Optimized low-altitude condition radio wave propagation prediction method and device and storage medium
By constructing a three-dimensional collaborative database and an intelligent model selection mechanism, and combining dynamic superposition calculations of terrain diffraction, atmospheric attenuation, and surface reflection loss, the accuracy and adaptability issues in low-altitude radio wave propagation prediction were solved, and high-precision low-altitude communication network planning was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHONGQING TELECOMM PLAN & DESIGN INST
- Filing Date
- 2026-01-23
- Publication Date
- 2026-05-12
AI Technical Summary
Existing low-altitude radio wave propagation prediction methods are not accurate enough in complex terrain and open space mixed environments, fail to calculate multiple loss factors in a coordinated manner, and lack intelligent model selection mechanisms, resulting in systematic errors.
A three-dimensional collaborative database is constructed, and a three-dimensional graphic obstacle recognition algorithm is combined to intelligently select a low-altitude or ground propagation model. Through dynamic superposition calculation of terrain diffraction, atmospheric attenuation and surface reflection loss, the fusion and collaborative prediction of multi-source data are realized.
It significantly improves the accuracy and adaptability of low-altitude radio wave propagation prediction, provides a reliable basis for network planning, overcomes the prediction inaccuracies of traditional methods in complex terrain, and enhances the scientific nature and reliability of low-altitude communication networks.
Smart Images
Figure CN122028069A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wireless communication technology, and more specifically to a method, device, and storage medium for optimizing low-altitude radio wave propagation prediction. Background Technology
[0002] With the rapid development of the low-altitude economy, applications such as drone logistics, urban air traffic (UAM), and emergency communications have created an urgent need for reliable low-altitude wireless communication networks. Unlike terrestrial cellular networks, the propagation environment of low-altitude communication nodes (such as drones and aerial platforms) is extremely complex, and signal propagation is affected by a combination of factors, including three-dimensional terrain, vertical changes in atmospheric conditions, and the diversity of surface materials.
[0003] Currently, in the field of radio wave propagation prediction, traditional methods mainly have the following limitations:
[0004] (1) The model is too simple and has poor scene adaptability. Most existing prediction models are designed for specific scenes. For example, the diffraction prediction method based on the digital elevation model (DEM) mainly focuses on regular terrain occlusion and lacks the ability to model complex terrain (such as continuous multi-peaks and dome shapes) in a refined way. Although the open-field channel model considers reflection and scattering, it does not take terrain diffraction as a core factor. This leads to a significant decrease in the prediction accuracy of a single model in the mixed environment of low altitude, which has both complex terrain and open space.
[0005] (2) Element separation, failure to achieve collaborative prediction. Most methods focus only on a single factor of propagation loss, such as only calculating terrain diffraction loss or only calculating atmospheric absorption loss. Low-altitude radio wave propagation is the result of the combined effects of multiple physical phenomena such as diffraction, reflection, and atmospheric attenuation. Calculating these elements separately cannot reflect the true end-to-end propagation characteristics and is difficult to meet the needs of high-precision planning of low-altitude networks.
[0006] (3) Lack of intelligent model selection mechanism. On complex low-altitude paths, the propagation mechanism may change dynamically with spatial location. Existing technologies lack a quantitative discrimination criterion based on real-time terrain analysis to automatically select the dominant propagation model (such as low-altitude diffraction model or ground multipath model) suitable for the current path segment, resulting in a mismatch between model application and actual situation, and introducing systematic errors.
[0007] Therefore, there is an urgent need for a low-altitude radio wave propagation prediction method that can integrate multi-source data, intelligently identify propagation mechanisms, and collaboratively calculate various loss factors, so as to improve the scientificity and accuracy of low-altitude communication network planning and optimization. Summary of the Invention
[0008] One of the objectives of this invention is to provide an optimized method for predicting radio wave propagation under low-altitude conditions, thereby solving the aforementioned problems.
[0009] To achieve the above objectives, an optimized method for predicting radio wave propagation under low-altitude conditions is provided, comprising the following steps:
[0010] Step S1: Construct a three-dimensional collaborative database, which includes high-resolution terrain elevation data, atmospheric profile data and surface material information of the target area;
[0011] Step S2: Based on the terrain elevation data, a three-dimensional graphics obstacle recognition algorithm is used to identify obstacles on the propagation path between the transmitting point and the receiving point, and to calculate the occlusion ratio of the obstacles on the first Fresnel zone.
[0012] Based on the comparison between the occlusion ratio and the preset threshold, either the low-altitude propagation loss model or the ground propagation model is selected as the dominant prediction model for the current propagation path.
[0013] If the low-altitude propagation loss model is selected, the terrain type of the obstacle is further identified, and the corresponding diffraction loss sub-model is matched according to the terrain type to calculate the terrain diffraction loss value.
[0014] Step S3: Based on the atmospheric profile data, the vertical space between the transmitting point and the receiving point is divided into layers, the atmospheric absorption loss of each layer is calculated, and the total atmospheric attenuation loss value is obtained by summing them up.
[0015] Step S4: Based on the surface material information, determine the reflection coefficient of the surface reflection point along the propagation path, and calculate the surface reflection loss value;
[0016] Step S5: If the dominant prediction model is the low-altitude propagation loss model, then the terrain diffraction loss value, the total atmospheric attenuation loss value, and the surface reflection loss value are superimposed to obtain the end-to-end total propagation loss prediction value.
[0017] Furthermore, the specific steps in step S2 for selecting the low-altitude propagation loss model or the ground propagation model based on the comparison result of the occlusion ratio and the preset threshold are as follows:
[0018] When the occlusion ratio is greater than 45%, the low-altitude propagation loss model is selected.
[0019] When the occlusion ratio is less than or equal to 45%, the ground propagation model is selected.
[0020] Furthermore, the terrain types in step S2 include: single-peak blade-shaped terrain, multi-peak blade-shaped terrain, dome-shaped terrain, and irregular terrain combination; the diffraction loss sub-model includes single-peak blade-shaped diffraction loss model, multi-peak blade-shaped diffraction loss model, dome-shaped diffraction loss model, and irregular terrain combination diffraction loss model, which correspond one-to-one with each terrain type.
[0021] Furthermore, the multi-peak blade-shaped diffraction loss model includes a scenario model without a main peak and a scenario model with a main peak;
[0022] The no-main-peak scenario model is used to solve the diffraction loss by calculating the equivalent single-peak height and introducing a multi-peak superposition correction coefficient when the height difference between multiple obstacle peaks is less than or equal to a set value and the peak spacing is uniform.
[0023] The main peak scene model is used to calculate the diffraction loss of the dominant peak when there is a dominant peak with a height difference greater than a set value, and to make corrections based on the height and spacing of the auxiliary peaks.
[0024] Furthermore, the dome-shaped diffraction loss model is calculated through the following steps:
[0025] The dome-shaped obstacle is equivalent to a blade-shaped obstacle to obtain an equivalent height;
[0026] Calculate the equivalent blade diffraction loss based on the equivalent height;
[0027] Based on the radius of curvature of the dome-shaped obstacle, the equivalent blade-shaped diffraction loss is smoothed to obtain the dome-shaped diffraction loss value.
[0028] Furthermore, step S3 specifically includes:
[0029] The vertical space involved in the propagation path is divided into multiple layers with a fixed thickness;
[0030] The temperature and humidity of each layer are obtained from the atmospheric profile data;
[0031] Calculate the oxygen absorption coefficient and water vapor absorption coefficient of each layer based on the temperature and humidity of each layer.
[0032] Based on the absorption coefficient and layer thickness of each layer, the atmospheric absorption loss of each layer is calculated, and the total atmospheric attenuation loss value is obtained by summing them.
[0033] Furthermore, the three-dimensional collaborative database in step S1 also includes a dynamic update mechanism, the specific steps of which are as follows:
[0034] The latest terrain change data of a local area is obtained by scanning with a drone, which is then used to update the terrain elevation data;
[0035] Real-time data from meteorological stations is accessed to update the atmospheric profile data.
[0036] Furthermore, the specific steps for determining the reflection coefficient of the surface reflection point along the propagation path in step S4 are as follows:
[0037] Based on the surface material information, a preset material-frequency-reflection coefficient mapping table is queried to obtain the reflection coefficient corresponding to the current communication frequency and surface material.
[0038] A second objective of this invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of an optimized low-altitude radio wave propagation prediction method as described above.
[0039] A third objective of this invention is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.
[0040] The optimized low-altitude radio wave propagation prediction method provided by this invention can produce a series of significant and synergistic technical effects compared with the prior art, fundamentally improving the scientificity, accuracy and practicality of low-altitude wireless network planning and design.
[0041] 1. This invention, by constructing a three-dimensional collaborative database integrating terrain, atmosphere, and surface material, and introducing a dynamic update mechanism, systematically integrates multi-dimensional physical environmental factors affecting low-altitude radio wave propagation into the prediction model for the first time. This innovative infrastructure enables the prediction model to perceive and reflect the true complexity and spatiotemporal variability of the propagation environment from the data source. Traditional single-dimensional modeling methods often lead to prediction results deviating from reality due to incomplete data sources. In contrast, this invention, through the fusion and collaboration of multi-source heterogeneous data, provides a comprehensive and reliable data foundation for subsequent high-precision calculations, achieving a digital holographic mapping of the complex low-altitude propagation environment and laying a solid data foundation for the accuracy of prediction results.
[0042] 2. The intelligent model selection mechanism proposed in this invention, based on 3D graphic obstacle recognition and Fresnel zone quantitative analysis, effectively solves the problem of ambiguous model applicability judgment in mixed propagation scenarios using traditional methods. By accurately calculating the occlusion ratio of obstacles in the first Fresnel zone and using this as a quantitative threshold to automatically identify and switch between the "low-altitude propagation loss model" and the "ground propagation model," this mechanism enables the prediction method to adapt to different scenarios. This avoids systematic errors caused by model misuse and ensures that the model can automatically match the dominant propagation physics mechanism regardless of whether it is in open airspace or complex terrain, thereby significantly improving the universality and reliability of the prediction method in different low-altitude environments.
[0043] 3. Addressing the core and complex issue of terrain diffraction in low-altitude propagation, this invention abandons the coarse approach of traditional single equivalent models (such as the single-blade model) and innovatively proposes a dedicated diffraction loss sub-model system matched to specific terrain features. This system can intelligently call upon corresponding refined models (such as a superposition correction model for multi-peak terrain and a curvature correction model for dome terrain) based on automatic terrain identification results. This "classification and identification, precise targeting" computational strategy greatly improves the calculation accuracy of diffraction loss for complex continuous and irregular terrain, overcoming the inherent flaw of traditional methods inaccurate predictions when facing complex real-world landforms.
[0044] 4. The dynamic superposition calculation framework of "terrain diffraction loss - atmospheric attenuation loss - surface reflection loss" constructed in this invention achieves, for the first time in the field of low-altitude prediction, the coordinated and integrated calculation of multiple loss mechanisms. This framework simulates the combined effects of various influences on radio waves during end-to-end propagation at low altitudes, rather than considering individual factors in isolation. Through this coordinated calculation, the final predicted total propagation loss value more closely approximates the comprehensive attenuation experienced by the signal in the real environment, providing a crucial and reliable quantitative basis for the budgeting of low-altitude communication links, the delineation of coverage areas, and the deployment of network nodes. In summary, through the above-mentioned systematic innovations, this invention comprehensively and significantly improves the accuracy, adaptability, and engineering practical value of low-altitude radio wave propagation prediction, and plays a significant technical role in promoting the scientific planning and reliable deployment of low-altitude communication networks. Attached Figure Description
[0045] Figure 1 This is a flowchart illustrating an optimized method for predicting radio wave propagation under low-altitude conditions, according to an embodiment of the present invention.
[0046] Figure 2 A schematic diagram of Fresnel zone analysis;
[0047] Figure 3 A schematic diagram of the segmentation line within the Fresnel zone analysis;
[0048] Figure 4 This is a schematic diagram for calculating atmospheric attenuation in stratified atmospheres. Detailed Implementation
[0049] The following detailed description illustrates the specific implementation method:
[0050] Example
[0051] An optimized method for predicting radio wave propagation under low-altitude conditions, basically as follows: Figure 1 As shown, it includes the following steps:
[0052] Step S1: Construct a three-dimensional collaborative database, which includes high-resolution terrain elevation data, atmospheric profile data and surface material information of the target area;
[0053] 1. Topographic elevation data: obtained from GIS map data.
[0054] Dynamic update mechanism: The latest terrain change data of a local area is obtained by scanning with drones, and the terrain elevation data is updated accordingly.
[0055] 2. Atmospheric profile data: Access real-time data from meteorological stations to obtain atmospheric profile data, such as temperature and humidity.
[0056] By combining satellite inversion data, information on water vapor distribution can be supplemented.
[0057] 3. Surface material information: obtained from GIS map data.
[0058] Multispectral imagery is used to classify surface materials and automatically generate a material-frequency-reflectance coefficient mapping table.
[0059] Step S2: Based on the terrain elevation data, a three-dimensional graphics obstacle recognition algorithm is used to identify obstacles on the propagation path between the transmitter and receiver, and to calculate the occlusion ratio of the obstacles on the first Fresnel zone; according to the comparison result of the occlusion ratio and the preset threshold, the low-altitude propagation loss model or the ground propagation model is selected as the dominant prediction model for the current propagation path.
[0060] The specific steps for selecting the low-altitude propagation loss model or the ground propagation model in step S2, based on the comparison result between the occlusion ratio and the preset threshold, are as follows:
[0061] Step S201: When the occlusion ratio is greater than 45%, select the low-altitude propagation loss model;
[0062] Step S202: When the occlusion ratio is less than or equal to 45%, select the ground propagation model. The ground propagation model adopts the model specified in 3GPP 38.901.
[0063] If the low-altitude propagation loss model is selected, the terrain type of the obstacle is further identified, and the corresponding diffraction loss sub-model is matched according to the terrain type to calculate the terrain diffraction loss value; the terrain types in step S2 include: single-peak blade-shaped terrain, multi-peak blade-shaped terrain, dome-shaped terrain and irregular terrain combination; the diffraction loss sub-model includes single-peak blade-shaped diffraction loss model, multi-peak blade-shaped diffraction loss model, dome-shaped diffraction loss model and irregular terrain combination diffraction loss model, which correspond one-to-one with each terrain type.
[0064] For 3D graphic obstacle recognition algorithms:
[0065] 1. Data Definition:
[0066] (1) Define the key data of the obstacle, including the number of peaks (single peak / multiple peaks), peak shape (blade shape / dome shape), distance between adjacent peaks (multiple peak scenarios), and the relative positional relationship between the obstacle height and the propagation path;
[0067] (2) Define the propagation path: obtain the coordinates of the base station's transmitting point (XS,YS) and receiving point (XE,YE).
[0068] Key parameters include: propagation path length (d1: distance from the transmitter to the peak of the obstacle, d2: distance from the peak of the obstacle to the receiver), relative height of the obstacle (h: height of the peak above the line connecting the transmitter and receiver); wavelength λ (calculated from the target frequency f, λ=c / f, where c is the speed of light).
[0069] 2. Establish a digital model
[0070] (1) Divide the propagation path: Divide (XS,YS)-(XE,YE) into N segments according to the length of each segment M. This forms a series of points, namely (XS,YS), (X1,Y1)…(Xn-1,Yn-1),(XE,YE).
[0071] (2) Obtain the segmentation line within the Fresnel zone analysis: Obtain the Fresnel zone analysis, then obtain the point (X1,Y1)…(Xn-1,Yn-1), perpendicular to the line (XS,YS)(XE,YE), and the length of the segment formed within the Fresnel zone analysis is:
[0072] The line segment array [(X1-S,Y1-S),(X1-E,Y1-E)]…[(Xn-1-S,Yn-1-S), (Xn-1-E,Yn-1-E)] forms lines named [L1,L2…Ln].
[0073] (3) Divide multiple line segments individually by λ / 2: Each line segment in [(X1-S,Y1-S), (X1-E,Y1-E)]…[(Xn-1-S,Yn-1-S), (Xn-1-E,Yn-1-E)] is divided into J parts to form a multi-point number.
[0074] [(X1-S-1,Y1-S-1),(X1-S-2,Y1-S-2)…(X1-EJ,Y1-EJ)]…[(Xn-1-S-1,Yn-1-S-1), (X1-S-2,Y1-S-2) ,(Xn-1-EJ,Yn-1-EJ)]; such as Figure 3 As shown.
[0075] (4) Get the elevation of each point in the point array [(X1-S-1,Y1-S-1), (X1-S-2,Y1-S-2)… (X1-EJ,Y1-EJ)].
[0076] 3. Fresnel zone analysis: Determine the model type through Fresnel zone analysis;
[0077] Determine whether the obstacle obscures 45% of the Fresnel zone area.
[0078] First Fresnel zone radius: , where d is the horizontal distance between the transmitter and receiver.
[0079] d1: Distance from the transmitter to the top of the obstacle peak; d2: Distance from the top of the obstacle peak to the receiver.
[0080] like Figure 2 As shown, a closed rectangle is generated using interpolation points and elevations on the digital model [L1, L2…Ln], thereby calculating the area S1 (obstacle occlusion area) of each cross-section, and then calculating the area S2 (the theoretical area of the first Fresnel zone) of the current cross-section. If S1 > S2 * 0.45, a low-altitude propagation model is used for calculation.
[0081] 4. Determine the scenario of the diffraction model through digital model.
[0082] Scene discrimination analysis of the blade-shaped diffraction model:
[0083] (1) The elevation data of each point on the line [L1,L2…Ln] is smoothed by Gaussian filtering to reduce the impact of abnormal data.
[0084] The formula for calculating the current elevation by using the coordinates of five points surrounding the given point and normalizing them based on distance is as follows:
[0085]
[0086] Based on the formula, a new set of elevation coordinates for each set of lines is obtained.
[0087] (2) Finding the crest terrain
[0088] Traverse [L1, L2…Ln]. For each line segment, starting from the coordinates [X1-S-1, Y1-S-1], calculate the first derivative of the model number. Points that transition from less than or equal to zero to greater than zero are valley points, and points that transition from greater than or equal to zero to less than zero are peak points.
[0089] Filter out valleys that are higher than the average of all peaks, and take the larger value of the peaks on both sides of the filtered valley; filter out peaks that are lower than the average of all valleys, and take the smaller value of the valleys on both sides of the filtered peak.
[0090] Iterate through each peak (index HP), mark the valley before the peak with index SP, and mark the valley after the peak with index EP.
[0091] (3) Find multi-peaked blade-shaped terrain
[0092] If there are multiple peaks, they are sorted by height. If the difference between the first and second highest peaks is greater than 3 meters, it is determined to be the dominant peak.
[0093] (4) Determining the dome shape of the obstacle
[0094] The shape of the obstacle is determined by analyzing the radius of curvature near the crest. The radius of curvature of the three points is calculated based on the subscripts SPi, HPi, and EPi of the i-th crest. The array is sampled at equal intervals, and the derivative is approximated by the central difference.
[0095] First derivative (slope):
[0096]
[0097] Second derivative (curvature related):
[0098]
[0099] Calculate the radius of curvature:
[0100] curvature
[0101]
[0102] The radius of curvature Ri = 1 / Ki.
[0103] (5) Determine the terrain type
[0104] a) Single-peaked blade-shaped terrain
[0105] Single-peak blade-shaped terrain refers to obstacles that are sharp and blade-shaped (peak curvature radius r ≤ 0.5 meters) and have only one dominant blocking peak (such as an isolated mountain blade or the top edge of a communication tower).
[0106] b) Multi-peaked blade-shaped terrain
[0107] Multi-peak blade-shaped terrain refers to the presence of two or more continuous blade-shaped obstacles (such as mountain ranges or the tops of multiple tall buildings), with a peak spacing d≤10λ (millimeter wave) or d≤5λ (sub 6G), requiring the signal to diffract through multiple peaks in sequence.
[0108] c) Dome-shaped terrain
[0109] Dome-shaped terrain refers to obstacles whose peaks are arc-shaped (with a radius of curvature greater than 0.5 meters, such as the top of a hill or the top of a circular building).
[0110] d) Irregular terrain
[0111] Irregular terrain combinations refer to combinations that simultaneously include various forms such as blade-shaped, dome-shaped, and multi-peaked (e.g., mountain + hill + town building combination). The propagation path needs to be divided into multiple segments according to the obstacle form ("single-peak blade-shaped segment - dome-shaped segment - multi-peak blade-shaped segment") and processed separately.
[0112] 5. Diffraction loss calculation:
[0113] (1) Diffraction loss model of single-peaked blade-shaped terrain
[0114] The diffraction loss model for single-peaked blade-shaped terrain was used to calculate the diffraction parameters. calculate:
[0115]
[0116] Where h is the relative height of the obstacle (unit: m), d1 and d2 are the segment lengths of the propagation path (unit: m), and λ is the wavelength (unit: m).
[0117] Diffraction loss calculation:
[0118] When v > -0.78,
[0119] When v ≤ −0.78, the loss is 0.
[0120] (2) The multi-peak blade-shaped diffraction loss model includes a scenario model without a main peak and a scenario model with a main peak;
[0121] 1) The main peak-less scenario model is used to solve the diffraction loss by calculating the equivalent single peak height and introducing a multi-peak superposition correction coefficient when the height difference of multiple obstacle peaks is less than or equal to a set value and the peak spacing is uniform.
[0122] No main peak scenario: all peaks are of similar height (height difference ≤ 3m), the peak spacing is uniform, and the angle between the propagation path and the line connecting each peak is ≤ 15°; number of peaks n (n≥2), distance between adjacent peaks dp (unit: m), relative heights of each peak h1, h2, ..., h n (Unit: m), Equivalent single-peak height heq = (h1 + h2 + ... + h n The total path length dtotal = d1 + (n-1) dp + d2 is d1 / n.
[0123] a) Calculate the equivalent single-peak diffraction parameter veq:
[0124]
[0125] b) Calculate the single-peak base diffraction loss Ld1 (using the single-peak blade model formula);
[0126] c) Introduce a multi-peak superposition correction coefficient kn, considering the inter-peak coupling effect:
[0127] kn=1+0.15(n-2)⋅exp(-dp / λ)
[0128] d) Total diffraction loss :
[0129]
[0130] in, This is a correction term for the cumulative loss caused by multi-peak shading.
[0131] 2) The main peak scene model is used to calculate the diffraction loss of the main peak when there is a main peak with a height difference greater than a set value, and to make corrections based on the height and spacing of the auxiliary peaks.
[0132] Scene with main peak:
[0133] Significant peak height differences (maximum height difference > 3m): Larger peak spacing (dp ≤ 10λ (high frequency band) or dp ≤ 5λ (low frequency band)): Select the highest peak as the dominant peak, and the remaining peaks as auxiliary peaks. The auxiliary peak correction coefficient ka = 0.08 × (hi / hmax) (hi is the auxiliary peak height, hmax is the dominant peak height), and the total loss... , This is the diffraction loss of the dominant peak in a single-peak blade-shaped terrain. For peaks with large spacing (dp>10λ (high frequency band) or dp>5λ (low frequency band)): the total loss is calculated using multiple independent single-peak blade-shaped models, and the maximum value of the diffraction loss of each peak is taken.
[0134] Total diffraction loss .
[0135] (3) Diffraction loss model for dome-shaped terrain
[0136] When diffracting signals, the interaction between spherical waves and circular surfaces needs to be considered, and the Bullington approximation model is used for optimization. The dome-shaped obstacle is equivalent to a blade-shaped obstacle, with an equivalent height... (h is the actual height of the dome, and r is the radius of curvature of the peak).
[0137] Calculate the equivalent blade diffraction loss based on the equivalent height;
[0138] Based on the radius of curvature of the dome-shaped obstacle, the equivalent blade-shaped diffraction loss is smoothed to obtain the dome-shaped diffraction loss value.
[0139] Diffraction parameters Calculation (same as the single-peak edge model):
[0140]
[0141] Diffraction loss of the dome .
[0142]
[0143] Among them, is the diffraction loss of the equivalent edge (calculated according to the single-peak edge model), and the second term is the loss reduction correction of the dome shape (the dome surface is smoother, and the diffraction loss is lower than that of the edge).
[0144] When r ≥ 5λ, the model error ≤ 1dB; when 0.5m < r < 5λ, a curvature correction term needs to be added , after correction ; when r ≤ 0.5m, it is the single-peak edge model.
[0145] (4) Diffraction loss model for irregular terrain combination
[0146] Adopt a hybrid algorithm of "partition calculation - path screening - loss superposition".
[0147] Segmented calculation: Call the corresponding diffraction model for each segment respectively to calculate the diffraction loss of the segment
[0148]
[0149] Path screening: Screen out 5 main propagation paths (direct + main diffraction + 3 secondary diffractions) through Fresnel zone analysis;
[0150] Screening algorithm: Sort according to the nearest distance of the partition. In the case of no obstruction, it is direct. In the case of obstruction and the nearest distance is the main diffraction, the 2nd to 4th in the sorting are the secondary diffractions;
[0151] Total loss synthesis: Use the vector superposition method to calculate the total diffraction loss, considering the phase difference of each path:
[0152]
[0153] Among them, is the phase difference between the i-th path and the direct path ( , is the path length difference).
[0154] Step S3: Based on the atmospheric profile data, layer the vertical space between the emission point and the receiving point, calculate the atmospheric absorption loss of each layer, and accumulate to obtain the total atmospheric attenuation loss value; The specific steps of step S3 include:
[0155] Step S301: As Figure 4 As shown, the vertical space involved in the propagation path is divided into multiple layers with a fixed thickness; based on the atmospheric stratification characteristics, the vertical range of h0-300 meters is divided into I 50-meter layers to simplify the calculation and control the loss error within 0.5dB.
[0156]
[0157] Step S302: Obtain the temperature and humidity of each layer from the atmospheric profile data;
[0158] Step S303: Calculate the oxygen absorption coefficient and water vapor absorption coefficient of each layer based on the temperature and humidity of each layer;
[0159] Step S304: Calculate the atmospheric absorption loss of each layer based on the absorption coefficient and layer thickness, and sum them to obtain the total atmospheric attenuation loss value.
[0160] Atmospheric attenuation formula:
[0161] in and The first Temperature and humidity of the layer. , The first The oxygen absorption coefficient and water vapor absorption coefficient of the layer.
[0162] Step S4: Based on the surface material information, determine the reflection coefficient of the surface reflection point along the propagation path and calculate the surface reflection loss value; query the preset material-frequency-reflection coefficient mapping table according to the surface material information to obtain the reflection coefficient corresponding to the current communication frequency and surface material.
[0163] The formula for surface reflection loss is:
[0164] in: and Here, denoted by , represents the height of the transmitter and receiver, respectively; d represents the horizontal distance between the transmitter and receiver; Γ represents the reflection coefficient, expressed as:
[0165]
[0166] Step S5: If the dominant prediction model is the low-altitude propagation loss model, then the terrain diffraction loss value, the total atmospheric attenuation loss value, and the surface reflection loss value are superimposed to obtain the end-to-end total propagation loss prediction value.
[0167]
[0168] A computer-readable storage medium having a computer program stored thereon, which, when executed, implements an optimized low-altitude condition radio wave propagation prediction method as described above.
[0169] Those skilled in the art will understand that implementing all or part of the processes in the above-described method for multimodal mechanical fault identification of high-voltage circuit breakers can be accomplished by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes described in various embodiments of the optimized low-altitude radio wave propagation prediction method. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0170] An electronic device includes a processor and a memory, the memory storing a computer program, the processor executing the program to implement an optimized low-altitude condition radio wave propagation prediction method as described above.
[0171] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, based on the guidance provided in this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.
Claims
1. An optimized method for predicting radio wave propagation under low-altitude conditions, characterized in that, Includes the following steps: Step S1: Construct a three-dimensional collaborative database, which includes high-resolution terrain elevation data, atmospheric profile data and surface material information of the target area; Step S2: Based on the terrain elevation data, a three-dimensional graphics obstacle recognition algorithm is used to identify obstacles on the propagation path between the transmitting point and the receiving point, and to calculate the occlusion ratio of the obstacles on the first Fresnel zone. Based on the comparison between the occlusion ratio and the preset threshold, either the low-altitude propagation loss model or the ground propagation model is selected as the dominant prediction model for the current propagation path. If the low-altitude propagation loss model is selected, the terrain type of the obstacle is further identified, and the corresponding diffraction loss sub-model is matched according to the terrain type to calculate the terrain diffraction loss value. Step S3: Based on the atmospheric profile data, the vertical space between the transmitting point and the receiving point is divided into layers, the atmospheric absorption loss of each layer is calculated, and the total atmospheric attenuation loss value is obtained by summing them up. Step S4: Based on the surface material information, determine the reflection coefficient of the surface reflection point along the propagation path, and calculate the surface reflection loss value; Step S5: If the dominant prediction model is the low-altitude propagation loss model, then the terrain diffraction loss value, the total atmospheric attenuation loss value, and the surface reflection loss value are superimposed to obtain the end-to-end total propagation loss prediction value.
2. The method for optimizing low-altitude radio wave propagation prediction according to claim 1, characterized in that: The specific steps for selecting the low-altitude propagation loss model or the ground propagation model in step S2, based on the comparison result of the occlusion ratio and the preset threshold, are as follows: When the occlusion ratio is greater than 45%, the low-altitude propagation loss model is selected. When the occlusion ratio is less than or equal to 45%, the ground propagation model is selected.
3. The method for predicting radio wave propagation under optimized low-altitude conditions according to claim 2, characterized in that: The terrain types in step S2 include: single-peaked blade-shaped terrain, multi-peaked blade-shaped terrain, dome-shaped terrain, and irregular terrain combination; the diffraction loss sub-models include single-peaked blade-shaped diffraction loss models, multi-peaked blade-shaped diffraction loss models, dome-shaped diffraction loss models, and irregular terrain combination diffraction loss models, which correspond one-to-one with each terrain type.
4. The method for predicting radio wave propagation under optimized low-altitude conditions according to claim 3, characterized in that: The multi-peak blade-shaped diffraction loss model includes a scenario model without a main peak and a scenario model with a main peak. The no-main-peak scenario model is used to solve the diffraction loss by calculating the equivalent single-peak height and introducing a multi-peak superposition correction coefficient when the height difference between multiple obstacle peaks is less than or equal to a set value and the peak spacing is uniform. The main peak scene model is used to calculate the diffraction loss of the dominant peak when there is a dominant peak with a height difference greater than a set value, and to make corrections based on the height and spacing of the auxiliary peaks.
5. The method for predicting radio wave propagation under optimized low-altitude conditions according to claim 4, characterized in that: The dome-shaped diffraction loss model is calculated through the following steps: The dome-shaped obstacle is equivalent to a blade-shaped obstacle to obtain an equivalent height; Calculate the equivalent blade diffraction loss based on the equivalent height; Based on the radius of curvature of the dome-shaped obstacle, the equivalent blade-shaped diffraction loss is smoothed to obtain the dome-shaped diffraction loss value.
6. The method for optimizing low-altitude radio wave propagation prediction according to claim 1, characterized in that: Step S3 specifically includes: The vertical space involved in the propagation path is divided into multiple layers with a fixed thickness; The temperature and humidity of each layer are obtained from the atmospheric profile data; Calculate the oxygen absorption coefficient and water vapor absorption coefficient of each layer based on the temperature and humidity of each layer. Based on the absorption coefficient and layer thickness of each layer, the atmospheric absorption loss of each layer is calculated, and the total atmospheric attenuation loss value is obtained by summing them.
7. The method for optimizing low-altitude radio wave propagation prediction according to claim 1, characterized in that: The three-dimensional collaborative database in step S1 also includes a dynamic update mechanism, the specific steps of which are as follows: The latest terrain change data of a local area is obtained by scanning with a drone, which is then used to update the terrain elevation data; Real-time data from meteorological stations is accessed to update the atmospheric profile data.
8. The method for optimizing low-altitude radio wave propagation prediction according to claim 1, characterized in that: The specific steps for determining the reflection coefficient of the surface reflection point along the propagation path in step S4 are as follows: Based on the surface material information, a preset material-frequency-reflection coefficient mapping table is queried to obtain the reflection coefficient corresponding to the current communication frequency and surface material.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of an optimized low-altitude condition radio wave propagation prediction method as described in any one of claims 1 to 8.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of an optimized low-altitude condition radio wave propagation prediction method as described in any one of claims 1 to 8.