Artificial rainfall cloud water resource development potential prediction method, system and device and medium

Abstract

The invention, which relates to the technical field of artificial influence weather, discloses an artificial rain-increasing cloud water resource development potential prediction method, system, device and medium, and the method comprises the steps: obtaining cloud cluster detection data of a playable area, and carrying out the screening to obtain a target cloud cluster; searching similar clouds of the target cloud cluster according to the screened target cloud cluster data, historical cloud cluster data and a playable area condition on the basis of the historical cloud cluster data; obtaining in-cloud particle image data of the similar cloud in a specified time, and tracking a track of the similar cloud in the specified time; processing the target cloud cluster based on the trajectory data to obtain prediction potential information; calculating the artificial precipitation potential of the target cloud cluster based on the predicted potential information; according to the method, the potential prediction evaluation result of the cloud water resource based on artificial precipitation can be obtained through calculation, a user can decide a specific precipitation enhancement strategy according to the potential prediction information, and a foundation is laid for carrying out targeted drainage basin cascade precipitation enhancement work.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial weather influencing, and in particular to a method, system, equipment and medium for predicting the development potential of artificial rainfall cloud water resources. Background Art

[0002] To increase precipitation, many regions implement artificial rainmaking through precisely timed operations. Based on the scientific principles of cloud physics and artificial rainmaking, the effectiveness of artificial rainmaking is primarily reflected in changes to the microstructure of target cloud systems and precipitation processes. This influence manifests itself in two ways: first, artificial intervention directly alters the macroscopic and microscopic physical properties of clouds, a direct effect; second, through catalytic action, it indirectly increases surface precipitation, the ultimate goal of artificial weather manipulation, a so-called indirect effect.

[0003] The existing research on the potential of artificial precipitation enhancement operation is to use multiple source and multi-temporal satellite remote sensing data, conventional ground meteorological observation data and aircraft precipitation enhancement operation information to analyze the influence area of aircraft precipitation enhancement operation, such as the invention patent CN116362425A discloses a method for analyzing seeding area based on airborne detection data of weather modification, which comprises obtaining detection data and screening, obtaining detection data; screening the detection data based on the seeding area conditions, obtaining the sampling point detection data; grid processing of the detection area is carried out, and the detection area grid space field is obtained; the detection area grid space field is colored based on the sampling point detection data, and the colored display seeding area information is obtained; based on the colored display seeding area information, the specified area in the detection area is analyzed and predicted, and the seeding analysis prediction index is obtained. The defect of the above scheme is that the scheme can only obtain the prediction of the seeding area at this moment based on the real-time detection data of a region, and the implementation of specific rainfall measures, the selection of rainfall measures and the rainfall efficiency after the implementation of rainfall measures are mostly obtained by experience or simple model, and the corresponding rainfall effect prediction cannot be given. Although the current deep learning technology can be applied to artificial rainfall effect prediction and process quality control, such method highly depends on large-scale and fully annotated data set. In the context of rainfall effect prediction, due to the regional sensitivity and confidentiality of geographical data such as topography, climate characteristics and other factors, the model trained based on the unique geographical and climate conditions of a single region is difficult to directly adapt to the actual situation of other regions. This data barrier seriously limits the cross-regional promotion ability of the trained model, and the effective reuse and sharing of rainfall prediction models in different regions cannot be realized. At the same time, for special terrain, such as the temporal and spatial variation characteristics of cloud water resources over highland and mountain, the distribution characteristics of water and electricity, the water energy of cascade hydropower stations and other factors, the rainfall effect is applied to multiple sections of the river, so as to promote the combination of potential energy, kinetic energy and energy utilization of hydropower stations, form a ladder-shaped water energy development system, realize the efficient layered utilization of water resources, and combine the evaluation results of regional air cloud water resources and rainfall efficiency in different seasons. The existing method is difficult to provide a targeted solution. Taking Guizhou Province as an example, Guizhou Province is located in the eastern slope of Yungui Plateau in the southwest of China, with large terrain undulation, high in the west and low in the east, belonging to the subtropical humid monsoon climate, with relatively abundant rainfall all the year round and rich climate resources. However, the rainfall is uneven in time and space, and the drought disaster occurs frequently every spring and summer. In addition, the typical karst topography has poor water storage and water retention capacity. The cloud distribution in mountainous area is affected by terrain uplift and sinking air flow, the cloud on the windward slope is thick and the precipitation is much, the cloud on the leeward slope is thin and the precipitation is less, resulting in serious problems of resource water shortage and engineering water shortage in Guizhou. Overall, the cloud in Guizhou area has the characteristics of various types, large thickness, uneven distribution and obvious seasonal change, which is significantly affected by terrain and monsoon. These characteristics provide rich target cloud clusters for artificial precipitation enhancement operation, but also put forward higher requirements for operation time and method. SUMMARY

[0004] In view of the above-mentioned problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is that the prior art cannot predict and evaluate the effect of cloud water resources above the cascade of special regional river basins after implementing rainfall measures.

[0006] To solve the above technical problems, the present application provides the following technical solutions.

[0007] In a first aspect, the embodiments of the present application provide an artificial rainfall cloud water resource development potential prediction method, comprising:

[0008] Obtain cloud cluster detection data in a seeding area, and screen target cloud clusters;

[0009] According to the target cloud cluster data screened out, find similar clouds of the target cloud cluster based on historical cloud cluster data and seeding area conditions;

[0010] Obtain cloud particle image data of the similar clouds within a specified time, and track the trajectories of the similar clouds within the specified time;

[0011] Process the target cloud cluster based on the trajectory data to obtain prediction potential information;

[0012] Calculate the artificial rainfall potential of the target cloud cluster based on the prediction potential information.

[0013] As an optional scheme of the artificial rainfall cloud water resource development potential prediction method, wherein:

[0014] The obtaining of the cloud cluster detection data in the seeding area and the screening of the target cloud clusters comprise:

[0015] Perform multi-source data fusion on the detection data obtained by the detection equipment as a data source to obtain detection data reflecting the physical characteristics of the cloud layer;

[0016] Judge the time consistency of the detection data, and use qualified detection data as construction data of the cloud cluster in the seeding area;

[0017] Perform grid processing on the target area, perform grade evaluation on the construction data according to user-set grade evaluation conditions based on seasons and cloud layer parameters, perform spatial interpolation on the target area based on an interpolation algorithm and the construction data of each evaluation grade, and obtain spatial interpolation results of each evaluation grade.

[0018] As an optional scheme of the artificial rainfall cloud water resource development potential prediction method, wherein:

[0019] The obtaining of the cloud cluster detection data in the seeding area and the screening of the target cloud clusters further comprise:

[0020] The spatial interpolation results of the constructed data are colored and displayed according to the coloring requirements of each evaluation level to obtain the cloud detection data of the broadcastable area in the colored display;

[0021] After obtaining the target cloud clusters within the current watershed cascade area, different parts of a single cloud cluster are classified based on seasonal characteristics and parameter indicators;

[0022] The user selects a single target cloud based on the rain enhancement potential of the operational clouds in the current area displayed in color.

[0023] The benefits of this preferred technical solution include: By color-coding cloud detection data within the broadcastable area, the distribution of clouds at different evaluation levels can be intuitively displayed, allowing users to quickly understand cloud conditions. Grading the different parts of a single cloud more carefully considers internal differences. Users can select target clouds based on their rainfall enhancement potential, improving the specificity and accuracy of target cloud selection.

[0024] As a preferred method for predicting the development potential of artificial rain-making cloud water resources, the following are some of the methods:

[0025] The method of searching for similar clouds of the target cloud cluster according to the filtered target cloud cluster data and the historical cloud cluster data according to the broadcastable area conditions includes:

[0026] Based on the data of the target cloud cluster, compare it with the historical cloud clusters, select the historical cloud clusters that meet the requirements, and use them as preliminary similar clouds;

[0027] According to the preset broadcast zone conditions or the broadcast zone conditions input by the user, the historical cloud cluster data of the operation cloud cluster is judged as qualified. If the historical cloud cluster data meets the conditions, the historical cloud cluster is marked as a similar cloud;

[0028] The preset broadcastable area conditions include: setting different types of cloud layers as screening criteria for similar clouds according to different seasons in the forecast area.

[0029] As a preferred method for predicting the development potential of artificial rain-making cloud water resources, the following are some of the methods:

[0030] The obtaining of the particle image data of the similar cloud within the prescribed time and tracking the trajectory of the similar cloud within the prescribed time comprises:

[0031] For the cloud particle image data of similar clouds in a preset area within a fixed period before and after rainfall, the cloud particle image data of similar clouds are sampled at intervals based on the preset sampling interval. The sampling time of the cloud particles is time-series aligned with the sampling time of the constructed data to obtain the time-series aligned cloud particle image dataset for trajectory analysis.

[0032] The beneficial effects of the preferred technical solution are that the cloud particle image data in the fixed period before and after the similar cloud precipitation are sampled and time-aligned, the time consistency of the data is ensured, high-quality data basis is provided for accurately tracking the trajectory of the similar cloud, and the accuracy of subsequent trajectory analysis is improved.

[0033] As an optional scheme of the method for predicting the artificial rain enhancement cloud water resource development potential, wherein:

[0034] The processing of the target cloud cluster based on the trajectory data to obtain the predicted potential information includes:

[0035] According to the meteorological screening rules, the cloud trajectory data of the similar clouds are processed, and a similar cloud that is most suitable for the current state of a target cloud cluster is calculated from the similar clouds.

[0036] The meteorological screening rules include dimensional features such as wind speed, wind direction, height layer, and terrain, the similarity between the target cloud cluster and the similar clouds in the dimensional feature vectors is measured by calculation, and then a similarity value sequence list of the target cloud cluster and each similar cloud is obtained.

[0037] As an optional scheme of the method for predicting the artificial rain enhancement cloud water resource development potential, wherein:

[0038] The processing of the target cloud cluster based on the trajectory data to obtain the predicted potential information further includes:

[0039] According to the similarity value sequence list, the cloud trajectory three-dimensional point cloud coordinates of the similar clouds are weighted and fused with the similarity size as the fusion weight, and fused point cloud coordinates are obtained.

[0040] The fused point cloud coordinates are mapped into the target region processed by grid according to the point cloud coloring rule, and the predicted potential information of the target cloud cluster in color display is obtained.

[0041] The beneficial effects of the preferred technical solution are that the similarity is used as the fusion weight for weighted fusion, which can make the fused point cloud coordinates more accurately reflect the situation of the target cloud cluster, and improve the reliability of the data. The fused point cloud coordinates are displayed by coloring mapping, which can intuitively present the predicted potential information of the target cloud cluster, and facilitate user analysis and decision-making.

[0042] In a second aspect, an embodiment of the present application provides an artificial rain enhancement cloud water resource development potential prediction system, comprising:

[0043] A target cloud cluster acquisition module is configured to acquire cloud cluster detection data in a seeding area, and screen a target cloud cluster.

[0044] The similar cloud acquisition module is configured to acquire similar clouds of the target cloud group according to the target cloud group data screened out, according to historical cloud group data, and according to a castable area condition.

[0045] The trajectory tracking module is configured to acquire cloud particle image data of the similar clouds within a specified time, and track trajectories of the similar clouds within the specified time.

[0046] The prediction potential acquisition module is configured to process the target cloud group based on the trajectory data, and obtain prediction potential information.

[0047] The artificial rainmaking potential acquisition module is configured to calculate an artificial rainmaking potential of the target cloud group based on the prediction potential information.

[0048] In a third aspect, an electronic device is provided, including:

[0049] a memory and a processor;

[0050] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, so that the one or more processors implement the artificial rainmaking cloud water resource development potential prediction method according to any of the embodiments of the present application.

[0051] In a fourth aspect, a computer readable storage medium is provided, which stores computer executable instructions, and the computer executable instructions are executed by a processor to implement the artificial rainmaking cloud water resource development potential prediction method.

[0052] The present application has the following beneficial effects: The present application can accurately obtain resource development potential prediction information of a target cloud group under current basin cascade and current season conditions, which is beneficial for a user to make a specific artificial rainmaking strategy according to the potential prediction information; the prediction logic is anchored on the morphological characteristics and dynamic trajectory of the cloud group; by extracting real-time state parameters of the target cloud group, samples with highly similar morphological and dynamic characteristics are screened out in a local historical cloud group database, and the historical evolution trajectory of the similar cloud after artificial rainmaking is directly used for mapping prediction, thereby completely avoiding the explicit dependence on sensitive geographical data such as topography, geomorphology and climate characteristics, and only the cloud image sequence and the corresponding artificial rainmaking operation record are required, thereby fundamentally solving the security restriction of cross-regional data sharing. When applied to a new region, only the historical cloud group image data set of the region needs to be imported, and the prediction can be quickly generated by automatically matching the local similar cloud samples through a preset similarity measurement algorithm, without modifying the underlying algorithm or retraining, thereby significantly improving the convenience and flexibility of cross-regional application, and being suitable for multi-region collaborative scenarios with large differences in geographical environment and high data sensitivity. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor under the premise of the drawings.

[0054] Figure 1 is the overall flowchart of the artificial precipitation cloud water resource development potential prediction method provided by the present application. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings in the specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0056] Embodiment 1, refer to Figure 1 The first embodiment of the present application provides an artificial precipitation cloud water resource development potential prediction method, which comprises the following steps:

[0057] S1: Obtain cloud cluster detection data in the seeding area, and screen to obtain a target cloud cluster;

[0058] S2: According to the target cloud cluster data screened out, and based on historical cloud cluster data, similar clouds of the target cloud cluster are searched according to the seeding area conditions;

[0059] S3: Obtain cloud particle image data of the similar cloud within a specified time, and track the trajectory of the similar cloud within the specified time;

[0060] S4: Process the target cloud cluster based on the trajectory data, and obtain prediction potential information;

[0061] S5: Calculate the artificial precipitation potential of the target cloud cluster based on the prediction potential information.

[0062] It should be noted that through steps S1-S5, the present embodiment can utilize the seedable area cloud cluster detection data, combine historical cloud cluster data, and use the cloud particle image and trajectory information of similar clouds to effectively obtain the prediction potential information of the target cloud cluster, thereby providing valuable reference for meteorological research, artificial weather modification, and other related fields, and helping to more accurately grasp the cloud cluster change trend and characteristics, thereby improving the ability to cope with weather changes and the scientific nature of decision-making; based on the situation of similar clouds after artificial rain enhancement in the past, the potential prediction and evaluation results of cloud water resources based on artificial rain enhancement are obtained under the current cascade water and electricity distribution situation, combined with regional air cloud water resources and precipitation characteristics in different seasons, which is beneficial to users to make specific rain enhancement strategies based on the potential prediction information, and lays a foundation for carrying out targeted cascade rain enhancement work in the basin.

[0063] Embodiment 2, refer to Figure 1 For an embodiment of the present application, a method for predicting the development potential of artificial rain cloud water resources is provided based on the previous embodiment, comprising:

[0064] In the present embodiment, the seedable area cloud cluster detection data obtained in step S1 is filtered to obtain the target cloud cluster, comprising:

[0065] The detection data obtained by multiple detection devices including cloud radar, satellite, and sounding instrument are used as data sources for multi-source data fusion to obtain detection data reflecting the physical characteristics of the cloud layer;

[0066] Further, the seedable area in the present embodiment refers to a region specified by the user that has rain enhancement potential and can perform rain enhancement operations.

[0067] Illustratively, the seedable area refers to a highland mountainous terrain region of Guizhou Province constructed according to the cascade water and electricity distribution characteristics and cascade water demand of Guizhou, and the cloud cluster detection data of the seedable area refers to the detection data of a specific region in the highland mountainous terrain region, including a cascade diagram of the basin (data of each cascade hydropower station), cloud particle image data of air cloud water resources above each cascade of the basin, i.e., three-dimensional point cloud coordinates, cloud layer liquid water content, cloud layer thickness, cloud top temperature, ground precipitation, near-surface atmospheric fine particulate pollutants, and ice crystal number concentration.

[0068] The time consistency of the detection data is determined, and the qualified detection data is used as the construction data of the seedable area cloud cluster, and the construction data is cleaned and removed based on the screening rules of abnormal data and invalid data;

[0069] Specifically, the effective construction data is obtained by eliminating the explicit outliers in the time dimension in the detection data. For example, when the cloud top temperature of the clear sky area is inconsistent with the ground temperature, the cloud top temperature of the clear sky area is lower than the surrounding temperature; when the data of adjacent cascade hydropower stations suddenly changes, the upstream precipitation increases sharply but the downstream precipitation does not change; when the short-time precipitation is extreme, the short-time precipitation of the current station exceeds three times of the historical precipitation of the same period.

[0070] The target area is subjected to grid processing, the construction data is subjected to level evaluation based on the level evaluation condition set by the user, the target area is subjected to spatial interpolation based on the interpolation algorithm and the construction data of each evaluation level, and the spatial interpolation result of each evaluation level is obtained;

[0071] Further, the level evaluation condition includes: when the season is spring and summer, the cloud liquid water content is greater than or equal to 0.1 g / m 3 , and the cloud thickness is greater than or equal to 2 km, which is excellent; when the season is autumn and winter, the cloud liquid water content is greater than or equal to 0.05 g / m 3 , the cloud top temperature is less than or equal to -5℃, and the ice-water surface saturated water vapor density difference is greater than 0.05 g / m 3 , which is excellent; when the season is spring and summer, the cloud liquid water content is less than 0.1 g / m 3 , and the cloud thickness is greater than or equal to 2 km, which is good; when the season is autumn and winter, the cloud liquid water content is less than 0.05 g / m 3 , which is good; when the season is spring and summer, the cloud liquid water content is less than 0.1 g / m 3 , and the cloud thickness is less than 2 km, which is low; when the season is autumn and winter, the cloud liquid water content is less than 0.05 g / m 3 , and the cloud top temperature is greater than -5℃, which is low.

[0072] Illustratively, the target area is a region where a cascade hydropower station is located in Guizhou Province, such as the administrative region where the Beipanjiang River is located. The mountainous terrain region of the administrative region is subjected to grid processing, and the administrative region where the Beipanjiang River is located is grid processed into a resolution of 1 km×1 km based on the distribution diagram of the cascade distribution of the river basin, and the construction data is processed.

[0073] The spatial interpolation result of the construction data is subjected to coloring display based on the coloring requirement of each evaluation level, and the cloud cluster detection data of the coloring display is obtained;

[0074] Illustratively, according to the cloud water resource construction data of the Beipanjiang River, the different evaluation levels in the cloud cluster are colored, and according to the coloring condition, all cloud cluster detection information with rain enhancement potential in the region can be directly obtained. When constructing the fine spatial and temporal distribution diagram of the cloud water resource characteristics in the plateau mountainous area, it is beneficial for the user to directly decide to further predict the specific target cloud cluster above the cascade of the specific river basin.

[0075] Further, the coloring requirement is: when the evaluation level is excellent, red coloring is used for display; when the evaluation level is good, yellow coloring is used for display, and when the evaluation level is low, green coloring is used for display.

[0076] It should be noted that after obtaining the target cloud cluster in the current basin cascade region, due to the difference in the microstate inside different parts of the same cloud cluster, the different parts of the single target cloud cluster are graded based on the seasonal characteristics and parameter indexes, so that the target cloud cluster point cloud data of different parts of the single target cloud cluster can be represented in different colors when displayed, and after different coloring displays, the user can distinguish different target cloud cluster conditions, thereby intuitively selecting a cloud cluster with better operation potential as the target cloud cluster.

[0077] Based on the target prediction area selected by the user, historical cloud cluster data of a plurality of operation cloud clusters in the target prediction area is obtained, the operation cloud cluster referring to a cloud cluster that has undergone artificial precipitation enhancement operation in the target prediction area in the past.

[0078] Specifically, the user selects a single target cloud cluster for which the precipitation enhancement potential is to be further predicted, based on the precipitation enhancement potential of the current region operable cloud cluster displayed after coloring.

[0079] For example, if the target cloud cluster is located in the administrative region of the Beipanjiang cascade hydropower station, the cloud cluster data of historical artificial precipitation enhancement operation in the region is obtained, and similar clouds that are adapted to the target cloud cluster are searched from the cloud clusters that have undergone the historical artificial precipitation enhancement operation. At the same time of displaying the current target cloud cluster data, the historical cloud cluster data of the region at the historical time is obtained.

[0080] For example, the Beipanjiang region has undergone more than 100 times of artificial precipitation enhancement operation in the past four years, and the historical data of the cloud clusters that have undergone the precipitation enhancement operation is obtained.

[0081] In another possible implementation, when the target cloud cluster is selected, the selection can be performed according to some pre-set threshold. For example, it is set that the region with cloud liquid water content greater than 0.08 g / m 3 and cloud top height higher than 3000 meters is a possible target cloud cluster. The extracted data is traversed, and the region that meets the conditions is marked as the target cloud cluster after preliminary screening. Then, the cloud cluster located in the leeward slope of the mountain and other regions not conducive to precipitation enhancement operation can be further excluded in combination with the terrain data, and finally the target cloud cluster is determined.

[0082] In the embodiment, the step S2 of searching for the similar cloud of the target cloud cluster based on the historical cloud cluster data according to the castable region condition includes:

[0083] According to the data of the target cloud cluster, historical cloud clusters in the same season as the target cloud cluster are screened out as similar clouds according to the season in which the target cloud cluster is located, the type of catalyst selected by the historical cloud cluster when the cloud cluster is performing the rain enhancement operation.

[0084] For example, by taking the watershed cascade in the mountainous plateau of Guizhou as the basis and taking different seasons as the search conditions, the subsequent output result is the precipitation potential prediction and evaluation effect of the air cloud water resources of the watershed cascade region generated according to the distribution characteristics of the cascade hydropower in Guizhou and different seasons.

[0085] According to the preset seedable area condition or the user input seedable area condition, it is judged whether the historical cloud cluster data of the several operation cloud clusters is qualified, and if qualified, the historical cloud cluster is marked as a similar cloud;

[0086] Specifically, based on the similar cloud that meets the condition, the future moving track of the target cloud cluster that has met the catalytic condition in multiple directions is predicted, and the prediction potential information based on artificial rain enhancement is obtained.

[0087] The preset seedable area condition includes: when the prediction area is in spring, the historical cloud cluster of stratiform cloud is marked as a similar cloud; when the prediction area is in summer, the historical cloud cluster of convective cloud is marked as a similar cloud; when the prediction area is in autumn, the historical cloud cluster of high-level cloud and stratocumulus is marked as a similar cloud; and when the prediction area is in winter, the historical cloud cluster of broken layer cloud is marked as a similar cloud.

[0088] For example, for the Guizhou region, in spring, the cloud layer is mainly stratiform cloud, the precipitation is stable but the intensity is small; in summer, the cloud layer is mainly convective cloud, there are thunderstorms in the afternoon, the local is strong and the precipitation occurs frequently; in autumn, the cloud layer is mainly high-level cloud and convective cloud, the precipitation amount decreases; and in winter, the stratiform cloud and broken layer cloud increase, accompanied by continuous light rain or fog. Therefore, according to the unique cloud layer characteristics of different seasons, the seedable area is set with a screening condition, and the cloud cluster in the same season as the target cloud cluster under the watershed cascade is selected as a similar cloud.

[0089] The input seedable area condition includes: the selection of the catalyst is silver iodide or hygroscopic catalyst, when the prediction area is in summer, if the user inputs the catalyst selection as the hygroscopic catalyst, the historical cloud cluster with the catalyst of silver iodide is removed; when the prediction area is in spring or winter, if the user inputs the catalyst selection as silver iodide, the historical cloud cluster with the catalyst of hygroscopic catalyst is removed.

[0090] Exemplarily, since Guizhou is a plateau region, the cloud layer characteristics of Guizhou region are that the supercooled water content in stratiform cloud is high in winter and spring, which is suitable for silver iodide catalysis, so when the user inputs the catalyst as silver iodide, the historical cloud clusters using hygroscopic catalysts such as sodium chloride and urea can be removed; and in summer, the convective cloud liquid water content is rich, which is suitable for hygroscopic catalysis, so when the user inputs the catalyst as a hygroscopic catalyst, the historical cloud clusters using silver iodide catalysis can be removed. Thus, the target cloud cluster meeting the conditions is retained as a similar cloud.

[0091] In the embodiment, the step S3 of acquiring the cloud particle image data of the similar cloud in the specified time includes:

[0092] For the cloud particle image data of the similar cloud in a fixed period before and after the rainfall in the preset area, such as one hour before the rainfall and one hour after the rainfall, the cloud particle image data of the similar cloud is interval sampled based on the preset sampling interval, the sampling time of the cloud particle is time-sequentially aligned with the sampling time of the constructed data, and a time-sequentially aligned similar cloud cloud particle image data set is obtained.

[0093] Exemplarily, the data of the similar cloud one hour before and after the rainfall operation is processed, the sampling time of the historical data of the similar cloud is first aligned with the current data of the target cloud cluster, and then a plurality of processed similar cloud cloud particle image data sets are obtained for subsequent trajectory analysis. Not only the sampling difficulty is reduced, but also the accuracy of the historical data and the target cloud cluster data is ensured.

[0094] In the embodiment, the step S4 of processing the target cloud cluster based on the trajectory data to obtain the prediction potential information includes:

[0095] The cloud trajectory data of a plurality of similar clouds is calculated according to the meteorological screening rule to obtain a similar cloud most suitable for the current state of a target cloud cluster.

[0096] The meteorological screening rule features include four-dimensional features of wind speed, wind direction, height layer, and terrain; the Euclidean distance between the four-dimensional feature vector of the target cloud cluster and the four-dimensional feature vector of the similar cloud is calculated, and is expressed as:

[0097]

[0098] Wherein, A is the feature vector of the target cloud cluster, B is the feature vector of the similar cloud, A i , B i are the i-th elements in the feature vectors of the target cloud cluster and the similar cloud respectively; the smaller the Euclidean distance C value is, the higher the meteorological feature similarity between the target cloud cluster and the similar cloud is; the similarity value sequence list of the target cloud cluster and each similar cloud can be obtained according to the Euclidean distance value.

[0099] It should be noted that, during rain enhancement operation, even if the type of cloud layer, the season in which the cloud layer is located, and the region where the cloud layer is located are the same, the meteorological environment in different years at the same location is different. Therefore, the plurality of characteristics in the specific meteorological environment of the cloud layer are taken as the feature vector, the similarity between the target cloud cluster and the similar cloud cluster is represented by calculating the Euclidean distance of the meteorological characteristics, the cloud cluster with higher similarity has smaller Euclidean distance value, and the simulation degree of the data is higher when the weighted fusion data is processed subsequently.

[0100] According to the similarity value sequence table, the cloud trajectories of the first five similar clouds with the highest similarity in the sequence table are overlapped with the target cloud cluster, the trajectories of the plurality of similar clouds are displayed in layers on the target cloud cluster in height, and the spatial coordinate data of the cloud trajectories of the five similar clouds after weighted fusion is calculated and represented as:

[0101]

[0102] P = ∑j=1nWjPj+ N(0, σ2) (1) final P is the fused point cloud coordinate, n represents the total number of similar clouds participating in the weighted fusion, Wj is the similarity value weight of the jth similar cloud, Pj is the three-dimensional point cloud coordinate of the cloud trajectory of the jth similar cloud, N(0, σ2) is Gaussian noise, and σ = (0.1Wj) (2) j j 2 2 j 2 .

[0103] It should be noted that the similarity is taken as the weight of fusion, the point cloud trajectory coordinate points of the similar clouds ranked in the top five in similarity are fused to obtain the final point cloud coordinate, compared with a single data source, and therefore the accuracy of potential prediction of the weighted fusion of a plurality of point cloud trajectories with high similarity is improved.

[0104] The fused point cloud coordinate is mapped into the target region which has been grid processed according to the point cloud coloring rule to obtain the prediction potential information of the colored and displayed target cloud cluster.

[0105] ​​​​​Specifically, due to the large number of similar clouds, the number of similar clouds needs to be further reduced. The scheme screens similar clouds based on the meteorological data of the current target cloud cluster, ensures that the similar cloud data is under similar meteorological conditions for subsequent processing of the target cloud cluster, calculates the Euclidean distance, and if the two weather state similar cloud cluster data, the smaller the Euclidean distance, otherwise the larger. If the Euclidean distance of a certain similar cloud obtained by screening is very large compared with the Euclidean distance between other similar clouds, it indicates that the similar cloud is an event under abnormal weather and should be reselected. After screening a plurality of suitable similar clouds, the cloud particle data sets of the plurality of similar clouds representing the future trajectory of the target cloud cluster are weighted and fused to obtain the weighted and fused point cloud coordinates, which can make the fused point cloud more accurate, and the data with high reliability is given higher weight, while the overall variance is reduced and the robustness is enhanced.

[0106] Further, the point cloud coloring rule includes:

[0107] The center coordinates of each evaluation level are calculated, the edge points of each evaluation level are selected, the average distance value between the center coordinates of each evaluation level and the edge points of each evaluation level is obtained, the route of the cloud trajectory is obtained according to the three-dimensional point cloud coordinates of the cloud trajectory of the similar cloud, and the color of each point on the cloud trajectory is filled according to the center coordinates of each evaluation level, the average distance value, and the route of the cloud trajectory.

[0108] Specifically, since the similar cloud point cloud data obtained after weighting and fusion is in an overlapping relationship with the point cloud data of the target cloud cluster, in order to ensure that the similar cloud point cloud data is also colored and displayed in different colors at different evaluation levels, and the edge of the cloud cluster is a curve, the average distance between the edge and the center point of a single evaluation level on the target cloud cluster is taken as the range reference for coloring the point cloud data of different evaluation levels of the similar cloud. For example, according to the distance value between the red point on the edge and the center point of the cloud cluster, the average distance value of the red point is calculated, and the point cloud of a single evaluation level on the target cloud cluster, such as the red evaluation level, is colored based on the distance value, so that the cloud trajectory is filled with different colors as a whole, and the trajectory situation of the target cloud cluster after the rain enhancement operation can be intuitively understood. The trajectory situation is the predicted potential information of the current target cloud cluster.

[0109] In another possible implementation, when performing trajectory analysis, the trajectory data of the similar cloud can be analyzed to fit its motion trend. A polynomial fitting method can be used to fit the trajectory data of the similar cloud as a polynomial function. For example, for the trajectory data (x, y) in a two-dimensional plane, a quadratic polynomial y = ax 2 + bx + c can be fitted, and the values of coefficients a, b, and c are determined by the least squares method.

[0110] When performing potential prediction, the initial position of a target cloud cluster is matched with the trajectories of similar clouds. The target cloud cluster's future trajectory is then extrapolated based on the fitted polynomial function. Combined with the cloud cluster's local meteorological conditions (such as humidity and temperature), the cloud cluster's future trajectory is predicted, including whether it will merge with other clouds or encounter updrafts. This provides information on the target cloud cluster's potential.

[0111] In this embodiment, the calculation of the artificial rainfall enhancement potential of the target cloud based on the predicted potential information in step S5 includes:

[0112] Artificial rainfall enhancement potential is defined as total precipitation × rainfall enhancement efficiency × rainfall enhancement probability, expressed as:

[0113] PEP=H h ×E W ×P e =∑(H ht ×H hs )×E w ×(P c ×P n ×(1-P k )×P a )

[0114] Among them, PEP represents the artificial precipitation potential, H h is the total precipitation, E W is the rain enhancement efficiency, P e The probability of precipitation.

[0115] H h =∑(H ht ×H hs )

[0116] Among them, H h is the total volume (mass) of precipitation in region S during period T, H ht is the total precipitation thickness observed at each station during the T period, H hs is the area represented by the site, H ht and H hs The product is the volume (mass) of precipitation observed at the station;

[0117] E W The ratio of artificial rainfall to natural precipitation. The average artificial rainfall efficiency obtained from a large number of tests is 10%-15%.

[0118] P e =P c ×P n ×(1-P k )×P a

[0119] Among them, Pc is the probability of the occurrence of suitable conditions, P n is the demand probability, P k is the probability of unsuitable operation, P a For operational ability.

[0120] It should be noted that the predicted potential information after color filling can intuitively understand the potential of the target cloud cluster. After further calculation through the artificial rainmaking potential formula in this scheme, the specific rainmaking potential value of the target cloud cluster can be obtained, thereby giving users a clearer picture of the post-rainfall potential values ​​of different target cloud clusters.

[0121] Example 3. The above is a schematic diagram of the method for predicting cloud water resource development potential for artificial rainfall enhancement in this embodiment. It should be noted that the technical solution of the artificial rainfall enhancement cloud water resource development potential prediction system and the technical solution of the artificial rainfall enhancement cloud water resource development potential prediction method described above are based on the same concept. For details not described in detail in the technical solution of the artificial rainfall enhancement cloud water resource development potential prediction system in this embodiment, please refer to the description of the technical solution of the artificial rainfall enhancement cloud water resource development potential prediction method described above.

[0122] This embodiment also provides a system for predicting the development potential of artificial rain-making cloud water resources, including:

[0123] The target cloud cluster acquisition module is used to obtain cloud cluster detection data in the broadcastable area and screen the target cloud cluster;

[0124] The similar cloud acquisition module is used to search for similar clouds to the target cloud cluster based on the filtered target cloud cluster data and the historical cloud cluster data according to the broadcastable area conditions;

[0125] The trajectory tracking module is used to obtain the image data of particles in the cloud within a specified time and track the trajectory of the similar cloud within a specified time;

[0126] The prediction potential acquisition module is used to process the target cloud based on the trajectory data to obtain the prediction potential information;

[0127] The artificial rainfall enhancement potential acquisition module is used to calculate the artificial rainfall enhancement potential of the target cloud based on the predicted potential information.

[0128] This embodiment further provides an electronic device applicable to the method for predicting the development potential of artificial rainfall cloud water resources, including:

[0129] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the artificial rain enhancement cloud water resource development potential prediction method proposed in the above embodiment.

[0130] The embodiment also provides a storage medium, which stores a computer program, and the computer program is executed by a processor to implement the artificial precipitation cloud water resource development potential prediction method proposed in the above embodiment.

[0131] The storage medium proposed in the embodiment and the artificial precipitation cloud water resource development potential prediction method proposed in the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for predicting the development potential of artificial rain-making cloud water resources, characterized in that: include: Obtain cloud detection data in the broadcastable area and screen out target clouds; Based on the filtered target cloud cluster data and historical cloud cluster data, similar clouds to the target cloud cluster are searched according to the broadcastable area conditions; Obtain the image data of particles in a similar cloud within a specified time, and track the trajectory of the similar cloud within a specified time; Process the target cloud based on trajectory data to obtain prediction potential information; Calculate the artificial rainfall enhancement potential of the target cloud cluster based on the predicted potential information.

2. The method for predicting the development potential of artificial rain-making cloud water resources according to claim 1, characterized in that: The acquisition of cloud cluster detection data in the broadcastable area and screening of target cloud clusters includes: The detection data obtained by the detection equipment is used as the data source for multi-source data fusion to obtain detection data that can reflect the physical characteristics of the cloud layer; Determine the temporal consistency of the detection data and use the qualified detection data as the construction data for the cloud cluster in the broadcastable area; The target area is gridded, and the constructed data is graded according to the grade evaluation conditions set by the user based on season and cloud parameters. Based on the interpolation algorithm and the constructed data of each evaluation grade, the target area is spatially interpolated to obtain the spatial interpolation results of each evaluation grade.

3. The method for predicting the development potential of artificial rain-making cloud water resources according to claim 2, characterized in that: The obtaining of cloud cluster detection data in the broadcastable area and screening out target cloud clusters further includes: The spatial interpolation results of the constructed data are colored and displayed according to the coloring requirements of each evaluation level to obtain the cloud detection data of the broadcastable area in the colored display; After obtaining the target cloud clusters within the current watershed cascade area, different parts of a single cloud cluster are classified based on seasonal characteristics and parameter indicators; The user selects a single target cloud based on the rain enhancement potential of the operational clouds in the current area displayed in color.

4. The method for predicting the development potential of artificial rain-making cloud water resources according to claim 3, characterized in that: The method of searching for similar clouds of the target cloud cluster according to the filtered target cloud cluster data and the historical cloud cluster data according to the broadcastable area conditions includes: Based on the data of the target cloud cluster, compare it with the historical cloud clusters, select the historical cloud clusters that meet the requirements, and use them as preliminary similar clouds; According to the preset broadcast zone conditions or the broadcast zone conditions input by the user, the historical cloud cluster data of the operation cloud cluster is judged as qualified. If the historical cloud cluster data meets the conditions, the historical cloud cluster is marked as a similar cloud; The preset broadcastable area conditions include: setting different types of cloud layers as screening criteria for similar clouds according to different seasons in the forecast area.

5. The method for predicting the development potential of artificial rain-making cloud water resources according to claim 4, characterized in that: The obtaining of the particle image data of the similar cloud within the prescribed time and tracking the trajectory of the similar cloud within the prescribed time comprises: For the cloud particle image data of similar clouds in a preset area within a fixed period before and after rainfall, the cloud particle image data of similar clouds are sampled at intervals based on the preset sampling interval. The sampling time of the cloud particles is time-series aligned with the sampling time of the constructed data to obtain the time-series aligned cloud particle image dataset for trajectory analysis.

6. The method for predicting the development potential of artificial rain-making cloud water resources according to claim 5, characterized in that: The processing of the target cloud based on the trajectory data to obtain prediction potential information includes: According to meteorological screening rules, the cloud trajectory data of multiple similar clouds are processed to calculate the similar cloud that best matches the current state of a target cloud cluster. The meteorological screening rules include dimensional features such as wind speed, wind direction, altitude, and terrain. The similarity is measured by calculating the distance between the target cloud and similar clouds in these dimensional feature vectors, and then a sequence list of similarity values ​​between the target cloud and each similar cloud is obtained.

7. The method for predicting the development potential of artificial rain-making cloud water resources according to claim 6, characterized in that: The processing of the target cloud based on the trajectory data to obtain prediction potential information further includes: According to the similarity value sequence table, the similarity value is used as the fusion weight to perform weighted fusion on the three-dimensional point cloud coordinates of the cloud trajectory of the similar cloud to obtain the fused point cloud coordinates; The fused point cloud coordinates are mapped to the target area after gridding according to the point cloud coloring rules to obtain the prediction potential information of the colored target cloud.

8. A system for predicting the development potential of artificial rainfall cloud water resources, using the method according to any one of claims 1 to 7, characterized in that: include: The target cloud cluster acquisition module is used to obtain cloud cluster detection data in the broadcastable area and screen the target cloud cluster; The similar cloud acquisition module is used to search for similar clouds to the target cloud cluster based on the filtered target cloud cluster data and the historical cloud cluster data according to the broadcastable area conditions; The trajectory tracking module is used to obtain the image data of particles in the cloud within a specified time and track the trajectory of the similar cloud within a specified time; The prediction potential acquisition module is used to process the target cloud based on the trajectory data to obtain the prediction potential information; The artificial rainfall enhancement potential acquisition module is used to calculate the artificial rainfall enhancement potential of the target cloud based on the predicted potential information.

9. An electronic device, characterized in that: include: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that It stores computer-executable instructions, which implement the steps of the method according to any one of claims 1 to 7 when executed by a processor.

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