A method and apparatus for predicting signal loss

Abstract

This invention provides a signal loss prediction method and apparatus. The method includes: acquiring an image of a tunnel cross-section, the image including an arch corresponding to the tunnel; obtaining at least one polygon based on the arch in the image; determining the near-field and far-field regions of a signal source based on the at least one polygon; acquiring a first predicted signal loss of the signal emitted by the signal source in the near-field region and a second predicted signal loss of the signal in the far-field region; and obtaining the signal loss in the tunnel based on the first and second predicted signal losses. This improves the consistency of calculating propagation loss values ​​in the near-field and far-field regions, and obtains the signal loss in the tunnel based on the first and second predicted signal losses, making the obtained loss results more consistent with reality, thereby improving the prediction accuracy of signal loss. It also helps to rationally determine antenna locations and source power in highway tunnel scenarios.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a method and apparatus for predicting signal loss. Background Technology

[0002] Currently, the main methods for tunnel signal coverage include: leaky cable coverage, panel / logarithmic periodic antenna coverage, and wall-mounted antenna coverage. Leaky cable coverage offers relatively stable signal, but traditional leaky cables do not support frequencies above 3GHz, and newer leaky cables suffer significant signal loss over 100 meters, resulting in high construction costs. Panel / logarithmic periodic antenna coverage uses logarithmic periodic antennas or directional panel antennas for tunnel coverage. It requires a large number of antennas and is relatively simple to construct, but has short construction windows, limited equipment installation locations, and high safety requirements. Wall-mounted antenna coverage features high gain and high reliability, but current products only support CDMA or mobile frequency bands, resulting in poor frequency sharing capabilities.

[0003] Signals can propagate in the form of electromagnetic waves, which experience energy loss during propagation. In highway tunnels, the electromagnetic waves used for mobile communication typically propagate in multimode, and the degree of loss varies in different areas due to the different propagation modes. To comprehensively consider antenna coverage and construction costs, signal loss needs to be predicted to determine parameters such as antenna type, location, and source power. However, existing technologies, based on test data and empirical models for different environments, are mostly applicable to open outdoor environments, and their accuracy in predicting signal loss in tunnel scenarios remains relatively low. Summary of the Invention

[0004] This invention provides a signal loss prediction method and apparatus to solve the problem of low prediction accuracy of signal loss in the prior art.

[0005] This invention provides a signal loss prediction method, the method comprising:

[0006] Acquire an image of a tunnel cross-section, the image including the arch shape corresponding to the tunnel;

[0007] Based on the arches in the image, at least one polygon is obtained;

[0008] Based on the at least one polygon, determine the near-field region and far-field region of the information source;

[0009] The first signal prediction loss of the signal emitted by the source in the near field region and the second signal prediction loss of the signal in the far field region are obtained.

[0010] The signal loss in the tunnel is obtained based on the first signal prediction loss and the second signal prediction loss.

[0011] Optionally, after determining the near-field and far-field regions of the signal source based on the at least one polygon, and before acquiring the first signal prediction loss of the signal emitted by the signal source in the near-field region and the second signal prediction loss of the signal in the far-field region, the method further includes:

[0012] Determine the refractive loss coefficient, which includes the horizontal polarization loss coefficient, the vertical polarization loss coefficient, and the 45-degree polarization loss coefficient;

[0013] The step of acquiring the first signal prediction loss of the signal emitted by the signal source in the near-field region and the second signal prediction loss of the signal in the far-field region includes:

[0014] Based on the preset frequency of the signal and the target distance, the predicted loss of the first signal is obtained;

[0015] The second signal prediction loss is obtained based on the preset frequency, the target distance, and the refractive loss coefficient. The second signal prediction loss includes horizontal polarization loss, vertical polarization loss, and 45-degree polarization loss coefficient.

[0016] The target distance is the distance between the transmitting point and the receiving point of the signal.

[0017] Optionally, determining the near-field and far-field regions of the signal source based on the at least one polygon includes:

[0018] Calculate multiple distances between the transmission and reception points of the signal when the sidewall of the tunnel is tangent to the Fresnel region, wherein the profile of the sidewall is the profile of at least one polygon;

[0019] Based on the multiple distances, determine the boundary point between the near-field region and the far-field region;

[0020] The near-field region and the far-field region are determined based on the boundary point.

[0021] Optionally, obtaining the signal loss in the tunnel based on the first signal prediction loss and the second signal prediction loss includes:

[0022] The predicted loss of the second signal is verified to obtain the deviation parameter;

[0023] The second signal prediction loss is corrected based on the deviation parameter to generate the third signal prediction loss;

[0024] The signal loss in the tunnel is obtained based on the first signal prediction loss and the third signal prediction loss.

[0025] Optionally, the at least one polygon includes a target isosceles trapezoid and a target rectangle;

[0026] Obtaining at least one polygon based on the arch in the image includes:

[0027] Based on the upper region of the arch, the target isosceles trapezoid is obtained, and the target isosceles trapezoid is the largest inscribed isosceles trapezoid of the upper region of the arch.

[0028] Based on the lower region of the arch, the target rectangle is obtained, which is the largest inscribed rectangle of the lower region of the arch;

[0029] Wherein, the target isosceles trapezoid coincides with the target side of the target rectangle.

[0030] This invention also provides a signal loss prediction device, the device comprising:

[0031] The first acquisition module is used to acquire an image of a tunnel cross-section, the image including the arch shape corresponding to the tunnel;

[0032] The second acquisition module is used to obtain at least one polygon based on the arch in the image;

[0033] The first determining module is used to determine the near-field region and far-field region of the information source based on the at least one polygon;

[0034] The third acquisition module is used to acquire the first signal prediction loss of the signal emitted by the signal source in the near field region and the second signal prediction loss of the signal in the far field region.

[0035] The fourth acquisition module is used to obtain the signal loss in the tunnel based on the first signal prediction loss and the second signal prediction loss.

[0036] Optionally, the device further includes:

[0037] The second determining module is used to determine the refractive loss coefficient, which includes the horizontal polarization loss coefficient, the vertical polarization loss coefficient, and the 45-degree polarization loss coefficient.

[0038] The third acquisition module includes:

[0039] The first acquisition submodule is used to acquire the predicted loss of the first signal based on the preset frequency of the signal and the target distance;

[0040] The second acquisition submodule is used to acquire the second signal prediction loss based on the preset frequency, the target distance and the refraction loss coefficient. The second signal prediction loss includes horizontal polarization loss, vertical polarization loss and 45-degree polarization loss coefficient.

[0041] The target distance is the distance between the transmitting point and the receiving point of the signal.

[0042] Optionally, the first determining module includes:

[0043] The calculation submodule is used to calculate multiple distances between the transmission point and the reception point of the signal when the sidewall of the tunnel is tangent to the Fresnel region, wherein the contour of the sidewall is the contour of the at least one polygon.

[0044] The first determining submodule is used to determine the boundary point between the near-field region and the far-field region based on the multiple distances;

[0045] The second determining submodule is used to determine the near-field region and the far-field region based on the dividing point.

[0046] Optionally, the fourth acquisition module includes:

[0047] The verification submodule is used to verify the prediction loss of the second signal and obtain the deviation parameters;

[0048] A generation submodule is used to correct the second signal prediction loss based on the deviation parameter and generate a third signal prediction loss.

[0049] The third acquisition submodule is used to obtain the signal loss in the tunnel based on the first signal prediction loss and the third signal prediction loss.

[0050] Optionally, the at least one polygon includes a target isosceles trapezoid and a target rectangle;

[0051] The second acquisition module includes:

[0052] The fourth acquisition submodule is used to obtain the target isosceles trapezoid based on the upper region of the arch, wherein the target isosceles trapezoid is the largest inscribed isosceles trapezoid of the upper region of the arch;

[0053] The fifth acquisition submodule is used to obtain the target rectangle based on the lower region of the arch, wherein the target rectangle is the maximum inscribed rectangle of the lower region of the arch;

[0054] Wherein, the target isosceles trapezoid coincides with the target side of the target rectangle.

[0055] In this embodiment of the invention, based on the image of the tunnel cross-section, polygonization processing is performed. Based on at least one obtained polygon, the near-field and far-field regions of the signal source are determined. A first predicted signal loss in the near-field region and a second predicted signal loss in the far-field region are obtained, improving the consistency of calculating propagation loss values ​​in the near-field and far-field regions. Based on the first and second predicted signal losses, the signal loss in the tunnel is obtained, making the obtained loss results more consistent with reality, thereby improving the accuracy of signal loss prediction. This helps to rationally determine antenna locations and signal source power in highway tunnel scenarios. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating the signal loss prediction method provided in an embodiment of the present invention;

[0058] Figure 2 This is a polygonal planar schematic diagram of the tunnel decoration drawings provided in an embodiment of the present invention;

[0059] Figure 3 This is a polygonal planar schematic diagram of the unfinished tunnel drawing provided in an embodiment of the present invention;

[0060] Figure 4 This is a schematic diagram of the three-dimensional structure of a tunnel in a spatial coordinate system provided in an embodiment of the present invention;

[0061] Figure 5 This is a schematic diagram of the signal loss prediction device provided in an embodiment of the present invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] The terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such usage can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, the first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0064] Please see Figure 1 , Figure 1 This is a flowchart illustrating the signal loss prediction method provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0065] Step 101: Obtain an image of the tunnel cross-section, the image including the arch shape corresponding to the tunnel;

[0066] In this step, a cross-sectional image of the highway tunnel is acquired to determine the tunnel's width and height, providing parameters for subsequent calculations of signal loss within the tunnel.

[0067] Step 102: Based on the arch in the image, obtain at least one polygon;

[0068] In this step, it is confirmed whether the tunnel sidewalls have been decorated. If so, such as Figure 2 As shown, the first arch in the highway tunnel decoration drawing is obtained. The first arch includes the decoration height h1 on both sides of the tunnel and the distance w1 between the two sides after decoration. After polygonization based on the first arch, a rectangle with length w1 and width h1 is obtained, as well as an isosceles trapezoid with upper base w1 / 2, lower base w1, and height h2 is obtained.

[0069] If the tunnel sidewalls are not finished, such as Figure 3 As shown, the second arch in the unfinished drawings of the highway tunnel is obtained. The second arch includes the maximum height h3 of the tunnel and the distance w2 between the two sides. After polygonization based on the second arch, a rectangle with a length of w2-2w3 and a width of h3 / 2 is obtained, as well as an isosceles trapezoid with an upper base of (w2-2w3) / 2, a lower base of w2-2w3, and a height of h4 is obtained.

[0070] Step 103: Determine the near-field region and far-field region of the signal source based on the at least one polygon;

[0071] In this step, a spatial coordinate system is established based on at least one polygon, P t and P rThe two points are the signal transmission and signal reception points, with coordinates P and P, respectively. t (S tf ,S th ,0)P r (S rf ,S rh ,z), where S tf S th These are the distance between the launch point and the left tunnel, and the altitude above the ground, S. rf S rh These are the distance between the receiving point and the left tunnel, and its height above the ground, respectively. According to P... t and P r The positions of the two points, and the frequency f of the transmitted signal, are used to calculate and determine the near-field and far-field regions of the signal source. When the frequency of the transmitted signal is f, the wavelength is λ = 1 / f.

[0072] Step 104: Obtain the first signal prediction loss of the signal emitted by the signal source in the near field region and the second signal prediction loss of the signal in the far field region;

[0073] In this step, within a highway tunnel, the electromagnetic waves in the frequency band used for mobile communication typically propagate in a multimode pattern. In the near-field region, since guided propagation has not yet been established, the electromagnetic wave propagation mode is similar to free-space propagation, resulting in significant fast fading. In the far-field region, however, the electromagnetic waves primarily propagate in the fundamental mode, similar to wave propagation in a waveguide, and the fading is significantly reduced. The locations where different fading occurs, i.e., the dividing points between the near-field and far-field regions, are determined by the Fresnel zone. The near-field region is typically defined as the vertical distance between two antennas in the tunnel when the first Fresnel zone begins to be blocked.

[0074] In the near-field region, using the free propagation mode, the first signal prediction loss can be calculated using Formula 1:

[0075] pl(dB) = 20lgd + 20lgf + 32.45;

[0076] Where pl(dB) is the predicted signal loss, d is the signal transmission distance, and f is the frequency of the transmitted signal;

[0077] In the far-field region, using the fundamental mode transmission mode, the second signal prediction loss can be calculated using Formula 2:

[0078] pl(dB)=pl d' +c(d-d')+k;

[0079] Where d' is the boundary point between the near-field and far-field regions, k is the deviation parameter, and pl d' It can be calculated using Formula 1:

[0080] pl d' =20lgd' + 20lgf + 32.45;

[0081] Thus, by using Formula 1 and Formula 2, we can obtain the first signal prediction loss in the near-field region and the second signal prediction loss in the far-field region of the signal emitted by the source.

[0082] Step 105: Obtain the signal loss in the tunnel based on the first signal prediction loss and the second signal prediction loss.

[0083] In the near-field region, the free propagation mode is adopted and the first signal prediction loss is calculated using Formula 1. In the far-field region, the fundamental mode transmission mode is adopted and the second signal prediction loss is calculated using Formula 2. Based on the first and second signal prediction losses, the comprehensive signal loss in the tunnel is obtained, and an accurate propagation loss prediction model is established.

[0084] In this embodiment, based on the image of the tunnel cross-section, polygonization processing is performed. Based on at least one obtained polygon, the near-field and far-field regions of the signal source are determined. The first predicted signal loss in the near-field region and the second predicted signal loss in the far-field region are obtained, improving the consistency of calculating propagation loss values ​​in the near-field and far-field regions. Furthermore, based on the first and second predicted signal losses, the signal loss in the tunnel is obtained, making the obtained loss results more realistic and thus improving the accuracy of signal loss prediction. This helps to rationally determine antenna locations and signal source power in highway tunnel scenarios.

[0085] Wherein, the at least one polygon includes a target isosceles trapezoid and a target rectangle;

[0086] Obtaining at least one polygon based on the arch in the image includes:

[0087] Based on the upper region of the arch, the target isosceles trapezoid is obtained, and the target isosceles trapezoid is the largest inscribed isosceles trapezoid of the upper region of the arch.

[0088] Based on the lower region of the arch, the target rectangle is obtained, which is the largest inscribed rectangle of the lower region of the arch;

[0089] like Figure 4 As shown, the length of the target rectangle can be w, and the width can be h1; the upper base of the target isosceles trapezoid is w / 2, the lower base is w, and the height is h2. This simplifies the calculation process by dividing the arch in the tunnel cross-section image into polygons, making it easier to obtain the parameters needed for subsequent calculations. The lower base of the target isosceles trapezoid coincides with one long side of the target rectangle.

[0090] It should be noted that the tunnel profile can also be processed into a polygon with more sides, such as a continuous isosceles trapezoid, to improve the accuracy of the calculation results. To avoid repetition, this will not be elaborated on here.

[0091] Optionally, determining the near-field and far-field regions of the signal source based on the at least one polygon includes:

[0092] Calculate multiple distances between the transmission and reception points of the signal when the sidewall of the tunnel is tangent to the Fresnel region, wherein the profile of the sidewall is the profile of at least one polygon;

[0093] Based on the multiple distances, determine the boundary point between the near-field region and the far-field region;

[0094] The near-field region and the far-field region are determined based on the boundary point.

[0095] The near-field range can be defined as the vertical distance between the transmitter and receiver points on the tunnel wall when the Fresnel region begins to tangent to the tunnel sidewall.

[0096] After performing polygon processing on the tunnel cross-section image, the target isosceles trapezoid and the target rectangle are obtained. The tunnel can be considered as a three-dimensional shape composed of target hexagons, such as... Figure 4 As shown, the target hexagon includes a target isosceles trapezoid and a target rectangle. The six sides of the target hexagon can be the outline of the tunnel sidewall, corresponding to the six sidewall surfaces ①, ②, ③, ④, ⑤, and ⑥ of the tunnel sidewall.

[0097] Calculate the multiple distances between the transmitting and receiving points of the signal when the six sidewalls of the tunnel (①, ②, ③, ④, ⑤, and ⑥) are tangent to the Fresnel region:

[0098] When the Fresnel region is tangent to the sidewall ①, the distance between the signal transmission point and the receiving point is:

[0099]

[0100] When the Fresnel region is tangent to the side wall ②, the distance between the signal transmission point and the receiving point is:

[0101]

[0102] When the Fresnel region is tangent to sidewall ③, the distance between the signal transmission point and the receiving point is:

[0103]

[0104] in,

[0105] When the Fresnel region is tangent to sidewall ④, the distance between the signal transmission point and the receiving point is:

[0106]

[0107] When the Fresnel region is tangent to the sidewall surface ⑤, the distance between the signal transmission point and the receiving point is:

[0108]

[0109] in,

[0110] When the Fresnel region is tangent to the sidewall surface ⑥, the distance between the signal transmission point and the receiving point is:

[0111]

[0112] Where f is the frequency of the transmitted signal, λ is the wavelength of the transmitted signal, and P t and P r The two points are the signal transmission and signal reception points, with coordinates P and P, respectively. t (S tf ,S th ,0)P r (S rf ,S rh ,z), where S tf S th These are the distance between the launch point and the left tunnel, and the altitude above the ground, S. rf S rh These are the distance between the receiving point and the tunnel on the left, and the height above the ground, respectively.

[0113] Then, based on d1, d2, d3, d4, d5, and d6, determine the target distance between the launch point and the receiver point when the Fresnel region begins to be tangent to the profile:

[0114] d = max{d1,d2,d3,d4,d5,d6};

[0115] Determine the boundary between the near-field and far-field regions based on the target distance:

[0116]

[0117] When the distance between the transmitting and receiving points is less than or equal to d', it can be considered the near-field region, and the first predicted signal loss is calculated using Formula 1. When the distance between the transmitting and receiving points is greater than d', it can be considered the far-field region, and the second predicted signal loss is calculated using Formula 2. The method for calculating the boundary between the near-field and far-field regions of a signal in a tunnel, provided by this invention, can improve the accuracy of signal loss prediction and can be extended to other shaft scenarios.

[0118] Optionally, after determining the near-field and far-field regions of the signal source based on the at least one polygon, and before acquiring the first signal prediction loss of the signal emitted by the signal source in the near-field region and the second signal prediction loss of the signal in the far-field region, the method may further include:

[0119] Determine the refractive loss coefficient, which includes the horizontal polarization loss coefficient, the vertical polarization loss coefficient, and the 45-degree polarization loss coefficient;

[0120] The step of acquiring the first signal prediction loss of the signal emitted by the signal source in the near-field region and the second signal prediction loss of the signal in the far-field region includes:

[0121] Based on the preset frequency of the signal and the target distance, the predicted loss of the first signal is obtained;

[0122] The second signal prediction loss is obtained based on the preset frequency, the target distance, and the refractive loss coefficient. The second signal prediction loss includes horizontal polarization loss, vertical polarization loss, and 45-degree polarization loss coefficient.

[0123] The target distance is the distance between the transmitting point and the receiving point of the signal.

[0124] In this embodiment, the refractive loss coefficient is further determined. Existing research indicates that the edge refractive loss coefficient for horizontal polarization can be:

[0125]

[0126] The edge refractive loss coefficient of vertical polarization can be:

[0127]

[0128] Where, ε r1 and ε r2 These are the relative permittivity of the materials on the tunnel side and the top and bottom surfaces, respectively.

[0129] Since 45-degree polarization can be decomposed into vertical polarization and horizontal polarization, the loss coefficient of 45-degree polarization can be:

[0130]

[0131] 45-degree polarization includes positive 45-degree polarization and negative 45-degree polarization, and the 45-degree polarization loss coefficient is also applicable to both positive and negative 45-degree polarization. Thus, by calculating the loss coefficients for different polarization modes, the signal loss under different polarization modes in the tunnel can be obtained, making the obtained loss results more consistent with reality and improving the accuracy of signal loss prediction.

[0132] The preset frequency of the signal can be f, and the target distance can be d. The first signal prediction loss can be calculated using Formula 1.

[0133] pl(dB)=20lgd+20lgf+32.45, d≤d'.

[0134] The overall signal transmission loss coefficient in the far field includes the refraction loss coefficient, the tunnel surface roughness scattering loss coefficient, and the tunnel wall tilt loss coefficient. The overall signal transmission loss coefficient in the far field can be:

[0135] c = tl + rl + fl;

[0136] Where c is the overall loss coefficient of the signal in the far field, tl is the signal scattering loss coefficient, rl is the tunnel surface roughness scattering loss coefficient, and fl is the tunnel wall tilt loss coefficient.

[0137] Given that the tunnel wall roughness follows a mean of 0 and a variance of σ, and the mean square value of the tunnel inclination angle is θ, the tunnel surface roughness scattering loss coefficient can be:

[0138]

[0139] The tunnel wall tilt loss coefficient can be:

[0140]

[0141] When the access antenna type is horizontally polarized, the first signal prediction loss in the near field region and the second signal prediction loss in the far field region can be expressed by the following formula:

[0142]

[0143] Where, k H It is the deviation parameter for horizontal polarization, c H It is the overall loss coefficient of the signal when it is horizontally polarized in the far field, c H It can be represented as:

[0144]

[0145] When the access antenna type is vertically polarized, the first signal prediction loss in the near field region and the second signal prediction loss in the far field region can be expressed by the following formula:

[0146]

[0147] Where, k V It is the deviation parameter for horizontal polarization, c V It is the overall loss coefficient of the signal when it is horizontally polarized in the far field, c VIt can be represented as:

[0148]

[0149] When the access antenna type is ±45° polarization (positive 45° and / or negative 45° polarization), the first signal prediction loss in the near field region and the second signal prediction loss in the far field region can be expressed by the following formula:

[0150]

[0151] Where, k 45 It is the deviation parameter for horizontal polarization, c 45 It is the overall loss coefficient of the signal when it is horizontally polarized in the far field, c 45 It can be represented as:

[0152]

[0153] Currently, dual-polarized end-fire antennas all use ±45° polarization. This invention proposes a propagation prediction loss calculation method that includes the refraction loss coefficient of ±45° polarized signals, which is more compatible with existing antenna polarization methods.

[0154] By comprehensively considering factors such as the boundary between the near-field and far-field regions of an arched tunnel, signal transmission loss within ±45° of the far-field region, and the consistency of propagation loss models in the near-field and far-field regions, a method for predicting propagation loss in highway tunnels is constructed, improving the accuracy of signal loss prediction. This allows for the rational determination of antenna locations and input power, thereby achieving low-cost and effective 2G / 3G / 4G / 5G network coverage in highway tunnel scenarios.

[0155] Optionally, obtaining the signal loss in the tunnel based on the first signal prediction loss and the second signal prediction loss includes:

[0156] The predicted loss of the second signal is verified to obtain the deviation parameter;

[0157] The second signal prediction loss is corrected based on the deviation parameter to generate the third signal prediction loss;

[0158] The signal loss in the tunnel is obtained based on the first signal prediction loss and the third signal prediction loss.

[0159] In this embodiment, the predicted loss of the second signal can be verified based on measured data to correct the deviation parameters. Then, the predicted loss of the second signal is corrected according to the corrected deviation parameters to generate the predicted loss of the third signal. Based on the predicted loss of the first signal and the predicted loss of the third signal, the signal loss in the tunnel is obtained to improve the prediction accuracy of the signal loss. The method for obtaining the deviation parameters can be described as follows:

[0160] First, a highway tunnel with a length greater than 1.5 km was selected as a pilot project to determine the near-far boundary point d'. Then, a vertically polarized log-periodic antenna (700-3700MHz) and a shared tunnel wall-mounted antenna (700-3700MHz) were selected as transmitting antennas. The vertically polarized log-periodic antenna was used to verify the propagation loss of vertically and horizontally polarized signals, while the shared tunnel wall-mounted antenna was used to verify the propagation loss of ±45° polarized signals. Next, 2G / 3G / 4G / 5G signal sources from various operators were sequentially loaded, and the antenna port power P was calculated. t Starting from the antenna and moving towards the transmission direction, perform fixed-point tests every 10 meters, recording the RSRP. Perform 10 tests at each point, and calculate the average Reference Signal Receiving Power (RSRP), which is P. r The measured signal loss is P. t -P r At each point in the far-field region under each polarization mode, calculate the value of k, which can be k1,...k n (n is not less than 100); the deviation parameter is obtained as follows:

[0161]

[0162] See Figure 5 , Figure 5 This is a schematic diagram of the signal loss prediction device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, a signal loss prediction device 500 may include:

[0163] The first acquisition module 501 is used to acquire an image of a tunnel cross-section, the image including the arch shape corresponding to the tunnel;

[0164] The second acquisition module 502 is used to obtain at least one polygon based on the arch in the image;

[0165] The first determining module 503 is used to determine the near-field region and far-field region of the signal source based on the at least one polygon.

[0166] The third acquisition module 504 is used to acquire the first signal prediction loss of the signal emitted by the signal source in the near field region and the second signal prediction loss of the signal in the far field region.

[0167] The fourth acquisition module 505 is used to obtain the signal loss in the tunnel based on the first signal prediction loss and the second signal prediction loss.

[0168] Optionally, the signal loss prediction device 500 further includes:

[0169] The second determining module is used to determine the refractive loss coefficient, which includes the horizontal polarization loss coefficient, the vertical polarization loss coefficient, and the 45-degree polarization loss coefficient.

[0170] The third acquisition module 504 includes:

[0171] The first acquisition submodule is used to acquire the predicted loss of the first signal based on the preset frequency of the signal and the target distance;

[0172] The second acquisition submodule is used to acquire the second signal prediction loss based on the preset frequency, the target distance and the refraction loss coefficient. The second signal prediction loss includes horizontal polarization loss, vertical polarization loss and 45-degree polarization loss coefficient.

[0173] The target distance is the distance between the transmitting point and the receiving point of the signal.

[0174] Optionally, the first determining module 503 includes:

[0175] The calculation submodule is used to calculate multiple distances between the transmission point and the reception point of the signal when the sidewall of the tunnel is tangent to the Fresnel region, wherein the contour of the sidewall is the contour of the at least one polygon.

[0176] The first determining submodule is used to determine the boundary point between the near-field region and the far-field region based on the multiple distances;

[0177] The second determining submodule is used to determine the near-field region and the far-field region based on the dividing point.

[0178] Optionally, the fourth acquisition module 505 includes:

[0179] The verification submodule is used to verify the prediction loss of the second signal and obtain the deviation parameters;

[0180] A generation submodule is used to correct the second signal prediction loss based on the deviation parameter and generate a third signal prediction loss.

[0181] The third acquisition submodule is used to obtain the signal loss in the tunnel based on the first signal prediction loss and the third signal prediction loss.

[0182] Optionally, the at least one polygon includes a target isosceles trapezoid and a target rectangle;

[0183] The second acquisition module 502 includes:

[0184] The fourth acquisition submodule is used to obtain the target isosceles trapezoid based on the upper region of the arch, wherein the target isosceles trapezoid is the largest inscribed isosceles trapezoid of the upper region of the arch;

[0185] The fifth acquisition submodule is used to obtain the target rectangle based on the lower region of the arch, wherein the target rectangle is the maximum inscribed rectangle of the lower region of the arch;

[0186] Wherein, the target isosceles trapezoid coincides with the target side of the target rectangle.

[0187] The signal loss prediction device 500 provided in this embodiment of the invention can achieve Figure 1 The various processes implemented in the method embodiments achieve the same technical effect, and will not be described again here to avoid repetition.

[0188] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of the present invention is not limited to performing functions in the order discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0189] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0190] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. A signal loss prediction method, characterized in that, The method includes: Acquire an image of a tunnel cross-section, the image including the arch shape corresponding to the tunnel; Based on the arches in the image, at least one polygon is obtained; Based on the at least one polygon, determine the near-field region and far-field region of the information source; The first signal prediction loss of the signal emitted by the source in the near field region and the second signal prediction loss of the signal in the far field region are obtained. The signal loss in the tunnel is obtained based on the first signal prediction loss and the second signal prediction loss; After determining the near-field and far-field regions of the signal source based on the at least one polygon, and before acquiring the first signal prediction loss of the signal emitted by the signal source in the near-field region and the second signal prediction loss of the signal in the far-field region, the method further includes: Determine the refractive loss coefficient, which includes the horizontal polarization loss coefficient, the vertical polarization loss coefficient, and the 45-degree polarization loss coefficient; The step of acquiring the first signal prediction loss of the signal emitted by the signal source in the near-field region and the second signal prediction loss of the signal in the far-field region includes: Based on the preset frequency of the signal and the target distance, the predicted loss of the first signal is obtained; The second signal prediction loss is obtained based on the preset frequency, the target distance, and the refractive loss coefficient. The second signal prediction loss includes horizontal polarization loss, vertical polarization loss, and 45-degree polarization loss coefficient. The target distance is the distance between the transmitting point and the receiving point of the signal.

2. The method according to claim 1, characterized in that, Determining the near-field and far-field regions of the information source based on the at least one polygon includes: Calculate multiple distances between the transmission and reception points of the signal when the sidewall of the tunnel is tangent to the Fresnel region, wherein the profile of the sidewall is the profile of at least one polygon; Based on the multiple distances, determine the boundary point between the near-field region and the far-field region; The near-field region and the far-field region are determined based on the boundary point.

3. The method according to claim 1, characterized in that, The step of obtaining the signal loss in the tunnel based on the first signal prediction loss and the second signal prediction loss includes: The predicted loss of the second signal is verified to obtain the deviation parameter; The second signal prediction loss is corrected based on the deviation parameter to generate the third signal prediction loss; The signal loss in the tunnel is obtained based on the first signal prediction loss and the third signal prediction loss.

4. The method according to claim 1, characterized in that, The at least one polygon includes a target isosceles trapezoid and a target rectangle; Obtaining at least one polygon based on the arch in the image includes: Based on the upper region of the arch, the target isosceles trapezoid is obtained, and the target isosceles trapezoid is the largest inscribed isosceles trapezoid of the upper region of the arch. Based on the lower region of the arch, the target rectangle is obtained, which is the largest inscribed rectangle of the lower region of the arch; Wherein, the target isosceles trapezoid coincides with the target side of the target rectangle.

5. A signal loss prediction device, characterized in that, The device includes: The first acquisition module is used to acquire an image of a tunnel cross-section, the image including the arch shape corresponding to the tunnel; The second acquisition module is used to obtain at least one polygon based on the arch in the image; The first determining module is used to determine the near-field region and far-field region of the information source based on the at least one polygon; The third acquisition module is used to acquire the first signal prediction loss of the signal emitted by the signal source in the near field region and the second signal prediction loss of the signal in the far field region. The fourth acquisition module is used to obtain the signal loss in the tunnel based on the first signal prediction loss and the second signal prediction loss; The device further includes: The second determining module is used to determine the refractive loss coefficient, which includes the horizontal polarization loss coefficient, the vertical polarization loss coefficient, and the 45-degree polarization loss coefficient. The third acquisition module includes: The first acquisition submodule is used to acquire the predicted loss of the first signal based on the preset frequency of the signal and the target distance; The second acquisition submodule is used to acquire the second signal prediction loss based on the preset frequency, the target distance and the refraction loss coefficient. The second signal prediction loss includes horizontal polarization loss, vertical polarization loss and 45-degree polarization loss coefficient. The target distance is the distance between the transmitting point and the receiving point of the signal.

6. The apparatus according to claim 5, characterized in that, The first determining module includes: The calculation submodule is used to calculate multiple distances between the transmission point and the reception point of the signal when the sidewall of the tunnel is tangent to the Fresnel region, wherein the contour of the sidewall is the contour of the at least one polygon. The first determining submodule is used to determine the boundary point between the near-field region and the far-field region based on the multiple distances; The second determining submodule is used to determine the near-field region and the far-field region based on the dividing point.

7. The apparatus according to claim 5, characterized in that, The fourth acquisition module includes: The verification submodule is used to verify the prediction loss of the second signal and obtain the deviation parameters; A generation submodule is used to correct the second signal prediction loss based on the deviation parameter and generate a third signal prediction loss. The third acquisition submodule is used to obtain the signal loss in the tunnel based on the first signal prediction loss and the third signal prediction loss.

8. The apparatus according to claim 5, characterized in that, The at least one polygon includes a target isosceles trapezoid and a target rectangle; The second acquisition module includes: The fourth acquisition submodule is used to obtain the target isosceles trapezoid based on the upper region of the arch, wherein the target isosceles trapezoid is the largest inscribed isosceles trapezoid of the upper region of the arch; The fifth acquisition submodule is used to obtain the target rectangle based on the lower region of the arch, wherein the target rectangle is the maximum inscribed rectangle of the lower region of the arch; Wherein, the target isosceles trapezoid coincides with the target side of the target rectangle.

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