Communication method and system for inspection of offshore wind power unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle communication management, in particular to a communication method and system for inspection of an offshore wind power unmanned aerial vehicle, and the method comprises the following steps: collecting the signal receiving frequency and power information of the unmanned aerial vehicle, extracting the signal intensity and fluctuation amplitude, analyzing the signal attenuation condition during the inspection of the unmanned aerial vehicle, and evaluating the signal interference degree. And generating a signal quality evaluation result. According to the invention, the communication data of the unmanned aerial vehicle, including signal receiving frequency and power information, are collected in detail, the signal attenuation condition of the signal source is accurately analyzed, the transmission error rate and the signal-to-noise ratio are further calculated, the interference degree of each signal source is evaluated, and the signal occupancy rate and the response speed of the channel are optimized according to the signal quality. The signal transmission path is optimized, the signal packet loss rate is reduced, the time delay change is improved, the communication continuity and stability are effectively improved, and the method is suitable for special environments such as offshore wind power and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV communication management, and particularly to a communication method and system for UAV inspection of offshore wind farms. Background Art

[0002] The technical field of UAV communication management involves the management and optimization of communication systems used by UAVs when performing various tasks such as monitoring, reconnaissance, and material transportation. This field mainly focuses on how to improve the performance of UAVs by improving communication protocols, enhancing signal stability, and expanding the operating range. The core content includes data transmission between the UAV and the control platform, real-time monitoring, and collaborative work among multiple UAVs. The systematic introduction covers everything from the selection of basic communication hardware to the development and implementation of advanced network management strategies and communication protocols.

[0003] Among them, the communication method for UAV inspection of offshore wind farms refers to specific communication technologies used to optimize the inspection activities of UAVs in offshore wind farms. The technical matters addressed cover how to maintain a stable communication link in a marine environment far from land, mainly achieved by using communication protocols designed specifically for the marine environment and enhanced signal transmission technologies, ensuring that the UAV can maintain effective data exchange with the control platform under complex marine conditions, thus supporting continuous inspection operations.

[0004] Although the prior art provides basic communication hardware selection and communication protocol development, in a marine environment far from land, the existing systems still have obvious deficiencies in terms of signal stability and data transmission reliability. In particular, signal instability leads to delays or losses in data transmission, which is particularly prominent when performing critical tasks such as offshore wind farm inspections. Signal instability can cause communication interruptions with the control platform, affecting inspection efficiency and potentially threatening the safety of the UAV and its payload. For example, in complex marine climate conditions, traditional communication protocols and signal transmission technologies are insufficient to cope with the highly dynamic environment, thus limiting the application effectiveness and operating range of UAVs. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art that in a marine environment far from land, the existing systems still have obvious deficiencies in terms of signal stability and data transmission reliability. In particular, signal instability leads to delays or losses in data transmission, which is particularly prominent when performing critical tasks such as offshore wind farm inspections. Signal instability can cause communication interruptions with the control platform, affecting inspection efficiency and potentially threatening the safety of the UAV and its payload. For example, in complex marine climate conditions, traditional communication protocols and signal transmission technologies are insufficient to cope with the highly dynamic environment, thus limiting the application effectiveness and operating range of UAVs, the embodiments of the present invention provide a communication method and system for UAV inspection of offshore wind farms. The technical solution is as follows: On the one hand, a communication method for unmanned aerial vehicle (UAV) inspection of offshore wind farms is provided, including the following steps: S1: Collect the signal reception frequency and power information of the UAV, extract the signal strength and fluctuation amplitude, analyze the signal attenuation situation during UAV inspection, evaluate the signal interference degree, and generate a signal quality evaluation result; S2: Based on the signal quality evaluation result, analyze the signal source distribution in the offshore wind farm, count the transmission data volume, calculate the signal occupancy rate, determine the channel response speed and data packet transmission time, and obtain a channel load index; S3: Based on the channel load index, analyze the congestion situation of the channel, determine the signal fluctuation range, and according to the signal stability change trend, screen the communication channels that meet the bandwidth requirements of the UAV inspection task to obtain channel preference indicators; S4: According to the channel preference indicators, adjust the signal reception frequency and power of the UAV, set the power output range, match the waveform characteristics and frequency bands of the target channel, optimize the signal transmission path, and generate signal parameter configuration; S5: Using the signal parameter configuration, monitor the coherence degree of signal transmission, calculate the signal packet loss rate and analyze the delay change, identify and extract abnormal communication data during the inspection of wind turbine units, verify and calibrate the error signal, and obtain a communication efficiency correction result.

[0006] On the other hand, the signal quality evaluation result includes the measured signal strength, fluctuation amplitude and signal attenuation index, the channel load index includes the counted number of signal sources, channel signal occupancy rate and data packet transmission delay, the channel preference indicator includes the congestion level of the channel, signal fluctuation range and bandwidth demand matching degree, the signal parameter configuration includes the set reception frequency, power output range and signal transmission optimization measures, and the communication efficiency correction result includes the monitored signal packet loss rate, delay change rate and calibration data of abnormal signals.

[0007] On the other hand, the steps for obtaining the signal quality evaluation result are specifically as follows: S101: Collect the signal reception frequency and power information of the UAV, extract the signal strength and fluctuation amplitude of the signal source, detect the relationship between frequency change and power fluctuation, identify abnormal power points, calculate the frequency offset value, and generate a signal fluctuation degree; S102: Based on the signal strength fluctuation characteristics, analyze the signal attenuation situation of the signal source, measure the attenuation trend of the signal strength with the distance during UAV inspection, calculate the transmission error rate and signal-to-noise ratio of the signal, and generate a signal attenuation analysis result; S103: Based on the signal attenuation analysis result, evaluate the signal interference degree of the signal source, analyze the stability and continuity of the signal, determine the signal coverage range of the offshore base station of the wind farm, calculate the utilization rate of the channel, and generate a signal quality evaluation result.

[0008] On the other hand, the steps for obtaining the channel load index are specifically as follows: S201: Based on the signal quality evaluation result, extract the distribution of the signal sources of the offshore wind farm in the communication channel, identify the positions and distribution densities of each signal source in the channel, analyze the interaction effects between the signal sources, and generate a signal source distribution map; S202: Based on the signal source distribution map, extract the transmission data volume of the signal sources of the offshore wind farm, analyze the data mobility and data transmission efficiency in the channel, calculate the contribution rate of each signal source to the total data volume, and generate a data transmission efficiency value; S203: Based on the data transmission efficiency value, calculate the signal occupancy rate of the channel, analyze the usage density of the channel in each time period, determine the channel response speed and packet transmission time, and generate a channel load index.

[0009] On the other hand, the steps for obtaining the channel preference index are specifically as follows: S301: Based on the channel load index, analyze the congestion situation of the channel, measure the occupancy of the signal in different time periods, calculate the fluctuation degree of the data traffic, identify the traffic peak interval and evaluate the load stability, determine the saturation degree of the channel, and generate a channel congestion level; S302: Based on the channel congestion level, analyze the continuity of the channel signal, detect the stability of the signal in different time periods, measure the time interval of signal interruption and recovery, calculate the signal fluctuation range, and identify the influence degree of signal interference, and generate a signal stability degree; S303: Based on the signal stability degree, analyze the change trend of signal stability, evaluate the performance ability of the channel under different load levels, screen the communication channels that meet the bandwidth requirements of the UAV inspection task, calculate the fitness of different channels, and determine the optimal channel to obtain the channel preference index.

[0010] On the other hand, when calculating the fluctuation index of the data traffic, the formula is used: ; Identify the traffic peak interval and evaluate the load stability, determine the saturation degree of the channel, and generate a channel congestion level; Wherein, represents the data traffic fluctuation index, represents the data traffic at time point , represents the data traffic at time point ; The time point of the timestamp, The time point of the timestamp, represents the number of time points, represents the average value of data traffic.

[0011] On the other hand, the steps for obtaining the signal parameter configuration are specifically as follows: S401: Based on the channel preference index, adjust the signal reception frequency of the UAV communication device, detect the signal strength within the real-time reception range, determine the communication frequency band during UAV inspection, correct the offset of the reception frequency, and generate a frequency adjustment parameter; S402: Based on the frequency adjustment parameter, set the power output range of the UAV communication device, analyze the transmission effect of the signal at each power level, calculate the optimal power interval, adjust the UAV signal transmission power, and generate a power distribution parameter; S403: Based on the power distribution parameter, match the waveform characteristics and frequency band of the target channel, measure and correct the channel waveform error, adjust the propagation direction and amplitude of the signal during UAV inspection, optimize the signal transmission path, and generate a signal parameter configuration.

[0012] On the other hand, the calculation of the signal strength equalization deviation adopts the formula: ; Adjust the signal reception frequency of the UAV communication device, detect the signal strength within the real-time reception range, determine the communication frequency band during UAV inspection, correct the offset of the reception frequency, and generate a frequency adjustment parameter; Among them, represents the signal strength equalization deviation, represents the total number of detection points, represents the th signal power value of the detection point, represents the total number of detection points, represents the signal power value trough, represents the number of sampling points for variance calculation.

[0013] On the other hand, the steps for obtaining the communication efficiency correction result are specifically as follows: S501: Based on the signal parameter configuration, monitor the coherence of signal transmission, detect the persistence of the signal at different time points, measure the frequency and duration of signal interruption, analyze the signal fluctuation characteristics, calculate the signal attenuation rate during UAV inspection, and generate a signal coherence index; S502: Based on the signal coherence index, calculate the signal packet loss rate, analyze the delay variation, measure the number of data packets lost at the receiving end, count the time interval during which packet loss occurs, extract the signal propagation delay data, identify the delay fluctuation range, and generate the signal transmission characteristics. S503: Based on the signal transmission characteristics, analyze the signal change characteristics, identify abnormal signal fluctuations, judge the influence range of the error signal, calibrate the error signal deviation, extract the abnormal communication data in the inspection data of the wind turbine generator set, adjust the data compensation value, and obtain the communication efficiency correction result.

[0014] On the other hand, a communication system for offshore wind power drone inspection is provided. This system is applied to the communication method for offshore wind power drone inspection and includes: The signal data analysis module collects the signal reception frequency and power information of the drone, extracts the signal strength and fluctuation amplitude, analyzes the signal attenuation situation during drone inspection, evaluates the signal interference degree, and generates the signal quality evaluation result. The channel load calculation module analyzes the signal source distribution in the offshore wind farm based on the signal quality evaluation result, counts the transmission data volume, calculates the signal occupancy rate, determines the channel response speed and data packet transmission time, and generates the channel load index. The channel optimization module analyzes the channel congestion situation according to the channel load index, determines the signal fluctuation range, and filters out the communication channels that meet the bandwidth requirements of the drone inspection task according to the signal stability change trend, and obtains the channel preference index. The signal configuration module adjusts the signal reception frequency and power of the drone according to the channel preference index, sets the power output range, matches the waveform characteristics and frequency band of the target channel, optimizes the signal transmission path, and generates the signal parameter configuration. The communication efficiency adjustment module uses the signal parameter configuration to monitor the signal transmission coherence, calculate the signal packet loss rate and analyze the delay variation, identify and extract the abnormal communication data during the inspection of the wind turbine generator set, verify and calibrate the error signal, and obtain the communication efficiency correction result.

[0015] The beneficial effects brought by the technical solution provided in the embodiment of the present invention at least include: The innovative solution accurately analyzes the signal attenuation of the signal source by collecting detailed communication data of the drone, including signal reception frequency and power information, and then calculates the transmission error rate and signal-to-noise ratio, evaluates the interference degree of each signal source, optimizes the signal occupancy rate and response speed of the channel according to the signal quality, optimizes the signal transmission path, reduces the signal packet loss rate and improves the delay variation, effectively improves the coherence and stability of communication, is applicable to special environments such as offshore wind power, can ensure more stable data exchange between the drone and the control platform, supports uninterrupted inspection operations, and significantly improves the performance of the drone and the accuracy of problem diagnosis. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 is the main step flow chart of the present invention; Figure 2 is the step flow chart of S1 of the present invention; Figure 3 is the step flow chart of S2 of the present invention; Figure 4 is the step flow chart of S3 of the present invention; Figure 5 is the step flow chart of S4 of the present invention; Figure 6 is the step flow chart of S5 of the present invention; Figure 7 is the system block diagram of the present invention. Detailed Embodiments

[0018] The following will describe the technical solutions in the present invention with reference to the drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either of the two can be selected.

[0020] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when their differences are not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when their differences are not emphasized, the meanings they express are the same.

[0021] In the embodiments of the present invention, sometimes subscripts such as W 1 will be written in non-subscript form such as W1. When their differences are not emphasized, the meanings they express are the same.

[0022] To make the technical problems, technical solutions, and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0023] An embodiment of the present invention provides a communication method for unmanned aerial vehicle (UAV) inspection of offshore wind farms, as Figure 1 shown, including the following steps: S1: Collect the signal reception frequency and power information of the UAV, extract the signal strength and fluctuation amplitude, analyze the signal attenuation of the signal source, calculate the transmission error rate and signal-to-noise ratio of the signal, evaluate the signal interference degree of each signal source, and generate a signal quality evaluation result; S2: Based on the signal quality evaluation result, analyze the signal source distribution in the offshore wind farm, count the transmission data volume of the signal source, calculate the signal occupancy rate of the channel, determine the channel response speed and data packet transmission time, and analyze the signal stability in the UAV inspection path to obtain a channel load index; S3: Based on the channel load index, analyze the congestion situation of the channel, monitor the continuity of the channel signal, determine the signal fluctuation range of the channel, and according to the signal stability change trend, screen the communication channels that meet the bandwidth requirements of the UAV inspection task to obtain channel preference indicators; S4: According to the channel preference indicators, adjust the signal reception frequency and power of the UAV communication device, set the power output range, match the waveform characteristics and frequency band of the target channel, optimize the signal transmission path, and generate signal parameter configuration; S5: Using the signal parameter configuration, monitor the coherence of signal transmission, calculate the signal packet loss rate and analyze the delay change, according to the signal change characteristics, identify abnormal signal fluctuations, extract abnormal communication data during the inspection of wind turbine units, and verify and calibrate the error signal to obtain a communication efficiency correction result.

[0024] The signal quality evaluation result includes the measured signal strength, fluctuation amplitude, and signal attenuation index. The channel load index includes the counted number of signal sources, channel signal occupancy rate, and data packet transmission delay. The channel preference indicators include the congestion level of the channel, signal fluctuation range, and bandwidth requirement matching degree. The signal parameter configuration includes the set reception frequency, power output range, and signal transmission optimization measures. The communication efficiency correction result includes the monitored signal packet loss rate, delay change rate, and calibration data of abnormal signals.

[0025] As Figure 2 shown, the steps for obtaining the signal quality evaluation result are specifically as follows: S101: Collect the signal reception frequency and power information of the UAV, extract the signal strength and fluctuation amplitude of the signal source, detect the relationship between frequency change and power fluctuation, identify abnormal power points, calculate the frequency offset value, and generate a signal fluctuation degree; Obtain real-time signal frequency data through the communication module on the drone, and combine with the power measurement device to record the instantaneous power value of the signal. Adopt high-speed sampling technology to record the signal power and received frequency value at each time point at a high frequency. Conduct statistical calculations on the received power at different time points, extract the signal strength change range, and use the maximum-minimum normalization method to calculate the normalized signal fluctuation amplitude. The calculation formula is as follows: , where is the signal power measured at a certain time point, and are the minimum and maximum power values within this time window respectively. After obtaining the normalized signal fluctuation data, analyze the signal spectrum through Fourier transform (FFT) to determine the main components of the frequency, and calculate the power spectral density (PSD) to observe the overall energy distribution of the signal. Further, adopt the sliding window method to compare the frequency change and power fluctuation within adjacent time windows, and calculate their correlation coefficient to measure the coupling relationship between the two. The formula is as follows: , where and are the received frequency and power values at the th time point respectively, and are the average frequency and average power within this time period respectively. If , it indicates that there is a strong correlation between the frequency and the power. By statistically analyzing the change trend of the power over time, calculate the power volatility using the first derivative: , where and represent the power values at two adjacent time points respectively. If the volatility exceeds the set threshold (such as ±2dBm / ms), then this point is considered an abnormal power point. Further calculate the frequency offset value within this time period. The frequency offset can be obtained through: . After calculating the offset, conduct statistical analysis on it to generate the signal fluctuation degree.

[0026] S102: Based on the signal strength fluctuation characteristics, analyze the signal attenuation situation of the signal source, determine the attenuation trend of the signal strength with the distance during the drone patrol process, calculate the transmission error rate and signal-to-noise ratio of the signal, and generate the signal attenuation analysis result; Calculate the signal mean and variance within a specific time window, and determine the fluctuation trend of the signal in different time periods through the sliding window technology. Further, use the free space path loss (FSPL) model to calculate the signal attenuation situation. The formula is as follows: ; where is the propagation distance between the drone and the signal source (unit: m), is the signal frequency (unit: Hz), is the speed of light ( m / s). By actually measuring the signal power received by the drone at different distances, calculate the deviation from the theoretical loss value. If the deviation is greater than the set threshold (such as ±3 dB), it means that the signal attenuation is greatly affected by environmental factors. Determine the attenuation trend of the signal intensity with distance, select the signal power values at multiple measurement points, and perform linear regression fitting: , where is the distance at which the received power is is the power at the reference point (usually the power measured at 1 meter), is the path loss exponent, which is affected by the environment. It is 2 in an open environment and reaches 3 - 4 in an urban environment. Subsequently, calculate the bit error rate (BER) of the signal using the formula: , where is the received energy per bit, is the noise power spectral density, and measure the signal-to-noise ratio (SNR): . If the SNR is below 10 dB, it is considered that the signal quality is poor, and generate signal attenuation trend data.

[0027] S103: Based on the signal attenuation analysis results, evaluate the signal interference degree of the signal source, analyze the stability and continuity of the signal, determine the signal coverage range of the offshore base station of the wind farm, and calculate the channel utilization rate to generate the signal quality evaluation result.

[0028] Evaluate the interference degree of the signal source. By measuring the signal intensity received by the drone at different azimuth angles, calculate the power distribution of different signal sources in the same channel. If there are multiple strong signal sources (power higher than -50 dBm) in the same frequency band, it indicates that the interference is large. Analyze the stability and continuity of the signal. By calculating the mean and standard deviation of the signal intensity, if the standard deviation exceeds 5 dB, it means that the signal fluctuates greatly. By measuring the signal loss rate within multiple consecutive time windows, calculate the channel utilization rate, and its calculation formula is: , where is the effective transmission time of the signal, is the total time. If the channel utilization rate is below 50%, it is considered that the communication efficiency of this channel is low. Further combine the signal power fluctuation range and packet loss rate to determine the stable range of the signal and generate the signal quality evaluation index.

[0029] As Figure 3 shown, the steps to obtain the channel load index are specifically as follows: S201: Based on the signal quality assessment results, extract the distribution of offshore wind farm signal sources in the communication channel, identify the positions and distribution densities of each signal source in the channel, analyze the interaction effects between signal sources, and generate a signal source distribution map; Screen the signal quality assessment data, extract the effective offshore wind farm signal source data, and mark the frequency, power, and geographical coordinates of each signal source. Use the spatial weighted interpolation method to perform fitting calculations on the signal source distribution. The formula is as follows: , where represents the signal strength at position , is the signal source at the received power at position represents the distance from the signal source to this position. Use the weighted average method to calculate the signal power distribution in each area. Use the K-Means clustering algorithm to classify the signal sources according to their geographical locations to form signal hot spots in different areas, and calculate the density of signal sources in each area: , where represents the signal density of area , is the number of signal sources in this area, is the total area of this area (unit: m²). If signal sources / m², it is considered that the signal sources in this area are dense. By calculating the distance matrix between signal sources, analyze the interference degree between each signal source, and generate a signal source distribution map.

[0030] S202: Based on the signal source distribution map, extract the transmission data volume of offshore wind farm signal sources, analyze the data mobility and data transmission efficiency in the channel, calculate the contribution rate of each signal source to the total data volume, and generate a data transmission efficiency value; Statistical the transmission data volume of each signal source within a specific time window, and use the packet counting method to obtain the total data volume of each signal source: , where represents the total data transmission volume of signal source within the time window , is the signal source at time the number of packets sent, is the average size of the packet (unit: KB). Further calculate the data mobility in the channel. By statistically analyzing the data exchange volume of each signal source, calculate the data flow rate: , where is the data flow rate of signal source in the channel, is the transmission data volume of the signal source per unit time, is the theoretical maximum bandwidth of the signal source. If , it indicates that the utilization rate of the signal source is low. Calculate the contribution rate of each signal source to the total data volume: , where represents the signal source 's contribution rate to the total data volume of the channel. is the actual data transmission volume of the signal source, is the total data volume of the channel during the same time period, and generate a data transmission efficiency value.

[0031] S203: Based on the data transmission efficiency value, calculate the signal occupancy rate of the channel, analyze the usage density of the channel in each time period, determine the channel response speed and the data packet transmission time, and generate a channel load index.

[0032] Statistically analyze the signal activity of each signal source in different time periods, and calculate the occupancy ratio of the channel: , where is the signal occupancy rate during the time period , is the effective data transmission time during this time period, is the total duration of this time period. If , it indicates that the channel is approaching saturation. Further calculate the usage density of the channel in each time period, analyze the distribution of data packets at different times, use the Poisson distribution model to estimate the signal congestion situation, and calculate the channel response speed: , where is the channel response time (ms), represents the time from the request of the signal source to data transmission, represents the total number of data packets. If ms, it indicates that the channel response is slow. Finally, calculate the data packet transmission time: , where is the data packet transmission time, is the size of a single data packet (KB), is the current channel bandwidth (Mbps), and obtain the channel load index.

[0033] As Figure 4 shown, the steps for obtaining the channel preference index are specifically as follows: S301: Based on the channel load index, analyze the congestion situation of the channel, measure the occupancy of the signal in different time periods, calculate the fluctuation index of the data traffic, identify the traffic peak interval and evaluate the load stability, and determine the saturation of the channel to generate the channel congestion level; Calculate the fluctuation index of the data traffic using the formula: ; Identify the peak traffic interval and evaluate the load stability, determine the saturation of the channel, and generate the channel congestion level; Among them, represents the data traffic fluctuation index, represents the time point of the data traffic, represents the time point of the data traffic, represents the time point timestamp, represents the time point timestamp, represents the number of time points, represents the average value of the data traffic; Collect data traffic values at different time points through the monitoring system. Assume that the data traffic values (unit: Mbps) at 10 time points are: 50, 55, 53, 60, 58, 62, 57, 59, 61, 56; Calculate the average value: ; Calculate the sum of the absolute values of the data traffic change rates between adjacent time points: Calculate the data traffic change rate between every two adjacent time points: |55 - 50| = 5; |53 - 55| = 2; |60 - 53| = 7; |58 - 60| = 2; |62 - 58| = 4; |57 - 62| = 5; |59 - 57| = 2; |61 - 59| = 2; |56 - 61| = 5; Add up the change rates: ; Calculate the standard deviation of the data traffic: Calculate the square of the difference between the data traffic at each time point and the average value: (50 - 57.1)² = 50.41; (55 - 57.1)² = 4.41; (53 - 57.1)² = 16.81; (60 - 57.1)² = 8.41; (58 - 57.1)² = 0.81; (62 - 57.1)² = 24.01; (57 - 57.1)² = 0.01; (59 - 57.1)² = 3.61; (61 - 57.1)² = 15.21; (56 - 57.1)² = 1.21; Add the squares of the differences: ; Calculate the standard deviation: ; Calculate the data traffic fluctuation index : Substitute the above results into the formula: ; Among them, , because the calculation of the change rate involves two adjacent time points, so the total number is 1 less than the original data points. This result shows that the data traffic fluctuation index is 13.4, indicating that there is a certain degree of fluctuation in the data traffic during the monitored time period. The higher this index, the greater the degree of data traffic fluctuation.

[0034] S302: Based on the channel congestion level, analyze the continuity of the channel signal, detect the stability of the signal in different time periods, measure the time interval of signal interruption and recovery, calculate the signal fluctuation range, and identify the degree of influence of signal interference to generate the signal stability; Extract the signal power data of different time periods and calculate the change rate of the signal mean value within adjacent time windows: , where is the change rate of the signal mean value within the time window , is the signal power at time , is the time stamp of the time point (unit: ms). If dB / ms, it is considered that the signal is unstable, and further calculate the signal interruption and recovery time, and count the duration of each signal interruption: , where represents the signal recovery time, is the signal recovery moment, is the signal interruption start moment. If ms, it indicates that the channel signal recovers slowly, and calculate the signal fluctuation range: , where is the signal fluctuation range within the time window , is the set of signal powers within this window. If dB, it indicates that the signal fluctuates greatly, analyze the degree of influence of signal interference, and generate the signal stability.

[0035] ​​​​​S303: Analyze the changing trend of signal stability based on signal stability, evaluate the performance ability of the channel under different load levels, screen communication channels that meet the bandwidth requirements of the UAV inspection task, calculate the adaptability of different channels, determine the optimal channel, and obtain channel preference indicators.

[0036] Extract signal fluctuation data under different loads and calculate the stability change rate at different time periods: , where is the stability change rate within the time period , represents the signal stability at time , is the time timestamp at, if , it indicates that the signal stability drops rapidly. Further evaluate the adaptability of the channel under different load levels and calculate the channel adaptability using regression analysis: , where is the channel adaptability, is the signal stability under the current load, is the theoretical maximum stability of the channel. If , the adaptability of the channel is strong. Finally, calculate the preference indicators of each channel: , where represents the optimal channel selection, is the set of adaptabilities of all channels. Determine the optimal channel and obtain channel preference indicators.

[0037] As Figure 5 shown, the steps for obtaining signal parameter configuration are specifically as follows: S401: Based on the channel preference indicators, calculate the signal strength equalization deviation, adjust the signal reception frequency of the UAV communication device, detect the signal strength within the real-time reception range, determine the communication frequency band during UAV inspection, correct the offset of the reception frequency, and generate frequency adjustment parameters; Calculate the signal strength equalization deviation using the formula: ; Adjust the signal reception frequency of the UAV communication device, detect the signal strength within the real-time reception range, determine the communication frequency band during UAV inspection, correct the offset of the reception frequency, and generate frequency adjustment parameters; Among them, represents the signal strength equalization deviation, represents the total number of detection points, represents the th detection point's signal power value, represents the total number of detection points, represents the signal power value trough, Represents the number of sampling points for variance calculation; By flying the drone in the inspection area, record the signal power value and the corresponding geographical location per second, and count the number of detection points. For example, if the drone flies in a certain area for 100 seconds and records 100 detection points, then Use the RF transceiver module carried by the drone to measure the received signal strength in real time. Assume that the signal power value of the first detection point is -50 dBm, the second is -55 dBm, and so on until the 100th detection point. For the current inspection task, is equal to , that is By comparing the values of all detection points, determine the minimum value. For example, the minimum value is -80 dBm, and let ; Average signal power : ; For example, if the sum of the signal powers of all detection points is -6000 dBm, then: ; Signal strength variance : ; Calculate the sum of the squares of the differences between the signal power of each detection point and the minimum value . For example, if the calculated result is 400 dBm , then: ; Signal strength equalization deviation : ; Calculate the sum of the absolute differences between the signal power of each detection point and the average value. For example, if the sum is 500 dBm, then: ; This result indicates that the signal strength equalization deviation in the current inspection area is 10 dBm. The signal strength equalization deviation reflects the dispersion degree of the signal strengths at each detection point. The larger the value, the more uneven the signal strength distribution, indicating the existence of interference sources or areas with insufficient signal coverage. Based on this result, the receiving frequency of the drone communication equipment can be further adjusted and the inspection path can be optimized to ensure communication quality.

[0038] S402: Based on the frequency adjustment parameter, set the power output range of the UAV communication device, analyze the transmission effect of the signal at each power level, calculate the optimal power range, adjust the UAV signal transmission power, and generate the power distribution parameter; Adjust the power level of the UAV communication device, increase it starting from the minimum power, record the signal transmission quality at each power level, calculate the signal attenuation at different powers, and adopt the path loss model: , where is the distance at which the received power is is the transmitted power, is the path loss factor (2 - 4 depends on the environment), calculate the signal coverage range at different power levels, and select the minimum transmitted power that meets the minimum received power requirement as the optimal power: , where is the optimal power range, is the current transmitted power, is the lowest power threshold available for the received signal, adjust the UAV signal transmission power, and generate the power distribution parameter.

[0039] S403: Based on the power distribution parameter, match the waveform characteristics and frequency band of the target channel, measure and correct the channel waveform error, adjust the propagation direction and amplitude of the signal during UAV inspection, optimize the signal transmission path, and generate the signal parameter configuration.

[0040] Measure the waveform error of the channel, and calculate the waveform matching degree using correlation analysis: , where is the waveform matching degree, is the received waveform signal, is the target channel waveform. If , waveform correction is required, adjust the propagation direction of the signal, and calculate the signal propagation angle offset: , where is the propagation angle, is the current signal direction, is the theoretically optimal propagation direction. If , direction adjustment is performed, optimize the signal transmission path, and generate the signal parameter configuration.

[0041] As Figure 6 shown, the steps to obtain the communication efficiency correction result are specifically as follows: S501: Based on the signal parameter configuration, monitor the coherence of signal transmission, detect the persistence of the signal at different time points, measure the frequency and duration of signal interruption, analyze the signal fluctuation characteristics, calculate the signal attenuation rate during UAV inspection, and generate the signal coherence index; Perform time series analysis on the signal data, record the signal power at different time points, and calculate the persistence of the signal. Use the sliding window method to count the effective transmission duration of the signal within adjacent time windows, and calculate the frequency of signal interruption. The formula is as follows: , where represents the signal interruption frequency, is the number of signal interruptions, is the total monitoring duration. If Hz, it indicates that the signal interruption is frequent. Further measure the duration of signal interruption, count the time lengths of all interruption events, and calculate the average interruption time: , where is the average interruption time, is the time length of each interruption. If ms, it is determined that the signal coherence is poor. Calculate the signal attenuation rate using the logarithmic attenuation model: , where represents the signal attenuation rate, are the signal powers at time points respectively, is the time interval. If dB / s, it indicates that the signal attenuation is fast, and generate the signal coherence index.

[0042] S502: Based on the signal coherence index, calculate the signal packet loss rate and analyze the delay variation. Measure the number of data packets lost at the receiving end, count the time intervals when packet loss occurs, extract the signal propagation delay data, and identify the delay fluctuation range to generate the signal transmission characteristics; Measure the number of data packets at the receiving end, count the total number of lost data packets, and calculate the packet loss rate: , where is the signal packet loss rate, is the number of lost data packets, is the total number of data packets. If , it indicates that the packet loss rate is high. Count the time intervals when packet loss occurs, use the time series analysis method to mark the packet loss periods, extract the signal propagation delay data, and calculate the average delay: , where is the average signal propagation delay, is the transmission delay of a single data packet, is the total number of data packets. If ms, it indicates that the signal delay is large. Identify the delay fluctuation range to generate the signal transmission characteristics.

[0043] S503: Analyze the signal change characteristics based on the signal transmission characteristics, identify abnormal signal fluctuations, determine the influence range of the error signal, calibrate the deviation of the error signal, extract abnormal communication data from the inspection data of the wind turbine, adjust the data compensation value, and obtain the communication efficiency correction result.

[0044] Conduct trend analysis on the time-series signal data, identify abnormal signal fluctuation points, and calculate the signal fluctuation amplitude: , where is the signal fluctuation amplitude, is the signal power in the time series. If dB, it indicates abnormal signal fluctuation. Determine the influence range of the error signal and calculate the deviation between the error signal and the normal signal: , where is the error signal deviation, is the error signal power, is the normal signal power, is the total number of error signals. If dB, perform error calibration, adjust the data compensation value, and obtain the communication efficiency correction result.

[0045] As Figure 7 shown, a communication system for offshore wind turbine inspection by unmanned aerial vehicle includes: The signal data analysis module collects the signal reception frequency and power information of the unmanned aerial vehicle, extracts the signal strength and fluctuation amplitude, analyzes the signal attenuation during the unmanned aerial vehicle inspection, evaluates the signal interference degree, and generates a signal quality evaluation result; The channel load calculation module analyzes the signal source distribution in the offshore wind farm based on the signal quality evaluation result, counts the transmission data volume, calculates the signal occupancy rate, determines the channel response speed and packet transmission time, and generates a channel load index; The channel optimization module analyzes the channel congestion situation according to the channel load index, determines the signal fluctuation range, and filters the communication channels that meet the bandwidth requirements of the unmanned aerial vehicle inspection task according to the signal stability change trend to obtain the channel optimization index; The signal configuration module adjusts the signal reception frequency and power of the unmanned aerial vehicle according to the channel optimization index, sets the power output range, matches the waveform characteristics and frequency bands of the target channel, optimizes the signal transmission path, and generates signal parameter configuration; The communication efficiency adjustment module uses the signal parameter configuration to monitor the signal transmission coherence, calculate the signal packet loss rate and analyze the delay change, identify and extract abnormal communication data during the wind turbine inspection, verify and calibrate the error signal, and obtain the communication efficiency correction result.

[0046] ​​​​It should be understood that the term "and / or" in this text is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this text generally represents an "or" relationship between the associated objects before and after, but it also represents an "and / or" relationship, and specific understanding can be made with reference to the context before and after.

[0047] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following items (pieces)" or similar expressions refer to any combination of these items, including any combination of single items (pieces) or plural items (pieces). For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0048] It should be understood that in various embodiments of the present invention, the magnitudes of the serial numbers of the above processes do not mean the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0049] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0050] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0051] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0052] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0053] In addition, in each embodiment of the present invention, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0054] If the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0055] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A communication method for offshore wind power UAV inspection, characterized in that: The method comprises: S1: Collect the signal reception frequency and power information of the drone, extract the signal strength and fluctuation amplitude, analyze the signal attenuation during the drone inspection, evaluate the signal interference degree, and generate the signal quality evaluation result; S2: Based on the signal quality evaluation result, analyze the distribution of offshore wind farm signal sources, count the amount of transmitted data, calculate the signal occupancy rate, determine the channel response speed and data packet transmission time, and obtain the channel load index; S3: Based on the channel load index, analyze the channel congestion, determine the signal fluctuation range, select the communication channel that meets the bandwidth requirements of the drone inspection task according to the signal stability change trend, and obtain the channel optimization index; S4: According to the channel optimization index, adjust the drone signal receiving frequency and power, set the power output range, match the waveform characteristics and frequency band of the target channel, optimize the signal transmission path, and generate signal parameter configuration; S5: Using the signal parameter configuration, monitor the continuity of signal transmission, calculate the signal packet loss rate and analyze the delay change, identify and extract abnormal communication data during wind turbine inspection, verify and calibrate the error signal, and obtain the communication performance correction result.

2. The communication method for offshore wind power UAV inspection according to claim 1 is characterized in that: The signal quality assessment results include measured signal strength, fluctuation amplitude and signal attenuation indicators; the channel load index includes the statistical number of signal sources, channel signal occupancy rate and data packet transmission delay; the channel optimization indicators include the channel congestion level, signal fluctuation range and bandwidth requirement matching degree; the signal parameter configuration includes the set receiving frequency, power output range and signal transmission optimization measures; the communication performance correction results include the monitored signal packet loss rate, delay change rate and calibration data of abnormal signals.

3. The communication method for offshore wind power UAV inspection according to claim 1 is characterized in that: The steps of obtaining the signal quality evaluation result are specifically as follows: S101: Collect the signal receiving frequency and power information of the drone, extract the signal strength and fluctuation amplitude of the signal source, detect the relationship between the frequency change and the power fluctuation, identify the abnormal power point, calculate the frequency offset value, and generate the signal fluctuation degree; S102: Based on the signal strength fluctuation characteristics, analyzing the signal attenuation of the signal source, determining the attenuation trend of the signal strength with the distance during the drone inspection process, calculating the signal transmission bit error rate and signal-to-noise ratio, and generating a signal attenuation analysis result; S103: Based on the signal attenuation analysis result, the signal interference degree of the signal source is evaluated, the stability and continuity of the signal are analyzed, the signal coverage range of the offshore base station of the wind farm is determined, and the utilization rate of the channel is calculated to generate a signal quality evaluation result.

4. The communication method for offshore wind power UAV inspection according to claim 1, characterized in that: The steps of obtaining the channel load index are specifically as follows: S201: based on the signal quality evaluation result, extract the distribution of offshore wind farm signal sources of the communication channel, identify the position and distribution density of each signal source in the channel, analyze the interaction between the signal sources, and generate a signal source distribution map; S202: based on the signal source distribution map, extract the transmission data volume of the offshore wind farm signal source, analyze the data fluidity and data transmission efficiency in the channel, calculate the contribution rate of each signal source to the total data volume, and generate a data transmission efficiency value; S203: Based on the data transmission efficiency value, calculate the signal occupancy rate of the channel, analyze the usage density of the channel in each time period, determine the channel response speed and data packet transmission time, and generate a channel load index.

5. The communication method for offshore wind power UAV inspection according to claim 1, characterized in that: The steps of obtaining the channel optimization index are specifically as follows: S301: Based on the channel load index, analyze the congestion of the channel, measure the occupancy of the signal in different time periods, calculate the fluctuation degree of data traffic, identify the traffic peak interval and evaluate the load stability, determine the saturation of the channel, and generate the channel congestion level; S302: Based on the channel congestion level, analyzing the continuity of the channel signal, detecting the stability of the signal in different time periods, determining the time interval between signal interruption and recovery, calculating the signal fluctuation range, and identifying the influence of signal interference to generate signal stability; S303: Based on the signal stability, analyze the signal stability change trend, evaluate the channel performance under different load levels, select communication channels that meet the bandwidth requirements of the drone inspection mission, calculate the adaptability of the different channels, and determine the optimal channel to obtain the channel optimization index.

6. The communication method for offshore wind power UAV inspection according to claim 5 is characterized in that: The calculation of the fluctuation index of data flow is carried out using the formula: ； Identify traffic peak intervals and evaluate load stability, determine channel saturation, and generate channel congestion levels; in, Represents the data traffic fluctuation index, Representing time point The data flow at Representing time point The data flow at Representing time point timestamp, Representing time point timestamp, Represents the number of time points, Represents the average value of data traffic.

7. The communication method for offshore wind power UAV inspection according to claim 1, characterized in that: The steps of acquiring the signal parameter configuration are specifically as follows: S401: Based on the channel optimization index, the signal strength balance deviation is calculated, the signal receiving frequency of the UAV communication device is adjusted, the signal strength within the real-time receiving range is detected, the frequency band of the UAV communication during inspection is determined, the offset of the receiving frequency is corrected, and the frequency adjustment parameter is generated; S402: Based on the frequency adjustment parameter, set the power output range of the UAV communication device, analyze the transmission effect of the signal at each power level, calculate the optimal power range, adjust the UAV signal transmission power, and generate the power allocation parameter; S403: Based on the power allocation parameters, the waveform characteristics and frequency band of the target channel are matched, the channel waveform error is measured and corrected, the propagation direction and amplitude of the signal during the drone inspection are adjusted, the signal transmission path is optimized, and the signal parameter configuration is generated.

8. The communication method for offshore wind power UAV inspection according to claim 7, characterized in that: The signal strength equalization deviation is calculated using the formula: ； Adjust the signal receiving frequency of the drone communication equipment, detect the signal strength within the real-time receiving range, determine the frequency band of the drone communication during inspection, correct the offset of the receiving frequency, and generate frequency adjustment parameters; in, represents the signal strength equalization deviation, Represents the total number of detection points, Representative The signal power value of each detection point is Represents the total number of detection points, Indicates the signal power value is low. Represents the number of sampling points for variance calculation.

9. The communication method for offshore wind power UAV inspection according to claim 1, characterized in that: The steps of obtaining the communication performance correction result are specifically as follows: S501: Based on the signal parameter configuration, monitor the coherence of signal transmission, detect the persistence of the signal at different time points, measure the frequency and duration of signal interruption, analyze the signal fluctuation characteristics, calculate the signal attenuation rate during drone inspection, and generate a signal coherence index; S502: Based on the signal coherence index, calculate the signal packet loss rate and analyze the delay variation, measure the number of data packets lost at the receiving end, count the time interval when the packet loss occurs, extract the signal propagation delay data, identify the delay fluctuation range, and generate the signal transmission characteristics; S503: Based on the signal transmission characteristics, analyze the signal change characteristics, identify abnormal signal fluctuations, determine the influence range of the error signal, calibrate the error signal deviation, extract abnormal communication data from the wind turbine inspection data, adjust the data compensation value, and obtain the communication efficiency correction result.

10. A communication system for offshore wind power UAV inspection, the communication system for offshore wind power UAV inspection is used to implement the communication method for offshore wind power UAV inspection as claimed in any one of claims 1 to 9, characterized in that: The system comprises: The signal data analysis module collects the signal reception frequency and power information of the drone, extracts the signal strength and fluctuation amplitude, analyzes the signal attenuation during the drone inspection, evaluates the signal interference level, and generates signal quality evaluation results; The channel load calculation module analyzes the signal source distribution of the offshore wind farm based on the signal quality evaluation result, counts the amount of transmitted data, calculates the signal occupancy rate, determines the channel response speed and data packet transmission time, and generates a channel load index; The channel optimization module analyzes the channel congestion according to the channel load index, determines the signal fluctuation range, selects the communication channel that meets the bandwidth requirements of the UAV inspection task according to the signal stability change trend, and obtains the channel optimization index; The signal configuration module adjusts the drone signal receiving frequency and power according to the channel optimization index, sets the power output range, matches the waveform characteristics and frequency band of the target channel, optimizes the signal transmission path, and generates signal parameter configuration; The communication efficiency adjustment module uses the signal parameter configuration to monitor signal transmission continuity, calculate the signal packet loss rate and analyze the delay change, identify and extract abnormal communication data during wind turbine inspection, verify and calibrate the error signal, and obtain the communication efficiency correction result.

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