A sample collection system and method for precipitation isotopes

Abstract

The present application relates to the field of isotope collection, and more particularly to a sample collection system and method for precipitation isotopes, which continuously acquires multi-source meteorological monitoring data through a data acquisition and processing module, determines rainfall characteristic values based on the multi-source meteorological monitoring data through a rainfall characteristic calculation module, classifies the rainfall into short or sustained types according to the rainfall characteristic calculation module, predicts the total duration and intensity time series of the rainfall type through a rainfall process prediction module, constructs a sampling strategy according to the rainfall type, the predicted total duration and intensity time series through a sampling strategy module, and executes the strategy to drive the sample collection device to perform actions through a sampling control module. The present application realizes the perception prediction and adaptive sampling of the rainfall process, ensures the matching of the sampling strategy and the rainfall process, improves the representativeness of the precipitation isotope sample, and provides reliable data basis for subsequent research.

Description

Technical Field

[0001] This invention relates to the field of isotope collection, and more particularly to a sample collection system and method for precipitation isotopes. Background Technology

[0002] Isotope technology provides precise tracing tools for scientific research by tracking the migration of elements in nature. Precipitation isotopes, as important tracing carriers for water cycle research, climate reconstruction, and water resource tracing, contain crucial information about water vapor sources, phase transition processes, and transport pathways through their spatiotemporal differentiation characteristics. They are essential for understanding the coupling mechanism between regional hydrological cycles and global climate change. The accuracy, timeliness, and representativeness of isotope sample collection determine the scientific value of the test data. An inappropriate sampling strategy will affect the reliability and scientific validity of subsequent isotope analysis results. Currently, most existing precipitation isotope sample collection devices are deployed at field monitoring stations, and collection operations typically rely on manual experience or fixed time-series patterns.

[0003] Chinese Patent Publication No. CN109827813B discloses a monthly cumulative sample collection device for atmospheric precipitation isotope analysis, comprising a water collection funnel, a sample storage bottle, a water guide pipe, a screen, a pressure relief pipe, and a float. The funnel's inlet is mounted on the upper part of the water guide pipe via a support, and the bottom of the water collection sleeve at the bottom of the sample storage bottle is closed. The lower part of the water guide pipe has an opening, and the water guide pipe is threadedly connected to the water collection sleeve. The upper part of the water guide pipe connects to the funnel's inlet, and the lower part passes through the sample storage bottle cap and connects to the water collection sleeve. The float is located at the inlet of the water guide pipe at the funnel's inlet. The sample storage bottle cap has a pressure relief hole, and the funnel's inlet has an exhaust port, which is connected to the exhaust port via a pressure relief pipe. This invention eliminates the need for manual intervention during the sampling process. The small liquid surface exposure, the small-diameter long pressure relief pipe, and the float effectively control water evaporation during sample collection, ensuring sample representativeness. The sampling process, which lasts up to one month, requires no manual intervention and can save on sampling costs.

[0004] However, the following problems still exist in the existing technology:

[0005] 1. Existing technologies do not consider the dynamic changes and differences in the types of rainfall events, making it difficult to accurately capture the key time points of isotopic changes for different types of rainfall.

[0006] 2. Existing technologies do not consider the spatial non-uniformity and movement direction characteristics of rainfall systems, lack the ability to predict rainfall, and cannot dynamically adjust the sampling time according to the development trend of rainfall, which may lead to insufficient isotopic representativeness of the samples. Summary of the Invention

[0007] To address these issues, the present invention provides a sample collection system and method for precipitation isotopes, which overcomes the problems of existing technologies that fail to consider the dynamic changes and type differences of rainfall events, making it difficult to accurately capture key time points of isotopic changes in different rainfall types, failing to consider the spatial non-uniformity and movement direction characteristics of rainfall systems, lacking the ability to predict rainfall, and potentially leading to insufficient isotopic representativeness of samples.

[0008] To achieve the above objectives, in one aspect, the present invention provides a sample collection system for precipitation isotopes, comprising:

[0009] The data acquisition and processing module is used to continuously acquire multi-source meteorological monitoring data, including meteorological radar reflection data, satellite cloud image data, and rainfall data from ground automatic weather stations.

[0010] The rainfall characteristic calculation module is used to calculate the spatial non-uniformity index, movement direction stability index, and intensity peak concentration index of the current rainfall event based on the multi-source meteorological monitoring data, so as to determine the rainfall characteristic values.

[0011] The rainfall type classification module is used to classify the current rainfall event into short-duration rainfall or continuous rainfall based on the rainfall feature values;

[0012] The rainfall event prediction module is used to predict the total predicted duration and intensity time series of the current rainfall event in response to the classified rainfall type.

[0013] The sampling strategy module is used to determine several sampling time points to construct a sampling strategy based on the rainfall type, total predicted duration, and intensity time series.

[0014] The sampling control module is used to execute the sampling strategy and drive the sample acquisition device to perform actions at the sampling time point;

[0015] The sampling time point is at least adapted to the predicted equivalent starting point, rainfall intensity peak point, and rainfall termination point.

[0016] Furthermore, the rainfall characteristic calculation module, based on the multi-source meteorological monitoring data, calculates the spatial non-uniformity index, movement direction stability index, and intensity peak concentration index of the current rainfall event, including...

[0017] Based on the meteorological radar reflection data, calculate the spatial non-uniformity index of the current rainfall event within the monitoring area;

[0018] Based on several satellite cloud image data arranged in chronological order, the movement trajectory of the core rainfall area is obtained, and the stability index of the movement direction of the core rainfall area is determined.

[0019] Based on the rainfall data from the aforementioned automatic weather stations, a rainfall intensity map is constructed, and the intensity peak concentration index is determined.

[0020] Furthermore, the rainfall feature calculation module, based on several satellite cloud image data arranged in temporal order, obtains the movement trajectory of the core rainfall region and determines the stability index of the movement direction of the core rainfall region, including...

[0021] Identify the core rainfall regions in each of the satellite cloud image data according to the time sequence, and determine the corresponding geographic centroid coordinates of each region;

[0022] By connecting the geographic centroid coordinates in chronological order, the movement trajectory of the core rainfall area is generated.

[0023] A sequence of orientation angles is calculated based on the movement trajectory;

[0024] The direction angle sequence is statistically analyzed, and the resulting statistics are used as the stability index of the movement direction.

[0025] Furthermore, the rainfall characteristic calculation module determines the rainfall characteristic values ​​including,

[0026] The ratio of the spatial inhomogeneity index to the standard value of the spatial index is determined as the spatial parameter;

[0027] The ratio of the stability index of the movement direction to the standard value of the movement direction is determined as the direction parameter;

[0028] The ratio of the intensity peak concentration index to the intensity standard value is determined as the intensity parameter;

[0029] Based on the spatial parameters, directional parameters, and intensity parameters, rainfall characteristic values ​​are determined.

[0030] Furthermore, the rainfall characteristic calculation module determines rainfall characteristic values ​​based on the spatial parameters, direction parameters, and intensity parameters, including:

[0031] Based on the intensity parameter and the preset weight threshold, the combination of weight coefficients is determined;

[0032] Based on the weighted coefficient combination, the spatial parameters, directional parameters, and intensity parameters are weighted and summed, and the summation result is used as the rainfall characteristic value;

[0033] The weighting coefficient combination includes a first weighting coefficient combination and a second weighting coefficient combination; if the intensity parameter is greater than a preset weighting threshold, the first weighting coefficient combination is used; if the intensity parameter is less than or equal to the preset weighting threshold, the second weighting coefficient combination is used.

[0034] Furthermore, the rainfall type classification module, used to classify the current rainfall event into short-duration rainfall or continuous rainfall based on the rainfall feature values, includes:

[0035] The rainfall characteristic value is compared with a preset rainfall threshold;

[0036] If the rainfall characteristic value is less than or equal to the preset rainfall threshold, the current rainfall event is classified as short-duration rainfall.

[0037] If the rainfall characteristic value is greater than the preset rainfall threshold, the current rainfall event is classified as continuous rainfall.

[0038] Furthermore, the rainfall event prediction module, in response to the classified rainfall type, predicts the total predicted duration and intensity time series of the current rainfall event, including:

[0039] If the rainfall type is classified as short-duration rainfall, the total predicted duration and the intensity time series are predicted based on the intensity attenuation trend of the latest meteorological radar reflection data and the movement speed of the core rainfall area.

[0040] If the rainfall type is classified as persistent rainfall, then based on the satellite cloud image data, the total predicted duration and the intensity time series are generated through a pre-trained rainfall-runoff prediction model.

[0041] Furthermore, the sampling strategy module is used to determine several sampling time points to construct a sampling strategy based on the rainfall type, predicted total duration, and intensity time series.

[0042] If the rainfall type is short-duration rainfall, then based on the total predicted duration and the intensity time series, the equivalent starting point, the peak rainfall intensity point, and the ending rainfall point are determined to construct the sampling time points;

[0043] If the rainfall type is continuous rainfall, then based on the equivalent starting point, the peak rainfall intensity point, and the ending point, according to the total predicted duration, a number of additional sampling points are uniformly inserted at preset fixed time intervals within the time period between the starting point and the ending point to construct the sampling time points.

[0044] Furthermore, the sampling strategy module, used to determine the equivalent starting point, includes:

[0045] If the rainfall in the area where the sample collection device is located is zero at the current moment, it is determined that the rainfall has not yet started. The equivalent starting point is the first time point in the intensity time series where the rainfall intensity is greater than zero.

[0046] If the rainfall in the area where the sample collection device is located is greater than zero at the current moment, it is determined that a rainfall event is in progress, and the current moment is taken as the equivalent starting point.

[0047] On the other hand, a method for collecting precipitation isotopes using a sample collection system for precipitation isotopes is also provided, including:

[0048] Continuously acquire multi-source meteorological monitoring data, including meteorological radar reflection data, satellite cloud image data, and rainfall data from ground automatic weather stations;

[0049] Based on the multi-source meteorological monitoring data, the spatial non-uniformity index, movement direction stability index, and intensity peak concentration index of the current rainfall event are calculated to determine the rainfall characteristic values.

[0050] Based on the rainfall characteristic values, the current rainfall event is classified as either short-duration rainfall or continuous rainfall;

[0051] In response to the classified rainfall type, predict the total predicted duration and intensity time series of the current rainfall event;

[0052] Based on the rainfall type, predicted total duration, and intensity time series, several sampling time points are determined to construct a sampling strategy;

[0053] The sampling strategy is executed, and the sample acquisition device is driven to perform an action at the sampling time point.

[0054] Compared with existing technologies, this invention continuously acquires multi-source meteorological monitoring data through a data acquisition and processing module, determines rainfall characteristic values ​​based on the multi-source meteorological monitoring data through a rainfall characteristic calculation module, classifies rainfall into short-duration or continuous types based on this data, a rainfall process prediction module responds to the total duration and intensity time series of rainfall type prediction, a sampling strategy module constructs a sampling strategy based on rainfall type, total predicted duration, and intensity time series, and a sampling control module executes this strategy to drive the sample collection device to perform actions. This invention achieves perception prediction and adaptive sampling of rainfall processes, ensures the matching of sampling strategies with rainfall processes, improves the representativeness of precipitation isotope samples, and provides a reliable data foundation for subsequent research.

[0055] In particular, this invention considers the dynamic characteristics and typological differences of rainfall events. In reality, rainfall systems exhibit uneven spatial distribution, movement trajectories, and intensity variations, making accurate prediction of long-term rainfall difficult. The isotopic distributions of short-duration and persistent rainfall may differ, and fixed sampling strategies often fail to adapt to various rainfall types due to untimely updates. This invention integrates multi-source meteorological monitoring data through a rainfall characteristic calculation module, calculating spatial inhomogeneity, movement direction stability, and intensity peak concentration indices. A rainfall type classification module accurately classifies rainfall events, and a differentiated sampling strategy is used to construct a mechanism that enables targeted capture of key nodes for different types of rainfall, improving the isotopic representativeness of the samples.

[0056] In particular, this invention considers the impact of spatial non-uniformity of rainfall events on the representativeness of sampling. In reality, the rainfall intensity and pattern of the same rainfall event vary at different geographical locations, causing single-point fixed sampling to fail to reflect the true characteristics of the region. This invention continuously acquires meteorological radar reflection data and satellite cloud image data through a data acquisition and processing module. Based on this, a rainfall characteristic calculation module calculates the movement trajectory and azimuth sequence of the core rainfall area, determines the stability index of the movement direction, and constructs a rainfall intensity map by combining rainfall data from ground automatic weather stations. This enables a quantitative assessment of the spatial structure and development trend of rainfall, allowing the sampling strategy module to optimize sampling timing based on the movement characteristics of the rainfall system. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the module connections of a sample collection system for precipitation isotopes according to an embodiment of the invention.

[0058] Figure 2 A logic block diagram for determining the combination of weighting coefficients in an embodiment of the invention;

[0059] Figure 3 This is a logic block diagram illustrating the classification of rainfall event types in an embodiment of the invention. Detailed Implementation

[0060] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0061] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0062] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0063] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0064] Please see Figure 1 The diagram shown is a schematic diagram of the module connections of a precipitation isotope sample collection system according to an embodiment of the present invention. The precipitation isotope sample collection system of the present invention includes:

[0065] The data acquisition and processing module is used to continuously acquire multi-source meteorological monitoring data, including meteorological radar reflection data, satellite cloud image data, and rainfall data from ground automatic weather stations.

[0066] The rainfall characteristic calculation module is used to calculate the spatial non-uniformity index, movement direction stability index, and intensity peak concentration index of the current rainfall event based on the multi-source meteorological monitoring data, so as to determine the rainfall characteristic values.

[0067] The rainfall type classification module is used to classify the current rainfall event into short-duration rainfall or continuous rainfall based on the rainfall feature values;

[0068] The rainfall event prediction module is used to predict the total predicted duration and intensity time series of the current rainfall event in response to the classified rainfall type.

[0069] The sampling strategy module is used to determine several sampling time points to construct a sampling strategy based on the rainfall type, total predicted duration, and intensity time series.

[0070] The sampling control module is used to execute the sampling strategy and drive the sample acquisition device to perform actions at the sampling time point;

[0071] The sampling time point is at least adapted to the predicted equivalent starting point, rainfall intensity peak point, and rainfall termination point.

[0072] Specifically, the rainfall characteristic calculation module, based on the multi-source meteorological monitoring data, calculates the spatial non-uniformity index, movement direction stability index, and intensity peak concentration index of the current rainfall event, including...

[0073] Based on the meteorological radar reflection data, calculate the spatial non-uniformity index of the current rainfall event within the monitoring area;

[0074] Based on several satellite cloud image data arranged in chronological order, the movement trajectory of the core rainfall area is obtained, and the stability index of the movement direction of the core rainfall area is determined.

[0075] Based on the rainfall data from the aforementioned automatic weather stations, a rainfall intensity map is constructed, and the intensity peak concentration index is determined.

[0076] Specifically, the core rainfall area refers to a continuous region with deep convective cloud characteristics identified in time-series satellite cloud imagery using satellite infrared cloud images. This region is then designated as the core rainfall area. Alternatively, if the rainfall clouds do not possess typical deep convective cloud structures, the thickest continuous region within the cloud system is selected as the core rainfall area based on cloud thickness characteristics. In this embodiment, the thickest 15% of the cloud layers are used. Specifically, based on the meteorological radar reflection data, the spatial variability of the current rainfall event within the monitoring area is calculated as a spatial non-uniformity index. It is understood that rainfall spatial variability, as an existing technical concept describing the uneven spatial distribution of hydrological and meteorological elements, has various calculation methods. Those skilled in the art can choose an appropriate calculation method based on the actual situation, which will not be elaborated upon here.

[0077] Specifically, the rainfall characteristic calculation module obtains the movement trajectory of the core rainfall region based on several satellite cloud image data arranged in temporal order, and determines the stability index of the movement direction of the core rainfall region, including...

[0078] Identify the core rainfall regions in each of the satellite cloud image data according to the time sequence, and determine the corresponding geographic centroid coordinates of each region;

[0079] By connecting the geographic centroid coordinates in chronological order, the movement trajectory of the core rainfall area is generated.

[0080] A sequence of orientation angles is calculated based on the movement trajectory;

[0081] The direction angle sequence is statistically analyzed, and the resulting statistics are used as the stability index of the movement direction.

[0082] Specifically, the rainfall characteristic calculation module determines rainfall characteristic values ​​including:

[0083] The ratio of the spatial inhomogeneity index to the standard value of the spatial index is determined as the spatial parameter;

[0084] The ratio of the stability index of the movement direction to the standard value of the movement direction is determined as the direction parameter;

[0085] The ratio of the intensity peak concentration index to the intensity standard value is determined as the intensity parameter;

[0086] Based on the spatial parameters, directional parameters, and intensity parameters, rainfall characteristic values ​​are determined.

[0087] Specifically, based on the rainfall data from the aforementioned automatic weather stations, a rainfall intensity process line is plotted. Based on the plotted rainfall intensity process line, time-series rainfall intensity data is extracted, and the average rainfall intensity is calculated. Simultaneously, the maximum half-hour rainfall intensity (I30) during the process is identified and calculated as the peak intensity. The ratio of the maximum half-hour rainfall intensity to the average intensity is determined as the intensity peak concentration index.

[0088] Specifically, the spatial index standard value is obtained by those skilled in the art through statistical analysis of several historical meteorological radar reflectance data samples of the monitoring area, and the median value of the spatial non-uniformity index of all rainfall events in the samples is taken as the spatial index standard value. The spatial index standard value aims to eliminate the interference of extreme cases, thereby characterizing the normal level of spatial non-uniformity of rainfall distribution in the monitoring area.

[0089] Specifically, the standard value of the movement direction is obtained by those skilled in the art through statistical analysis of several historical satellite cloud image data samples of the monitored area, and the median value of the stability index of the movement direction of all rainfall events in the samples is taken as the standard value of the movement direction. This standard value of the movement direction is intended to capture the central tendency of the weather system's movement path stability in the region, providing an objective benchmark for determining whether the movement of the current rainfall event is abnormal.

[0090] Specifically, the intensity standard value is obtained by those skilled in the art through statistical analysis of rainfall data samples from several automatic weather stations in the monitoring area, and the median value of the concentration index of intensity peaks of all rainfall events in the samples is taken as the intensity standard value. The intensity standard value can reduce the impact of occasional extreme rainfall events, thereby reflecting the general pattern of the concentration of rainfall intensity in the monitoring area over time.

[0091] It is understood that the samples collected by those skilled in the art should cover historical meteorological data from different seasons over several years in the monitored area. By collecting long-term historical data samples covering different seasons, it is possible to ensure that the statistical samples have sufficient temporal representativeness and climate coverage, thereby comprehensively covering various typical rainfall patterns and weather system characteristics that may occur in the region. This allows the standard values ​​determined based on the median of this sample to robustly characterize the long-term climate norms of the region, providing a stable and regionally adaptable reference benchmark for the subsequent calculation of characteristic parameters.

[0092] Specifically, the rainfall characteristic calculation module determines rainfall characteristic values ​​based on the spatial parameters, direction parameters, and intensity parameters, including:

[0093] Based on the intensity parameter and the preset weight threshold, the combination of weight coefficients is determined;

[0094] Based on the weighted coefficient combination, the spatial parameters, directional parameters, and intensity parameters are weighted and summed, and the summation result is used as the rainfall characteristic value;

[0095] Please see Figure 2 As shown, Figure 2 The present invention provides a logic block diagram for determining a weight coefficient combination according to an embodiment of the invention. The weight coefficient combination includes a first weight coefficient combination and a second weight coefficient combination. If the intensity parameter is greater than a preset weight threshold, the first weight coefficient combination is used; if the intensity parameter is less than or equal to the preset weight threshold, the second weight coefficient combination is used.

[0096] Specifically, the weight threshold is calculated by a person skilled in the art based on statistical analysis of historical data samples from several years of different seasons in the monitoring area, calculating the intensity parameters of all rainfall events in the historical samples, obtaining the historical average value, and multiplying the historical average value by an adjustment coefficient, with the resulting product being the weight threshold.

[0097] Specifically, the adjustment coefficient ranges from [0.7, 1.3]. This is intended to provide a reasonable and operable calibration range on a regional climate baseline, adapting to different monitoring accuracy requirements or application scenarios under microclimate conditions.

[0098] In this embodiment of the invention, the first weighting coefficient combination has a spatial parameter weight of 0.25, a directional parameter weight of 0.25, and an intensity parameter weight of 0.5. The second weighting coefficient combination has an inter-parameter weight of 0.35, a directional parameter weight of 0.35, and an intensity parameter weight of 0.3.

[0099] Understandably, for heavy rainfall such as thunderstorms, the isotopic composition often changes drastically in a short period of time. Therefore, the first weighted coefficient combination is used to prioritize capturing and responding to rapid fluctuations in intensity, ensuring that the sampling strategy can accurately pinpoint its key evolution stages.

[0100] Conversely, for moderate-intensity rainfall events such as widespread stratiform cloud precipitation, isotopic variations are more influenced by macroscopic factors such as water vapor source and transport path. A second weighting coefficient combination is used to comprehensively determine whether the event constitutes a stable, widespread, and persistent rainfall event, and a coverage sampling plan is formulated accordingly.

[0101] Please see Figure 3 As shown, Figure 3 This is a logical block diagram illustrating the classification of rainfall event types according to an embodiment of the invention. The rainfall type classification module is used to classify the current rainfall event into short-duration rainfall or continuous rainfall based on the rainfall feature values, including:

[0102] The rainfall characteristic value is compared with a preset rainfall threshold;

[0103] If the rainfall characteristic value is less than or equal to the preset rainfall threshold, the current rainfall event is classified as short-duration rainfall.

[0104] If the rainfall characteristic value is greater than the preset rainfall threshold, the current rainfall event is classified as continuous rainfall.

[0105] Specifically, the rainfall threshold is calculated by those skilled in the art from historical data to form a historical feature value sequence, and the sequence is statistically analyzed. The product of the historical average value and the amplification factor is the rainfall threshold.

[0106] In this embodiment of the invention, the amplification factor ranges from [1.15, 1.35].

[0107] Specifically, the rainfall event prediction module, in response to the classified rainfall type, predicts the total predicted duration and intensity time series of the current rainfall event, including:

[0108] If the rainfall type is classified as short-duration rainfall, the total predicted duration and the intensity time series are predicted based on the intensity attenuation trend of the latest meteorological radar reflection data and the movement speed of the core rainfall area.

[0109] If the rainfall type is classified as persistent rainfall, then based on the satellite cloud image data, the total predicted duration and the intensity time series are generated through a pre-trained rainfall-runoff prediction model.

[0110] Specifically, those skilled in the art can select the publicly available rainfall-runoff prediction model based on the actual situation, and fine-tune the model using historical meteorological data of the monitored area. It is understood that those skilled in the art can also train their own prediction models to meet the prediction requirements, which will not be elaborated upon here.

[0111] Specifically, the sampling strategy module is used to determine several sampling time points to construct a sampling strategy based on the rainfall type, predicted total duration, and intensity time series.

[0112] If the rainfall type is short-duration rainfall, then based on the total predicted duration and the intensity time series, the equivalent starting point, the peak rainfall intensity point, and the ending rainfall point are determined to construct the sampling time points;

[0113] If the rainfall type is continuous rainfall, then based on the equivalent starting point, the peak rainfall intensity point, and the ending point, according to the total predicted duration, a number of additional sampling points are uniformly inserted at preset fixed time intervals within the time period between the starting point and the ending point to construct the sampling time points.

[0114] Specifically, the sampling strategy module, used to determine the equivalent starting point, includes:

[0115] If the rainfall in the area where the sample collection device is located is zero at the current moment, it is determined that the rainfall has not yet started. The equivalent starting point is the first time point in the intensity time series where the rainfall intensity is greater than zero.

[0116] If the rainfall in the area where the sample collection device is located is greater than zero at the current moment, it is determined that a rainfall event is in progress, and the current moment is taken as the equivalent starting point.

[0117] Specifically, there are no restrictions on the specific forms of the data acquisition and processing module, rainfall characteristic calculation module, rainfall type classification module, rainfall process prediction module, and sampling control module. They can be composed of logic components, including field-programmable processors, computers, or microprocessors in computers, which will not be elaborated further here.

[0118] This embodiment also provides a method for collecting precipitation isotopes using a sample collection system applied to precipitation isotopes, including:

[0119] Step S1: Continuously acquire multi-source meteorological monitoring data, including meteorological radar reflection data, satellite cloud image data, and rainfall data from ground automatic weather stations;

[0120] Step S2: Based on the multi-source meteorological monitoring data, calculate the spatial non-uniformity index, movement direction stability index, and intensity peak concentration index of the current rainfall event to determine the rainfall characteristic values;

[0121] Step S3: Based on the rainfall characteristic values, classify the current rainfall event into short-duration rainfall or continuous rainfall;

[0122] Step S4: In response to the classified rainfall type, predict the total predicted duration and intensity time series of the current rainfall event;

[0123] Step S5: Based on the rainfall type, predicted total duration, and intensity time series, determine several sampling time points to construct a sampling strategy;

[0124] Step S6: Execute the sampling strategy and drive the sample acquisition device to perform actions at the sampling time point.

[0125] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A sample collection system for precipitation isotopes, characterized in that, include: The data acquisition and processing module is used to continuously acquire multi-source meteorological monitoring data, including meteorological radar reflection data, satellite cloud image data, and rainfall data from ground automatic weather stations. The rainfall characteristic calculation module is used to calculate the spatial non-uniformity index, movement direction stability index, and intensity peak concentration index of the current rainfall event based on the multi-source meteorological monitoring data, so as to determine the spatial parameters, direction parameters, and intensity parameters, and to determine the rainfall characteristic value based on the spatial parameters, direction parameters, and intensity parameters; The rainfall type classification module is used to classify the current rainfall event into short-duration rainfall or continuous rainfall based on the rainfall feature values; The rainfall event prediction module is used to predict the total predicted duration and intensity time series of the current rainfall event in response to the classified rainfall type. The sampling strategy module is used to determine several sampling time points to construct a sampling strategy based on the rainfall type, total predicted duration, and intensity time series. The sampling control module is used to execute the sampling strategy and drive the sample acquisition device to perform actions at the sampling time point; The sampling time points are at least adapted to the predicted equivalent start point, rainfall intensity peak point, and rainfall termination point; The rainfall characteristic calculation module, based on the multi-source meteorological monitoring data, calculates the spatial non-uniformity index, movement direction stability index, and intensity peak concentration index of the current rainfall event, including... Based on the meteorological radar reflection data, the spatial non-uniformity index of the current rainfall event within the monitoring area is calculated; based on several satellite cloud image data arranged in chronological order, the movement trajectory of the core rainfall area is obtained, and the movement direction stability index of the core rainfall area is determined; based on the rainfall data from the ground automatic weather station, a rainfall intensity map is constructed, and the intensity peak concentration index is determined. The rainfall characteristic calculation module determines the rainfall characteristic values, including: The ratio of the spatial non-uniformity index to the standard value of the spatial index is determined as a spatial parameter; the ratio of the movement direction stability index to the standard value of the movement direction is determined as a directional parameter; the ratio of the intensity peak concentration index to the standard value of intensity is determined as an intensity parameter; based on the spatial parameter, directional parameter, and intensity parameter, rainfall characteristic values ​​are determined.

2. The sample collection system for precipitation isotopes according to claim 1, characterized in that, The rainfall characteristic calculation module, based on a series of satellite cloud image data arranged in chronological order, obtains the movement trajectory of the core rainfall region and determines the stability index of the movement direction of the core rainfall region, including... Identify the core rainfall regions in each of the satellite cloud image data according to the time sequence, and determine the corresponding geographic centroid coordinates of each region; By connecting the geographic centroid coordinates in chronological order, the movement trajectory of the core rainfall area is generated. A sequence of orientation angles is calculated based on the movement trajectory; The direction angle sequence is statistically analyzed, and the resulting statistics are used as the stability index of the movement direction.

3. The sample collection system for precipitation isotopes according to claim 1, characterized in that, The rainfall characteristic calculation module determines rainfall characteristic values ​​based on the spatial parameters, direction parameters, and intensity parameters, including: Based on the intensity parameter and the preset weight threshold, the combination of weight coefficients is determined; Based on the weighted coefficient combination, the spatial parameters, directional parameters, and intensity parameters are weighted and summed, and the summation result is used as the rainfall characteristic value; The weighting coefficient combination includes a first weighting coefficient combination and a second weighting coefficient combination. If the intensity parameter is greater than the preset weight threshold, the first weight coefficient combination is used; if the intensity parameter is less than or equal to the preset weight threshold, the second weight coefficient combination is used.

4. The sample collection system for precipitation isotopes according to claim 1, characterized in that, The rainfall type classification module is used to classify the current rainfall event into short-duration rainfall or continuous rainfall based on the rainfall feature values, including: The rainfall characteristic value is compared with a preset rainfall threshold; If the rainfall characteristic value is less than or equal to the preset rainfall threshold, the current rainfall event is classified as short-duration rainfall. If the rainfall characteristic value is greater than the preset rainfall threshold, the current rainfall event is classified as continuous rainfall.

5. The sample collection system for precipitation isotopes according to claim 1, characterized in that, The rainfall event prediction module, in response to the classified rainfall type, predicts the total predicted duration and intensity time series of the current rainfall event, including... If the rainfall type is classified as short-duration rainfall, the total predicted duration and the intensity time series are predicted based on the intensity attenuation trend of the latest meteorological radar reflection data and the movement speed of the core rainfall area. If the rainfall type is classified as persistent rainfall, then based on the satellite cloud image data, the total predicted duration and the intensity time series are generated through a pre-trained rainfall-runoff prediction model.

6. The sample collection system for precipitation isotopes according to claim 1, characterized in that, The sampling strategy module is used to determine several sampling time points based on the rainfall type, predicted total duration, and intensity time series to construct a sampling strategy, including: If the rainfall type is short-duration rainfall, then based on the total predicted duration and the intensity time series, the equivalent starting point, the peak rainfall intensity point, and the ending rainfall point are determined to construct the sampling time points; If the rainfall type is continuous rainfall, then based on the equivalent starting point, the peak rainfall intensity point, and the ending point, according to the total predicted duration, a number of additional sampling points are uniformly inserted at preset fixed time intervals within the time period between the starting point and the ending point to construct the sampling time points.

7. The sample collection system for precipitation isotopes according to claim 6, characterized in that, The sampling strategy module, used to determine the equivalent starting point, includes: If the rainfall in the area where the sample collection device is located is zero at the current moment, it is determined that the rainfall has not yet started. The equivalent starting point is the first time point in the intensity time series where the rainfall intensity is greater than zero. If the rainfall in the area where the sample collection device is located is greater than zero at the current moment, it is determined that a rainfall event is in progress, and the current moment is taken as the equivalent starting point.

8. A method for using the sample collection system for precipitation isotopes as described in any one of claims 1-7, characterized in that, include, Continuously acquire multi-source meteorological monitoring data, including meteorological radar reflection data, satellite cloud image data, and rainfall data from ground automatic weather stations; Based on the multi-source meteorological monitoring data, the spatial non-uniformity index, movement direction stability index, and intensity peak concentration index of the current rainfall event are calculated to determine the rainfall characteristic values. Based on the rainfall characteristic values, the current rainfall event is classified as either short-duration rainfall or continuous rainfall; In response to the classified rainfall type, predict the total predicted duration and intensity time series of the current rainfall event; Based on the rainfall type, predicted total duration, and intensity time series, several sampling time points are determined to construct a sampling strategy; The sampling strategy is executed, and the sample acquisition device is driven to perform an action at the sampling time point.

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