Method and apparatus for identifying type of precipitation particles, storage medium, and electronic device

Abstract

Embodiments of the present disclosure provide a method and apparatus for identifying the type of precipitation particles, a storage medium, and an electronic device. The method is applied to a server, and comprises: by means of a base station, acquiring a reflection signal value of precipitation particles under detection; by means of the base station and a receiver, acquiring a field intensity attenuation amount of a wireless signal transmitted by the base station; and on the basis of the reflection signal value and the field intensity attenuation amount, determining the type of said precipitation particles. The present disclosure solves the problem of low accuracy of identifying the type of precipitation particles, thereby achieving the effect of improving the accuracy of identifying precipitation particles.

Description

Method, device, storage medium and electronic device for identifying precipitation particle types

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure is based on Chinese patent application CN202311247975.X, filed on September 22, 2023, entitled “Method, device, storage medium and electronic device for identifying precipitation particle types”, and claims the priority of the patent application, and all the contents disclosed therein are incorporated into this disclosure by reference. Technical Field

[0003] The embodiments of the present disclosure relate to the field of communications, and in particular, to a method, device, storage medium, and electronic device for identifying precipitation particle types. Background Art

[0004] The study of precipitation is of great significance to the prevention of natural disasters such as floods, landslides, and mudslides. When measuring and studying precipitation, it is first necessary to distinguish the types of precipitation particles - rain, snow, hail, etc.

[0005] Currently, the identification of rain, snow, and hail relies primarily on volume-scan weather radar data and dual-polarization Doppler radar polarization parameters. While volume-scan weather radar is simpler, it also has low resolution and ignores the microphysical properties of precipitation particles. Dual-polarization Doppler radar, on the other hand, offers higher resolution than conventional weather radar but is susceptible to interference, resulting in larger errors.

[0006] Alternatively, microwave attenuation characteristics of microwave links can be used to invert the droplet spectra and particle shape distributions of rain, snow, and hail. However, this requires a large amount of labeled microwave attenuation data. In real-world scenarios, the amount of microwave attenuation data labeled with precipitation type is often insufficient, resulting in low accuracy in classification monitoring solutions for rain, snow, and hail.

[0007] Summary of the Invention

[0008] The embodiments of the present disclosure provide a method, apparatus, storage medium, and electronic device for identifying precipitation particle types, so as to at least solve the problem of low accuracy in identifying precipitation particle types in related technologies.

[0009] According to one embodiment of the present disclosure, a method for identifying precipitation particle types is provided. The method is applied to a server and includes:

[0010] Obtain the reflection signal value of the measured precipitation particles through the base station;

[0011] Obtaining, through the base station and the receiver, a field intensity attenuation of a wireless signal transmitted by the base station;

[0012] The type of the detected precipitation particles is determined based on the reflection signal value and the field intensity attenuation.

[0013] According to another embodiment of the present disclosure, a method for identifying precipitation particle types is provided. The method is applied to a base station and includes:

[0014] Analyze the reflection signal value of the measured precipitation particles;

[0015] Transmitting a wireless signal and recording a wireless signal value, so that a receiver obtains an attenuated signal value and sends the attenuated signal value to a server;

[0016] The reflected signal value and the wireless signal value are sent to a server, so that the server obtains a field intensity attenuation based on the wireless signal value and the attenuated signal value, and determines the type of the measured precipitation particles according to the reflected signal value and the field intensity attenuation.

[0017] According to yet another embodiment of the present disclosure, a method for identifying precipitation particle types is provided. The method is applied to a receiver and includes:

[0018] Acquire a wireless signal sent by a base station, obtain an attenuated signal value, and send the attenuated signal value to a server;

[0019] The server obtains the field intensity attenuation based on the attenuated signal value and the wireless signal value sent by the base station, and determines the type of the measured precipitation particles based on the field intensity attenuation and the reflected signal value sent by the base station.

[0020] According to another embodiment of the present disclosure, a device for identifying precipitation particle types is provided, comprising:

[0021] An acquisition module is configured to acquire the reflection signal value of the measured precipitation particles and the field intensity attenuation of the wireless signal transmitted by the base station;

[0022] The processing module is configured to determine the type of the detected precipitation particles based on the reflection signal value and the field intensity attenuation.

[0023] According to another embodiment of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0024] According to another embodiment of the present disclosure, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any one of the above method embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG1 is a schematic diagram of a network architecture for executing a method for identifying precipitation particle types according to an embodiment of the present disclosure;

[0026] FIG2 is a flow chart of a method for identifying precipitation particle types according to an embodiment of the present disclosure;

[0027] 3 is a flow chart of determining the type of precipitation particles being measured based on the reflection signal value and the field intensity attenuation according to an embodiment of the present disclosure;

[0028] FIG4 is a flow chart of a method for obtaining a classification model according to an embodiment of the present disclosure;

[0029] 5 is a flowchart of constructing a training data set and a verification data set based on the reflection signal value and the field intensity attenuation in the historical data according to an embodiment of the present disclosure;

[0030] FIG6 is a flowchart of a method for acquiring a first data set and a second data set according to an embodiment of the present disclosure;

[0031] FIG7 is a flow chart of obtaining a combined data set based on differential reflectivity and differential attenuation according to an embodiment of the present disclosure;

[0032] FIG8 is a flowchart of obtaining a fifth data set and a sixth data set according to an embodiment of the present disclosure;

[0033] FIG9 is a diagram of precipitation particle distribution obtained using differential reflectivity and differential attenuation according to an embodiment of the present disclosure;

[0034] FIG10 is a graph showing a trend of changes in K value versus error rate according to an embodiment of the present disclosure;

[0035] FIG11 is a schematic structural diagram of a device for identifying precipitation particle types according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings and in conjunction with embodiments.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0038] The embodiment of the present application can be run on the network architecture shown in Figure 1. As shown in Figure 1, the network architecture includes: a server, a communication base station, and a receiver. The communication base station is communicatively connected to the server and the receiver, and the receiver is communicatively connected to the server. The communication base station is used to transmit wireless signals and receive echoes; the receiver is used to receive wireless signals transmitted by the communication base station and can be a terminal or another communication base station. The server is used to collect, analyze, process, and output data. Specifically, the communication base station can transmit detection signals to the sky, receive reflected signals fed back by precipitation particles, and send wireless signals to the receiver.

[0039] In this embodiment, a method for identifying precipitation particle types running on the above-mentioned network architecture is provided. FIG2 is a flow chart of a method for identifying precipitation particle types according to an embodiment of the present disclosure. As shown in FIG2 , the flow chart includes the following steps:

[0040] Step S201, obtaining the reflection signal value of the measured precipitation particles through the base station;

[0041] In an exemplary embodiment, the specific implementation is as follows:

[0042] The base station transmits a detection signal: The base station will transmit a beam of electromagnetic waves (usually a signal in the microwave band). This detection signal will be transmitted at a certain power and specific frequency.

[0043] Interaction between the detection signal and precipitation particles: The emitted detection signal will interact with precipitation particles. When the detection signal encounters precipitation particles, it will be scattered, reflected, or absorbed.

[0044] Receiving and processing reflected signals: The base station receives signals reflected from precipitation particles. These reflected signals are received by the receiving antenna and transmitted to the signal processing system for processing.

[0045] Analyzing reflected signals: The signal processing system analyzes and processes the received reflected signals. By analyzing parameters such as the intensity, frequency, and phase of the reflected signals, the reflected signal values ​​of the precipitation particles can be obtained, and further information such as the intensity, type, and distribution of the precipitation can be inferred.

[0046] Step S202: obtaining, through the base station and the receiver, the field intensity attenuation of the wireless signal transmitted by the base station;

[0047] In an exemplary embodiment, the specific implementation is as follows:

[0048] The base station transmits wireless signals: The base station transmits wireless signals, which are transmitted at a certain power and specific frequency.

[0049] Wireless signal propagation process: The transmitted wireless signal will be affected by precipitation particles during the propagation process, which will cause the wireless signal to attenuate.

[0050] Receiving wireless signals: A receiving antenna near a receiver receives the transmitted wireless signal. The receiving antenna converts the wireless signal into an electrical signal and transmits it to the receiver.

[0051] Wireless signal strength measurement: The receiver measures the strength of the received wireless signal. By measuring the power or voltage of the received wireless signal, the field strength of the wireless signal can be obtained.

[0052] Field strength attenuation calculation: By comparing the field strength of the received wireless signal with the field strength of the wireless signal when transmitted by the base station, the attenuation experienced by the wireless signal during propagation can be calculated.

[0053] Step S203: determining the type of the detected precipitation particles based on the reflection signal value and the field intensity attenuation.

[0054] Through the above steps, the method of using base stations to identify precipitation particle types solves the problem of low accuracy in identifying precipitation particle types by obtaining multi-dimensional information, multi-parameter analysis, and real-time feedback optimization, thereby improving the accuracy of identifying precipitation particles.

[0055] FIG3 is a flow chart of determining the type of precipitation particles to be measured based on the reflection signal value and the field intensity attenuation according to an embodiment of the present disclosure. As shown in FIG3 ,

[0056] Determine the type of precipitation particles being measured based on the reflected signal value and field strength attenuation, including:

[0057] Step S301, obtaining a differential reflectivity based on the reflection signal value;

[0058] In an exemplary embodiment, a reference signal value is set, and the reflection signal value of a known reference object or position can be selected as a reference value, or a background noise signal can be used as a benchmark. The reflection signal value and the reference signal value are differenced to obtain a differential signal. This differential signal represents the reflection ability of the target object relative to the reference signal. Depending on the specific application requirements, calibration and calibration steps may be required. This may include calibrating the response of the sensor and calibrating the relationship between the reflection signal value and the reference signal value to obtain a more accurate and comparable differential reflectivity. According to the definition and unit of the differential signal, the differential signal can be converted into a differential reflectivity. The differential reflectivity is an indicator that measures the reflection ability of the target object relative to the reference object. The specific conversion method may involve a calibration curve, calibration coefficients, etc. to ensure the accuracy and comparability of the differential reflectivity.

[0059] Step S302, obtaining a differential attenuation rate based on the field intensity attenuation;

[0060] In an exemplary embodiment, a reference field strength attenuation is set. The field strength attenuation of a known reference object or position can be selected as a reference value, or the background noise field strength attenuation can be used as a benchmark. The field strength attenuation is calculated to differ from the reference field strength attenuation to obtain a differential field strength attenuation. This differential field strength attenuation represents the attenuation capability of the target object relative to the reference signal. Depending on the specific application requirements, calibration and calibration steps can be performed. This can include calibration of the sensor response and calibration of the relationship between the field strength attenuation and the reference field strength attenuation to obtain a more accurate and comparable differential attenuation rate. Based on the definition and unit of the differential field strength attenuation, the differential field strength attenuation can be converted into a differential attenuation rate. The differential attenuation rate is an indicator that measures the attenuation capability of the target object relative to the reference object. The specific conversion method may involve a calibration curve, calibration coefficient, etc. to ensure the accuracy and comparability of the differential attenuation rate.

[0061] Step S303, obtaining a combined data set based on the differential reflectivity and the differential attenuation rate;

[0062] In an exemplary embodiment, a differential reflectivity dataset and a differential attenuation dataset are aligned and matched. This can be done through methods such as timestamps or spatial coordinates to ensure that the data in the two datasets correspond to the same target object or location. The aligned and matched differential reflectivity dataset and differential attenuation dataset are then combined to form a new combined dataset. Differential reflectivity and differential attenuation can be treated as two dimensions or combined into a single dimension. The specific combination method depends on the characteristics of the data and application requirements and is not limited here.

[0063] Step S304: input the combined data set into a classification model to determine the type of the detected precipitation particles.

[0064] In an exemplary embodiment, the combined data set is input into a well-trained classification model so that the classification model processes the combined data and determines the type of the detected precipitation particles.

[0065] FIG4 is a flow chart of a method for obtaining a classification model according to an embodiment of the present disclosure. As shown in FIG4 ,

[0066] The way to obtain the classification model is as follows:

[0067] Step S401, constructing a training data set and a verification data set based on the reflection signal value and field intensity attenuation in the historical data;

[0068] In an exemplary embodiment, the specific implementation is as follows:

[0069] Data collection: Collect historical data on reflected signal values ​​and field strength attenuation. This data can be measured by sensors or equipment, or extracted from existing data sets.

[0070] Data preprocessing: Preprocess the collected data, including data cleaning, removing outliers, filling missing values, etc., to ensure the quality and integrity of the data.

[0071] Data partitioning: Divide the preprocessed data into a training dataset and a validation dataset. Generally speaking, random partitioning or chronological partitioning can be used.

[0072] Random partitioning: The dataset is randomly divided into training and validation datasets. Cross-validation methods, such as K-fold cross-validation, can be used to partition the dataset into K subsets, with K-1 subsets used as training at a time and the remaining subset used as validation. Chronological partitioning: The first part of the dataset is used as the training dataset, and the second part as the validation dataset, following chronological order. This partitioning method is suitable for time series data and can better simulate real-world application scenarios.

[0073] Feature Engineering: Based on specific application requirements, perform feature engineering on the data to extract features related to the reflected signal value and field strength attenuation. Feature extraction can be performed using statistical features, frequency domain features, time domain features, and other methods.

[0074] Data normalization: Normalize the extracted features to ensure that they have the same scale. Common normalization methods include Z-score normalization and Min-Max normalization.

[0075] Constructing training and validation datasets: Divide the feature-engineered and standardized data into training and validation datasets. Generally speaking, the training dataset is used to train the model, and the validation dataset is used to evaluate the model's performance and generalization ability.

[0076] Step S402: constructing an initial classification model using a training data set and a preset algorithm;

[0077] In an exemplary embodiment, the preset algorithm may be a KNN algorithm, an ANN algorithm, or the like.

[0078] Step S403: Use the validation data set to train the initial classification model until the initial classification model converges to obtain a classification model.

[0079] FIG5 is a flow chart of constructing a training data set and a verification data set according to the reflected signal value and the field intensity attenuation in the historical data according to an embodiment of the present disclosure. As shown in FIG5 ,

[0080] The training and validation datasets are constructed based on the reflected signal values ​​and field strength attenuation in the historical data, including:

[0081] Step S501, calculating a differential reflectivity according to a first data set, and constructing a third data set based on the differential reflectivity, wherein the first data set is a historical data set of reflection signal values;

[0082] Step S502, calculating the differential attenuation rate according to the second data set, and constructing a fourth data set based on the differential attenuation rate, where the second data set is a historical data set of field intensity attenuation;

[0083] Step S503: The third data set and the fourth data set are combined and then split into a training data set and a validation data set.

[0084] FIG6 is a flow chart of a method for obtaining a first data set and a second data set according to an embodiment of the present disclosure. As shown in FIG6 ,

[0085] The method for obtaining the first and second datasets is as follows:

[0086] Step S601: Acquire the vertical polarization transmission field strength, horizontal polarization transmission field strength, and vertical polarization echo field strength and horizontal polarization echo field strength reflected by precipitation particles transmitted by the base station, annotate the time and precipitation particle type, and record the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization echo field strength, horizontal polarization echo field strength, precipitation particle type, and time at the same time as a set of first data to obtain multiple sets of first data, which are then recorded as a first data set.

[0087] Step S602: Obtain the vertical polarization transmission field strength and horizontal polarization transmission field strength transmitted by the base station and the vertical polarization reception field strength and horizontal polarization reception field strength transmitted by the receiver, annotate the time and type of precipitation particles, and record the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization reception field strength, horizontal polarization reception field strength, precipitation particle type, and time at the same time as a set of second data to obtain multiple sets of second data, which are recorded as a second data set.

[0088] The first data and the second data are two sets of data at the same time.

[0089] FIG7 is a flow chart of obtaining a combined data set based on differential reflectivity and differential attenuation rate according to an embodiment of the present disclosure. As shown in FIG7 ,

[0090] A combined data set is obtained based on differential reflectivity and differential attenuation, including:

[0091] Step S701, calculating multiple sets of differential reflectivity based on the fifth data set, the differential reflectivity being marked with a timestamp, and constructing the multiple sets of differential reflectivity into a seventh data set based on the timestamp, where the fifth data set is a real-time collection data set of reflection signal values;

[0092] Step S702: Calculate multiple sets of differential attenuation rates based on the sixth data set, where the differential attenuation rates are marked with a timestamp. The multiple sets of differential attenuation rates are constructed into an eighth data set based on the timestamp. The sixth data set is a real-time data set of field intensity attenuation.

[0093] Step S703: merge the first data and the second data with the same timestamp into a set of data in a combined data set.

[0094] FIG8 is a flowchart of obtaining the fifth data set and the sixth data set according to an embodiment of the present disclosure. As shown in FIG8 ,

[0095] The method for obtaining the fifth and sixth data sets is as follows:

[0096] Step S801: Acquire the vertical polarization transmission field strength, horizontal polarization transmission field strength, and vertical polarization echo field strength and horizontal polarization echo field strength reflected by precipitation particles of the base station, mark the time, and record the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization echo field strength, horizontal polarization echo field strength, and time at the same time as a set of fifth data to obtain multiple sets of fifth data, and record the multiple sets of fifth data as a fifth data set;

[0097] Step S802: Obtain the vertical polarization transmit field strength and horizontal polarization transmit field strength transmitted by the base station and the vertical polarization receive field strength and horizontal polarization receive field strength transmitted by the receiver, annotate the time, and record the vertical polarization transmit field strength, horizontal polarization transmit field strength, vertical polarization receive field strength, horizontal polarization receive field strength, and time at the same time as a set of sixth data to obtain multiple sets of sixth data, and record the multiple sets of sixth data as a sixth data set;

[0098] The fifth data and the sixth data are two sets of data at the same time.

[0099] In an exemplary embodiment, the process of the above method is explained by taking an example:

[0100] 1. Data accumulation and processing part

[0101] This part analyzes and processes the data under known precipitation particle conditions to build a classification model.

[0102] 1: Original data collection.

[0103] The base station detects the reflected field intensity of precipitation particles through echoes, including both vertical and horizontal polarization, and identifies the particle type. This is recorded as the first data set.

[0104] Specifically, let the base station's vertically polarized transmit field strength be Pv, and the horizontally polarized transmit field strength be Ph. The received vertically polarized echo field strength is Sv, and the horizontally polarized echo field strength is Sh. The first data set can be expressed as: D1 = {(Pv1, Ph1, Sv1, Sh1, T1), (Pv2, Ph2, Sv2, Sh2, T2) ... (Pvn, Phn, Svn, Shn, Tn)}, where 1 to n represent different times, and Tn represents the type of precipitation particle at time n.

[0105] The base station detects the transmit and receive field strengths, including both vertical and horizontal polarizations, and identifies the particle types. This is recorded as the second data set.

[0106] Specifically, let the base station's vertically polarized transmit field strength be Tv, and the horizontally polarized transmit field strength be Th. The received vertically polarized field strength is Hv, and the received horizontally polarized field strength is Hh. The second data set can be expressed as: D2 = {(Tv1, Th1, Hv1, Hh1, T1), (Tv2, Th2, Hv2, Hh2, T2) ... (Tvn, Thn, Hvn, Hhn, Tn)}, where 1 to n represent different times, and Tn represents the type of precipitation particle at time n.

[0107] 2: Data preprocessing.

[0108] Obvious outliers in the first and second data sets were removed respectively.

[0109] Specifically, the data that obviously deviates from the normal value in the first and second data sets are removed. The commonly used method is to remove the data that deviates from the mean by more than 3 times the variance.

[0110] The differential reflectance of the first data set is calculated to construct the third data set.

[0111] Specifically, according to the first data set, the vertical polarization reflectivity at the nth moment can be calculated as Rvn=Svn / Pvn, the horizontal polarization reflectivity is Rhn=Shn / Phn, and the differential reflectivity is The differential reflectivity of all data in the first data set is calculated to construct a third data set. The third data set can be expressed as: D3 = {(R1, T1), (R2, T2).....(Rn, Tn)}.

[0112] The differential attenuation rate is calculated for the second data set to construct a fourth data set.

[0113] Specifically, according to the second data set, the vertical polarization attenuation field strength at the nth moment can be calculated as Avn=Tvn-Hvn; the vertical polarization attenuation rate per kilometer is Fvn=Avn / (Tvn*L), where L is the distance (in kilometers); the horizontal polarization attenuation field strength is Ahn=Thn-Hhn; the horizontal polarization attenuation rate per kilometer is Fhn=Ahn / (Thn*L), where L is the distance (in kilometers); the differential attenuation rate is The differential reflectivity of all data in the second data set is calculated to construct a fourth data set. The fourth data set can be expressed as: D4 = {(F1, T1), (F2, T2).....(Fn, Tn)}.

[0114] The third data set and the fourth data set are used to obtain a training data set and a validation data set.

[0115] Specifically, the third and fourth datasets are combined into a mixed dataset, which is then split into a training dataset and a validation dataset in a ratio of 6:4. The training dataset can be expressed as: DT = {(R1, F1, T1), (R2, F2, T2) ... (Rn1, Fn1, Tn1)}; the validation dataset can be expressed as: DV = {(R1, F1, T1), (R2, F2, T2) ... (Rn2, Fn2, Tn2)}.

[0116] 3: Algorithm part.

[0117] This section takes the KNN algorithm as an example, including but not limited to the KNN algorithm.

[0118] A classification model is constructed for the training data set (DT) using the KNN algorithm.

[0119] Figure 9 is a precipitation particle distribution diagram obtained using differential reflectivity and differential attenuation rate according to an embodiment of the present disclosure. As shown in Figure 9, the horizontal axis is the differential reflectivity R, the vertical axis is the differential attenuation rate F, and points of different shapes represent different particle types T.

[0120] Validate based on the validation dataset and select the optimal K value.

[0121] FIG10 is a changing trend diagram of K value-error rate according to an embodiment of the present disclosure, and the relationship between the change of K value and the change of error rate is shown in FIG10 .

[0122] Specifically, set K to x, and substitute multiple sets of data in the validation dataset into the classification model. Let the substituted data be (Rm, Fm, Tm), where Rm is the differential reflectivity of the precipitation particles at the mth moment in the validation dataset, Fm is the differential attenuation rate of the precipitation particles at the mth moment in the validation dataset, Tm is the mth moment, and (Rm, Fm, Tm) is a set of data at the mth moment in the validation dataset. The calculation steps are:

[0123] Calculate the distance value between all the data in the training data set. There are many distance value algorithms. Taking Euclidean distance as an example, the Euclidean distance between (Rm, Fm, Tm) and the data in the training data set (Ri, Fi, Ti) is Where Ri is the differential reflectivity of the precipitation particles at the i-th moment in the training dataset, Fi is the differential attenuation rate of the precipitation particles at the i-th moment in the training dataset, Ti is the i-th moment, and (Ri, Fi, Ti) is a set of data at the i-th moment in the training dataset;

[0124] Select the smallest x distance values ​​from all the distance values ​​calculated in the previous step. If the category of the training dataset corresponding to these x distance values ​​is Tk at most, then the category of the validation dataset data is determined to be Tk;

[0125] The calculated category value Tk is then compared with the actual category value Tm in the validation dataset to draw a conclusion, correct or wrong.

[0126] Perform the above calculations on all validation datasets in turn and calculate the category error rate Wx;

[0127] By varying the value of K and calculating the error rates for different K values, we obtain the corresponding set of K values ​​and error rates: {(W1, K1), (W2, K2) ... (Wn, Kn)}. The K value that gives the lowest error rate is selected as the optimal K value. For example, as shown in Figure 10, when the K value is 10, the error rate is the lowest, approximately 3%, making the optimal K value 10.

[0128] 2. Real-time data collection and judgment

[0129] This part collects data in real time under unknown weather conditions, and analyzes and processes the data based on the above-mentioned classification algorithm to distinguish different precipitation particles.

[0130] 1: Real-time data collection.

[0131] The communication base station detects the reflected field strength of precipitation particles through echoes, including both vertical and horizontal polarizations. This is recorded as the fifth data set.

[0132] Assume the base station's vertically polarized transmit field strength is Pv, and the horizontally polarized transmit field strength is Ph. The received vertically polarized echo field strength is Sv, and the received horizontally polarized echo field strength is Sh. The fifth data set can be expressed as: D1 = {(Pv1, Ph1, Sv1, Sh1), (Pv2, Ph2, Sv2, Sh2) ... (Pvn, Phn, Svn, Shn)}, where 1 to n represent different moments in time.

[0133] The communication base station calculates the attenuation field strength of precipitation particles by transmitting and receiving field strength, including both vertical and horizontal polarization. This is recorded as the sixth data set.

[0134] Assume that the vertically polarized transmit field strength of the base station is Tv, and the horizontally polarized transmit field strength is Th. The received vertically polarized field strength is Hv, and the received horizontally polarized field strength is Hh. The sixth data set can be expressed as: D2 = {(Tv1, Th1, Hv1, Hh1), (Tv2, Th2, Hv2, Hh2) ... (Tvn, Thn, Hvn, Hhn)}, where 1 to n represent different time instants.

[0135] 2: Data preprocessing.

[0136] Obvious outliers in the fifth and sixth data sets were removed respectively.

[0137] The specific steps are the same as the data preprocessing part in the data accumulation and processing part.

[0138] The differential reflectance of the fifth data set is calculated to construct the seventh data set.

[0139] The specific steps are the same as the data preprocessing part in the data accumulation and processing part.

[0140] The differential attenuation rate is calculated for the sixth data set, and the eighth data set is constructed.

[0141] The specific steps are the same as the data preprocessing part in the data accumulation and processing part.

[0142] The seventh data set and the eighth data set are combined into a ninth data set.

[0143] Specifically, the ninth data set after synthesis is: D9={(R1, F1), (R2, F2)...(Rn, Fn)}, where 1...n represents n different moments.

[0144] 3: Data classification.

[0145] The ninth data set is calculated according to the classification algorithm in the third part to obtain the precipitation particle category.

[0146] Specifically, let the input data be (Rm, Fm), where Rm is the differential reflectivity of the precipitation particles at the mth moment, Fm is the differential attenuation rate of the precipitation particles at the mth moment, and (Rm, Fm) is a set of real-time data collected at the mth moment. The calculation steps are:

[0147] a) Calculate the distance between all data in the training dataset. There are many distance value algorithms. Take the Euler distance as an example: the Euclidean distance between (Rm, Fm) and the data in the training dataset (Ri, Fi, Ti) is Calculate all distance values. Where Ri is the differential reflectivity of the precipitation particles at the i-th moment in the training dataset, Fi is the differential attenuation rate of the precipitation particles at the i-th moment in the training dataset, Ti is the i-th moment, and (Ri, Fi, Ti) is a set of data at the i-th moment in the training dataset.

[0148] b) Select the smallest K distance values ​​from all the distance values ​​calculated in the previous step. If the category Tk is the most common in the training data set corresponding to these K distance values, then the precipitation particle category of the data is determined to be Tk.

[0149] This embodiment also provides a method for identifying precipitation particle types. The method is applied to a base station and includes:

[0150] Analyze the reflection signal value of the measured precipitation particles;

[0151] Transmitting a wireless signal and recording a wireless signal value, so that the receiver obtains an attenuated signal value and sends the attenuated signal value to the server;

[0152] The reflected signal value and the wireless signal value are sent to the server, so that the server obtains the field intensity attenuation based on the wireless signal value and the attenuated signal value, and determines the type of the measured precipitation particles according to the reflected signal value and the field intensity attenuation.

[0153] In one embodiment, determining the type of the detected precipitation particles based on the reflection signal value and the field intensity attenuation includes:

[0154] The differential reflectivity is obtained based on the reflection signal value;

[0155] The differential attenuation rate is obtained based on the field intensity attenuation;

[0156] A combined data set is obtained based on the differential reflectivity and the differential attenuation rate;

[0157] The combined dataset is input into a classification model to determine the type of precipitation particles being measured.

[0158] In one embodiment, the classification model is obtained as follows:

[0159] Construct training and validation datasets based on the reflected signal values ​​and field strength attenuation in historical data;

[0160] Build an initial classification model using the training dataset and preset algorithm;

[0161] The initial classification model is trained using the validation data set until the initial classification model converges to obtain a classification model.

[0162] In one embodiment, constructing a training data set and a validation data set based on the reflected signal values ​​and field intensity attenuation values ​​in historical data includes:

[0163] Calculating a differential reflectivity according to a first data set, and constructing a third data set based on the differential reflectivity, wherein the first data set is a historical data set of reflection signal values;

[0164] Calculating a differential attenuation rate according to the second data set, and constructing a fourth data set based on the differential attenuation rate, wherein the second data set is a historical data set of field intensity attenuation;

[0165] The third and fourth datasets are combined and split into a training dataset and a validation dataset.

[0166] In one embodiment, a method for obtaining the first data set and the second data set is as follows:

[0167] Obtaining the vertical polarization transmission field strength and horizontal polarization transmission field strength transmitted by the base station, and the vertical polarization echo field strength and horizontal polarization echo field strength reflected by precipitation particles, and marking the time and the type of precipitation particles, recording the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization echo field strength, horizontal polarization echo field strength, precipitation particle type, and time at the same time as a set of first data, obtaining multiple sets of first data, and recording the multiple sets of first data as a first data set;

[0168] Obtaining the vertical polarization transmission field strength and horizontal polarization transmission field strength transmitted by the base station and the vertical polarization reception field strength and horizontal polarization reception field strength transmitted by the receiver, and marking the time and the type of precipitation particles, recording the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization reception field strength, horizontal polarization reception field strength, precipitation particle type, and time at the same time as a set of second data, obtaining multiple sets of second data, and recording the multiple sets of second data as a second data set;

[0169] The first data and the second data are two sets of data at the same time.

[0170] In one embodiment, obtaining a combined data set based on differential reflectivity and differential attenuation includes:

[0171] Calculating multiple sets of differential reflectivities based on the fifth data set, the differential reflectivities are marked with timestamps, and constructing the multiple sets of differential reflectivities into a seventh data set based on the timestamps, wherein the fifth data set is a real-time collection data set of reflection signal values;

[0172] Calculating multiple sets of differential attenuation rates based on the sixth data set, the differential attenuation rates are marked with timestamps, and constructing the multiple sets of differential attenuation rates into an eighth data set based on the timestamps, the sixth data set being a real-time collection data set of field intensity attenuation;

[0173] The first data and the second data having the same timestamp are merged into a set of data within the combined data set.

[0174] In one embodiment, a method for obtaining the fifth and sixth data sets is as follows:

[0175] Obtaining the vertical polarization transmission field strength, horizontal polarization transmission field strength, and vertical polarization echo field strength and horizontal polarization echo field strength reflected by precipitation particles of the base station, marking the time, and recording the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization echo field strength, horizontal polarization echo field strength, and time at the same time as a set of fifth data, obtaining multiple sets of fifth data, and recording the multiple sets of fifth data as a fifth data set;

[0176] Obtaining the vertical polarization transmit field strength and the horizontal polarization transmit field strength transmitted by the base station and the vertical polarization receive field strength and the horizontal polarization receive field strength transmitted by the receiver, marking the time, and recording the vertical polarization transmit field strength, the horizontal polarization transmit field strength, the vertical polarization receive field strength, the horizontal polarization receive field strength, and the time at the same time as a set of sixth data to obtain multiple sets of sixth data, and recording the multiple sets of sixth data as a sixth data set;

[0177] The fifth data and the sixth data are two sets of data at the same time.

[0178] This embodiment also provides a method for identifying precipitation particle types. The method is applied to a receiver and includes:

[0179] Obtain the wireless signal sent by the base station, obtain the attenuated signal value, and send the attenuated signal value to the server;

[0180] The server obtains the field intensity attenuation based on the attenuated signal value and the wireless signal value sent by the base station, and determines the type of the measured precipitation particles based on the field intensity attenuation and the reflected signal value sent by the base station.

[0181] In one embodiment, determining the type of the detected precipitation particles based on the reflection signal value and the field intensity attenuation includes:

[0182] The differential reflectivity is obtained based on the reflection signal value;

[0183] The differential attenuation rate is obtained based on the field intensity attenuation;

[0184] A combined data set is obtained based on the differential reflectivity and the differential attenuation rate;

[0185] The combined dataset is input into a classification model to determine the type of precipitation particles being measured.

[0186] In one embodiment, the classification model is obtained as follows:

[0187] Construct training and validation datasets based on the reflected signal values ​​and field strength attenuation in historical data;

[0188] Build an initial classification model using the training dataset and preset algorithm;

[0189] The initial classification model is trained using the validation data set until the initial classification model converges to obtain a classification model.

[0190] In one embodiment, constructing a training data set and a validation data set based on the reflected signal values ​​and field intensity attenuation values ​​in historical data includes:

[0191] Calculating a differential reflectivity according to a first data set, and constructing a third data set based on the differential reflectivity, wherein the first data set is a historical data set of reflection signal values;

[0192] Calculating a differential attenuation rate according to the second data set, and constructing a fourth data set based on the differential attenuation rate, wherein the second data set is a historical data set of field intensity attenuation;

[0193] The third and fourth datasets are combined and split into a training dataset and a validation dataset.

[0194] In one embodiment, a method for obtaining the first data set and the second data set is as follows:

[0195] Obtaining the vertical polarization transmission field strength and horizontal polarization transmission field strength transmitted by the base station, and the vertical polarization echo field strength and horizontal polarization echo field strength reflected by precipitation particles, and marking the time and the type of precipitation particles, recording the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization echo field strength, horizontal polarization echo field strength, precipitation particle type, and time at the same time as a set of first data, obtaining multiple sets of first data, and recording the multiple sets of first data as a first data set;

[0196] Obtaining the vertical polarization transmission field strength and horizontal polarization transmission field strength transmitted by the base station and the vertical polarization reception field strength and horizontal polarization reception field strength transmitted by the receiver, and marking the time and the type of precipitation particles, recording the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization reception field strength, horizontal polarization reception field strength, precipitation particle type, and time at the same time as a set of second data, obtaining multiple sets of second data, and recording the multiple sets of second data as a second data set;

[0197] The first data and the second data are two sets of data at the same time.

[0198] In one embodiment, obtaining a combined data set based on differential reflectivity and differential attenuation includes:

[0199] Calculating multiple sets of differential reflectivities based on the fifth data set, the differential reflectivities are marked with timestamps, and constructing the multiple sets of differential reflectivities into a seventh data set based on the timestamps, wherein the fifth data set is a real-time collection data set of reflection signal values;

[0200] Calculating multiple sets of differential attenuation rates based on the sixth data set, the differential attenuation rates are marked with timestamps, and constructing the multiple sets of differential attenuation rates into an eighth data set based on the timestamps, the sixth data set being a real-time collection data set of field intensity attenuation;

[0201] The first data and the second data having the same timestamp are merged into a set of data within the combined data set.

[0202] In one embodiment, a method for obtaining the fifth and sixth data sets is as follows:

[0203] Obtaining the vertical polarization transmission field strength, horizontal polarization transmission field strength, and vertical polarization echo field strength and horizontal polarization echo field strength reflected by precipitation particles of the base station, marking the time, and recording the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization echo field strength, horizontal polarization echo field strength, and time at the same time as a set of fifth data, obtaining multiple sets of fifth data, and recording the multiple sets of fifth data as a fifth data set;

[0204] Obtaining the vertical polarization transmit field strength and the horizontal polarization transmit field strength transmitted by the base station and the vertical polarization receive field strength and the horizontal polarization receive field strength transmitted by the receiver, marking the time, and recording the vertical polarization transmit field strength, the horizontal polarization transmit field strength, the vertical polarization receive field strength, the horizontal polarization receive field strength, and the time at the same time as a set of sixth data to obtain multiple sets of sixth data, and recording the multiple sets of sixth data as a sixth data set;

[0205] The fifth data and the sixth data are two sets of data at the same time.

[0206] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by adding the necessary general hardware platform with the help of software, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present disclosure is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD-ROM), including a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0207] This embodiment also provides a device for identifying precipitation particle types, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented using software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0208] FIG11 is a schematic diagram of a structure of a device for identifying precipitation particle types according to an embodiment of the present disclosure. As shown in FIG11 , the device includes:

[0209] An acquisition module 111 is configured to acquire a reflection signal value of the measured precipitation particles and a field intensity attenuation of a wireless signal transmitted by a base station;

[0210] The processing module 112 is configured to determine the type of the detected precipitation particles based on the reflection signal value and the field intensity attenuation.

[0211] In one embodiment, the device further includes: an output module 113 configured to output the type of the detected precipitation particles.

[0212] In one embodiment, the device is further configured to determine the type of precipitation particles being measured based on the reflection signal value and the field intensity attenuation, including:

[0213] The differential reflectivity is obtained based on the reflection signal value;

[0214] The differential attenuation rate is obtained based on the field intensity attenuation;

[0215] A combined data set is obtained based on the differential reflectivity and the differential attenuation rate;

[0216] The combined dataset is input into a classification model to determine the type of precipitation particles being measured.

[0217] In one embodiment, the apparatus is further configured to obtain a classification model in the following manner:

[0218] Construct training and validation datasets based on the reflected signal values ​​and field strength attenuation in historical data;

[0219] Build an initial classification model using the training dataset and preset algorithm;

[0220] The initial classification model is trained using the validation data set until the initial classification model converges to obtain a classification model.

[0221] In one embodiment, the device is further configured to construct a training data set and a verification data set based on the reflection signal values ​​and field intensity attenuation values ​​in the historical data, including:

[0222] Calculating a differential reflectivity according to a first data set, and constructing a third data set based on the differential reflectivity, wherein the first data set is a historical data set of reflection signal values;

[0223] Calculating a differential attenuation rate according to the second data set, and constructing a fourth data set based on the differential attenuation rate, wherein the second data set is a historical data set of field intensity attenuation;

[0224] The third and fourth datasets are combined and split into a training dataset and a validation dataset.

[0225] In one embodiment, the apparatus is further configured to: obtain the first data set and the second data set in the following manner:

[0226] Obtaining the vertical polarization transmission field strength and horizontal polarization transmission field strength transmitted by the base station, and the vertical polarization echo field strength and horizontal polarization echo field strength reflected by precipitation particles, and marking the time and the type of precipitation particles, recording the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization echo field strength, horizontal polarization echo field strength, precipitation particle type, and time at the same time as a set of first data, obtaining multiple sets of first data, and recording the multiple sets of first data as a first data set;

[0227] Obtaining the vertical polarization transmission field strength and horizontal polarization transmission field strength transmitted by the base station and the vertical polarization reception field strength and horizontal polarization reception field strength transmitted by the receiver, and marking the time and the type of precipitation particles, recording the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization reception field strength, horizontal polarization reception field strength, precipitation particle type, and time at the same time as a set of second data, obtaining multiple sets of second data, and recording the multiple sets of second data as a second data set;

[0228] The first data and the second data are two sets of data at the same time.

[0229] In one embodiment, the apparatus is further configured to obtain a combined data set based on the differential reflectivity and the differential attenuation rate, comprising:

[0230] Calculating multiple sets of differential reflectivities based on the fifth data set, the differential reflectivities are marked with timestamps, and constructing the multiple sets of differential reflectivities into a seventh data set based on the timestamps, wherein the fifth data set is a real-time collection data set of reflection signal values;

[0231] Calculating multiple sets of differential attenuation rates based on the sixth data set, the differential attenuation rates are marked with timestamps, and constructing the multiple sets of differential attenuation rates into an eighth data set based on the timestamps, the sixth data set being a real-time collection data set of field intensity attenuation;

[0232] The first data and the second data having the same timestamp are merged into a set of data within the combined data set.

[0233] In one embodiment, the apparatus is further configured to: obtain the fifth data set and the sixth data set in the following manner:

[0234] Obtaining the vertical polarization transmission field strength, horizontal polarization transmission field strength, and vertical polarization echo field strength and horizontal polarization echo field strength reflected by precipitation particles of the base station, marking the time, and recording the vertical polarization transmission field strength, horizontal polarization transmission field strength, vertical polarization echo field strength, horizontal polarization echo field strength, and time at the same time as a set of fifth data, obtaining multiple sets of fifth data, and recording the multiple sets of fifth data as a fifth data set;

[0235] Obtaining the vertical polarization transmit field strength and the horizontal polarization transmit field strength transmitted by the base station and the vertical polarization receive field strength and the horizontal polarization receive field strength transmitted by the receiver, marking the time, and recording the vertical polarization transmit field strength, the horizontal polarization transmit field strength, the vertical polarization receive field strength, the horizontal polarization receive field strength, and the time at the same time as a set of sixth data to obtain multiple sets of sixth data, and recording the multiple sets of sixth data as a sixth data set;

[0236] The fifth data and the sixth data are two sets of data at the same time.

[0237] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0238] An embodiment of the present disclosure further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when run.

[0239] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0240] An embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0241] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0242] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0243] Obviously, those skilled in the art should understand that the modules or steps of the present disclosure described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present disclosure is not limited to any particular combination of hardware and software.

[0244] The foregoing description is merely a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will readily appreciate that various modifications and variations of the present disclosure are possible. Any modifications, equivalent substitutions, or improvements made within the principles of the present disclosure shall be included within the scope of protection of the present disclosure.

Claims

1. A method for identifying precipitation particle types, the method being applied to a server, comprising: Acquire the reflection signal value of the measured precipitation particles through the base station; Acquire, through the base station and the receiver, a field strength attenuation of a wireless signal transmitted by the base station; The type of the detected precipitation particle is determined based on the reflection signal value and the field intensity attenuation.

2. The method according to claim 1, wherein: Determining the type of the detected precipitation particle based on the reflection signal value and the field intensity attenuation includes: Obtaining a differential reflectivity based on the reflection signal value; Obtaining a differential attenuation rate based on the field intensity attenuation; obtaining a combined data set based on the differential reflectivity and the differential attenuation rate; The combined data set is input into a classification model to determine the type of the detected precipitation particles.

3. The method according to claim 2, wherein: The classification model is obtained as follows: Construct training data set and validation data set according to the reflection signal value and field strength attenuation in the historical data; Using the training data set and the preset algorithm to build an initial classification model; The initial classification model is trained using the validation data set until the initial classification model converges to obtain the classification model.

4. The method according to claim 3, wherein: The training data set and validation data set are constructed based on the reflection signal values ​​and field strength attenuation in the historical data, including: Calculating a differential reflectivity according to a first data set, and constructing a third data set based on the differential reflectivity, wherein the first data set is a historical data set of reflection signal values; Calculating a differential attenuation rate according to a second data set, and constructing a fourth data set based on the differential attenuation rate, wherein the second data set is a historical data set of field intensity attenuation; The third data set and the fourth data set are combined and then split into the training data set and the verification data set.

5. The method according to claim 4, wherein: The method for obtaining the first data set and the second data set is as follows: Obtaining the vertical polarization transmission field strength and the horizontal polarization transmission field strength transmitted by the base station and the vertical polarization echo field strength and the horizontal polarization echo field strength reflected by the precipitation particles, and marking the time and the type of the precipitation particles, recording the vertical polarization transmission field strength, the horizontal polarization transmission field strength, the vertical polarization echo field strength, the horizontal polarization echo field strength, the precipitation particle type, and the time at the same time as a group of first data, obtaining multiple groups of first data, and recording the multiple groups of first data as a first data set; Obtaining the vertical polarization transmission field strength and the horizontal polarization transmission field strength transmitted by the base station and the vertical polarization reception field strength and the horizontal polarization reception field strength sent by the receiver, and marking the time and the type of the precipitation particles, recording the vertical polarization transmission field strength, the horizontal polarization transmission field strength, the vertical polarization reception field strength, the horizontal polarization reception field strength, the type of precipitation particles, and the time at the same time as a set of second data, obtaining multiple sets of second data, and recording the multiple sets of second data as a second data set; The first data and the second data are two sets of data at the same time.

6. The method according to claim 2, wherein: Obtaining a combined data set based on the differential reflectivity and the differential attenuation rate, comprising: Calculating multiple sets of differential reflectivity according to the fifth data set, wherein the differential reflectivity is marked with a timestamp, and constructing the multiple sets of differential reflectivity into a seventh data set based on the timestamp, wherein the fifth data set is a real-time collection data set of the reflection signal value; Calculating multiple groups of differential attenuation rates according to the sixth data set, wherein the differential attenuation rates are marked with timestamps, and constructing the multiple groups of differential attenuation rates into an eighth data set based on the timestamps, wherein the sixth data set is a real-time collection data set of the field intensity attenuation amount; The first data and the second data with the same time stamp are combined into a set of data in a combined data set.

7. The method according to claim 6, wherein: The method for obtaining the fifth data set and the sixth data set is as follows: Obtaining the vertical polarization transmission field strength, the horizontal polarization transmission field strength, and the vertical polarization echo field strength and the horizontal polarization echo field strength reflected by the precipitation particles of the base station, and marking the time, recording the vertical polarization transmission field strength, the horizontal polarization transmission field strength, the vertical polarization echo field strength, the horizontal polarization echo field strength, and the time at the same time as a set of fifth data, obtaining multiple sets of fifth data, and recording the multiple sets of fifth data as a fifth data set; Obtaining the vertical polarization transmission field strength and the horizontal polarization transmission field strength transmitted by the base station and the vertical polarization reception field strength and the horizontal polarization reception field strength sent by the receiver, and marking the time, recording the vertical polarization transmission field strength, the horizontal polarization transmission field strength, the vertical polarization reception field strength, the horizontal polarization reception field strength, and the time at the same time as a set of sixth data, obtaining multiple sets of sixth data, and recording the multiple sets of sixth data as a sixth data set; The fifth data and the sixth data are two sets of data at the same time.

8. A method for identifying precipitation particle types, the method being applied to a base station, comprising: Analyze the reflection signal value of the measured precipitation particles; Transmitting a wireless signal and recording a wireless signal value, so that a receiver obtains an attenuated signal value and sends the attenuated signal value to a server; The reflected signal value and the wireless signal value are sent to a server, so that the server obtains a field intensity attenuation based on the wireless signal value and the attenuated signal value, and determines the type of the measured precipitation particles according to the reflected signal value and the field intensity attenuation.

9. A method for identifying precipitation particle types, the method being applied to a receiver, comprising: Acquire a wireless signal sent by a base station, obtain an attenuated signal value, and send the attenuated signal value to a server; The server obtains the field intensity attenuation based on the attenuated signal value and the wireless signal value sent by the base station, and determines the type of the measured precipitation particles based on the field intensity attenuation and the reflected signal value sent by the base station.

10. A device for identifying precipitation particle types, comprising: An acquisition module, configured to acquire the reflection signal value of the measured precipitation particles and the field strength attenuation of the wireless signal transmitted by the base station; The processing module is configured to determine the type of the detected precipitation particles based on the reflection signal value and the field intensity attenuation.

11. A computer-readable storage medium having a computer program stored therein, wherein: When the computer program is executed by a processor, the computer program implements the steps of the method described in any one of claims 1 to 7, or implements the steps of the method described in claim 8, or implements the steps of the method described in claim 9.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processor implements the steps of the method described in any one of claims 1 to 7, or implements the steps of the method described in claim 8, or implements the steps of the method described in claim 9.

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