54 results about "Precipitation particle" patented technology

Provided are a device and a method for multi-angle stereoscopic imaging measurement of precipitation particles, including an optical illumination unit used for illuminating a sampling space; a multi-angle imaging unit used for performing multi-angle imaging on the precipitation particles; a data acquisition control unit used for controlling the optical illumination unit and the multi-angle imaging unit; a data processing unit used for processing and displaying image data of the precipitation particles; and a protection unit used for protecting the optical illumination unit, the multi-angle imaging unit and the data acquisition control unit.

The invention discloses a three-dimensional Gaussian splash modeling method and system based on scene prior in complex weather. The method comprises the following steps of: 1, acquiring a group of low-illumination original image sequences, corresponding camera parameters and a pose relation between viewpoints; 2, removing rainfall particle shielding in the original image sequence to obtain a clear image sequence; obtaining a corresponding mask by adopting a segmentation large model; 3, extracting scene geometric priori information with illumination invariance, wherein the scene geometric priori information comprises a depth priori image and an edge gradient priori image; 4, constructing a three-dimensional Gaussian splashing model containing edge gradient, depth and illumination attributes, and rendering a prediction result by using a microrasterization method; and constructing a total loss function in combination with the mask, and optimizing the three-dimensional Gaussian splash model by optimizing the total loss function. According to the method, all-around elimination of rainfall particle and lens rainwater interference is realized, a robust scene geometry priori acquisition mechanism is innovatively constructed, and a multi-attribute constrained three-dimensional Gaussian splash model is established.

The invention belongs to the field of microwave remote sensing, and relates to a Doppler precipitation radar precipitation and three-dimensional wind field synchronous inversion method and system. The Doppler precipitation radar precipitation and three-dimensional wind field synchronous inversion method comprises the following steps: determining a reflectivity factor of an observation area through echo power of a Doppler precipitation radar; according to the reflectivity factor, determining a rainfall area and calculating the size of rainfall particles; calculating the vertical falling speed of the precipitation particles according to the size of the precipitation particles; calculating the radial velocity of precipitation particles according to the echo phase of the Doppler precipitation radar; removing the vertical falling speed of the precipitation particles to obtain the radial movement speed of the precipitation particles caused by the wind field; and according to the rainfall particle radial motion speed caused by the wind field, a radar observation geometry and a three-dimensional wind field inversion algorithm are combined, and a three-dimensional wind field of the rainfall area is obtained through inversion. By adopting the method provided by the invention, the wind field inversion precision is improved.

The embodiment of the invention provides a precipitation particle type identification method and device, a storage medium and electronic equipment, and the method is applied to a server, and comprises the steps: obtaining a reflection signal value of a measured precipitation particle through a base station; acquiring the field intensity attenuation of the wireless signal transmitted by the base station through the base station and the receiver; and determining the type of the measured rainfall particles based on the reflected signal value and the field intensity attenuation amount. Through the method and the device, the problem of low accuracy of identifying the type of the rainfall particles is solved, and the effect of improving the accuracy of identifying the rainfall particles is further achieved.

The invention discloses a data feature enhanced artificial intelligence dual-polarization radar quantitative rainfall estimation method, which comprises the following steps: S1, acquiring dual-polarization radar observation data, and constructing a three-dimensional space grid structure data set; s2, constructing a double-Gaussian probability density function, and performing data feature enhancement on a single variable in the three-dimensional space grid structure data set; s3, constructing a multi-dimensional joint probability density function based on the double-Gaussian probability density function, and performing joint data feature enhancement on all variables in the three-dimensional space grid structure data set; and S4, training an artificial intelligence model reflecting the relation between the radar data and the rainfall data. According to the method, the probability density function characteristics are creatively adopted to describe the rolling change of the dual-polarization radar variable within one hour. By constructing three-dimensional joint probability density function features, not only can the distribution characteristics of dual-polarization radar variables be extracted, but also the space-time evolution rule of precipitation particles can be effectively captured.

To realize to control output of weather alerts by using weather-related posted information that is posted by users.SOLUTION: A weather management system of the embodiment analyzes weather observation data, performs an analysis process for outputting precipitation particles that will fall to the ground based on a predetermined criterion, and outputs alert information according to the result of the analysis process. The predetermined criterion used in the analysis process can be changed based on weather-related posted information posted via a poster terminal. Here, the effective time and effective area set according to a predetermined posting time zone are stored for each predetermined posting area. By performing the analysis process applying the revised predetermined criterion to the observation data located within the effective area from the posting location of the weather-related posted information during the effective time corresponding to the posting time of the weather-related posted information, appropriate disaster prevention calls can be made taking into account the posting frequency of the weather-related posted information.SELECTED DRAWING: Figure 1

The present invention discloses an artificial intelligence dual-polarization radar quantitative precipitation estimation method with enhanced data features, including: S1, obtaining dual-polarization radar observation data and constructing a three-dimensional spatial grid structure data set; S2, constructing a double Gaussian probability density function to enhance the data features of a single variable in the three-dimensional spatial grid structure data set; S3, constructing a multi-dimensional joint probability density function based on the double Gaussian probability density function to perform joint data feature enhancement on all variables in the three-dimensional spatial grid structure data set; S4, training an artificial intelligence model that reflects the relationship between radar data and precipitation data. The present invention innovatively uses the probability density function feature to describe the rolling change of dual-polarization radar variables within one hour. By constructing the three-dimensional joint probability density function feature, not only can the distribution characteristics of dual-polarization radar variables be extracted, but also the spatio-temporal evolution law of precipitation particles can be effectively captured.

The invention discloses an alpine region rainfall phase state identification and multi-source data storage and retrieval system, which relates to the technical field of multi-source rainfall data storage and retrieval, and comprises a multi-source rainfall data acquisition module, a remote sensing positioning module, a rainfall type identification module, a rainfall intensity evaluation module and a storage and retrieval module, satellite remote sensing data and ground observation rainfall data of an alpine region are collected, the collected data are preprocessed and standardized, feature extraction and analysis are carried out on the preprocessed rainfall data, and then rainfall particle features and rainfall amount feature parameters are obtained; the data acquisition technology, the feature analysis technology, the remote sensing positioning technology and the model construction technology in the system are closely combined with the modern information technology, and the problems that in the prior art, multi-source rainfall data are difficult to effectively integrate and analyze, different types of rainfall data are difficult to accurately distinguish, and rainfall intensity and spatial distribution are difficult to accurately evaluate are solved.

The invention relates to the technical field of meteorological prediction, in particular to a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data. According to the system, the cloud bottom height of a target cloud cluster is determined based on a laser ceilometer, radiation corresponding to the cloud top height of the target cloud cluster is collected based on a meteorological satellite, the cloud top temperature of the target cloud cluster is calculated based on a radiation transmission mode, and the cloud top height of the target cloud cluster is determined based on the cloud top temperature. And detecting precipitation particles of the marked segmented cloud layer, deducing the water content density of the marked segmented cloud layer according to the precipitation particles, obtaining an aerosol particle extinction coefficient and a backscattering coefficient of the marked segmented cloud layer, and obtaining a meteorological characterization value of the target cloud cluster based on the water content density, the aerosol particle extinction coefficient and the backscattering coefficient. The reasonability of the target cloud cluster is analyzed based on the meteorological characterization value of the target cloud cluster, the cloud layer meteorological prediction precision can be improved, and the meteorological prediction timeliness is improved.

The present invention belongs to the field of microwave remote sensing and relates to a method and system for synchronous inversion of Doppler precipitation radar precipitation and three-dimensional wind field. The method includes: a method for synchronous inversion of Doppler precipitation radar precipitation and three-dimensional wind field, comprising: determining the reflectivity factor of the observation area through the echo power of the Doppler precipitation radar; determining the precipitation area and calculating the size of the precipitation particles based on the reflectivity factor; calculating the vertical falling velocity of the precipitation particles based on the precipitation particle size; calculating the radial velocity of the precipitation particles based on the echo phase of the Doppler precipitation radar; removing the vertical falling velocity of the precipitation particles to obtain the radial motion velocity of the precipitation particles caused by the wind field; and inverting the three-dimensional wind field of the precipitation area based on the radial motion velocity of the precipitation particles caused by the wind field, combining the radar observation geometry and the three-dimensional wind field inversion algorithm. The method of the present invention improves the accuracy of wind field inversion.

The invention provides an airborne cloud precipitation particle measuring instrument which comprises a probe cabin with an elliptical cross section, the top end of the probe cabin is located on one side of an elliptical long axis perpendicular to the cross section, a first supporting arm and a second supporting arm which are oppositely arranged are fixed to the top end, and a fixing seat is arranged on one side face of the probe cabin; reflective mirror cover plates inclined inwards by 45 degrees are arranged on the outer walls of the free ends, away from the probe bin, of the first supporting arm and the second supporting arm, windows are arranged between the top ends of the inner walls of the free ends and the outer edges of the tops of the reflective mirror cover plates, and window glass is fixed through window glass pressing rings; a 45-degree reflector is mounted on the inner side of the reflector cover plate; a laser assembly is arranged in the first supporting arm; a lens group and a camera are arranged in the second supporting arm. The lens group is located at a light inlet of the camera. The overall modeling of the device is streamline, wind resistance is reduced, collision and breakage of particles are reduced, the device has high precision and reliability, and real-time, high-speed and three-dimensional measurement and synchronous extraction of cloud precipitation particles can be achieved.

The application discloses a high-precision precipitation particle phase state identification method and system, and relates to the technical field of atmospheric detection.The application solves the problems of difficult precipitation particle tracking, many misjudgments of phase state identification and no accurate basis for repairing misjudgment points in the prior art.The application comprises the following steps: synchronously sampling continuous frames of precipitation particles, generating a unique identification of the particles through double-feature coupling, constructing and completing integrity verification of a space-time trajectory; based on the qualified trajectory, carrying out consistency verification in time and space dimensions, and marking suspicious misjudgment points and effective sampling points; constructing an exclusive traceability sample set for the misjudgment points, and deducing and repairing the real phase state according to the phase state correlation type of the sample set.The application realizes unique tracking of the particles in the whole process, accurately identifies and repairs the misjudgment points, improves the overall precision of the precipitation particle phase state identification, and guarantees the continuity of the phase state evolution.

The invention relates to the technical field of rainfall inversion, in particular to a multi-source data fused rainfall radar high-precision rainfall inversion method, which comprises the steps of S1, defining an inversion target and selecting multi-source data; s2, multi-source data preprocessing and cloud layer influence pre-judgment; s3, fusion algorithm type selection and correction model construction; s4, assimilating historical data; s5, dynamic weight optimization is carried out; the judgment logic can effectively distinguish distorted data and effective data which are shielded by clouds, blind use of shielded rainfall particle detection data for inversion is avoided, false rainfall areas or missed judgment areas are reduced, rainfall particle information which is originally large in deviation due to signal reflection and absorption of the clouds is closer to the real situation, and the accuracy of rainfall particle detection is improved. The accuracy of rainfall intensity inversion is remarkably improved, short-term historical data processed by a physical model and a long-term climate background field are fused into a real-time inversion process, a smoother, more stable and more reasonable rainfall inversion result can be provided, and the accuracy of rainfall products in short-term and imminent forecasting is greatly enhanced.

The invention discloses a raindrop spectrum parameter profile inversion method and a raindrop spectrum parameter profile inversion system, which aim at the problem that the existing rainfall micro-physical parameterization scheme is insufficient in characterization of raindrop spectrum regional difference and vertical evolution characteristics, and optimize the raindrop spectrum parameter profile inversion process by fusing rainfall particle phase state classification. And the precision and reliability of an inversion result are improved. According to the method, collaborative inversion is carried out through multi-source vertical detection data by using a micro-rain radar (high-sensitivity small particle detection) and a laser raindrop spectrometer (ground drop spectrum verification), the vertical gradient change rule of raindrop spectrum parameters is quantified, the mu-lambda relation of different heights is constructed, and raindrop spectrum parameter profile inversion of rainfall particle phase state classification is realized.

This manufacturing method is for manufacturing an observation device (110) for observing precipitation particles such as raindrops. The manufacturing method comprises: a step for preparing an antenna module (30) that transmits electromagnetic waves (E) and receives electromagnetic waves (E) reflected by a storage container (20) and electromagnetic waves (E) reflected by precipitation particles; a step for outputting information relating to a placement position for placing the antenna module (30) in the storage container (20) on the basis of the value of the dielectric constant of the wall (21) of the storage container (20), the value of the thickness of the wall (21), and a theoretical formula for converting a function indicating the relationship between the electromagnetic waves (E) transmitted from the antenna module (30) and the electromagnetic waves (E) reflected by the storage container (20) into a function having, as variables, the dielectric constant of the wall (21), the thickness of the wall (21), and the placement position; and a step for storing the antenna module (30) in the storage container (20) and placing the antenna module (30) at the placement position.

The utility model provides an airborne holographic cloud precipitation particle imager. The airborne holographic cloud precipitation particle imager comprises a cylindrical pod, a conical pod cover and a strip-shaped ellipsoid end cover, the two ends of the rear side face of the strip-shaped ellipsoid end cover are each provided with a laser module installation shell and a camera assembly installation shell. The first far-away end of the laser module is provided with a first reflective mirror which reflects emergent light of the laser module, and the emergent light penetrates through a first light hole in the inner side of the first far-away end and then is vertically emitted to the camera assembly mounting shell. A second far end of the camera assembly mounting shell is provided with a second reflective mirror, and the inner side of the second far end is provided with a second light hole directly facing the first light hole; and an optical transceiver unit is fixed in the front end of the cylindrical pod. The device is simple in structure and convenient to use and maintain, particularly, the whole shape is streamline, the phenomena of wind resistance and particle breaking are greatly reduced, and the device has high timeliness, accuracy and reliability.

The invention discloses a method for realizing dual-polarization phased array weather radar target simulation by using a digital receiver, and relates to the technical field of radar signal processing. According to the method for realizing dual-polarization phased array weather radar target simulation by utilizing the digital receiver, a weather target micro-physical parameter model is established, abundant FPGA resources in the phased array radar digital receiver are combined, and a similar direct digital frequency synthesis (DDS) technology is utilized, so that a high-fidelity digital intermediate frequency echo signal is generated; dynamic target simulation is realized in an active array digital receiver of a phased array radar. Controllable and repeatable simulation of various weather phenomena (rain, snow, hail and the like) can be realized in a laboratory environment. Under the condition of not depending on external hardware, echo characteristics of weather targets (such as rainfall particles), including parameters such as distance, speed, amplitude and dual-polarization parameter quantity, are simulated, and debugging, performance verification and algorithm testing of a radar system are effectively supported.

The application relates to the technical field of signal processing, and discloses an intelligent anti-interference signal processing method and system for a portable laser wind measurement radar, the method comprising the following steps: constructing a physical-inspired feature network, outputting a feature vector and mapping the feature vector to a low-dimensional physical parameter vector, constructing a composite loss function according to the low-dimensional physical parameter vector; constructing a raindrop particle size distribution according to the low-dimensional physical parameter vector, calculating the backscattering cross section and the terminal falling velocity of each particle, obtaining a Doppler frequency shift according to the terminal falling velocity, and obtaining the spectrum of an interference signal according to the backscattering cross section and the Doppler frequency shift; obtaining a pure wind field signal according to the spectrum of the interference signal and a mixed spectrum, and obtaining a horizontal wind vector according to the pure wind field signal. The application realizes high-precision wind speed inversion.

The present application relates to the technical field of vehicles and discloses a wiper control method, a wiper control device and a vehicle.The wiper control method comprises the following steps: acquiring visual form features and impact pulse features of precipitation particles in a front windshield area of a vehicle, wherein the visual form features are used to represent the overall form and distribution law of the precipitation particles in the spatial and motion levels, and the impact pulse features are used to reflect the vibration change trend caused by the impact of the precipitation particles on the front windshield; determining a precipitation type based on the visual form features, the impact pulse features and real-time temperature features, wherein the real-time temperature features are used to describe the temperature influence of a driving environment on the front windshield area; and controlling the vehicle to execute a corresponding wiper control strategy based on the precipitation type.The present application can reduce the situation of over-frequent or untimely wiping, improve the visual clarity of the front windshield in bad weather, and thus enhance the driving safety and driving comfort.

The invention relates to the technical field of raindrop spectrum inversion, in particular to a method for inverting an aerial raindrop spectrum by using characteristic parameters of a dual-polarization radar, and the method comprises the steps of data collection, quality control, calculation and fusion. Modeling and counting; the statistics specifically comprises the statistics of a radar and rainfall type relationship and the statistics of a typing and raindrop spectrum type fitting relationship; and the business application process comprises a four-dimensional raindrop spectrum product and a rainfall estimation product. The research of the invention can bring significant improvements in three aspects: in the aspect of monitoring capability, a radar secondary product system can be enriched, and visual visualization of spatial precipitation particle distribution characteristics is realized; in the aspect of early warning and forecasting, the heavy rainfall falling area can be researched and judged in advance through the distribution characteristics of particles in the air, and the disastrous weather early warning timeliness is effectively prolonged; in the aspect of artificial influence weather, refined rainfall micro-physical structure information is provided, and reliable technical support is provided for operation condition identification and effect evaluation.

This invention discloses a method for inverting atmospheric precipitation particle spectra, belonging to the field of inversion technology. It addresses the limitations of existing technologies in accurately obtaining inversion parameters for ground and atmospheric microphysical parameters, thus hindering understanding of the formation, development, and variation mechanisms of atmospheric cloud precipitation and consequently preventing accurate prediction and interpretation of atmospheric processes and meteorological disasters. The method includes: acquiring the observed power spectrum using C-band frequency-modulated continuous wave radar and employing denoising methods to remove noise levels; constructing the atmospheric power spectrum based on horizontal wind data acquired from wind profiler radar; constructing the raindrop spectrum using ground raindrop data acquired from a ground raindrop spectrometer and a standardized gamma distribution; and performing ground and atmospheric microphysical parameter inversion based on the above results. This invention is used for inverting and obtaining ground and atmospheric microphysical parameters.

The present application relates to the field of radar technology, and more particularly to a precipitation particle phase state recognition method for an X-band dual-polarization micro-weather radar, which comprises the following steps: Step 1, scanning at a set pitch angle between horizontal and vertical, obtaining spectrum reflectivity, spectrum differential reflectivity and spectrum correlation coefficient along Doppler velocity distribution for each range bin; Step 2, determining a separation boundary according to a valley value formed by first decreasing and then increasing the spectrum correlation coefficient, dividing the range bin along Doppler velocity into a plurality of spectrum components corresponding to single phase states respectively; Step 3, obtaining component differential reflectivity, component correlation coefficient and relative order along fall velocity for each spectrum component, and obtaining phase state spatial distribution by summarizing and splicing. The present application can separate and recognize multiple coexisting phase states in a single resolution volume, and improves the accuracy of phase state recognition.

The invention relates to an L-band radar precipitation particle signal identification and extraction method, which mainly comprises the following steps of: firstly, preprocessing acquired L-band radar data, and sequentially finishing direct current removal, three-point smoothing, noise level estimation and spectral moment calculation so as to suppress non-target signal interference and provide reliable initial reference; then, based on multi-beam radial velocity analysis, whether a rainfall signal exists or not is automatically judged, and subsequent processing is started only when it is confirmed that the rainfall signal exists; performing double-Gaussian or single-Gaussian model fitting on the power spectrum data with precipitation signals, and extracting parameters such as amplitude, expected value and standard deviation of the signals; and finally, through signal reconstruction and characteristic parameter analysis, signal attributes are judged in combination with the radial velocity mean value and the standard deviation, precipitation signals are extracted, and parameters are recorded. According to the invention, precipitation signals can be accurately extracted under the condition of multiple signals and superposition, redundancy calculation is reduced, and processing efficiency and signal identification accuracy are improved.

The invention provides a rainfall micro-physical parameter profile inversion method and system based on active radar and passive microwave joint, and the method comprises the steps: carrying out the constraint of particle micro-physical parameters through employing the active radar and passive microwave detection multi-source observation data, and building a dual optimization inversion model; an observation operator of the inversion model comprises micro-physical characteristic changes of different types of particles and an electromagnetic wave attenuation effect, and inversion uncertainty caused by particle micro-physical assumption and the attenuation effect can be reduced, so that optimal state variables of different types of precipitation particles on a profile can be obtained through inversion. And the inversion precision of the rainfall micro-physical parameter profile is improved.

This invention discloses a high-precision precipitation particle detection method and system. It relates to the field of optical and meteorological measurement equipment technology, solving the problems of poor accuracy and insufficient adaptability in precipitation particle parameter measurement in existing technologies. This invention corrects the distortion of precipitation particle slice contours caused by these movements through horizontal and vertical velocities, thereby accurately identifying the shape, size, flatness, three-dimensional orientation, and type of precipitation particles during their descent. Based on the degree of change in the upper and lower sets of precipitation particle contours, the rotation rate of the precipitation particles is estimated. An artificial intelligence recognition algorithm is used to train and analyze the precipitation particle contours to identify the type of precipitation particles. A photoelectric detection unit detects the difference in scattering intensity signals of precipitation particles to two light sources, and a threshold is used to determine the phase state of the precipitation particles, thus improving the ability to identify precipitation particle types.

The present application relates to the technical field of rainfall inversion, in particular to a multi-source data fusion rain measuring radar high-precision rainfall inversion method, comprising S1: inversion target definition and multi-source data selection; S2: multi-source data preprocessing and cloud layer influence prediction; S3: fusion algorithm selection and correction model construction; S4: history data assimilation; S5: dynamic weight optimization; The judgment logic can effectively distinguish the distorted data and the effective data shielded by the cloud layer, avoid blindly using the shielded precipitation particle detection data for inversion, reduce the appearance of false rainfall area or missed area, make the originally large-deviation precipitation particle information due to cloud layer reflection and absorption signal closer to the real situation, significantly improve the accuracy of rain intensity inversion, and the short-term historical data processed through the physical model and the long-term climate background field are integrated into the real-time inversion process, so that more smooth, stable and reasonable rainfall inversion results can be provided, and the accuracy of the short-term forecast of the rainfall product is greatly improved.

1. The name of the design product: holographic precipitation weather phenomenon detection sensor. 2. The use of the design product: mainly used in the field of meteorological observation, through holographic imaging technology, real-time monitoring and analysis of precipitation particle (raindrop, snowflake, hail, etc.) data, providing high-precision observation data support for weather forecasting, disaster warning, climate research and artificial weather modification. 3. The design points of the design product: in the perspective drawing. 4. The picture or photo that best indicates the design points: perspective drawing.

The present invention relates to the field of radar identification technology, and specifically to a method for a single-channel millimeter-wave radar to identify the bright band at the zero-degree level. From the height of the zero-degree level, the reflectivity factor, radial velocity, and spectral width profile are filtered, and the height that simultaneously satisfies the conditions of the reflectivity factor and radial velocity is searched as the top height of the bright band at the zero-degree level. The height that satisfies the conditions of the radial velocity and velocity spectral width is searched from bottom to top as the bottom height of the bright band at the zero-degree level. For the bright band height in the time dimension, outliers, isolated values, and mutation points are removed, and interpolation and filtering are performed. Compared with the prior art, the present invention utilizes the fact that the reflectivity factor and radial velocity detected by the millimeter-wave radar increase rapidly in the bright band interval at the zero-degree level, the radial velocity is sensitive to the phase state transition of particles at the bottom of the bright band at the zero-degree level, and the velocity spectral width increases and reaches a high value when the precipitation particles are converted from the ice phase to the mixed phase. Through two-way multi-condition search, it is realized that a single-channel millimeter-wave radar can also identify the bright band at the zero-degree level.

The present invention relates to the field of meteorological prediction technology, and particularly to a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data. The system includes: The present invention determines the cloud base height of the target cloud cluster based on a ceilometer, collects the radiation corresponding to the cloud top height of the target cloud cluster based on a meteorological satellite, calculates the cloud top temperature of the target cloud cluster based on a radiative transfer model, and determines the cloud top height of the target cloud cluster based on the cloud top temperature, detects the precipitation particles of the labeled segmented cloud layer, infers the water content density of the labeled segmented cloud layer according to the precipitation particles, obtains the extinction coefficient and backscattering coefficient of the aerosol particles of the labeled segmented cloud layer, obtains the meteorological characterization value of the target cloud cluster based on the water content density, extinction coefficient and backscattering coefficient of the aerosol particles, and analyzes the rationality of the existence of the target cloud cluster based on the meteorological characterization value of the target cloud cluster, which can improve the accuracy of cloud meteorological prediction and improve the timeliness of meteorological prediction.

The invention provides a channel modeling method, a model application method, electronic equipment and a storage medium. The channel modeling method comprises the steps that a rainfall type factor and a rainfall rate in a current environment are obtained, the value of the rainfall type factor is larger than or equal to a first preset value and smaller than or equal to a second preset value, the first preset value represents that the rainfall type in the current environment is rain, and the second preset value represents that the rainfall type in the current environment is snow; according to the rainfall rate, determining a rainfall intensity grade in the current environment; and generating a signal attenuation model of the wireless communication channel in the current environment based on the rainfall type factor and the rainfall intensity grade. According to the embodiment of the invention, the rainfall type factor and the rainfall intensity grade are adopted to represent the rain and snow channel attenuation in a unified manner, and the unified channel model based on the equivalent rainfall particles is constructed to describe the mixed rainfall scene in a unified manner, improve the model precision and reduce the error of the signal attenuation prediction result.