CN-122028069-A - Prediction method, equipment and storage medium for optimizing low-altitude condition wave propagation

CN122028069ACN 122028069 ACN122028069 ACN 122028069ACN-122028069-A

Abstract

The invention relates to the technical field of wireless communication, in particular to a method, equipment and a storage medium for optimizing low-altitude condition wave propagation prediction. Based on a three-dimensional pattern obstacle recognition algorithm, a low-altitude propagation loss model or a ground propagation model is adaptively selected as a dominant prediction model by analyzing the shielding proportion of an obstacle on a propagation path to a first Fresnel zone. If the low-altitude model is selected, the corresponding diffraction loss sub-model is further intelligently matched according to the terrain features, and the terrain diffraction loss is calculated. Meanwhile, atmospheric attenuation loss is calculated in a layering mode, and surface reflection loss is calculated by combining surface material information. And finally, superposition calculation. According to the invention, through multi-source data fusion, intelligent discrimination of a propagation mechanism and cooperative calculation of multiple physical effects, the accuracy and the self-adaptive capacity of wave propagation prediction in a low-altitude complex environment are remarkably improved.

Inventors

CHENG LIJIE
XIE GANG
LIU GUANGJING
YOU JIA
LI PENGPENG
YANG SHUANG
QI YONG
SHI TAO

Assignees

重庆市信息通信咨询设计院有限公司

Dates

Publication Date: 20260512
Application Date: 20260123

Claims (10)

1. The method for predicting the propagation of the optimized low-altitude condition electric wave is characterized by comprising the following steps of: s1, constructing a three-dimensional collaborative database, wherein the three-dimensional collaborative database comprises high-resolution topographic elevation data, atmospheric profile data and earth surface material information of a target area; s2, identifying an obstacle on a propagation path between a transmitting point and a receiving point by adopting a three-dimensional pattern obstacle identification algorithm based on the terrain elevation data, and calculating the shielding proportion of the obstacle to a first Fresnel zone; according to a comparison result of the shielding proportion and a preset threshold value, a low-altitude propagation loss model or a ground propagation model is selected as a dominant prediction model of the current propagation path; if the low-altitude propagation loss model is selected, further identifying the terrain type of the obstacle, matching a corresponding diffraction loss sub-model according to the terrain type, and calculating to obtain a terrain diffraction loss value; Step S3, layering the vertical space between the transmitting point and the receiving point based on the atmospheric profile data, calculating the atmospheric absorption loss of each layer, and accumulating to obtain a total atmospheric attenuation loss value; s4, determining the reflection coefficient of the surface reflection point of the propagation path based on the surface material information, and calculating a surface reflection loss value; and S5, if the dominant prediction model is the low-altitude propagation loss model, superposing the topography diffraction loss value, the total atmospheric attenuation loss value and the ground surface reflection loss value to obtain an end-to-end total propagation loss prediction value.
2. The method for optimizing low-altitude condition wave propagation prediction as set forth in claim 1, wherein the specific step of selecting the low-altitude propagation loss model or the ground propagation model in step S2 according to the comparison result between the shielding ratio and the preset threshold is: When the shielding proportion is greater than 45%, selecting the low-altitude propagation loss model; and selecting the ground propagation model when the shielding proportion is less than or equal to 45%.
3. The method for predicting propagation of an optimized low-altitude condition wave according to claim 2, wherein the topography type in the step S2 includes a combination of a single-peak blade-shaped topography, a multi-peak blade-shaped topography, a dome-shaped topography, and an irregular topography, and the diffraction loss submodel includes a combination of a single-peak blade-shaped diffraction loss model, a multi-peak blade-shaped diffraction loss model, a dome-shaped diffraction loss model, and an irregular topography, which are in one-to-one correspondence with each topography type.
4. The method for predicting propagation of optimized low-altitude-condition electric waves as set forth in claim 3, wherein said multimodal blade diffraction loss model comprises a no-main-peak scene model and a main-peak scene model; The non-main peak scene model is used for solving diffraction loss by calculating equivalent single peak height and introducing a multi-peak superposition correction coefficient when the height difference of a plurality of obstacle peaks is smaller than or equal to a set value and the peak intervals are uniform; and the scene model with main peaks is used for calculating diffraction loss of the main peaks when the main peaks with the height difference larger than a set value exist, and correcting according to the height and the distance of the auxiliary peaks.
5. The method for predicting propagation of an optimized low-altitude-condition wave according to claim 4, wherein said dome-shaped diffraction loss model is calculated by: the dome-shaped barrier is equivalent to the blade-shaped barrier, so that the equivalent height is obtained; calculating equivalent blade diffraction loss based on the equivalent height; And carrying out smooth correction on the equivalent blade diffraction loss based on the curvature radius of the dome-shaped obstacle to obtain a dome-shaped diffraction loss value.
6. The method for predicting propagation of an optimized low-altitude condition wave according to claim 1, wherein said step S3 comprises: Dividing a vertical space related to a propagation path into a plurality of layers according to a fixed thickness; acquiring the temperature and humidity of each layering from the atmospheric profile data; According to the temperature and humidity of each layer, respectively calculating the oxygen absorption coefficient and the water vapor absorption coefficient of the layer; and according to the absorption coefficient and the layering thickness of each layer, calculating the atmospheric absorption loss of each layer, and summing to obtain the total atmospheric attenuation loss value.
7. The method for predicting propagation of low altitude condition electric waves according to claim 1, wherein said three-dimensional collaborative database in step S1 further comprises a dynamic update mechanism, and the specific steps are: the method comprises the steps of obtaining latest terrain change data of a local area through unmanned aerial vehicle scanning, and updating the terrain elevation data; and accessing real-time data of a weather station for updating the atmospheric profile data.
8. The method for predicting propagation of an optimized low-altitude-condition electric wave according to claim 1, wherein the determining the reflection coefficient of the propagation path surface reflection point in the step S4 comprises the following specific steps: And inquiring a preset material-frequency-reflection coefficient mapping table according to the earth surface material information to obtain a reflection coefficient corresponding to the current communication frequency and the earth surface material.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of an optimized low-altitude conditional electric wave propagation prediction method according to any one of claims 1 to 8 when the program is executed.
10. A computer-readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the steps of an optimized low-altitude condition wave propagation prediction method according to any one of claims 1 to 8.

Description

Prediction method, equipment and storage medium for optimizing low-altitude condition wave propagation Technical Field The present invention relates to the field of wireless communications technologies, and in particular, to a method, an apparatus, and a storage medium for optimizing low-altitude condition wave propagation prediction. Background With the rapid development of low-altitude economy, unmanned aerial vehicle logistics, urban air traffic (UAM), emergency communication and other applications are in urgent need for a reliable low-altitude wireless communication network. Unlike ground cellular networks, the propagation environment of low-altitude communication nodes (such as unmanned aerial vehicles and air platforms) is extremely complex, and signal propagation is compositely influenced by various factors such as three-dimensional topography, vertical change of atmospheric conditions, surface material diversity and the like. At present, in the field of radio wave propagation prediction, the conventional method mainly has the following limitations: (1) The model is single, and the scene adaptability is poor. Most of the existing prediction models are designed aiming at specific scenes, for example, a diffraction prediction method based on a Digital Elevation Model (DEM) mainly focuses on regular terrain shielding, and lacks the capability of fine modeling on complex terrains (such as continuous multimodal and dome-shaped), while an open-earth channel model takes reflection and scattering into consideration, but does not take terrain diffraction as a core factor. This results in a significant decrease in the accuracy of single model prediction in a hybrid environment with complex terrain and open space at low altitudes. (2) Element separation, and no collaborative prediction is achieved. Most approaches focus on only a single factor of propagation loss, such as only the topographic diffraction loss, or only the atmospheric absorption loss. The low-altitude wave propagation is the result of the combined action of various physical phenomena such as diffraction, reflection, atmospheric attenuation and the like, and the elements are subjected to fracture calculation, so that the real end-to-end propagation characteristics cannot be reflected, and the requirement of high-precision planning of a low-altitude network is difficult to meet. (3) Lacks an intelligent model selection mechanism. On complex low-altitude paths, the propagation mechanism may vary dynamically with spatial position. The prior art lacks a quantitative discriminant criterion based on real-time topography analysis to automatically select a dominant propagation model (such as a low-altitude diffraction model or a ground multipath model) suitable for a current path segment, so that the model application is not matched with the actual situation, and systematic errors are introduced. Therefore, there is an urgent need for a low-altitude wave propagation prediction method that can integrate multi-source data, intelligently distinguish propagation mechanisms, and cooperatively calculate multiple loss factors, so as to improve the scientificity and accuracy of low-altitude communication network planning and optimization. Disclosure of Invention It is an object of the present invention to provide a method for optimizing low-altitude condition wave propagation prediction, which solves the above-mentioned problems. In order to achieve the above object, there is provided a method for optimizing propagation prediction of low altitude condition electric waves, comprising the steps of: s1, constructing a three-dimensional collaborative database, wherein the three-dimensional collaborative database comprises high-resolution topographic elevation data, atmospheric profile data and earth surface material information of a target area; s2, identifying an obstacle on a propagation path between a transmitting point and a receiving point by adopting a three-dimensional pattern obstacle identification algorithm based on the terrain elevation data, and calculating the shielding proportion of the obstacle to a first Fresnel zone; according to a comparison result of the shielding proportion and a preset threshold value, a low-altitude propagation loss model or a ground propagation model is selected as a dominant prediction model of the current propagation path; if the low-altitude propagation loss model is selected, further identifying the terrain type of the obstacle, matching a corresponding diffraction loss sub-model according to the terrain type, and calculating to obtain a terrain diffraction loss value; Step S3, layering the vertical space between the transmitting point and the receiving point based on the atmospheric profile data, calculating the atmospheric absorption loss of each layer, and accumulating to obtain a total atmospheric attenuation loss value; s4, determining the reflection coefficient of the surface reflection point of the propagation path