5G signal coverage optimization method for wind power plant

Abstract

The invention relates to the technical field of wireless communication, and discloses a 5G signal coverage optimization method for a wind power plant, and the method comprises the steps: obtaining the real-time data of the rotation angle and the rotation speed of a blade in a wind turbine generator, and generating a fan operation feature set according to the mapping relation between a fan motion track and signal attenuation in the real-time data after time alignment; fusing the network state feature set and the fan operation feature set to obtain a digital twinborn model of the wind power plant under electromagnetic propagation; based on a digital twinborn model, analyzing space-time distribution characteristics of the wind power plant to obtain a coverage blind area prediction result of the wind power plant; according to a coverage blind area prediction result, adjusting a beam directional angle and a beam width in the wind power plant to obtain a phase compensation parameter; phase compensation parameters are injected into the periodic characteristics of the fan motion trail, and an anti-shielding beam configuration instruction is obtained; issuing the anti-shielding beam configuration instruction to a 5G base station; according to the invention, the 5G signal coverage optimization effect in the wind power plant can be improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a method for optimizing 5G signal coverage in a wind farm. Background Art

[0002] In the 5G signal coverage scenario of a wind farm, due to the dynamic obstruction caused by the continuous rotation of the wind turbine blades and the metal structure of the wind turbine during operation, which is prone to multipath interference, existing signal coverage optimization methods mostly use static beam configuration or parameter adjustment based on fixed scenarios, failing to effectively associate the dynamic mapping relationship between the wind turbine motion trajectory and signal attenuation, resulting in violent fluctuations in the signal during the blade obstruction period and poor coverage stability.

[0003] At the same time, existing technologies lack accurate modeling of the electromagnetic propagation environment in wind farms. They fail to integrate the cyclical characteristics of wind turbine operation with network status data, and are unable to predict in advance the spatiotemporal blind spots caused by periodic blade occlusion. This causes beam adjustment to lag behind occlusion changes, making it difficult to offset the effects of multipath interference and dynamic occlusion. This ultimately leads to recurring coverage blind spots and poor signal coverage optimization. Based on the beneficial effects outlined in the document, the emphasis should be on how the technology presented can improve these effects. This should be summarized in two paragraphs, avoiding the use of "for example" or comparisons with existing technologies. Simply state the improvements and benefits. Summary of the Invention

[0004] The present invention provides a 5G signal coverage optimization method for a wind farm to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides a method for optimizing 5G signal coverage in a wind farm, comprising:

[0006] S1. Acquire real-time data of blade rotation angle and speed in a wind turbine, and generate a wind turbine operation feature set of the wind turbine based on a mapping relationship between wind turbine motion trajectory and signal attenuation in the real-time data after time alignment;

[0007] S2. Fusing the network status feature set of the wind farm and the wind turbine operation feature set to obtain a digital twin model of the wind farm under electromagnetic propagation;

[0008] S3. Analyze the spatiotemporal distribution characteristics of the wind farm under multipath interference and signal shielding based on the digital twin model to obtain a coverage blind spot prediction result of the wind farm;

[0009] S4. Adjust the beam pointing angle and beam width of the 5G base station in the wind farm according to the coverage blind area prediction result to obtain a phase compensation parameter of the 5G base station;

[0010] S5. Injecting the periodic characteristics of the wind turbine motion trajectory into a phase compensation parameter to obtain an anti-blocking beam configuration instruction of the 5G base station;

[0011] S6. Send the anti-blocking beam configuration instruction to the 5G base station.

[0012] In a preferred embodiment, generating the wind turbine operation feature set of the wind turbine generator system according to the mapping relationship between the wind turbine motion trajectory and signal attenuation in the real-time data after time alignment includes:

[0013] Extract signal strength time series data from real-time data after time alignment;

[0014] Performing sliding window segmentation on the blade rotation angle and speed data to obtain motion slices of the wind turbine;

[0015] extracting an occlusion correlation factor between the paddle's spatial position change and the signal intensity fluctuation within the motion slice;

[0016] A speed-attenuation transfer function model is established according to the speed periodicity characteristics and the shading correlation factor;

[0017] The parameter set of the speed-attenuation transfer function model is fused with the blade spatial position change rate of the wind turbine generator set to obtain a wind turbine operation feature set of the wind turbine generator set.

[0018] In a preferred embodiment, the speed-attenuation transfer function model includes:

[0019]

[0020] Where H(*) is the wind turbine speed attenuation value, α is the shielding correlation factor, e is a natural constant, β is the dynamic attenuation factor of the rotating blade shielding in the wind turbine, and f r is the real-time speed of the wind turbine in the wind turbine generator set, γ is the multipath interference amplitude of the metal surface in the wind turbine generator set, π is, φ is the offset of the yaw angle phase of the wind turbine in the wind turbine generator set, and t is the time factor.

[0021] In a preferred embodiment, the fusion of the network status feature set of the wind farm and the wind turbine operation feature set to obtain the digital twin model of the wind farm under electromagnetic propagation includes:

[0022] Performing time stamp alignment on base station signal strength data in the network status feature set to obtain a synchronization signal strength sequence of the wind farm;

[0023] Extracting blade rotation angle data from the wind turbine operation feature set, and determining the real-time spatial position of the blade edge in the blade rotation angle data through three-dimensional spatial coordinate transformation to obtain a dynamic shielding contour of the wind farm;

[0024] Taking the position of the terminal in the 5G base station as the end point, performing electromagnetic wave diffraction simulation on the wind farm based on the synchronization signal strength sequence and the dynamic shielding profile to obtain a path loss distribution map of the wind farm;

[0025] A digital twin model of the wind farm is constructed based on the path loss distribution map and the channel quality index in the network status feature set.

[0026] In a preferred embodiment, the method of performing electromagnetic wave diffraction simulation on the wind farm based on the synchronization signal strength sequence and the dynamic shielding profile with the terminal position in the 5G base station as the end point to obtain a path loss distribution map of the wind farm includes:

[0027] identifying the spatial geometric relationship between the blade edge and the base station-terminal line-of-sight path based on the spatial coordinate data of the dynamic obstruction profile, and obtaining an obstacle obstruction determination result of the wind farm;

[0028] When the obstacle occlusion determination result is partial occlusion, extracting the direct signal component in the synchronization signal strength sequence;

[0029] Determining a propagation distance difference between a ground reflection path and a diffuse reflection path based on terrain elevation data of the terminal;

[0030] Performing weighted fusion on the direct signal component and the propagation distance difference to obtain a synthetic field intensity attenuation value of the wind farm;

[0031] The synthetic field intensity attenuation value is mapped to the base station-terminal line-of-sight path to obtain a path loss distribution map of the wind farm.

[0032] In a preferred embodiment, the analyzing, based on the digital twin model, the spatiotemporal distribution characteristics of the wind farm under multipath interference and signal shielding to obtain the coverage blind area prediction result of the wind farm includes:

[0033] Constructing an attenuation distribution heat map of the wind farm according to the intensity value of the signal attenuation factor in the digital twin model;

[0034] Marking the continuous area in the attenuation distribution heat map where the signal attenuation exceeds a predetermined threshold as a spatial occlusion area;

[0035] The spatial blocking area is integrated with the periodic blocking time window of the wind turbine operation feature set to obtain a spatiotemporal joint blind area prediction result of the wind farm.

[0036] In a preferred embodiment, adjusting the beam pointing angle and beam width of the 5G base station in the wind farm according to the coverage blind area prediction result to obtain the phase compensation parameter of the 5G base station includes:

[0037] Parsing the blind spot position coordinate set in the coverage blind spot prediction result, and determining the horizontal azimuth and elevation angle of the blind spot center point relative to the 5G base station;

[0038] Determining a beam scanning range of an antenna array in the 5G base station based on the horizontal azimuth angle and the elevation angle;

[0039] Dividing the beam dwell time window of the 5G base station according to the time interval characteristics in the coverage blind area prediction result;

[0040] Combining the beam scanning range and the beam dwell time window to obtain a dynamic beam pointing sequence of the 5G base station;

[0041] Matching a preset beam width adjustment strategy according to the interference type in the coverage blind area prediction result;

[0042] The phase compensation parameters of the 5G base station are output according to the adjusted strategy.

[0043] In a preferred embodiment, matching a preset beam width adjustment strategy according to the interference type in the coverage blind area prediction result includes:

[0044] When the interference type is multipath-dominated, a narrow beam high gain strategy is adopted;

[0045] When the interference type is dominated by obstruction, a wide beam diversity strategy is adopted.

[0046] In a preferred embodiment, injecting the periodic characteristics of the wind turbine motion trajectory into the phase compensation parameter to obtain the anti-blocking beam configuration instruction of the 5G base station includes:

[0047] Extracting blade rotation angular velocity time series data from the wind turbine operation feature set;

[0048] Calculating a Doppler shift correlation function between the angular velocity time series data and the carrier frequency of the 5G base station;

[0049] deriving a phase compensation gradient of the antenna array according to a phase change rate of a Doppler frequency shift correlation value in the Doppler frequency shift correlation function;

[0050] Mapping the phase compensation gradient into a phase shifter control codeword of an antenna element in the antenna array;

[0051] According to the time interval of the coverage blind area prediction result, the phase shifter control codeword is injected into the phase compensation parameter to obtain the phase compensation parameter set of the 5G base station.

[0052] In a preferred embodiment, the calculating the Doppler shift correlation function between the angular velocity time series data and the carrier frequency of the 5G base station includes:

[0053] Normalizing the angular velocity time series data to obtain a standard angular velocity sequence;

[0054] Determining a maximum theoretical Doppler shift of blade tip motion based on carrier frequency parameters of the 5G base station;

[0055] Establishing a time alignment data set of the standard angular velocity sequence and the real-time received signal carrier offset;

[0056] Fitting the angular velocity change in the angular velocity time series data with the carrier offset of the real-time received signal in the 5G base station to obtain a linear transfer coefficient of the 5G base station;

[0057] Based on the linear transfer coefficient and the maximum theoretical Doppler shift, constructing an angular velocity-frequency shift ratio function of the 5G base station;

[0058] Performing Fourier transform on the angular velocity-frequency shift ratio function to obtain a phase response characteristic of the 5G base station;

[0059] The linear transfer coefficient and the phase response characteristic are integrated to obtain a Doppler shift correlation function of the 5G base station, wherein the Doppler shift correlation function is as follows:

[0060]

[0061] Where F(*) is the Doppler frequency shift correlation value, K d is the linear transfer coefficient, ω(t) is the real-time angular velocity of the wind turbine blade, θ(t) is the real-time angle between the blade and the base station-terminal connection, φ0 is the initial phase offset, N is the maximum harmonic order in the phase response characteristic, A n is the amplitude of the nth harmonic, n is the harmonic number, f b is the blade passing frequency, t is the time factor, is the initial phase of the harmonic in the phase response characteristic, and R is the blade characteristic radius.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. This invention acquires real-time data on wind turbine blade rotation angle and speed, establishes a mapping relationship between wind turbine motion trajectory and signal attenuation, and generates a set of wind turbine operational characteristics. This is then integrated with the network status characteristics to construct a digital twin model of electromagnetic propagation in the wind farm. This model accurately analyzes the spatiotemporal distribution characteristics of multipath interference and signal obstruction, enabling precise prediction of coverage blind spots. This provides a precise decision-making basis for subsequent signal coverage optimization, fundamentally enhancing the pertinence and effectiveness of 5G signal coverage optimization.

[0064] 2. This method adjusts the beam pointing angle and width based on the coverage blind spot prediction results to obtain phase compensation parameters, and injects the periodic characteristics of the wind turbine's motion trajectory to generate anti-obstruction beam configuration instructions. This approach enables the 5G base station's beam configuration to dynamically adapt to signal changes caused by the wind turbine's periodic motion, effectively offsetting the effects of obstruction and multipath interference, and significantly improving the stability and reliability of 5G signal coverage in wind farms. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of a flow chart of a method for optimizing 5G signal coverage in a wind farm according to an embodiment of the present invention;

[0066] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0067] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0068] An embodiment of the present application provides a method for optimizing 5G signal coverage in a wind farm. The execution subject of the method for optimizing 5G signal coverage in a wind farm includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for optimizing 5G signal coverage in a wind farm can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0069] Reference Figure 1 FIG. 1 is a flow chart of a method for optimizing 5G signal coverage in a wind farm according to an embodiment of the present invention. In this embodiment, the method for optimizing 5G signal coverage in a wind farm includes:

[0070] S1. Acquire real-time data of blade rotation angle and speed in a wind turbine, and generate a wind turbine operation feature set of the wind turbine based on a mapping relationship between wind turbine motion trajectory and signal attenuation in the real-time data after time alignment;

[0071] In an embodiment of the present invention, generating a wind turbine operation feature set of the wind turbine group according to a mapping relationship between wind turbine motion trajectory and signal attenuation in real-time data after time alignment includes:

[0072] Extract signal strength time series data from real-time data after time alignment;

[0073] Performing sliding window segmentation on the blade rotation angle and speed data to obtain motion slices of the wind turbine;

[0074] extracting an occlusion correlation factor between the paddle's spatial position change and the signal intensity fluctuation within the motion slice;

[0075] Establishing a speed-attenuation transfer function model based on the speed periodicity characteristics and the shading correlation factor;

[0076] The parameter set of the speed-attenuation transfer function model is fused with the blade spatial position change rate of the wind turbine generator set to obtain a wind turbine operation feature set of the wind turbine generator set.

[0077] The speed-attenuation transfer function model includes:

[0078]

[0079] Where H(*) is the wind turbine speed attenuation value, α is the shielding correlation factor, e is a natural constant, β is the dynamic attenuation factor of the rotating blade shielding in the wind turbine, and f r is the real-time speed of the wind turbine in the wind turbine generator set, γ is the multipath interference amplitude of the metal surface in the wind turbine generator set, π is, φ is the offset of the yaw angle phase of the wind turbine in the wind turbine generator set, and t is the time factor.

[0080] Specifically, real-time records related to signal strength are screened out from the real-time data that have completed time alignment. These data have been synchronized in the time dimension through timestamp matching. These signal strength data are arranged in chronological order to form continuous signal strength time series data.

[0081] Furthermore, a sliding window of fixed length is set, and the window length is determined according to the time it takes for the blade to rotate one circle. The starting point of the blade rotation angle and speed data is taken as the starting point of the window, and all blade rotation angle data and speed data within the window range are intercepted. Then, the window is moved backward according to a fixed step size, and the step size is set to one tenth of the window length. After each movement, the data in the corresponding range is repeatedly intercepted. The set of blade rotation angle and speed data within the window obtained each time is the motion slice of the wind turbine.

[0082] Furthermore, based on the blade rotation angle data in the motion slice, the spatial position of the blade at each moment is calculated, and the change in the blade's spatial position is obtained by comparing the spatial positions at adjacent moments. At the same time, the change in signal intensity in the signal intensity time series data in the motion slice is extracted as the signal intensity fluctuation. The frequency and amplitude correlation of the signal intensity fluctuation when the blade's spatial position changes are statistically analyzed. When the blade position change reaches a certain amplitude, if the signal intensity fluctuates accordingly, the degree of correlation between the two is recorded. This degree of correlation is the occlusion correlation factor.

[0083] Furthermore, the changes in speed data over time are observed, and the length and regularity of the period in which the speed repeatedly shows the same change trend are determined as the speed periodic characteristics. The occlusion correlation factor is matched with the speed periodic characteristics. When the speed is at a certain stage within the cycle, the corresponding occlusion correlation factor value is recorded at this time, and the impact of the occlusion correlation factor on signal attenuation at different speed stages is analyzed. For example, the signal attenuation amplitude corresponding to the occlusion correlation factor at a certain stage when the speed is accelerated is used to establish a corresponding relationship model between speed change and signal attenuation, namely, the speed-attenuation transfer function model.

[0084] Furthermore, all key information used to describe the model characteristics is extracted from the speed-attenuation transfer function model as a parameter set. The blade spatial position change rate is calculated through the change of the blade rotation angle over time. Each parameter in the parameter set is matched with the blade spatial position change rate. For example, the attenuation characteristics of a certain parameter at the corresponding speed are combined with the blade spatial position change rate at the speed. All matched information is integrated to form a comprehensive information set that can reflect the operating status of the wind turbine, that is, the wind turbine operation feature set of the wind turbine.

[0085] Specifically, α is derived from the calculation result of the occlusion correlation factor between the blade spatial position change and the signal intensity fluctuation in the motion slice, that is, the degree of correlation is obtained by statistically analyzing the frequency and amplitude of the signal intensity fluctuation when the blade spatial position changes; β is determined by analyzing the occlusion process of the rotating blades in the wind turbine under different rotation states. Specifically, it records the dynamic changes of signal attenuation when the blades occlude the signal, and summarizes the degree of influence of the occlusion behavior on the attenuation; f ris the wind turbine speed data monitored in real time by the wind turbine, which is already included in the blade rotation angle and speed data; γ is determined by measuring the strength of the multipath interference formed by the metal surface reflection signal in the wind turbine, specifically by collecting the signal changes after the superposition of the metal surface reflection signal and the direct signal, and statistically calculating the amplitude of the interference part; φ is determined by measuring the phase change corresponding to the deviation between the yaw angle of the wind turbine and the reference angle, that is, recording the angle offset when the wind turbine yaws and converting it into the corresponding phase offset value; t is the time record corresponding to the real-time data. These time data have been used for time alignment, and this time record is directly used.

[0086] Furthermore, H(*) represents the speed attenuation value of the wind turbine. This formula establishes a corresponding relationship between the real-time speed of the wind turbine and the speed attenuation value by combining the shielding correlation factor, the dynamic attenuation factor of the rotating blade shielding, the real-time speed of the wind turbine, the multipath interference amplitude of the metal surface, the offset of the wind turbine yaw angle phase and the time factor. That is, when the real-time speed of the wind turbine changes, the corresponding speed attenuation value is calculated through the combined action of various parameters in the formula, thereby reflecting the influence of the speed on the signal attenuation and the effect of other related factors on the attenuation.

[0087] Furthermore, when the real-time speed of the fan increases, the value of α multiplied by the natural constant to the power of negative β times the real-time speed of the fan in the formula will gradually decrease, because the exponential part of the natural constant is negative and its absolute value increases with the increase of the speed, resulting in the corresponding attenuation contribution of this part gradually weakening; at the same time, the value of γ multiplied by the sine function will show periodic fluctuations with changes in time and speed, because the sine function itself is periodic, and its fluctuation period is jointly determined by the real-time speed of the fan, the time factor and the offset of the yaw angle phase. Overall, the speed attenuation value will gradually decrease with the increase of the speed, and will be superimposed with periodic fluctuations.

[0088] In general, by obtaining real-time data on the blade rotation angle and speed in the wind turbine, accurate and timely first-hand information can be provided for the operating status monitoring of the wind turbine.

[0089] In general, in the complex operating environment of wind turbines, the rotation angle and speed of the blades are constantly changing, and the collection of real-time data ensures that these changes are captured sensitively.

[0090] In general, after time-aligning the collected real-time data, the mapping relationship between the fan motion trajectory and signal attenuation can be accurately sorted out, and then a fan operation feature set can be generated.

[0091] Overall, this process is equivalent to building a unique "operation profile" for the wind turbine, which is of great significance. The wind turbine operation feature set integrates multi-dimensional key information, providing a solid data foundation for subsequent fault warning, performance optimization, and intelligent control.

[0092] For example, through in-depth analysis of feature-set data, it is possible to predict abnormal wear of blades in advance so that maintenance can be arranged in a timely manner, avoiding downtime caused by blade failure. This greatly improves the stability and reliability of wind turbine operation, and ultimately improves the overall power generation efficiency and economic benefits of the wind farm.

[0093] S2. Fusing the network status feature set of the wind farm and the wind turbine operation feature set to obtain a digital twin model of the wind farm under electromagnetic propagation;

[0094] In an embodiment of the present invention, the fusion of the network status feature set of the wind farm and the wind turbine operation feature set to obtain the digital twin model of the wind farm under electromagnetic propagation includes:

[0095] Performing time stamp alignment on base station signal strength data in the network status feature set to obtain a synchronization signal strength sequence of the wind farm;

[0096] Extracting blade rotation angle data from the wind turbine operation feature set, and determining the real-time spatial position of the blade edge in the blade rotation angle data through three-dimensional spatial coordinate transformation to obtain a dynamic shielding contour of the wind farm;

[0097] Taking the position of the terminal in the 5G base station as the end point, performing electromagnetic wave diffraction simulation on the wind farm based on the synchronization signal strength sequence and the dynamic shielding profile to obtain a path loss distribution map of the wind farm;

[0098] A digital twin model of the wind farm is constructed based on the path loss distribution map and the channel quality index in the network status feature set.

[0099] The method of performing electromagnetic wave diffraction simulation on the wind farm based on the synchronization signal strength sequence and the dynamic shielding profile with the terminal position in the 5G base station as the end point to obtain a path loss distribution diagram of the wind farm includes:

[0100] identifying the spatial geometric relationship between the blade edge and the base station-terminal line-of-sight path based on the spatial coordinate data of the dynamic obstruction profile, and obtaining an obstacle obstruction determination result of the wind farm;

[0101] When the obstacle occlusion determination result is partial occlusion, extracting the direct signal component in the synchronization signal strength sequence;

[0102] Determining a propagation distance difference between a ground reflection path and a diffuse reflection path based on terrain elevation data of the terminal;

[0103] Performing weighted fusion on the direct signal component and the propagation distance difference to obtain a synthetic field intensity attenuation value of the wind farm;

[0104] The synthetic field intensity attenuation value is mapped to the base station-terminal line-of-sight path to obtain a path loss distribution map of the wind farm.

[0105] Specifically, all base station signal strength data are screened out from the network status feature set. These data contain their respective timestamp records. These timestamps are matched with the preset unified time base, and the time mark corresponding to each base station signal strength data is adjusted to make all base station signal strength data consistent in the time dimension, that is, there are corresponding signal strength records at the same time point. Then, these base station signal strength data with completed timestamp alignment are arranged in chronological order to form a synchronous signal strength sequence of the wind farm.

[0106] Furthermore, the blade rotation angle data is found from the wind turbine operation feature set, and a three-dimensional spatial coordinate system is established with the center point of the wind turbine base as the origin. The X-axis is along the horizontal direction of the wind farm, the Y-axis is along the vertical ground direction, and the Z-axis is along another horizontal direction of the wind farm. According to the blade length and rotation direction corresponding to each angle in the blade rotation angle data, the coordinate value of the blade edge in the three-dimensional coordinate system is calculated. As the blade rotation angle changes, the coordinate value of the blade edge is updated in real time. The continuous contour formed by these continuously updated blade edge coordinates is the dynamic shielding contour of the wind farm.

[0107] Furthermore, the specific spatial position of the terminal in the 5G base station is determined as the end point of the electromagnetic wave propagation, and the initial value of the signal strength at each time point in the synchronous signal strength sequence is obtained. Combined with the real-time spatial position of the blades in the dynamic shading profile, the propagation path of the electromagnetic wave from the signal emission source to the terminal position is simulated. When the electromagnetic wave propagation path encounters the blades in the dynamic shading profile, the signal attenuation degree when the electromagnetic wave diffracts through the blade edge is calculated. The attenuation degree is combined with the initial signal strength in the synchronous signal strength sequence to obtain the signal loss values ​​at different positions. The signal loss values ​​at all positions are presented in graphical form, which is the path loss distribution map of the wind farm.

[0108] Furthermore, the loss data corresponding to each position in the path loss distribution map is associated with the channel quality index of the position in the network status feature set. For example, the path loss value of a certain area corresponds to the channel transmission rate, bit error rate and other indicators of the area. A virtual scene that is completely consistent with the actual geographical environment, wind turbine location, and base station location of the wind farm is constructed in the virtual space. The associated path loss data and channel quality index are loaded into the corresponding position of the virtual scene, so that the virtual scene can reflect the actual signal propagation loss and channel status of the wind farm in real time. This virtual scene that can map the actual wind farm network and wind turbine-related status is the digital twin model of the wind farm.

[0109] Specifically, the spatial coordinates of the base station and the terminal are obtained, and the two coordinates are connected to form a straight line segment of the base station-terminal line of sight path. All spatial coordinate points of the blade edge in the dynamic occlusion contour are extracted, and the vertical distance of each blade edge coordinate point to the straight line segment of the line of sight path is calculated. At the same time, it is determined whether the blade edge coordinate point is within the range between the two end points of the straight line segment of the line of sight path. If the vertical distances of all blade edge coordinate points to the line of sight path are greater than a preset threshold and are not within the end point range of the line of sight path, it is determined that there is no occlusion; if the vertical distances of some blade edge coordinate points to the line of sight path are less than or equal to the preset threshold and are within the end point range of the line of sight path, it is determined that there is partial occlusion; if all blade edge coordinate points completely cover the straight line segment of the line of sight path and the vertical distances are all less than or equal to the preset threshold, it is determined that there is complete occlusion. These determination results together constitute the obstacle occlusion determination result of the wind farm.

[0110] Furthermore, when the obstacle obstruction is determined to be partial obstruction, the synchronous signal strength sequence contains a direct signal component, a reflected signal component, and a scattered signal component caused by the obstruction. Since the direct signal component is not completely blocked, its signal strength fluctuation amplitude is small and maintains a relatively stable propagation characteristic. By observing the change law of the signal strength in the synchronous signal strength sequence, signal segments with a fluctuation amplitude within a preset range and a duration that meets the direct signal propagation characteristics are screened out, and the signal strength values ​​corresponding to these signal segments are extracted, which are the direct signal components in the synchronous signal strength sequence.

[0111] Furthermore, terrain elevation data of the terminal location and surrounding areas are collected. This data contains the altitude information of each point on the ground. The ground reflection path from the base station to the terminal is determined. This path is the propagation path from the base station through a certain reflection point on the ground to the terminal, and the actual length of this path is measured. At the same time, the diffuse reflection path is determined. This path is the propagation path from the base station through multiple diffuse reflection points on the ground to the terminal. The actual length of this path is measured. The propagation length of the ground reflection path is subtracted from the propagation length of the diffuse reflection path. The resulting value is the propagation distance difference between the ground reflection path and the diffuse reflection path.

[0112] Furthermore, the weighting coefficient is determined according to the propagation distance difference. The greater the propagation distance difference, the more significant the influence of ground reflection and diffuse reflection on the signal. The larger the corresponding reflection-related weighting coefficient is, the smaller the weighting coefficient of the direct signal component is. Conversely, the weighting coefficient is adjusted. The direct signal component is multiplied by its corresponding weighting coefficient to obtain the contribution value of the direct signal. The attenuation influence value of the reflection and diffuse reflection on the signal is calculated in combination with the propagation distance difference. The direct signal contribution value is added to the attenuation influence value, and the result obtained is the synthetic field strength attenuation value of the wind farm.

[0113] Furthermore, based on the straight segment of the base station-terminal line-of-sight path, the line-of-sight path is divided into several equidistant spatial points, each of which corresponds to a specific position coordinate. The synthetic field intensity attenuation value is matched to the corresponding spatial point on the line-of-sight path according to the corresponding spatial coordinate during calculation. Each spatial point has a corresponding synthetic field intensity attenuation value. The attenuation values ​​of all spatial points are presented on a two-dimensional or three-dimensional map in the form of color gradients or numerical annotations, forming a graph that can intuitively display the signal attenuation conditions at different positions on the line-of-sight path. This graph is the path loss distribution map of the wind farm.

[0114] In general, integrating the network status feature set of the wind farm and the wind turbine operation feature set to build a digital twin model can deeply correlate the real-time status of the 5G network in the wind farm (such as signal strength, channel quality, etc.) with the dynamic operation status of the wind turbine (such as blade position, rotation period, etc.), and accurately reproduce the propagation process of electromagnetic signals in the wind farm under the influence of wind turbine movement.

[0115] In general, this model breaks through the information gap between network status and wind turbine operating status, and can realistically simulate the electromagnetic propagation laws under complex scenarios such as multipath interference and signal blocking, providing a high-fidelity simulation carrier for subsequent analysis of the spatiotemporal characteristics of signal coverage.

[0116] In general, the digital twin model can dynamically reflect the real-time impact of wind turbine movement on signal propagation, upgrading the characterization of the electromagnetic environment of the wind farm from static description to dynamic deduction.

[0117] In general, based on this model, the dynamic changes of signal attenuation can be captured more accurately, providing reliable model support for coverage blind spot prediction, and laying a scientific foundation for subsequent beam adjustment and anti-obstruction configuration, fundamentally improving the ability to control the 5G signal propagation characteristics of wind farms, and ensuring the accuracy and foresight of signal coverage optimization.

[0118] S3. Analyze the spatiotemporal distribution characteristics of the wind farm under multipath interference and signal shielding based on the digital twin model to obtain a coverage blind spot prediction result of the wind farm;

[0119] In an embodiment of the present invention, analyzing the spatiotemporal distribution characteristics of the wind farm under multipath interference and signal shielding based on the digital twin model to obtain a coverage blind spot prediction result of the wind farm includes:

[0120] Constructing an attenuation distribution heat map of the wind farm according to the intensity value of the signal attenuation factor in the digital twin model;

[0121] Marking the continuous area in the attenuation distribution heat map where the signal attenuation exceeds a predetermined threshold as a spatial occlusion area;

[0122] The spatial blocking area is integrated with the periodic blocking time window of the wind turbine operation feature set to obtain a spatiotemporal joint blind area prediction result of the wind farm.

[0123] Specifically, the intensity values ​​of all signal attenuation factors are extracted from the digital twin model. These intensity values ​​have been bound to the specific spatial coordinates within the wind farm. Each coordinate point corresponds to a unique intensity value. These coordinate points are arranged on the virtual map according to the actual geographical layout of the wind farm, and a color gradient rule is set. The area with the smallest signal attenuation factor intensity value is represented by light blue. As the intensity value gradually increases, the color transitions to blue and dark blue in turn. The area with the largest intensity value is represented by dark red. The color corresponding to the intensity value of each coordinate point is filled in the corresponding position of the virtual map, finally forming an attenuation distribution heat map that can intuitively display the degree of signal attenuation at different locations in the wind farm.

[0124] Furthermore, based on the minimum signal quality required for normal data transmission of terminal equipment in the wind farm, a predetermined threshold for signal attenuation is determined. The threshold is a fixed attenuation strength value. When the signal attenuation reaches this value, the communication quality of the terminal equipment will drop to a level where data cannot be transmitted normally. The signal attenuation strength value of each coordinate point is checked one by one in the attenuation distribution heat map. When the strength value of a coordinate point exceeds the predetermined threshold, the position of the coordinate point is recorded, and the adjacent coordinate points are checked. If the strength value of the adjacent coordinate points also exceeds the predetermined threshold, it is determined that these coordinate points constitute a continuous area, and the continuous area is completely circled with a black border, and the interior of the area is filled with gray as an identifier. The area marked in this way is the spatial occlusion area.

[0125] Furthermore, time records related to blade shading are screened out from the wind turbine operation feature set. These records contain the start and end time of each shading of the base station-terminal signal path during the blade rotation process. Due to the periodicity of blade rotation, the same shading phenomenon will recur at fixed time intervals. The shading time intervals that recur more than three times in a row are determined as periodic shading time windows. Each window contains a clear start and end time. The coordinate range of the spatial shading area on the virtual map is associated with these periodic shading time windows. That is, it is determined in which time windows a certain spatial shading area will cause signal attenuation exceeding a predetermined threshold due to blade shading. The coordinate ranges of all spatial shading areas and their corresponding periodic shading time windows are organized into a table. Each row in the table records the location information of a spatial area and all corresponding shading time windows. The information presented in this table is the spatiotemporal joint blind spot prediction result of the wind farm.

[0126] In general, analyzing the spatiotemporal distribution characteristics of wind farms under multipath interference and signal obstruction based on the digital twin model and obtaining coverage blind area prediction results can accurately capture the laws of how signal propagation is affected by the dynamic operation of wind turbines.

[0127] In general, the digital twin model itself integrates the key characteristics of network status and wind turbine operation, and can truly reproduce the electromagnetic propagation scenario. On this basis, it analyzes the signal superposition effect of multipath interference, the temporal and spatial range of signal obstruction and other characteristics, and can directly link the formation of coverage blind spots with factors such as the periodicity of wind turbine movement and the path loss of signal propagation. The prediction results not only include the spatial location information of the blind spots, but can also synchronously output the time interval and duration of the blind spots, thereby realizing accurate prediction of the spatial and temporal dual dimensions of the coverage blind spots.

[0128] In general, this prediction result provides a clear optimization target for subsequent 5G base station beam adjustment.

[0129] In general, by knowing the temporal and spatial distribution characteristics of blind spots, beam adjustment can specifically match the time window and spatial location where the blind spots appear, avoiding aimless generalization adjustments, greatly improving the accuracy of beam configuration, and providing a reliable basis for the subsequent generation of anti-obstruction beam configuration instructions, thereby ensuring the effectiveness of 5G signal coverage optimization measures from the source.

[0130] S4. Adjust the beam pointing angle and beam width of the 5G base station in the wind farm according to the coverage blind area prediction result to obtain a phase compensation parameter of the 5G base station;

[0131] In an embodiment of the present invention, adjusting the beam pointing angle and beam width of the 5G base station in the wind farm according to the coverage blind area prediction result to obtain the phase compensation parameter of the 5G base station includes:

[0132] Parsing the blind spot position coordinate set in the coverage blind spot prediction result, and determining the horizontal azimuth and elevation angle of the blind spot center point relative to the 5G base station;

[0133] Determining a beam scanning range of an antenna array in the 5G base station based on the horizontal azimuth angle and the elevation angle;

[0134] Dividing the beam dwell time window of the 5G base station according to the time interval characteristics in the coverage blind area prediction result;

[0135] Combining the beam scanning range and the beam dwell time window to obtain a dynamic beam pointing sequence of the 5G base station;

[0136] Matching a preset beam width adjustment strategy according to the interference type in the coverage blind area prediction result;

[0137] The phase compensation parameters of the 5G base station are output according to the adjusted strategy.

[0138] The matching of a preset beam width adjustment strategy according to the interference type in the coverage blind area prediction result includes:

[0139] When the interference type is multipath-dominated, a narrow beam high gain strategy is adopted;

[0140] When the interference type is dominated by obstruction, a wide beam diversity strategy is adopted.

[0141] Specifically, the horizontal azimuth and elevation angles of the antenna array are obtained from the equipment parameters of the 5G base station. The horizontal azimuth angle represents the pointing angle of the antenna array in the horizontal plane, and the elevation angle represents the tilt angle of the antenna array in the vertical plane. A three-dimensional spatial coordinate system is established with the base station as the origin, with the horizontal directions as the X-axis and Y-axis, and the vertical direction as the Z-axis. The scanning angle range of the antenna array in the XY plane is determined according to the horizontal azimuth angle. The range is centered on the horizontal azimuth angle and extends to a certain angle to the left and right. This angle value is determined by the hardware characteristics of the antenna array and is usually ±60 degrees. The scanning angle range of the antenna array in the XZ plane is determined according to the elevation angle. The range is centered on the elevation angle and extends to a certain angle up and down. This angle value is also determined by the hardware characteristics of the antenna array and is usually ±30 degrees. The horizontal and vertical scanning angle ranges are combined to form a three-dimensional conical space area, which is the beam scanning range of the antenna array in the 5G base station.

[0142] Furthermore, all time interval features are extracted from the coverage blind spot prediction results. These features include the start time, end time and duration of each coverage blind spot. The time axis is divided into several equal time segments at fixed intervals. The length of each time segment is 5 milliseconds. The number of times the coverage blind spot appears in each time segment is counted. If the number of times the coverage blind spot appears in a time segment reaches a preset threshold, the time segment is marked as a high-priority time segment. If the number of occurrences is lower than the threshold, it is marked as a low-priority time segment. Continuous high-priority time segments are merged into a larger time window. This time window is the beam residence time window. Within this window, the antenna array needs to concentrate the beam in a specific area to cover the blind spot, while in the low-priority time segment, the antenna array can perform a wider scan.

[0143] Furthermore, the beam scanning range is gridded in a three-dimensional spatial coordinate system and divided into several grid cells of equal size. Each grid cell corresponds to a specific spatial direction, and a unique identification number is assigned to each grid cell. According to the division result of the beam dwell time window, the grid cells that need to be scanned in each time window are determined. These grid cells are determined based on the spatial position information in the coverage blind spot prediction result. At the beginning of each beam dwell time window, these key grid cells are pointed to in sequence according to a preset order, and one grid cell is pointed to each time and stayed for a certain time. The length of this time is determined by the total length of the beam dwell time window and the number of grid cells that need to be scanned. The beam pointing order and the corresponding time point in each time window are recorded to form an ordered list. This list is the dynamic beam pointing sequence of the 5G base station.

[0144] Furthermore, all interference type information is extracted from the coverage blind spot prediction results. These interference types include multipath fading, co-channel interference, and Doppler frequency shift. For each interference type, the corresponding adjustment strategy is found in the preset interference type and beam width adjustment strategy comparison table. The comparison table is formulated based on a large amount of experimental data and actual operation experience. For example, when the interference type is multipath fading, the corresponding adjustment strategy is to narrow the beam width to enhance the directionality of the signal and reduce the impact of the reflected signal. When the interference type is co-channel interference, the corresponding strategy is to appropriately widen the beam width and increase the beam transmission power to improve the signal-to-interference ratio. The adjustment strategy corresponding to each interference type in the coverage blind spot prediction results is extracted to form a strategy set. This set is the matched beam width adjustment strategy.

[0145] Furthermore, according to the matched beam width adjustment strategy, the beam parameters that need to be adjusted within each beam dwell time window are determined. These parameters include the horizontal width, vertical width and phase offset of the beam. For each beam parameter that needs to be adjusted, the corresponding phase compensation value is searched in the preset parameter adjustment table. The parameter adjustment table is pre-calculated based on the characteristics of the antenna array and the signal processing algorithm. For example, when the beam width needs to be narrowed, the corresponding phase compensation value is searched. These values ​​are used to adjust the signal phase of each antenna unit in the antenna array so that the synthesized beam width becomes narrower. All phase compensation values ​​within each beam dwell time window are arranged in chronological order to form a sequence. This sequence is the phase compensation parameter of the 5G base station. These parameters are input into the beamforming controller of the 5G base station. The controller adjusts the signal phase of the antenna array according to these parameters to achieve dynamic control of the beam width and pointing.

[0146] Specifically, when the interference type is determined to be multipath-dominated through the coverage blind spot prediction results, the narrow beam high-gain strategy is activated. During operation, the horizontal beam width of the antenna array is reduced from the conventional 60 degrees to 30 degrees, and the vertical beam width is reduced from the conventional 30 degrees to 15 degrees. By adjusting the signal phase of each antenna unit in the antenna array, the beam energy is concentrated in a narrower spatial range. At the same time, the transmission power of the beam is increased by 20% on the original basis, so that the signal intensity on the main propagation path is enhanced, and the interference of multipath signals such as reflection and refraction on the main signal is reduced, ensuring that the signal of the direct path can stably reach the terminal.

[0147] Furthermore, when the interference type is determined to be dominated by obstruction through the coverage blind spot prediction results, the wide-beam diversity strategy is activated. During operation, the horizontal beam width of the antenna array is expanded from the conventional 60 degrees to 90 degrees, and the vertical beam width is expanded from the conventional 30 degrees to 45 degrees. At the same time, the diversity transmission mode of the antenna array is enabled, and three independent beams are transmitted from three different spatial angles. The coverage of these three beams overlaps with each other and includes hollow positions that may appear in the obstruction area. Even if 1-2 beams are blocked by obstructions such as blades, the remaining beams can still transmit signals to the terminal through unobstructed paths, ensuring that the terminal can always receive valid signals.

[0148] In general, adjusting the beam pointing angle and beam width of the 5G base station according to the coverage blind spot prediction results to obtain the phase compensation parameters can enable the beam configuration to point directly to areas with weak signal coverage, achieving targeted optimization.

[0149] In general, the coverage blind spot prediction results clearly identify the spatial locations and spatiotemporal distribution characteristics of severe signal attenuation in wind farms. Based on this, the beam pointing angle is adjusted to enable the beam to be precisely focused in the direction of the blind spot. Adjusting the beam width can adapt to the size of the blind spot - wide beam coverage is used for large blind spots, and narrow beams are used to enhance signal strength for small concentrated blind spots, effectively improving the matching degree between the beam and the blind spot.

[0150] In general, the phase compensation parameters generated by this process are the quantitative optimization results of the beam shape. They can be directly used to correct the signal transmission status of the base station antenna, reduce signal waste and insufficient coverage caused by beam pointing deviation or width mismatch, and lay a precise parameter foundation for the subsequent injection of periodic characteristics of wind turbine motion and generation of anti-obstruction beam configuration instructions, further improving the effectiveness of 5G signal coverage.

[0151] S5. Injecting the periodic characteristics of the wind turbine motion trajectory into a phase compensation parameter to obtain an anti-blocking beam configuration instruction of the 5G base station;

[0152] In an embodiment of the present invention, injecting the periodic characteristics of the wind turbine motion trajectory into the phase compensation parameter to obtain the anti-blocking beam configuration instruction of the 5G base station includes:

[0153] Extracting blade rotation angular velocity time series data from the wind turbine operation feature set;

[0154] Calculating a Doppler shift correlation function between the angular velocity time series data and the carrier frequency of the 5G base station;

[0155] deriving a phase compensation gradient of the antenna array according to a phase change rate of a Doppler frequency shift correlation value in the Doppler frequency shift correlation function;

[0156] Mapping the phase compensation gradient into a phase shifter control codeword of an antenna element in the antenna array;

[0157] According to the time interval of the coverage blind area prediction result, the phase shifter control codeword is injected into the phase compensation parameter to obtain the phase compensation parameter set of the 5G base station.

[0158] The calculating a Doppler shift correlation function between the angular velocity time series data and the carrier frequency of the 5G base station includes:

[0159] Normalizing the angular velocity time series data to obtain a standard angular velocity sequence;

[0160] Determining a maximum theoretical Doppler shift of blade tip motion based on carrier frequency parameters of the 5G base station;

[0161] Establishing a time alignment data set of the standard angular velocity sequence and the real-time received signal carrier offset;

[0162] Fitting the angular velocity change in the angular velocity time series data with the carrier offset of the real-time received signal in the 5G base station to obtain a linear transfer coefficient of the 5G base station;

[0163] Based on the linear transfer coefficient and the maximum theoretical Doppler shift, constructing an angular velocity-frequency shift ratio function of the 5G base station;

[0164] Performing Fourier transform on the angular velocity-frequency shift ratio function to obtain a phase response characteristic of the 5G base station;

[0165] The linear transfer coefficient and the phase response characteristic are integrated to obtain a Doppler shift correlation function of the 5G base station, wherein the Doppler shift correlation function is as follows:

[0166]

[0167] Where F(*) is the Doppler frequency shift correlation value, K d is the linear transfer coefficient, ω(t) is the real-time angular velocity of the wind turbine blade, θ(t) is the real-time angle between the blade and the base station-terminal connection, φ0 is the initial phase offset, N is the maximum harmonic order in the phase response characteristic, A n is the amplitude of the nth harmonic, n is the harmonic number, f b is the blade passing frequency, t is the time factor, is the initial phase of the harmonic in the phase response characteristic, and R is the blade characteristic radius.

[0168] Specifically, data related to blade rotation are extracted from the wind turbine operation feature set. These data include the rotation angle of the blade at each time point. The angle difference between two adjacent time points is calculated and then divided by the time interval between these two time points to obtain the average angular velocity in each time interval. The average angular velocities of all time intervals are arranged in chronological order to form the angular velocity time series data of blade rotation. Each angular velocity value corresponds to a precise timestamp.

[0169] Furthermore, the carrier frequency of the 5G base station is fixed to a known reference value, and each angular velocity value in the angular velocity time series data of the blade rotation is substituted into the Doppler shift calculation formula. This formula describes the frequency change relationship caused by the relative motion between the wave source and the observer, and the Doppler shift value corresponding to each time point is calculated. The Doppler shift value at each time point is correlated with the angular velocity value at that time point to form a series of data pairs. The functional relationship between these data pairs is analyzed, and a function that can accurately describe the relationship between angular velocity and Doppler shift is determined by fitting method. This function is the Doppler shift correlation function.

[0170] Furthermore, the Doppler shift correlation value in the Doppler shift correlation function is analyzed, and the phase difference of the Doppler shift correlation value between two adjacent time points is calculated. The phase difference is then divided by the time interval between the two time points to obtain the phase change rate in each time interval. The phase change rates of all time intervals are arranged in chronological order to form a phase change rate sequence. According to the geometric structure of the antenna array and the signal propagation principle, the correspondence between the phase change rate and the phase compensation gradient of the antenna array is determined. Through this correspondence, the phase change rate sequence is converted into a phase compensation gradient sequence of the antenna array, and each phase compensation gradient value corresponds to a specific time point and antenna array position.

[0171] Furthermore, the antenna array is divided into several independently controlled antenna array element groups, each antenna array element group contains multiple antenna array elements, a unique identifier is assigned to each antenna array element group, and a mapping table between phase compensation gradient values ​​and phase shifter control codewords is established. The mapping table corresponds phase compensation gradient values ​​in different ranges to different phase shifter control codewords. These codewords are binary-coded and are used to control the working state of the phase shifter. According to each gradient value in the phase compensation gradient sequence, the corresponding phase shifter control codeword is searched in the mapping table, and the found control codewords are arranged in chronological order to form a phase shifter control codeword sequence. Each control codeword corresponds to a specific antenna array element group and time point.

[0172] Furthermore, all time interval information is extracted from the coverage blind spot prediction results. This information includes the start time and end time of each coverage blind spot. The parameter segment corresponding to each time interval is found in the phase compensation parameter sequence, and the control codeword of the corresponding time point in the phase shifter control codeword sequence is inserted into the corresponding parameter segment of the phase compensation parameter sequence to ensure that the phase compensation parameters contain the corresponding phase shifter control codeword in the time period when each coverage blind spot appears. The phase compensation parameter sequence with the inserted phase shifter control codeword is sorted and verified to ensure the continuity and correctness of the parameters. The final sequence obtained is the phase compensation parameter set of the 5G base station.

[0173] Specifically, the maximum and minimum values ​​of all angular velocity values ​​are found from the angular velocity time series data, the difference between the maximum and minimum values ​​is calculated, the minimum value is subtracted from each angular velocity value, and the result is divided by the difference between the maximum and minimum values. Through such calculation, all angular velocity values ​​are converted to the range of 0 to 1. These converted values ​​are arranged in the original chronological order to obtain a standard angular velocity sequence.

[0174] Furthermore, the carrier frequency parameter of the 5G base station is obtained. This parameter is the fixed frequency value of the base station's transmitted signal. The length of the wind turbine blade is known. The blade length is multiplied by the maximum angular velocity value in the angular velocity time series data to obtain the maximum linear velocity of the blade tip movement. According to the carrier frequency, the speed of light and the maximum linear velocity of the blade tip, when the direction of movement of the blade tip is consistent with the propagation direction of the electromagnetic wave, the Doppler frequency shift value generated at this time is calculated. This value is the maximum theoretical Doppler frequency shift of the blade tip movement.

[0175] Furthermore, the timestamp corresponding to each angular velocity value in the standard angular velocity sequence is extracted, and the carrier offset of the real-time received signal of the 5G base station and its corresponding timestamp are also extracted. The timestamps of the two sequences are compared, and the angular velocity values ​​and carrier offsets with completely consistent timestamps are retained. For cases where the timestamps are close but not completely consistent, value pairs with a time difference within 0.001 second are selected. The corresponding carrier offset is retained based on the timestamp of the angular velocity value. These matched angular velocity values ​​and carrier offsets are combined into data pairs in chronological order to form a time-aligned dataset.

[0176] Furthermore, the angular velocity difference between two adjacent time points is calculated from the angular velocity time series data to obtain the angular velocity change. At the same time, the carrier offset difference corresponding to the adjacent time points is calculated from the real-time received signal of the 5G base station to obtain the carrier offset change. All angular velocity changes are used as the horizontal axis data, and the corresponding carrier offset changes are used as the vertical axis data. All data points are marked in the coordinate system, and a straight line that is closest to all data points is drawn. The inclination of the straight line is the linear transfer coefficient, which reflects the proportional relationship between the angular velocity change and the carrier offset change.

[0177] Furthermore, based on the linear transfer coefficient, the proportional relationship between the angular velocity change and the Doppler frequency shift is determined, and the maximum theoretical Doppler frequency shift is used as the upper limit of the function. That is, when the angular velocity reaches the maximum value, the frequency shift value output by the function is equal to the maximum theoretical Doppler frequency shift. When the angular velocity is 0, the frequency shift value output by the function is 0. Within the range of 0 to the maximum angular velocity, according to the proportional relationship corresponding to the linear transfer coefficient, the corresponding relationship between the angular velocity value and the Doppler frequency shift value is established. This corresponding relationship is the angular velocity-frequency shift proportional function.

[0178] Furthermore, the values ​​of the angular velocity-frequency shift ratio function in the time domain are sampled at fixed time intervals to obtain a series of discrete function values. These function values ​​correspond to the frequency shift prediction results at different time points. These time domain function values ​​are converted to the frequency domain through Fourier transform to obtain the amplitude and phase corresponding to different frequency components in the frequency domain. The law of phase change with frequency is extracted, that is, the phase values ​​and change trends at different frequencies. These laws are the phase response characteristics.

[0179] Furthermore, the linear transfer coefficient is used as the amplitude parameter, which determines the strength of the influence of angular velocity changes on Doppler frequency shift. The phase response characteristic is used as the phase parameter, which reflects the phase change characteristics of the frequency shift signal at different frequencies. The amplitude parameter and the phase parameter are combined to form a function that can reflect both the amplitude correlation and the phase correlation between angular velocity and frequency shift. This function is the Doppler frequency shift correlation function of the 5G base station.

[0180] Specifically, K d The linear transfer coefficient is derived from the angular velocity change in the fitted angular velocity time series data and the carrier offset of the real-time received signal of the 5G base station. It is determined by taking the angular velocity change and the carrier offset change as the inclination of the fitting straight line of the data points; ω(t) is the real-time angular velocity value in the blade rotation angular velocity time series data extracted from the wind turbine operation feature set, which changes dynamically with time; θ(t) determines the real-time position of the blade through the blade rotation angle data, and is calculated by combining the position connection line between the base station and the terminal, reflecting the real-time angle between the blade and the line-of-sight path; φ0 is the phase offset value at the initial moment in the phase response feature, which is determined by the starting phase of the phase response feature after Fourier transform; N is the maximum harmonic order determined when performing harmonic analysis on the phase response feature, which is determined by the highest order of effective harmonics in the phase response feature; A n is the amplitude value of the nth harmonic in the phase response characteristic, which is determined by the amplitude of each harmonic obtained by Fourier transform decomposition; f b The frequency is the number of times a propeller rotates through the base station-terminal line-of-sight path divided by the rotation period. It is calculated based on the propeller rotation period and the number of propellers. t is the time factor used in time-aligned data and corresponds to the timestamp of the real-time data. It is the initial phase of the nth harmonic in the phase response characteristic, which is determined by the initial phase of each harmonic obtained by Fourier transform decomposition; R is the characteristic radius of the wind turbine blade, which comes from the inherent parameters of the wind turbine and is half of the blade length.

[0181] Furthermore, F(*) represents the Doppler frequency shift correlation value. The formula describes the effect of blade rotation on Doppler frequency shift through two parts, where K dThe part composed of , ω(t), R, and cos(θ(t)+φ0) reflects the linear correlation between the real-time angular velocity, blade position, and Doppler frequency shift, and embodies the immediate influence of the blade motion state on the frequency shift; the sum of each harmonic term reflects the periodic fluctuation in the phase response characteristics, and embodies the periodic change of frequency shift brought about by the periodicity of blade rotation. The overall function combines the linear real-time change with the periodic fluctuation, and fully associates the blade motion with the Doppler frequency shift.

[0182] Furthermore, when ω(t) increases, if θ(t) remains unchanged and cos(θ(t)+φ0) is positive, K d The ω(t)·R·cos(θ(t)+φ0) part will increase with the increase of angular velocity, and if the cos value is negative, it will decrease with the increase of angular velocity. When θ(t) changes, cos(θ(t)+φ0) will fluctuate between -1 and 1 as the angle changes, causing the linear part to fluctuate accordingly. At the same time, Some of them fluctuate periodically as integer multiples of the blade passing frequency over time, and the superposition of each harmonic forms a periodic change. Overall, the Doppler frequency shift correlation value is the superposition of a linear change trend and a periodic fluctuation, which changes dynamically with the angular velocity and angle and presents a periodic fluctuation characteristic.

[0183] In general, injecting the periodic characteristics of the wind turbine's motion trajectory into the phase compensation parameters to obtain anti-blocking beam configuration instructions can enable the beam adjustment of the 5G base station to dynamically adapt to the wind turbine movement.

[0184] In general, the rotation of wind turbine blades has a clear periodicity, and the impact of their motion trajectory on signal obstruction also shows a periodic pattern. After injecting this feature into the phase compensation parameters, the beam configuration can predict the time node and spatial range of blade obstruction in advance, so that the beam can complete the adaptive adjustment of direction and width before obstruction occurs, thereby reducing the interference of obstruction on signal coverage from the root.

[0185] In summary, this process extracts blade angular velocity time series data, derives the Doppler shift correlation function and phase compensation gradient, and incorporates dynamic compensation logic into beam configuration instructions. This allows the instructions to not only meet current signal coverage requirements but also continuously adapt to real-time changes caused by the wind turbine's periodic motion. This predictive beam configuration significantly improves the 5G base station's ability to combat dynamic obstruction and ensures the stability and continuity of 5G signal coverage within the wind farm.

[0186] S6. Send the anti-blocking beam configuration instruction to the 5G base station.

[0187] Specifically, the generated anti-obstruction beam configuration instructions are extracted from the system. The instructions contain specific parameters such as beam pointing angle, beam width, transmit power, and effective time window. These parameters are determined based on the coverage blind spot prediction results and the wind turbine operation feature set, and are used to guide the 5G base station to adjust the beam to resist the influence of obstruction.

[0188] Furthermore, the anti-obstruction beam configuration instruction is converted into an instruction format supported by the 5G base station. This format is a binary instruction code preset inside the base station, which includes an instruction header, a parameter segment, and a check segment. The instruction header is used to identify the instruction type as anti-obstruction beam configuration, the parameter segment is filled with the encoded value of the beam-related parameters, and the check segment is generated by calculating the checksum of the parameter segment to ensure that no errors occur during the instruction transmission process.

[0189] Furthermore, the converted anti-obstruction beam configuration instructions are transmitted through the wired Ethernet link between the 5G base station and the control center. The link uses the TCP / IP protocol for data transmission. The control center, as the sending end, first establishes a network connection with the 5G base station. After the connection is successful, the instruction data is encapsulated into a data packet according to the protocol format and sent to the instruction receiving port of the 5G base station.

[0190] Furthermore, the command receiving module of the 5G base station listens to the designated port. When a data packet is received, it first decapsulates the data packet, extracts the command header, parameter segment and check segment, calculates the checksum of the parameter segment and compares it with the value of the check segment. If they are consistent, the command is determined to be complete and valid. If they are inconsistent, a retransmission request is sent to the control center until a complete and valid command is received.

[0191] Furthermore, after confirming that the anti-obstruction beam configuration instruction is valid, the 5G base station parses the parameters in the instruction into a control signal that can be recognized by the base station beam control module, and records the reception time and effective time window of the instruction. When the effective time window arrives, the beam control module adjusts the beam parameters of the antenna array according to the control signal, completes the beam configuration, and feeds back a response message of successful configuration to the control center.

[0192] In summary, issuing anti-obstruction beam configuration instructions to 5G base stations transforms optimization strategies generated based on actual wind farm scenarios into actual base station operations, achieving a closed loop from theoretical optimization to actual signal coverage improvement. This instruction incorporates coverage blind spot prediction results, beam adjustment parameters, and the periodic characteristics of wind turbine motion, providing a precise configuration solution tailored to the dynamic electromagnetic environment of wind farms. After receiving and executing this instruction, the base station can adjust beam pointing, width, and phase compensation in real time.

[0193] In general, this process ensures that the 5G base station can dynamically adapt the signal transmission strategy according to the obstruction changes and multipath interference caused by the movement of wind turbines in the wind farm, effectively offset the periodic obstruction caused by blade rotation, reduce signal attenuation and coverage blind spots caused by obstruction and interference, and ultimately achieve a steady improvement in the 5G signal coverage quality of the wind farm, ensuring the continuity and reliability of communications within the wind farm.

[0194] In the several embodiments provided by the present invention, it should be understood that the disclosed methods can be implemented in other ways.

[0195] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0196] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method and technology of using digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to achieve optimal results.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing 5G signal coverage in a wind farm, characterized in that: The method comprises: S1. Acquire real-time data of blade rotation angle and speed in a wind turbine, and generate a wind turbine operation feature set of the wind turbine based on a mapping relationship between wind turbine motion trajectory and signal attenuation in the real-time data after time alignment; S2. Fusing the network status feature set of the wind farm and the wind turbine operation feature set to obtain a digital twin model of the wind farm under electromagnetic propagation; S3. Analyze the spatiotemporal distribution characteristics of the wind farm under multipath interference and signal shielding based on the digital twin model to obtain a coverage blind spot prediction result of the wind farm; S4. Adjust the beam pointing angle and beam width of the 5G base station in the wind farm according to the coverage blind area prediction result to obtain a phase compensation parameter of the 5G base station; S5. Injecting the periodic characteristics of the wind turbine motion trajectory into a phase compensation parameter to obtain an anti-blocking beam configuration instruction of the 5G base station; S6. Send the anti-blocking beam configuration instruction to the 5G base station.

2. The 5G signal coverage optimization method for a wind farm according to claim 1, characterized in that: Generating a wind turbine operation feature set of the wind turbine generator set according to a mapping relationship between wind turbine motion trajectory and signal attenuation in the real-time data after time alignment includes: Extract signal strength time series data from real-time data after time alignment; Performing sliding window segmentation on the blade rotation angle and speed data to obtain motion slices of the wind turbine; extracting an occlusion correlation factor between the paddle's spatial position change and the signal intensity fluctuation within the motion slice; A speed-attenuation transfer function model is established according to the speed periodicity characteristics and the shading correlation factor; The parameter set of the speed-attenuation transfer function model is fused with the blade spatial position change rate of the wind turbine generator set to obtain a wind turbine operation feature set of the wind turbine generator set.

3. The 5G signal coverage optimization method for a wind farm according to claim 2, wherein: The speed-attenuation transfer function model includes: Where H(*) is the wind turbine speed attenuation value, α is the shielding correlation factor, e is a natural constant, β is the dynamic attenuation factor of the rotating blade shielding in the wind turbine, and f r is the real-time speed of the wind turbine in the wind turbine generator set, γ is the multipath interference amplitude of the metal surface in the wind turbine generator set, π is, φ is the offset of the yaw angle phase of the wind turbine in the wind turbine generator set, and t is the time factor.

4. The 5G signal coverage optimization method for a wind farm according to claim 1, wherein: The network status feature set and the wind turbine operation feature set of the wind farm are integrated to obtain a digital twin model of the wind farm under electromagnetic propagation, including: Performing time stamp alignment on base station signal strength data in the network status feature set to obtain a synchronization signal strength sequence of the wind farm; Extracting blade rotation angle data from the wind turbine operation feature set, and determining the real-time spatial position of the blade edge in the blade rotation angle data through three-dimensional spatial coordinate transformation to obtain a dynamic shielding contour of the wind farm; Taking the position of the terminal in the 5G base station as the end point, performing electromagnetic wave diffraction simulation on the wind farm based on the synchronization signal strength sequence and the dynamic shielding profile to obtain a path loss distribution map of the wind farm; A digital twin model of the wind farm is constructed based on the path loss distribution map and the channel quality index in the network status feature set.

5. The 5G signal coverage optimization method for a wind farm according to claim 4, characterized in that: The method of performing electromagnetic wave diffraction simulation on the wind farm based on the synchronization signal strength sequence and the dynamic shielding profile with the terminal position in the 5G base station as the end point to obtain a path loss distribution diagram of the wind farm includes: identifying the spatial geometric relationship between the blade edge and the base station-terminal line-of-sight path based on the spatial coordinate data of the dynamic obstruction profile, and obtaining an obstacle obstruction determination result of the wind farm; When the obstacle occlusion determination result is partial occlusion, extracting the direct signal component in the synchronization signal strength sequence; Determining a propagation distance difference between a ground reflection path and a diffuse reflection path based on terrain elevation data of the terminal; Performing weighted fusion on the direct signal component and the propagation distance difference to obtain a synthetic field intensity attenuation value of the wind farm; The synthetic field intensity attenuation value is mapped to the base station-terminal line-of-sight path to obtain a path loss distribution map of the wind farm.

6. The 5G signal coverage optimization method for a wind farm according to claim 1, wherein: The analyzing, based on the digital twin model, the spatiotemporal distribution characteristics of the wind farm under multipath interference and signal shielding to obtain the coverage blind area prediction result of the wind farm includes: Constructing an attenuation distribution heat map of the wind farm according to the intensity value of the signal attenuation factor in the digital twin model; Marking the continuous area in the attenuation distribution heat map where the signal attenuation exceeds a predetermined threshold as a spatial occlusion area; The spatial blocking area is integrated with the periodic blocking time window of the wind turbine operation feature set to obtain a spatiotemporal joint blind area prediction result of the wind farm.

7. The 5G signal coverage optimization method for a wind farm according to claim 1, wherein: The step of adjusting the beam pointing angle and beam width of the 5G base station in the wind farm according to the coverage blind area prediction result to obtain the phase compensation parameter of the 5G base station includes: Parsing the blind spot position coordinate set in the coverage blind spot prediction result, and determining the horizontal azimuth and elevation angle of the blind spot center point relative to the 5G base station; Determining a beam scanning range of an antenna array in the 5G base station based on the horizontal azimuth angle and the elevation angle; Dividing the beam dwell time window of the 5G base station according to the time interval characteristics in the coverage blind area prediction result; Combining the beam scanning range and the beam dwell time window to obtain a dynamic beam pointing sequence of the 5G base station; Matching a preset beam width adjustment strategy according to the interference type in the coverage blind area prediction result; The phase compensation parameters of the 5G base station are output according to the adjusted strategy.

8. The 5G signal coverage optimization method for a wind farm according to claim 7, characterized in that: The matching of a preset beam width adjustment strategy according to the interference type in the coverage blind area prediction result includes: When the interference type is multipath-dominated, a narrow beam high gain strategy is adopted; When the interference type is dominated by obstruction, a wide beam diversity strategy is adopted.

9. The method for optimizing 5G signal coverage of a wind farm according to claim 8, wherein: The step of injecting the periodic characteristics of the wind turbine motion trajectory into a phase compensation parameter to obtain an anti-blocking beam configuration instruction of the 5G base station includes: Extracting blade rotation angular velocity time series data from the wind turbine operation feature set; Calculating a Doppler shift correlation function between the angular velocity time series data and the carrier frequency of the 5G base station; deriving a phase compensation gradient of the antenna array according to a phase change rate of a Doppler frequency shift correlation value in the Doppler frequency shift correlation function; Mapping the phase compensation gradient into a phase shifter control codeword of an antenna element in the antenna array; According to the time interval of the coverage blind area prediction result, the phase shifter control codeword is injected into the phase compensation parameter to obtain the phase compensation parameter set of the 5G base station.

10. The 5G signal coverage optimization method for a wind farm according to claim 9, characterized in that: The calculating a Doppler shift correlation function between the angular velocity time series data and the carrier frequency of the 5G base station includes: Normalizing the angular velocity time series data to obtain a standard angular velocity sequence; Determining a maximum theoretical Doppler shift of blade tip motion based on carrier frequency parameters of the 5G base station; Establishing a time alignment data set of the standard angular velocity sequence and the real-time received signal carrier offset; Fitting the angular velocity change in the angular velocity time series data with the carrier offset of the real-time received signal in the 5G base station to obtain a linear transfer coefficient of the 5G base station; Based on the linear transfer coefficient and the maximum theoretical Doppler shift, constructing an angular velocity-frequency shift ratio function of the 5G base station; Performing Fourier transform on the angular velocity-frequency shift ratio function to obtain a phase response characteristic of the 5G base station; The linear transfer coefficient and the phase response characteristic are integrated to obtain a Doppler shift correlation function of the 5G base station, wherein the Doppler shift correlation function is as follows: Where F(*) is the Doppler frequency shift correlation value, K d is the linear transfer coefficient, ω(t) is the real-time angular velocity of the wind turbine blade, θ(t) is the real-time angle between the blade and the base station-terminal connection, φ0 is the initial phase offset, N is the maximum harmonic order in the phase response characteristic, A n is the amplitude of the nth harmonic, n is the harmonic number, f b is the blade passing frequency, t is the time factor, is the initial phase of the harmonic in the phase response characteristic, and R is the blade characteristic radius.

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