Method, system, device and medium for rapid rainfall detection based on rainfall radar

Abstract

The present invention provides a method, system, device and medium for rapid detection of rainfall based on a rainfall radar, the method comprising: in response to the presence of rainfall in a target area: obtaining rainfall environment data; determining the estimated rainfall intensity of a future preset period of time in a monitoring area based on the rainfall environment data; the monitoring area is the area to be monitored within the target area; determining the estimated rainfall center of a future preset period of time based on the rainfall environment data; and determining the transmit pulse width of an X-band rainfall radar based on the position of the rainfall radar, the estimated rainfall center, and the estimated rainfall intensity. The detection method can realize rapid adjustment of the transmit pulse width of the X-band rainfall radar to ensure the accuracy of rainfall measurement and realize real-time monitoring of rapidly changing precipitation processes.

Description

Technical Field

[0001] The present invention relates to the technical field of rainfall monitoring equipment, and in particular to a method, system, device and medium for rapid rainfall detection based on a rainfall radar. Background Art

[0002] X-band rain radar is a radar system working in the X-band, mainly used to measure rainfall, especially local heavy rainfall. The wavelength of the X-band is between 2.4 and 3.75 cm. Due to its short wavelength, it is very sensitive to the detection of small meteorological phenomena (such as rainfall, clouds and fog). However, compared with radars with longer wavelengths (such as S-band radars), X-band radars are more susceptible to factors such as atmospheric attenuation and raindrops. For example, for different rainfall types (such as convective rain, orographic rain, frontal rain, etc.) and different rainfall intensities, the signal attenuation of X-band rain radars is different, and the accuracy of rainfall measurement results may also be different. Therefore, it is necessary to adaptively adjust the X-band rain radar to improve the efficiency and reliability of rainfall measurement. At present, the X-band rain radar mainly adjusts the rainfall measurement parameters (such as the transmission pulse width, etc.) of the X-band rain radar manually based on experience, which has the defects of untimely adjustment of rainfall measurement parameters and low adjustment accuracy.

[0003] Therefore, it is hoped to provide a rapid rainfall detection method, system, device and medium based on a rain measuring radar, which can quickly adjust the transmission pulse width of the X-band rain measuring radar according to known partial rainfall environment data to ensure the accuracy of rainfall measurement and realize real-time monitoring of rapidly changing precipitation processes. Summary of the invention

[0004] One of the embodiments of the present specification provides a method for rapid rainfall detection based on a rain measuring radar, the method comprising: in response to the presence of rainfall in a target area: acquiring rainfall environment data; determining an estimated rainfall intensity of a monitoring area for a preset future period of time based on the rainfall environment data; the monitoring area is an area monitored within the target area; determining an estimated rainfall center for the preset future period of time based on the rainfall environment data; and determining a transmit pulse width of an X-band rain measuring radar based on the position of the rain measuring radar, the estimated rainfall center, and the estimated rainfall intensity.

[0005] One or more embodiments of the present specification provide a rapid rainfall detection system based on a rain measuring radar, the system comprising: an acquisition module, a rainfall intensity module, a rainfall center module and a first determination module; the acquisition module is configured to acquire rainfall environment data; the rainfall intensity module is configured to determine the estimated rainfall intensity of a monitoring area for a future preset time period based on the rainfall environment data; the monitoring area is the area to be monitored within the target area; the rainfall center module is configured to determine the estimated rainfall center for the future preset time period based on the rainfall environment data; the first determination module is configured to determine the transmit pulse width of the X-band rain measuring radar based on the position of the rain measuring radar, the estimated rainfall center, and the estimated rainfall intensity.

[0006] One or more embodiments of the present specification provide a rapid rainfall detection device based on a rain measuring radar, the device comprising at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least part of the computer instructions to implement any of the above-mentioned rapid rainfall detection methods based on a rain measuring radar.

[0007] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes any one of the above-described methods for rapid rainfall detection based on a rain measuring radar.

[0008] Through the rapid rainfall detection method, system, device and medium based on the rainfall radar, it is possible to quickly adjust the transmission pulse width of the X-band rainfall radar according to the known partial rainfall environment data to ensure the accuracy of rainfall measurement and realize real-time monitoring of the rapidly changing precipitation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0010] Figure 1 is an exemplary module diagram of a rapid rainfall detection system based on a rainfall radar according to some embodiments of this specification;

[0011] Figure 2 is an exemplary flow chart of a method for rapid rainfall detection based on a rainfall radar according to some embodiments of this specification;

[0012] Figure 3 is an exemplary flow chart of determining the transmit pulse width of an X-band rain detection radar according to some embodiments of this specification;

[0013] Figure 4 This is an exemplary flow chart for determining the scanning frequency of an X-band rain detection radar according to some embodiments of this specification. DETAILED DESCRIPTION

[0014] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0015] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0016] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0017] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0018] Figure 1 It is a module diagram of a rapid rainfall detection system based on a rain measuring radar according to some embodiments of this specification.

[0019] In some embodiments, a rapid rainfall detection system 100 based on a rainfall radar (hereinafter referred to as the system 100 ) may include an acquisition module 110 , a rainfall intensity module 120 , a rainfall center module 130 , and a first determination module 140 .

[0020] The acquisition module 110 may be configured to acquire rainfall environment data.

[0021] The rainfall intensity module 120 may be configured to determine an estimated rainfall intensity of a monitoring area in a future preset period of time based on the rainfall environment data, wherein the monitoring area is an area to be monitored within the target area.

[0022] The rainfall center module 130 may be configured to determine an estimated rainfall center for a future preset period of time based on the rainfall environment data.

[0023] The first determination module 140 may be configured to determine a transmit pulse width of the X-band rain measuring radar based on the position of the rain measuring radar, the estimated rainfall center, and the estimated rainfall intensity.

[0024] In some embodiments, the first determination module 140 can be further configured to: loop the following steps until the transmit pulse width of the X-band rain measuring radar is obtained: based on the distance between the position of the rain measuring radar and the estimated rainfall center, and the estimated rainfall intensity, generate multiple candidate transmit pulse widths; for each candidate transmit pulse width, based on the candidate transmit pulse width, the transmit power of the X-band rain measuring radar, the position of the rain measuring radar, the estimated rainfall center, and the estimated rainfall intensity, determine the first signal attenuation value corresponding to the candidate transmit pulse width through a first attenuation value prediction model, the first attenuation value prediction model being a machine learning model; in response to the first signal attenuation values ​​corresponding to the multiple candidate transmit pulse widths being greater than a preset attenuation threshold, adjust

[0025] The rain measuring radar position is adjusted; and in response to the presence of a first signal attenuation value less than a preset attenuation threshold value among the first signal attenuation values ​​corresponding to the plurality of candidate transmit pulse widths, the transmit pulse width of the X-band rain measuring radar is determined.

[0026] In some embodiments, the first determination module 140 can be further configured to: determine the recommended rain measuring radar position based on the first signal attenuation values ​​corresponding to multiple candidate transmission pulse widths and the estimated rainfall center; and adjust the rain measuring radar position based on the recommended rain measuring radar position.

[0027] In some embodiments, the system 100 may further include a second determination module 150 .

[0028] The second determination module 150 may be configured to determine scanning parameters of the X-band rainfall radar based on the estimated rainfall intensity, where the scanning parameters include a scanning range.

[0029] In some embodiments, the scanning parameters also include a scanning frequency, and the second determination module 150 may be further configured to determine the scanning frequency of the X-band rain measuring radar based on the estimated rainfall intensity and the transmit pulse width of the X-band rain measuring radar.

[0030] For more information about the acquisition module 110, the rainfall intensity module 120, the rainfall center module 130, the first determination module 140 and the second determination module 150, see Figure 2-Figure 4 Related description in .

[0031] It should be understood that Figure 1 The system and its modules shown can be implemented in various ways. It should be noted that the above description of the rainfall rapid detection system based on rain radar and its modules is only for the convenience of description and cannot limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, Figure 1 The acquisition module 110, rainfall intensity module 120, rainfall center module 130, first determination module 140 and second determination module 150 disclosed in the specification can be different modules in a system, or one module can realize the functions of two or more modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the protection scope of this specification.

[0032] Figure 2 is an exemplary flow chart of a method for rapid rainfall detection based on a rainfall radar according to some embodiments of this specification. In some embodiments, the first process 200 may be executed by the system 100. Figure 2 As shown, the first process 200 includes the following steps 210 to 250.

[0033] In some embodiments, the system 100 may execute the following steps 210 to 240 or the following steps 210 to 250 in response to rainfall in the target area.

[0034] Step 210, obtaining rainfall environment data.

[0035] Rainfall environment data refers to various data related to rainfall.

[0036] In some embodiments, the rainfall environment data includes at least one rainfall area in the target area and its corresponding rainfall type.

[0037] The target area refers to the area covered by the rain radar, such as Chengdu, etc. Rain radar is a device that conducts meteorological observations based on the principle of electromagnetic wave scattering.

[0038] In some embodiments, the system 100 may divide the target area according to administrative regions to obtain multiple sub-regions. For example, if the target area is Chengdu, the sub-regions may be divided into High-tech Zone, Wuhou District, Tianfu New District, etc.

[0039] A rainfall area is a sub-area where it is raining.

[0040] In some embodiments, the system 100 may determine a sub-area where it is raining as a rainfall area. The rainfall type may include one of convective rain, orographic rain, frontal rain, and the like.

[0041] In some embodiments, the system 100 may obtain rainfall environment data from a third party (eg, a weather bureau).

[0042] Step 220, based on the rainfall environment data, determine the estimated rainfall intensity of the monitoring area in a future preset period of time.

[0043] The monitoring area refers to the sub-area within the target area where rainfall monitoring is required. For example, the monitoring area can be Tianfu New District.

[0044] The future preset time period refers to a specific time period in the future. The future preset time period can be preset by those skilled in the art based on experience.

[0045] The estimated rainfall intensity refers to the rainfall intensity in the monitoring area during the future preset period. The rainfall intensity refers to the amount of rainfall per unit time. The rainfall intensity is usually expressed in millimeters per hour (mm / h).

[0046] The estimated rainfall intensity is a key parameter to measure the intensity of the rainfall process in the monitoring area within a preset period of time in the future.

[0047] In some embodiments, the system 100 may determine a first target feature vector based on rainfall environment data; determine a first associated feature vector based on the first target feature vector through a first vector database; determine a reference rainfall intensity corresponding to the first associated feature vector as an estimated rainfall intensity for a future preset period of time in the monitoring area, wherein the reference rainfall intensity is the historical actual rainfall intensity of the historical monitoring area within a historical preset period of time after a first historical moment. The length of the historical preset period of time may be equal to the length of the future preset period of time. The historical preset period of time may be preset by a person skilled in the art based on experience.

[0048] The first vector database includes a plurality of first reference feature vectors, wherein each first reference feature vector has a corresponding reference rainfall intensity, wherein the reference rainfall intensity is the historical actual rainfall intensity in the historical monitoring area within the historical preset period after the first historical moment. In some embodiments, the system 100 may determine the ratio of the rainfall actually collected in the historical monitoring area within the historical preset period to the historical preset period as the historical actual rainfall intensity. The first reference feature vector is a feature vector constructed based on the historical rainfall environment data of the monitoring area.

[0049] In some embodiments, the system 100 may determine, based on the first target feature vector, a first reference feature vector that meets a first preset condition in a first vector database, and determine the first reference feature vector that meets the first preset condition as a first associated feature vector. In some embodiments, the first preset condition may include a minimum vector distance, etc.

[0050] In some embodiments, after determining the estimated rainfall intensity of the monitoring area in the future preset period of time, the system 100 may perform the following steps 230 to 240, and / or step 250.

[0051] Step 230, based on the rainfall environment data, determine the estimated rainfall center for a future preset period of time.

[0052] The estimated rainfall center refers to the location with the largest rainfall intensity in the monitoring area. In some embodiments, the estimated rainfall center may be one or more.

[0053] In some embodiments, system 100 can determine a second target feature vector based on rainfall environment data; determine a second associated feature vector based on the second target feature vector through a second vector database; and determine a reference rainfall center corresponding to the second associated feature vector as an estimated rainfall center for a future preset period of time, wherein the reference rainfall center is an actual rainfall center in a historical monitoring area within a historical preset period of time after a first historical moment.

[0054] The second vector database includes a plurality of second reference feature vectors, wherein each second reference feature vector has a corresponding reference rainfall center, wherein the reference rainfall intensity is the actual rainfall center in the historical monitoring area within the historical preset time period after the first historical moment. In some embodiments, the actual rainfall center can be obtained by manual marking by a person skilled in the art. The second reference feature vector is a feature vector constructed based on the historical rainfall environment data of the historical monitoring area. The historical preset time period can be preset by a person skilled in the art based on experience.

[0055] In some embodiments, the system 100 may determine, based on the second target feature vector, a second reference feature vector that meets a second preset condition in the second vector database, and determine the second reference feature vector that meets the second preset condition as the second associated feature vector. In some embodiments, the second preset condition may include a minimum vector distance, etc.

[0056] Step 240, determining the transmit pulse width of the X-band rain measuring radar based on the position of the rain measuring radar, the estimated rainfall center, and the estimated rainfall intensity.

[0057] The transmit pulse width refers to the length of time that the rain measuring radar transmits a single pulse signal. The unit of the transmit pulse width may be μs. In some embodiments, the system 100 may determine the transmit pulse width of the X-band rain measuring radar through a pulse width algorithm based on the rain measuring radar position, the estimated rainfall center, and the estimated rainfall intensity.

[0058] As an example only, the pulse width algorithm formula may be:

[0059] Transmitted pulse width = k1×d+k2 / Q, where d is the distance between the rain radar position and the estimated rainfall center, and the unit of d is m; Q is the estimated rainfall intensity, and the unit of Q is mm / h; k1 and k2 are coefficients greater than 0 and less than 1, the unit of k1 is μs / m, and the unit of k2 is μs×(mm / h). The values ​​of k1 and k2 can be preset by technicians in this field based on experience. The "time / distance (μs / m)" dimension of k1 needs to correspond to the time requirement when the radar detection range is expanded. For example, k1 corresponds to the relationship between the pulse repetition period and the maximum detection distance.

[0060] The "time × rainfall intensity (μs × (mm / h))" dimension of k2 needs to reflect the impact of rainfall interference on the time domain characteristics of the signal. For example, k2 reflects the pulse broadening effect caused by raindrop scattering. In some embodiments, the system 100 can obtain experimental data through experiments; and fit the experimental data (such as least squares fitting, etc.) to obtain the values ​​of k1 and k2. The experimental data can include a table consisting of measured data of pulse widths at different distances and rainfall intensities.

[0061] In some embodiments, the system 100 may determine the distance between the position of the rain measuring radar and the estimated rainfall center by using a distance calculation formula (eg, Euclidean distance, etc.) based on the position of the rain measuring radar and the estimated rainfall center.

[0062] It can be understood that the greater the distance between the rain measuring radar position and the estimated rainfall center, the larger the required detection range and the longer the required transmission pulse width; if the estimated rainfall intensity is greater, it means that the interference is greater, and in order to improve the measurement accuracy, the transmission pulse width needs to be shortened.

[0063] In some embodiments, the system 100 may also execute the following steps in a loop until the transmit pulse width of the X-band rain measuring radar is obtained: based on the distance between the position of the rain measuring radar and the estimated rainfall center, and the estimated rainfall intensity, generate multiple candidate transmit pulse widths; for each candidate transmit pulse width, based on the candidate transmit pulse width, the transmit power of the X-band rain measuring radar, the position of the rain measuring radar, the estimated rainfall center, and the estimated rainfall intensity, determine the first signal attenuation value corresponding to the candidate transmit pulse width through a first attenuation value estimation model, where the first attenuation value estimation model is a machine learning model; in response to the first signal attenuation values ​​corresponding to multiple candidate transmit pulse widths being greater than a preset attenuation threshold, adjust the position of the rain measuring radar; in response to the presence of a signal attenuation value less than the preset attenuation threshold among the first signal attenuation values ​​corresponding to multiple candidate transmit pulse widths, determine the transmit pulse width of the X-band rain measuring radar. For more information about this section, please see below. Figure 3 Instructions in .

[0064] Step 250, based on the estimated rainfall intensity, determine the scanning parameters of the X-band rainfall radar.

[0065] Scanning parameters refer to parameters related to rainfall radar scanning.

[0066] In some embodiments, the scan parameters include a scan range.

[0067] Scanning range refers to the angular coverage of the X-band rain measuring radar within a plane.

[0068] In some embodiments, the system 100 can determine a third target feature vector based on the estimated rainfall intensity; determine multiple third associated feature vectors through a third vector database based on the third target feature vector; and determine the reference scanning parameters that meet the first specific condition among the reference scanning parameters corresponding to the multiple third associated feature vectors as the scanning parameters. The first specific condition means that when the X-band rainfall radar performs subsequent scanning according to the reference scanning parameter, the actual historical signal attenuation value is the lowest.

[0069] The third vector database includes a plurality of third reference feature vectors, wherein each third reference feature vector has a corresponding reference scanning parameter. In some embodiments, the reference scanning parameter may be an actual historical parameter corresponding to the historical rainfall environment data. The third reference feature vector is a feature vector constructed based on the historical rainfall intensity of the monitoring area.

[0070] In some embodiments, the system 100 may determine a plurality of third reference feature vectors that meet the third preset condition in the third vector database based on the third target feature vector, and determine the third reference feature vectors that meet the third preset condition as the third associated feature vector. In some embodiments, the third preset condition may include (estimated rainfall intensity - historical rainfall intensity) / estimated rainfall intensity < preset ratio, etc. The preset ratio may be preset by those skilled in the art based on experience.

[0071] In some embodiments, when there are multiple locations with the highest rainfall intensity at the same time, that is, when there are multiple estimated rainfall centers, the scanning range is also related to the distribution of the estimated rainfall centers in the monitoring area.

[0072] The distribution of predicted rainfall centers in the monitoring area refers to the location distribution of multiple predicted rainfall centers in the monitoring area.

[0073] In some embodiments, in response to the estimated scattered distribution of rainfall centers in the monitoring area, the system 100 can increase the scanning range so that the X-band rainfall radar has sufficient layers to accurately scan the monitoring area; in response to the estimated dense distribution of rainfall centers in the monitoring area, the system 100 can reduce the scanning range, thereby improving the resolution of the X-band rainfall radar.

[0074] In some embodiments, the system 100 can calculate the distance from each estimated rainfall center in multiple estimated rainfall centers to its nearest adjacent estimated rainfall center, and then take the average distance of each distance; in response to the average distance being greater than a distance threshold, the estimated rainfall centers are dispersedly distributed in the monitoring area; otherwise, the estimated rainfall centers are densely distributed in the monitoring area.

[0075] In some embodiments, the larger the average distance, the more dispersed the distribution of the estimated rainfall centers in the monitoring area; the smaller the average distance, the denser the distribution of the estimated rainfall centers in the monitoring area. The distance threshold can be preset by those skilled in the art based on experience. The distance from each estimated rainfall center to its nearest neighboring estimated rainfall center can be the straight-line distance from each estimated rainfall center to its nearest neighboring estimated rainfall center. In some embodiments, the system 100 can calculate the distance from each estimated rainfall center to its nearest neighboring estimated rainfall center using the straight-line distance formula between two points.

[0076] In some embodiments of the present specification, when there are multiple estimated rainfall centers that may represent rainfall processes of different types or stages (e.g., developing thunderstorms, mature precipitation belts, etc.), sufficient stratification is required to accurately describe the development and intensity of each rainfall process, that is, the X-band rainfall radar needs a sufficiently large scanning range to cover the different height levels of multiple precipitation processes. Therefore, the scanning range is also related to the distribution of the estimated rainfall centers in the monitoring area to ensure that the precipitation characteristics of each rainfall center are fully detected. When the estimated rainfall centers are dispersed in the monitoring area, the scanning range can be increased to accurately scan the monitoring area; when the estimated rainfall centers are densely distributed in the monitoring area, the resolution can be increased to obtain a more accurate image.

[0077] Improve the quality of scan results.

[0078] In some embodiments, the scanning parameters also include a scanning frequency, and the system 100 may also determine the scanning frequency of the X-band rain measuring radar based on the estimated rainfall intensity and the transmit pulse width of the X-band rain measuring radar.

[0079] Scanning frequency refers to the number of scans of the X-band rainfall radar per unit time.

[0080] In some embodiments, the system 100 can determine a fourth target feature vector based on the estimated rainfall intensity and the transmit pulse width of the X-band rain measuring radar; determine multiple fourth associated feature vectors through a fourth vector database based on the fourth target feature vector; and determine the reference scanning frequency that satisfies the second specific condition among the reference scanning frequencies corresponding to the multiple fourth associated feature vectors as the scanning frequency of the X-band rain measuring radar. The second specific condition refers to the X-band rain measuring radar transmitting a reference transmit pulse width according to the reference scanning frequency, and when performing subsequent scanning, the historical actual signal attenuation value is the lowest.

[0081] The fourth vector database includes a plurality of fourth reference feature vectors, wherein each fourth reference feature vector has a corresponding reference scanning frequency. In some embodiments, the reference scanning frequency may be a historical actual scanning frequency corresponding to historical rainfall environment data. The fourth reference feature vector is a feature vector constructed based on the historical rainfall intensity of the historical monitoring area and the historical transmission pulse width of the X-band rainfall radar.

[0082] In some embodiments, the system 100 may determine a fourth reference feature vector that meets a fourth preset condition in a fourth vector database based on the fourth target feature vector, and determine the fourth reference feature vector that meets the fourth preset condition as a fourth associated feature vector. In some embodiments, the fourth preset condition may include a minimum vector distance, etc.

[0083] In some embodiments of the present specification, the scanning frequency of the X-band rain measuring radar is determined based on the estimated rainfall intensity and the transmission pulse width of the X-band rain measuring radar, which can improve the accuracy of determining the scanning frequency, thereby improving the adaptability and monitoring effect of the X-band rain measuring radar under dynamic weather conditions.

[0084] In some embodiments, the system 100 may also generate multiple candidate scanning frequencies; for each candidate scanning frequency, based on the estimated rainfall intensity, the transmit pulse width of the X-band rain radar, the transmit power of the X-band rain radar, the candidate scanning frequency, and the location of the rain radar, a second signal attenuation value is determined through a second attenuation value estimation model, where the second attenuation value estimation model is a machine learning model; and based on the second signal attenuation values ​​of the multiple candidate scanning frequencies, the scanning frequency of the X-band rain radar is determined. For more information about this part, please see below. Figure 4 Instructions in .

[0085] In some embodiments of the present specification, combined with rainfall environment data, the estimated rainfall intensity and estimated rainfall center of the monitoring area in a preset future period of time can be quickly estimated, and then combined with the position of the rain measuring radar to control the X-band rain measuring radar to operate at a reasonable transmission pulse width to obtain accurate measurement data, thereby monitoring the rapidly changing rainfall process in real time.

[0086] In addition, since the scanning parameters of the X-band rain measuring radar are important factors affecting the detection capability, data quality and application effect of the X-band rain measuring radar, the X-band rain measuring radar can effectively obtain the meteorological information of the monitoring area, improve the accuracy of rainfall measurement, and timely warn of extreme weather events by reasonably setting and adjusting this scanning parameter through the aforementioned rapid rainfall detection method based on rain measuring radar.

[0087] Figure 3 FIG. 1 is an exemplary flow chart of determining the transmit pulse width of an X-band rain radar according to some embodiments of this specification. Figure 3 As shown, the second process 300 includes the following steps 310 to 340. In some embodiments, the second process 300 can be performed by the system 100 to perform the following steps 310 to 340.

[0088] In some embodiments, the system 100 may execute the following steps 310 to 340 in a loop until the transmit pulse width of the X-band rain measuring radar is obtained.

[0089] Step 310, generating a plurality of candidate transmit pulse widths based on the distance between the rain measuring radar position and the estimated rainfall center, and the estimated rainfall intensity.

[0090] In some embodiments, the system 100 can determine the straight-line distance between the two points based on the coordinates of the rain measuring radar position and the estimated rainfall center by using the straight-line distance formula between the two points as the distance between the rain measuring radar position and the estimated rainfall center.

[0091] The candidate transmit pulse width refers to the transmit pulse width of the rain measuring radar to be determined.

[0092] In some embodiments, the system 100 can determine the transmit pulse width of an X-band rain radar based on the distance between the location of the rain radar and the estimated rainfall center, and the estimated rainfall intensity, through a pulse width algorithm; and based on the transmit pulse width, randomly adjust (e.g., increase or decrease) a random number of unit adjustments, thereby obtaining multiple candidate transmit pulse widths. The unit adjustment refers to the amount of transmit pulse width that is increased or decreased at one time based on the transmit pulse width. The unit adjustment can be preset by those skilled in the art based on experience. For a description of the radar algorithm formula, see Figure 2 Relevant instructions in step 240.

[0093] Step 320, for each candidate transmit pulse width, based on the candidate transmit pulse width, the transmit power of the X-band rain measuring radar, the location of the rain measuring radar, the estimated rainfall center, and the estimated rainfall intensity, a first attenuation value estimation model is used to determine the first signal attenuation value corresponding to the candidate transmit pulse width.

[0094] In some embodiments, the first attenuation value estimation model is a machine learning model. In some embodiments, the first attenuation value estimation model can be a neural network (NN) model or a deep neural network (DNN) model.

[0095] In some embodiments, the input of the first attenuation value estimation model may include the candidate transmit pulse width, the transmit power of the X-band rain radar, the location of the rain radar, the estimated rainfall center, and the estimated rainfall intensity, and the output may include the first signal attenuation value corresponding to the candidate transmit pulse width. For a description of the location of the rain radar, the estimated rainfall center, and the estimated rainfall intensity, see Figure 2 See the relevant instructions in .

[0096] Transmitting power refers to the power output when the rain measuring radar transmits electromagnetic waves. Transmitting power can be preset by those skilled in the art according to actual needs.

[0097] The first signal attenuation value refers to the signal strength difference of the electromagnetic wave emitted by the rain measuring radar according to the candidate emission pulse width, which gradually decreases as the transmission distance increases.

[0098] In some embodiments, the input of the first attenuation value prediction model also includes the scanning frequency of the X-band rain detection radar.

[0099] For a description of the scanning frequency, see Figure 2 Relevant instructions in step 250.

[0100] In some embodiments of the present specification, when predicting the first signal attenuation value corresponding to the candidate transmission pulse width, the scanning frequency of the X-band rain measuring radar is also considered, which can further improve the accuracy of predicting the first signal attenuation value corresponding to the candidate transmission pulse width.

[0101] In some embodiments, the first attenuation value estimation model can be obtained through training based on a plurality of first training samples with first labels.

[0102] In some embodiments, each group of first training samples in the first training samples may include a historical sample transmission pulse width, a historical sample transmission power, a historical sample rainfall radar position, a historical sample rainfall center, and a historical sample rainfall intensity corresponding to a first historical moment of an X-band rainfall radar in a historical monitoring area. In some embodiments, the first training samples may be acquired based on historical data.

[0103] In some embodiments, the first tag may be the actual historical signal attenuation value after the X-band rain measuring radar works according to the historical sample emission pulse width. In some embodiments, the system 100 may obtain the historical emission signal and the captured reflection signal after the rain measuring radar works according to the historical sample emission pulse width, and calculate the actual signal attenuation value obtained according to the preset method as the first tag. The preset method may include a signal attenuation calculation formula, etc.

[0104] For example only, the signal attenuation value L ( dB )=32.45+20log10( D )+20log10( Pt ), where D is the propagation distance of the electromagnetic wave emitted by the X-band rainfall radar, Pt is the transmission power of the X-band rain radar, and 32.45 is a preset constant in dB. The propagation distance of the electromagnetic wave transmitted by the X-band rain radar can be determined based on the actually measured historical transmission signal and the captured reflection signal. The transmission power of the X-band rain radar can be the actual transmission power of the X-band rain radar. 32.45 is preset by those skilled in the art based on experience.

[0105] In some embodiments, each group of first training samples in the first training samples may further include a historical sample scanning frequency of the X-band rainfall radar.

[0106] Step 330: In response to the first signal attenuation values ​​corresponding to the plurality of candidate transmission pulse widths being greater than a preset attenuation threshold, the position of the rain measuring radar is adjusted.

[0107] The preset attenuation threshold refers to a critical value of the attenuation value of the first signal. The preset attenuation threshold can be preset by those skilled in the art based on experience.

[0108] In some embodiments, in response to the first signal attenuation values ​​corresponding to the plurality of candidate transmission pulse widths being greater than a preset attenuation threshold, the system 100 may adjust the position of the rain measuring radar to bring the position of the rain measuring radar closer to the estimated rainfall center by a unit distance, and re-execute the above steps 310 to 320 based on the adjusted position of the rain measuring radar. The unit distance may be preset by those skilled in the art based on experience.

[0109] In some embodiments, the system 100 may also determine a recommended rain measuring radar position based on first signal attenuation values ​​corresponding to a plurality of candidate transmission pulse widths and an estimated rainfall center; and adjust the rain measuring radar position based on the recommended rain measuring radar position.

[0110] Recommended rain radar locations refer to the specific locations where rain radars are recommended to be located.

[0111] In some embodiments, the system 100 may use the following steps 331 to 335 to determine the recommended rain measuring radar position.

[0112] Step 331 , performing linear fitting (or other nonlinear fitting) based on the first signal attenuation values ​​x corresponding to the plurality of candidate transmit pulse widths and the plurality of candidate transmit pulse widths y, to obtain a relational expression F(X).

[0113] Linear fitting methods may include least squares method, gradient descent method, etc. Nonlinear fitting methods may include least squares method, etc.

[0114] Step 332: Substitute the signal attenuation threshold x0 into F(X) to obtain the threshold pulse width y.

[0115] The signal attenuation threshold refers to the size of the first signal attenuation value expected by the user. The signal attenuation threshold can be specified by the user. The signal attenuation threshold can be different from the preset attenuation threshold.

[0116] The threshold pulse width refers to the transmit pulse width corresponding to the preset signal attenuation value.

[0117] Step 333, according to the threshold pulse width, through the detection range table, obtain the detection range of the X-band rain measuring radar corresponding to the threshold pulse width.

[0118] The detection range table contains a corresponding relationship between the threshold pulse width and the detection range of the X-band rain measuring radar. The detection range table can be constructed by those skilled in the art based on prior knowledge.

[0119] Step 334, based on the detection range, obtain the distance between the recommended rain measuring radar position and the estimated rainfall center. In some embodiments, the system 100 can obtain the distance between the recommended rain measuring radar position and the estimated rainfall center in a variety of ways based on the detection range. For example, the system 100 can select the median in the detection range as the distance between the recommended rain measuring radar position and the estimated rainfall center.

[0120] Step 335, obtaining a recommended rain measuring radar position based on the distance between the rain measuring radar position and the estimated rainfall center, and the estimated rainfall center.

[0121] In some embodiments, the system 100 can reversely calculate the recommended rain measuring radar position based on the distance between the rain measuring radar position and the estimated rainfall center and the estimated rainfall center according to the straight-line distance formula between the two points.

[0122] In some embodiments, the system 100 may adjust the position of the rain measuring radar to a recommended rain measuring radar position.

[0123] In some embodiments of the present specification, the first attenuation value prediction model is used to estimate in advance the first signal attenuation values ​​corresponding to multiple candidate transmission pulse widths, and the candidate transmission pulse widths whose first signal attenuation values ​​are greater than a preset attenuation threshold are excluded in advance, so that the remaining candidate transmission pulse widths at least meet the preset attenuation threshold requirement, thereby avoiding the time waste caused by the rain measuring radar repeatedly testing inappropriate candidate transmission pulse widths, thereby achieving the requirement of rapid detection.

[0124] Step 340: In response to the presence of a first signal attenuation value less than a preset attenuation threshold value among the first signal attenuation values ​​corresponding to the plurality of candidate transmit pulse widths, determine the transmit pulse width of the X-band rain measuring radar.

[0125] In some embodiments, in response to the presence of a first signal attenuation value less than a preset attenuation threshold among the first signal attenuation values ​​corresponding to multiple candidate transmit pulse widths, the system 100 selects the candidate transmit pulse width with the smallest first signal attenuation value, determines it as the transmit pulse width of the X-band rain measuring radar and ends the loop.

[0126] In some embodiments of the present specification, by predicting the first signal attenuation values ​​of multiple candidate transmission pulse widths, it is possible to effectively reflect whether the candidate transmission pulse widths can be effectively measured, and then quickly determine the most suitable candidate transmission pulse width among the candidate transmission pulse widths, so that the application of the transmission pulse width in rainfall monitoring is more accurate and reliable.

[0127] Figure 4 is an exemplary flow chart of a method for determining the scanning frequency of an X-band rain radar according to some embodiments of this specification. In some embodiments, the third process 400 may be executed by the system 100. Figure 4 As shown, the third process 400 includes the following steps 410 to 430.

[0128] Step 410: Generate multiple candidate scanning frequencies.

[0129] The candidate scanning frequency refers to the scanning frequency to be determined.

[0130] In some embodiments, the system 100 may randomly generate candidate scanning frequencies in a variety of ways. For example, the system 100 may randomly select a plurality of historical scanning frequencies as a plurality of candidate scanning frequencies.

[0131] Step 420, for each candidate scanning frequency, based on the estimated rainfall intensity, the transmit pulse width of the X-band rain measuring radar, the transmit power of the X-band rain measuring radar, the candidate scanning frequency, and the position of the rain measuring radar, determine the second signal attenuation value through the second attenuation value estimation model.

[0132] For the estimated rainfall intensity, the location of the rain radar and the transmission pulse width of the X-band rain radar, please refer to Figure 2 See the relevant instructions in .

[0133] The second signal attenuation value refers to the signal strength difference of the electromagnetic waves emitted by the rain measuring radar according to the candidate scanning frequency, which gradually decreases as the transmission distance increases.

[0134] In some embodiments, the second attenuation value estimation model is a machine learning model. In some embodiments, the second estimation model can be a neural network (NN) model or a deep neural network (DNN) model.

[0135] In some embodiments, the second attenuation value estimation model can be obtained through training based on a plurality of second training samples with second labels.

[0136] In some embodiments, each group of second training samples in the second training samples may include historical sample rainfall intensity, historical sample transmission power, historical sample transmission pulse width, historical sample scanning frequency, and historical sample rainfall radar position corresponding to the first historical moment of the X-band rainfall radar in the historical monitoring area. In some embodiments, the second training samples may be acquired based on historical data.

[0137] In some embodiments, the second tag may be the actual historical signal attenuation value obtained after the X-band rain measuring radar transmits the historical sample power according to the historical sample scanning frequency. In some embodiments, the system 100 may obtain the actual measured historical transmission signal and the captured reflection signal after the X-band rain measuring radar transmits the historical sample power according to the historical sample scanning frequency, and calculates the actual signal attenuation value obtained according to the preset method as the second tag. For more information about the preset method, see Figure 3 Relevant instructions in step 320.

[0138] Step 430: Determine the scanning frequency of the X-band rain measuring radar based on the second signal attenuation values ​​of the plurality of candidate scanning frequencies.

[0139] In some embodiments, the system 100 may determine the candidate scanning frequency corresponding to the minimum second signal attenuation value as the scanning frequency of the X-band rain measuring radar based on the second signal attenuation values ​​of multiple candidate scanning frequencies.

[0140] In some embodiments of the present specification, multiple candidate scanning frequencies are generated, and combined with the second signal attenuation value prediction model, the subsequent signal data quality of each candidate scanning frequency is evaluated, and a more appropriate scanning frequency is selected from the perspective of ensuring the accuracy of the measurement results after transmission.

[0141] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0142] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0143] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0144] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0145] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0146] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification is hereby incorporated by reference in its entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0147] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for rapid rainfall detection based on a rainfall radar, characterized in that: In response to the presence of rainfall in the target area: Acquiring rainfall environment data, wherein the rainfall environment data includes at least one rainfall area in the target area and its corresponding rainfall type; Based on the rainfall environment data, determining the estimated rainfall intensity of the monitoring area in a future preset period of time; The monitoring area is the area to be monitored within the target area; Based on the rainfall environment data, determining the estimated rainfall center for the future preset period; as well as, Determining the transmit pulse width of the X-band rain measuring radar based on the position of the rain measuring radar, the estimated rainfall center, and the estimated rainfall intensity, including: The following steps are executed in a loop until the transmit pulse width of the X-band rain measuring radar is obtained: Generate a plurality of candidate transmit pulse widths based on the distance between the rain measuring radar position and the estimated rainfall center and the estimated rainfall intensity; For each of the candidate transmit pulse widths, based on the candidate transmit pulse width, the transmit power of the X-band rain measuring radar, the position of the rain measuring radar, the estimated rainfall center, and the estimated rainfall intensity, a first signal attenuation value corresponding to the candidate transmit pulse width is determined by a first attenuation value estimation model, where the first attenuation value estimation model is a machine learning model; In response to the first signal attenuation values ​​corresponding to the plurality of candidate transmission pulse widths being all greater than a preset attenuation threshold, adjusting the position of the rain measuring radar; In response to the first signal attenuation values ​​corresponding to the multiple candidate transmit pulse widths having a first signal attenuation value that is less than the preset attenuation threshold, the transmit pulse width of the X-band rain measuring radar is determined.

2. The method according to claim 1, characterized in that In response to the first signal attenuation values ​​corresponding to the plurality of candidate transmission pulse widths being greater than a preset attenuation threshold, adjusting the position of the rain measuring radar comprises: Determining a recommended rain measuring radar position based on the first signal attenuation values ​​corresponding to the multiple candidate transmit pulse widths and the estimated rainfall center; and Based on the recommended rain measuring radar position, adjust the rain measuring radar position.

3. The method according to claim 1, characterized in that: The method further comprises: Based on the estimated rainfall intensity, scanning parameters of the X-band rainfall measuring radar are determined, and the scanning parameters include a scanning range.

4. The method according to claim 3, characterized in that The scanning parameters also include scanning frequency. The scanning parameters of the X-band rainfall radar determined based on the estimated rainfall intensity include: The scanning frequency of the X-band rain measuring radar is determined based on the estimated rainfall intensity and the transmission pulse width of the X-band rain measuring radar.

5. A rapid rainfall detection system based on rain measuring radar, characterized in that: It includes an acquisition module, a rainfall intensity module, a rainfall center module and a first determination module; The acquisition module is configured to acquire rainfall environment data, wherein the rainfall environment data includes at least one rainfall area in the target area and its corresponding rainfall type; The rainfall intensity module is configured to determine the estimated rainfall intensity of a future preset period of time in a monitoring area based on the rainfall environment data; the monitoring area is the area to be monitored within the target area; The rainfall center module is configured to determine the estimated rainfall center of the future preset period based on the rainfall environment data; The first determination module is configured to determine the transmit pulse width of the X-band rain measuring radar based on the position of the rain measuring radar, the estimated rainfall center, and the estimated rainfall intensity, including: The following steps are executed in a loop until the transmit pulse width of the X-band rain measuring radar is obtained: Generate a plurality of candidate transmit pulse widths based on the distance between the rain measuring radar position and the estimated rainfall center and the estimated rainfall intensity; For each of the candidate transmit pulse widths, based on the candidate transmit pulse width, the transmit power of the X-band rain measuring radar, the position of the rain measuring radar, the estimated rainfall center, and the estimated rainfall intensity, a first signal attenuation value corresponding to the candidate transmit pulse width is determined by a first attenuation value estimation model, where the first attenuation value estimation model is a machine learning model; In response to the first signal attenuation values ​​corresponding to the plurality of candidate transmission pulse widths being all greater than a preset attenuation threshold, adjusting the position of the rain measuring radar; In response to the first signal attenuation values ​​corresponding to the multiple candidate transmit pulse widths having a first signal attenuation value that is less than the preset attenuation threshold, the transmit pulse width of the X-band rain measuring radar is determined.

6. The system according to claim 5, characterized in that The system also includes a second determination module; The second determination module is configured to determine a scanning parameter of the X-band rainfall measuring radar based on the estimated rainfall intensity, wherein the scanning parameter includes a scanning range.

7. A rapid rainfall detection device based on a rainfall radar, characterized in that: The apparatus comprises at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is used to execute at least part of the computer instructions to implement the rapid rainfall detection method based on the rain measuring radar as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the rapid rainfall detection method based on the rain measuring radar as described in any one of claims 1 to 4.

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