normalization, the raw rainfall data is standardized and calculated, and the baseline deviation is subtracted to obtain standardized initial rainfall data:

[0154]

[0155] If R Ci If < 0, then set to 0, that is:

[0156]

[0157] In the formula:

[0158] R Ci Standardize the initial rainfall data for rain gauge i;

[0159] R i The original data collected by the rain gauge for each unit collection cycle;

[0160] B i For rain gauge i, there is no rainfall baseline value.

[0161] In this embodiment:

[0162] Taking rain gauge 1 as an example:

[0163] The original data collection R1 = 3.5 mm, then R C1 =3.5−0.1=3.4 mm

[0164] The same calculation was performed on all 20 rain gauges to obtain standardized initial rainfall data RCI for all stations, forming intermediate data with a unified format and consistent time sequence, laying the foundation for subsequent multi-level cleaning.

[0165] S2.3. Data format unification processing involves removing frame headers, frame trailers, checksums, and manufacturer-defined redundant fields from the equipment protocol, retaining only four core contents: standard timestamp, site number, rainfall value, and equipment status. This forms intermediate data with a consistent structure and unified fields, providing a standardized data foundation for subsequent cleaning and fusion.

[0166] S3, Multi-level Data Cleaning

[0167] The edge gateway performs local time-series multi-level cleaning on intermediate data based on a sliding time window, including: single-site time-series anomaly removal, multi-site spatial correlation verification, and outlier data imputation, to obtain effective standardized rainfall data R.Ei .

[0168] S3.1 Single-site timing anomaly removal

[0169] Set the maximum allowable value ΔR for rainfall variation between adjacent periods at a single site. max Calculate the time series variation difference:

[0170]

[0171] In the formula:

[0172] ΔR Ci The absolute value of the rainfall change in adjacent periods for rain gauge i;

[0173] R Ci (t) represents the standardized rainfall data for the current period of rain gauge i;

[0174] R Ci (t−1) represents the standardized rainfall data of rain gauge i in the previous period;

[0175] If ΔR Ci >ΔR max It was determined to be a timing anomaly and was removed.

[0176] In this embodiment:

[0177] Taking rain gauge 1 in sub-region 1 as an example:

[0178] Current period R C1 (t)=3.4, R in the previous period C1 If (t−1)=3.1, then

[0179]

[0180] That is, the data timing is normal.

[0181] S3.2 Multi-site spatial correlation verification

[0182] Set the maximum allowable value ΔR for spatial variation in the sub-region. jmax For any two rain gauges within a sub-region, calculate the spatial difference between them:

[0183]

[0184] In the formula:

[0185] ΔR ab The difference between standardized rainfall data from two rain gauges within a sub-region;

[0186] R Ci,a R Ci,b These are standardized rainfall data from two different rain gauges.

[0187] If ΔR ab >ΔR jmax Then, stations with a larger deviation from the regional mean are excluded;

[0188] If three or more sites are abnormal, the site that deviates the furthest from the median will be removed.

[0189] The rain gauge data in sub-region 1 was 6.0 mm, which exceeded the limit compared to other stations, so it was judged as abnormal and removed.

[0190] S3.3, Abnormal Data Filling

[0191] For missing values ​​at abnormal station i, the average value of normal rainfall gauges in the same area is used to fill the gaps.

[0192]

[0193] In the formula:

[0194] R0 Ci Replacement value for abnormal data from rain gauge i;

[0195] x = 1, 2, ..., K0 j ;

[0196] K0 j The number of normal rain gauges remaining in sub-region j after removing abnormal stations;

[0197] R Cx This represents the standardized data of the x-th normal rain gauge within the sub-region.

[0198] In this embodiment:

[0199] Taking sub-region 1 as an example:

[0200] If the remaining four normal stations in sub-region 1 have data values ​​of 3.4, 3.2, 3.3, and 3.5, then:

[0201]

[0202] After anomaly removal and filling, the effective standardized data R of each rain gauge was obtained. Ci and R0 Ci and relabel it as R Ei .

[0203] In this embodiment, abnormal station data are not included in the calculation of the average value of normal rain gauges in the same area; only normal station data are used for interpolation.

[0204] If all stations within a sub-region are abnormal, interpolation of data from neighboring regions with the same period will be used to determine the cause.

[0205] Example 1 constructs a monitoring network by deploying multiple types of rain gauges. An edge gateway aggregates multi-source data and calculates baseline values ​​in the absence of rainfall, eliminating the impact of equipment drift. Time alignment, unit standardization, and format normalization resolve the issues of heterogeneous data and time-series misalignment inherent in traditional equipment. A multi-level cleaning process, employing single-site time-series removal, multi-site spatial verification, and mean filling within the same region, accurately identifies and repairs abnormal data, ensuring the authenticity and validity of rainfall data. This provides a standardized and reliable raw data source for the intelligent stormwater and sewage drainage system, adapting to basic monitoring and local preliminary scheduling needs.

[0206] As a second embodiment of the present invention:

[0207] In its specific implementation, compared to Embodiment 1, the technical solution of this embodiment differs from that of Embodiment 1 only in that this embodiment further includes the following steps:

[0208] S4, Rainfall Characteristics

[0209] After cleaning, rainfall characteristics are constructed from the effective rainfall data, and three levels of standard rainfall data—station-level, sub-region-level, and system-level—are generated:

[0210] The generation method is as follows:

[0211] S4.1 Calculation of Standard Rainfall Values ​​at Station Level

[0212] First, calculate the distance weights:

[0213] The distance from rain gauge i to the center within sub-region j is L i The total distance is:

[0214]

[0215] Distance weight:

[0216]

[0217] In the formula:

[0218] W Li The distance weight for rain gauge i;

[0219] L i The distance from rain gauge i to the center of the sub-region;

[0220] L sumj This is the sum of the distances of all rain gauges within the sub-region;

[0221] K j The total number of rain gauges within the sub-region;

[0222] in, .

[0223] In this embodiment:

[0224] Taking rain gauge 1 in sub-region 1 as an example:

[0225] L1 = 120m, and the distances between stations are 120, 150, 90, 110, and 130m.

[0226]

[0227]

[0228] Next, calculate the stability weights:

[0229]

[0230] In the formula:

[0231] W Si The data stability weight for rain gauge i;

[0232] C i The number of normal data points within a consecutive P-cycle of the rain gauge i;

[0233] P represents the total number of statistical periods.

[0234] If Ci=0, then W Si =0, this site does not participate in the weight calculation;

[0235] In this embodiment, if rain gauge 1 functions normally for 10 consecutive times, then

[0236]

[0237] Overall weighting:

[0238]

[0239] In the formula:

[0240] W i The overall weight of rain gauge i;

[0241] 0.6 is the distance weighting coefficient;

[0242] 0.4 is the stability weight allocation coefficient.

[0243] In this embodiment:

[0244]

[0245] Normalized weights:

[0246]

[0247] In this embodiment:

[0248] If the total weight of subregion 1 is 2.6, then:

[0249]

[0250] Standard rainfall values ​​at each station level:

[0251]

[0252] In the formula:

[0253] R Bi This represents the standard rainfall value at the i-station level of the rain gauge;

[0254] W Ni Normalized comprehensive weight for rain gauge i;

[0255] R Ei This provides effective standardized rainfall data for rain gauge i.

[0256] In this embodiment:

[0257]

[0258] The data R of all stations within the sub-region were calculated sequentially. Bi It is used for fine-grained scheduling and sharing within sub-regions.

[0259] Station-level standard rainfall values ​​can be directly shared and accessed among drainage control nodes within a sub-region, and can be used for scenarios such as refined prediction of stormwater and sewage flow, gate opening adjustment, and pump station load allocation.

[0260] S4.2 Calculation of shared rainfall values ​​at the sub-region level

[0261] The initial shared rainfall value for sub-region j is the station-level average:

[0262]

[0263] In the formula:

[0264] R Zj For sub-region j, share the sub-region-level rainfall value;

[0265] K j The total number of rainfall gauges within sub-region j;

[0266] R Bi Let be the standard rainfall value at the i-th rain gauge station level within the sub-region.

[0267] In this embodiment:

[0268] The data for the five stations within sub-region 1 are 0.68, 0.67, 0.70, 0.69, and 0.66.

[0269]

[0270] In this embodiment, a three-phase smoothing correction is performed to improve the stability of cross-regional sharing:

[0271]

[0272] In the formula:

[0273] R0 Zj This is the corrected sub-region level shared rainfall value;

[0274] RZ j (t) represents the current periodic sub-region level rainfall value for sub-region j;

[0275] RZ j (t−1) represents the sub-regional rainfall value of the previous cycle;

[0276] RZ j (t−2) represents the sub-regional rainfall values ​​from the previous two cycles.

[0277] In this embodiment:

[0278] Substituting the values ​​of the current period (0.68), the previous period (0.67), and the two periods prior (0.66):

[0279] Corrected R0 Zj Used for data sharing between sub-regions.

[0280] The sub-regional standard rainfall values ​​are adapted to the data reception standards of the dispatching systems between different sub-regions, enabling stable data sharing between sub-regions.

[0281] S4.3, Calculation of system-level shared rainfall values

[0282] Weights are determined by the area proportion of each sub-region:

[0283] Total area:

[0284]

[0285] Sub-region weights:

[0286]

[0287] In the formula:

[0288] W Zj Let be the weight of subregion j in the system-level computation;

[0289] S j Let j be the area of ​​subregion j;

[0290] S total This represents the total coverage area of ​​the system.

[0291] In this embodiment:

[0292] In this example, the areas of the four sub-regions are 2.5, 3.0, 3.5, and 1.0 km².

[0293]

[0294] Therefore:

[0295] W Z1 =0.25;

[0296] W Z2 =0.3;

[0297] W Z3 =0.35;

[0298] W Z4 =0.1.

[0299] System-level uniform rainfall value:

[0300]

[0301] In the formula:

[0302] R S Rainfall values ​​are shared at the system level;

[0303] W Zj is the weight of subregion j, which is weighted according to the confluence area of ​​the subregion;

[0304] R0 Zj The sub-region-level shared rainfall value after correction for sub-region j;

[0305] M represents the total number of sub-regions within the system.

[0306] In this embodiment:

[0307] Substituting the corrected sub-region values ​​of 0.67, 0.72, 0.75, and 0.65:

[0308]

[0309] The RS is pushed to the central platform to achieve unified data sharing across the entire domain.

[0310] The system-level three-level standard rainfall value is used for overall platform scheduling, regional flood warning, and overall stormwater and sewage interception decision-making. It is the highest level of shared data. After its value is generated, it is simultaneously pushed to the overall platform of the intelligent stormwater and sewage drainage system, the control terminals of each sub-region, and the emergency dispatch unit. At the same time, it serves as the core input for constructing the characteristics of streaming computing nodes and is used to calculate higher-order characteristics such as regional rainfall intensity field and upstream and downstream rainfall difference.

[0311] S4.4, Hierarchical Sharing and Update Rules

[0312] Site-level R Bi Update cycle T, real-time sharing;

[0313] Subregion level R0 Zj Update cycle 2TB, stable sharing;

[0314] System-level R S Update cycle 3T, decision sharing.

[0315] In this embodiment, T=5 minutes, then the site-level update cycle is 5 minutes, the sub-region-level update cycle is 10 minutes, and the system-level update cycle is 15 minutes;

[0316] In this embodiment, a streaming computing node is also set up as a dedicated intelligent processing hub for rainfall data, which executes dynamic programmable rule chain decisions, as follows:

[0317] First, based on the faulty or drifting sensor information identified in step S3, normal rain gauges within a 500m radius of the abnormal station are designated as compensation nodes, and compensation data is obtained by inversely weighting by distance.

[0318] Next, the valid data from multiple local rain gauges are spatiotemporally aligned with the requested compensation data to reconstruct the rainfall time series of missing or abnormal areas.

[0319] Subsequently, the multi-source rainfall data were fused using a dynamic weighting method, with the fusion weights dynamically adjusted based on the real-time signal-to-noise ratio of the sensors and historical monitoring accuracy.

[0320] Finally, based on the three-level rainfall data, high-order characteristic indicators such as the regional rainfall intensity field and the upstream and downstream rainfall difference are determined for drainage decision-making, forming high-precision rainfall field data that can accurately reflect the overall rainfall distribution in the region.

[0321] In this embodiment, the calculation methods for the regional rainfall intensity field and the upstream and downstream rainfall difference are all existing technologies, and therefore will not be described in detail.

[0322] Example 2, based on data cleaning, calculates station-level standard rainfall values ​​according to distance and stability weights. After averaging and three-phase smoothing, sub-regional values ​​are obtained, and then system-level values ​​are generated according to area proportions. These three levels of data are updated differentially. This method upgrades single-point data into high-precision rainfall field data adaptable to different management levels. Station-level data meets the requirements for refined scheduling, sub-regional-level data ensures stable cross-regional sharing, and system-level data supports comprehensive decision-making. This significantly improves data fusion accuracy and sharing adaptability, providing stable, layered data support for drainage scheduling and urban flooding early warning.

[0323] As an embodiment of the present invention:

[0324] In its specific implementation, compared to Embodiment 1 and Embodiment 2, the technical solution of this embodiment combines the solutions of Embodiment 1 and Embodiment 2. The difference between the technical solution of this embodiment and Embodiment 1 and Embodiment 2 lies only in that this embodiment further includes the following steps:

[0325] S5, Data Publishing and Sharing

[0326] The fused three-level rainfall data, along with regional rainfall intensity fields and upstream-downstream rainfall differences, are connected to a unified regional data sharing bus. The bus supports MQTT and DDS low-latency protocols.

[0327] The main platform can call R S All R0 Zj All R Bi ;

[0328] Sub-region cells can call up the local and neighboring R0 regions. Zj and this area R Bi ;

[0329] Field control nodes only call local R Bi With R0 Zj .

[0330] Before the shared data is published, deviation verification is also performed:

[0331]

[0332] In the formula:

[0333] E j The shared data verification deviation for sub-region j (unit: millimeters);

[0334] R0 Zj The sub-region-level shared rainfall value (in millimeters) for sub-region j after smoothing correction.

[0335] K j Let i be the total number of effective rainfall meters (positive integers) within sub-region j, i = 1, 2, ..., K. j ;

[0336] In this embodiment, N = K1 + K2 + ... + K M ;

[0337] R Ei For the effective standardized rainfall data (unit: mm) of the i-th rain gauge in sub-region j;

[0338] Extract the pre-set maximum permissible deviation E max If E j ≤E maxIf sharing is allowed, then recalculate;

[0339] In this embodiment:

[0340] If E j ≤E max Execute the rollback mechanism:

[0341] Repeat step S3. If it still exceeds the allowable error, mark the sub-region as suspicious. If it exceeds the allowable error for Q consecutive cycles, trigger a device alarm.

[0342] In this embodiment:

[0343] The verification deviation of sub-region 1 is 0.12 < 0.3, so the data is acceptable.

[0344] Example 3 integrates previous technical solutions, connecting three levels of data to a unified shared bus. It supports low-latency protocols and hierarchical access permissions, along with deviation verification, rollback recalculation, and device alarm mechanisms. This achieves efficient data interoperability across the entire domain, meeting the different calling needs of the main platform, sub-regions, and field nodes. Furthermore, deviation control ensures the quality of shared data, preventing abnormal data from affecting scheduling decisions and guaranteeing uninterrupted data sharing. This significantly improves the collaborative scheduling capabilities of the intelligent stormwater and sewage drainage system and the accuracy of urban flood control.

[0345] As an embodiment of the present invention:

[0346] In specific implementation, compared with Embodiment 1, Embodiment 2 and Embodiment 3, the technical solution of this embodiment is to combine the solutions of Embodiment 1, Embodiment 2 and Embodiment 3.

[0347] Example 4 integrates the core technologies of all examples to form a complete closed-loop method from multi-source acquisition, normalization processing, multi-level cleaning, intelligent fusion to hierarchical sharing. It is fully compatible with multiple types of rain gauges, solving pain points such as data heterogeneity, anomalies, inaccurate fusion, and poor sharing. It outputs high-precision, high-real-time, and high-stability three-level rainfall data, comprehensively supporting applications across all scenarios such as stormwater and sewage pipe network scheduling, pump station linkage, and urban flooding early warning, maximizing the overall operational efficiency and safety control level of the smart drainage system.

[0348] It should be stated that all user data collected in this application was collected with the user's consent and authorization, and the use of user data is legal and compliant, and the use and processing of user data comply with the relevant laws, regulations and standards of the relevant regions.

[0349] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0350] It should be noted that, in this document, relational terms such as "first" and "second" are used only 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 process, method, article, or apparatus.

[0351] The above formulas are all dimensionless calculations. Dimensionless calculation involves introducing a reference benchmark, such as the maximum, minimum, standard deviation, or theoretical extreme value of a physical quantity, to transform the original physical quantity into a dimensionless relative value. This value is usually mapped to a specific interval, such as [0,1] or [-1,1], which eliminates the influence of units while preserving the relative size relationship of the physical quantities. The formula is derived from software simulation based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0352] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0353] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for sharing regional rain gauge data in a smart rainwater and sewage drainage system, characterized in that, Includes the following steps: S1. Deploy tipping bucket, radar, and weighing rain gauges to form a monitoring network in the area covered by the intelligent rain and sewage drainage system. Collect multi-source raw rainfall data through the edge gateway and calculate the rainfall reference value of the rain gauges. S2. Perform time alignment, unit unification to millimeters, and data format unification on the raw data. Subtract the baseline value without rainfall to obtain standardized initial rainfall data, and set negative values ​​to zero. S3. Based on the sliding window, single-site temporal anomaly removal, sub-regional multi-site spatial correlation verification, and outlier value filling with the mean of the same region are performed sequentially to obtain effective standardized rainfall data. S4. Calculate the comprehensive weight based on distance weight and stability weight, generate station-level standard rainfall value, average the station-level data and smooth it over three periods to obtain the sub-region-level shared rainfall value, and weight it according to the sub-region area ratio to obtain the system-level unified rainfall value. The three-level data are updated in different periods. S5. Data publishing and sharing: Connect the three levels of data to a unified shared bus, and share and call the data after the deviation verification is qualified.

2. The method for sharing regional rain gauge data in a smart rainwater and sewage drainage system according to claim 1, characterized in that: In step S1, the no-rainfall baseline value is calculated by weighting the factory-calibrated baseline value and the average of three no-rainfall data collected on site. The standardized initial rainfall data is the original data minus the baseline value, and negative values ​​are set to zero.

3. The method for sharing regional rain gauge data in a smart rainwater and sewage drainage system according to claim 1, characterized in that: In step S2, radar rain gauges are converted to millimeters according to the collection cycle, tipping bucket rain gauges are converted according to the counting and calibration coefficients, and weighing rain gauges are converted according to the fixed coefficient of the weighing value.

4. The method for sharing regional rain gauge data in a smart rainwater and sewage drainage system according to claim 1, characterized in that: In step S3, a single-site time-series anomaly is determined when the absolute value of the rainfall change in adjacent cycles exceeds a preset threshold, and a multi-site anomaly is determined when the difference between stations in a sub-region exceeds the limit and the station with the largest deviation is removed.

5. The method for sharing regional rain gauge data in a smart rainwater and sewage drainage system according to claim 1, characterized in that: In step S3, abnormal data is filled using the average value of normal stations in the same area, and when all sub-areas are abnormal, interpolation of neighboring area data in the same period is used.

6. The method for sharing regional rain gauge data in a smart rainwater and sewage drainage system according to claim 1, characterized in that: In step S4, the comprehensive weight is a combination of distance weight and stability weight in a fixed ratio, and the station-level standard rainfall value is calculated after normalization.

7. The method for sharing regional rain gauge data in a smart rainwater and sewage drainage system according to claim 1, characterized in that: In step S4, the sub-regional rainfall values ​​are smoothed and corrected for three periods: the current period, the previous period, and the two periods before that, and then used for cross-regional sharing.

8. The method for sharing regional rain gauge data in a smart rainwater and sewage drainage system according to claim 7, characterized in that: In step S4, the system-level unified rainfall value is obtained by weighting the rainfall values ​​of each sub-region after correction, with the sub-region area ratio as the weight.

9. A method for sharing regional rain gauge data in a smart rainwater and sewage drainage system according to claim 1, characterized in that: Site-level shared rainfall values ​​are updated according to a pre-specified collection cycle, sub-region-level shared rainfall values ​​are updated according to twice the collection cycle, and system-level shared rainfall values ​​are updated according to three times the collection cycle.

10. A method for sharing regional rain gauge data in a smart rainwater and sewage drainage system according to claim 1, characterized in that: In step S5, the deviation check is the absolute difference between the sub-region level shared value and the mean of the valid data. If the deviation exceeds the maximum allowable deviation, the process returns to step S2. If the deviation exceeds the maximum allowable deviation continuously, a device alarm is triggered.

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