A method for unmanned aerial vehicle visual communication and navigation

Abstract

This invention discloses a visual communication and navigation method for unmanned aerial vehicles (UAVs), relating to the field of UAV communication and navigation technology. This invention comprehensively analyzes pre-selected flight paths by considering multiple flight impact parameters, including flight distance, the number of data collection points required to mark delayed tasks, and the total delay time. First, paths with estimated total time exceeding the ideal total time are eliminated. Then, the flight path evaluation value is calculated, and the path with the highest evaluation value is selected as the optimal flight path. This not only ensures that the UAV completes the task within the specified time but also minimizes delays in data collection points, improving the timeliness and accuracy of task execution and solving the problem of inefficient path planning in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication and navigation technology, and in particular to a UAV visual communication and navigation method. Background Technology

[0002] With the rapid development of drone technology, its application in fields such as power line inspection, emergency rescue, and agricultural monitoring is becoming increasingly widespread, making it a key tool for improving operational efficiency and safety. However, existing UAV visual communication and navigation methods still have the following shortcomings in practical applications: In terms of UAV flight path planning, traditional path planning often takes the shortest flight distance or the least time as the single objective, ignoring the time sensitivity of mission data collection and failing to fully consider the ideal data collection time period for mission collection points, resulting in delays at some key nodes and affecting the timeliness and accuracy of mission execution. In addition, drone data communication transmission mostly uses fixed rules and lacks dynamic adaptation capabilities. When the communication environment changes suddenly, it cannot adjust the transmission parameters based on real-time communication quality and historical data, resulting in data loss or transmission delay.

[0003] To address this, a visual communication and navigation method for unmanned aerial vehicles (UAVs) is proposed. Summary of the Invention

[0004] In view of this, the present invention provides a visual communication and navigation method for unmanned aerial vehicles (UAVs) to solve the problems mentioned in the background art.

[0005] The objective of this invention can be achieved through the following technical solution: a visual communication and navigation method for unmanned aerial vehicles (UAVs), comprising; Path planning: Based on the current UAV starting point and mission requirements, generate various pre-selected flight paths, comprehensively analyze the flight impact parameters of each pre-selected flight path, select the best flight path based on the analysis results, and control the UAV to start the flight mission; among which the flight impact parameters include flight distance k1, number of data collection points required to mark the delayed mission k2, and total delay time k3; Communication effect analysis: When the UAV arrives at a certain task data collection point during its flight along the set optimal flight path, the communication impact parameters of the UAV at the current task data collection point are comprehensively analyzed to determine the communication impact index Ma of the environment where the UAV is located at the corresponding task data collection point; the communication impact parameters include illumination, communication signal strength and electromagnetic interference; Transmission strategy selection: The communication impact index Ma of the UAV in the environment of the corresponding task data collection point is compared with the set threshold index. If it is less than the set threshold index, the communication optimization signaling is triggered and the corresponding steps are executed to determine the optimal optimization strategy of the UAV in the environment of the corresponding task data collection point. Ground control and navigation: After optimization based on the selected optimal strategy, the flight data collected by the UAV at the corresponding task data collection point is transmitted in real time.

[0006] Specifically, the process of comprehensively analyzing the flight impact parameters of each pre-selected flight path is as follows: Obtain the estimated total time for different pre-selected flight paths, and use the required completion time marked in the mission requirements as the ideal total time for the UAV; The estimated total time for different pre-selected flight paths is compared with the ideal total time. Pre-selected flight paths with estimated total time higher than ideal total time are eliminated, and the remaining pre-selected flight paths are marked as candidate paths. The flight distance of different candidate paths is extracted and denoted as k1. Identify the location of the data collection points required for each group of drone tasks on different candidate paths, and obtain the estimated time period for the drone to reach the data collection points required for each group of tasks on different candidate paths, starting from the set task start time. Based on the task requirements, the ideal data collection time period for the collection points required for each group of tasks is set; the estimated time period for the UAV to reach the collection points required for each group of tasks from different candidate paths is compared with the set ideal data collection time period. If the earliest estimated time in the estimated time period for a certain task's collection point is later than the latest ideal time in the corresponding ideal data collection time period, it is marked as delayed; the number of collection points required for tasks marked as delayed in different candidate paths is counted and denoted as k2. For the number of data collection points required for a task marked as delayed, the highest estimated time and the latest ideal time are extracted and the difference is calculated to obtain the delay duration. The delay durations calculated for each group in different candidate paths are summed to obtain the total delay duration corresponding to different candidate paths, denoted as k3.

[0007] Specifically, the process of selecting the optimal flight path is as follows: Through formula The flight path evaluation values ​​corresponding to different candidate paths are obtained and labeled as Ra; where a, b, and c represent the preset weight factors corresponding to k1, k2, and k3, respectively. Based on the flight path evaluation value Ra corresponding to different candidate paths, the candidate path with the highest flight path evaluation value Ra is selected as the best flight path for the UAV.

[0008] Specifically, the process of comprehensively analyzing the communication impact parameters of the UAV at its current mission data collection point is as follows: An evaluation time window is defined for the UAV's visual communication process. The illumination intensity of the UAV's environment at each time point within the evaluation time window is extracted, denoted by Ki, where i represents the time point number. A reference illumination intensity corresponding to the preset illumination intensity is defined by... This indicates; a preliminary optimal light intensity setting; through The illumination deviation value Ei at each time point is obtained. The average value of the illumination deviation value of the environment where the UAV is located at each time point within the evaluation time window is calculated to obtain the illumination evaluation value L1 of the environment where the UAV is located. Obtain the signal strength values ​​between the UAV and the ground control station at different equal distances in the current environment, and preset the acceptable signal strength between the UAV and the ground control station; extract the signal strength values ​​of the UAV at each time point within the evaluation time window in the current environment, and take the average value as the average signal strength of the UAV within the evaluation time window, denoted by Sp. Obtain the anti-interference parameters of the UAV tested in an interference-free environment, and extract the bit error rate (BER) and signal-to-noise ratio (SNR) of the UAV under the interference-free test environment. Calculate the ratio using BER as the numerator and SNR as the denominator to obtain the reference anti-interference benchmark value for the UAV. express; An evaluation time window is set for the UAV visual communication process. The bit error rate and signal-to-noise ratio of the environment where the UAV is located are extracted at each time point within the evaluation time window. The ratio is calculated to obtain the anti-interference performance value at each time point. The average value of the anti-interference performance value at each time point is calculated to obtain the anti-interference estimate of the environment where the UAV is located within the evaluation time window, which is denoted as Fp.

[0009] Specifically, the process of determining the communication impact index of the environment where the UAV is located at the corresponding task data collection point is as follows: According to the formula The communication impact index Ma of the drone in the current environment is obtained by weighting the illumination assessment value, signal strength assessment value, and anti-interference assessment value of the drone in the current environment. , as well as c1, c2, and c3 represent the preset allowable illumination assessment value, signal strength pass value, and anti-interference benchmark value corresponding to the UAV illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp, respectively. c1, c2, and c3 are the preset influence weight factors corresponding to the illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp, respectively.

[0010] Specifically, the process of triggering communication optimization signaling and executing corresponding steps is as follows: The communication impact index Ma of the UAV in the environment of the corresponding task data collection point is compared with the set threshold index. If it is less than the set threshold index, the communication optimization signaling is triggered and the corresponding steps are executed to determine the optimal optimization strategy of the UAV in the environment of the corresponding task data collection point. Ma is analyzed to obtain the UAV's illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp in the current environment, as well as the weighting factors c1, c2, and c3 of the communication impact index Ma and the baseline values ​​of each parameter. Extract each flight case from the case database, where each flight case includes a communication impact index Ma and the applied optimization strategy. Analyze the communication impact index Ma for each flight case to obtain the corresponding illumination assessment value, signal strength assessment value, and anti-interference estimate. Then, calculate the differences between these differences and the UAV's illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp under the current environment, and the pre-stored optimization cases in the database. These differences are then labeled as follows: Through formula Calculate the comprehensive difference value for each group of flight cases, denoted as Ve, where z1, z2, and z3 are the corresponding preset weighting factors; Specifically, the process of determining the optimal strategy for the UAV in the environment of the corresponding task data collection point is as follows: The expected comprehensive difference value corresponding to the preset comprehensive difference value is selected. Flight cases with a comprehensive difference value less than the comprehensive difference value are selected as reference cases. The case with the smallest comprehensive difference value Ve is selected from the reference cases, and the applied optimization strategy is extracted as the most suitable optimization strategy for the current UAV in the environment of the corresponding task data collection point.

[0011] Specifically, if the overall difference value Ve of each group of flight cases is higher than the expected overall difference value, the execution process is as follows: Get all maintenance personnel who are currently working and mark them as candidates; get the number of times each candidate has formulated optimization strategies before the current time, denoted as i; Extract the time spent formulating optimization strategies for each candidate and take the average value to obtain the average strategy formulation time, denoted as p; For each candidate's optimization strategy, a pre-evaluation score for data transmission effectiveness is obtained. The data transmission effectiveness score is scored by technical personnel based on the final display effect of the transmitted data after each application of the optimization strategy. The data transmission effectiveness scores of each candidate's optimization strategy are compared with the set passing score, and optimization strategies with scores higher than the passing score are recorded as excellent strategies. Obtain the number of times each candidate has a good strategy and the total number of times they have formulated an optimization strategy in history, and calculate the ratio. The success rate of each candidate's historical optimization strategy before the current time point is recorded as 0. Through formula The comprehensive ability value Xt of each candidate is obtained by weighting the number of times i, the average time p, and the success rate o of the historical optimization strategy formulation for each candidate. Here, s1, s2, and s3 correspond to the weighting factors of the number of times i, the average time p, and the success rate o of the historical optimization strategy formulation, respectively. Based on the comprehensive ability value Xt of each candidate, the candidate with the highest comprehensive ability value Xt is selected as the maintenance personnel for the UAV.

[0012] Compared with the prior art, the beneficial effects of the present invention are: This invention comprehensively analyzes the pre-selected flight path by taking into account multiple flight impact parameters, including flight distance, the number of data collection points required for marking delayed tasks, and the total delay time. First, it eliminates paths whose estimated total time is higher than the ideal total time. Then, it calculates the flight path evaluation value and selects the path with the highest flight path evaluation value as the optimal flight path. This not only ensures that the UAV completes the task within the specified time, but also minimizes the delay of task data collection points, improves the timeliness and accuracy of task execution, and solves the problem of inefficient path planning in the prior art. This invention, by comprehensively considering three key communication impact parameters—light intensity, communication signal strength, and electromagnetic interference—in the communication effect analysis stage, determines a communication impact index, which fully reflects the communication quality of the environment in which the UAV is located, thus overcoming the problem of one-sided communication evaluation in existing technologies. This invention compares the communication impact index with a set threshold index. If the index is less than the threshold, a communication optimization signal is triggered. The invention calculates the comprehensive difference between the current environmental parameters and the flight case parameters in the case database, and selects the most suitable optimization strategy. When there are no suitable cases in the case database, the invention assesses the comprehensive capabilities of maintenance personnel and selects suitable personnel to formulate strategies. This solves the problem of rigid transmission strategies in the prior art, ensuring that effective optimization measures can be taken in various communication environments and guaranteeing the reliability of data transmission. Attached Figure Description

[0013] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0014] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0015] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0016] Example

[0017] Please see Figure 1 As shown, a visual communication and navigation method for unmanned aerial vehicles (UAVs) includes: Path planning: Based on the current UAV starting point and mission requirements, generate various pre-selected flight paths; path planning needs to consider avoiding obstacles to ensure mission completion efficiency, and may also combine inertial measurement unit (IMU) and other sensor data to form a multi-source fusion navigation scheme to improve the accuracy and reliability of planning; select the best flight path and control the UAV to start the flight mission; Obtain the estimated total time for different pre-selected flight paths, and use the required completion time marked in the mission requirements as the ideal total time for the UAV; The estimated total time for different pre-selected flight paths is compared with the ideal total time. Pre-selected flight paths with estimated total time higher than ideal total time are eliminated, and the remaining pre-selected flight paths are marked as candidate paths. The flight distance of different candidate paths is extracted and denoted as k1. Identify the location of the data collection points required for each group of drone tasks on different candidate paths, and obtain the estimated time period for the drone to reach the data collection points required for each group of tasks on different candidate paths, starting from the set task start time. Based on the task requirements, the ideal data collection time period for the collection points required for each group of tasks is set; the estimated time period for the UAV to reach the collection points required for each group of tasks from different candidate paths is compared with the set ideal data collection time period. If the earliest estimated time in the estimated time period for a certain task's collection point is later than the latest ideal time in the corresponding ideal data collection time period, it is marked as delayed; the number of collection points required for tasks marked as delayed in different candidate paths is counted and denoted as K2. For the number of collection points required for a task marked as delayed, the highest estimated time and the latest ideal time are extracted and the difference is calculated to obtain the delay duration. The delay durations calculated for each group in different candidate paths are summed to obtain the total delay duration corresponding to different candidate paths, denoted as k3. Through formula The flight path evaluation values ​​corresponding to different candidate paths are obtained and labeled as Ra, where a, b, and c represent the preset weight factors corresponding to K1, K2, and K3, respectively. Based on the flight path evaluation value Ra corresponding to different candidate paths, the candidate path with the highest flight path evaluation value Ra is selected as the best flight path for the UAV. To further explain, the system first filters out paths with excessively long durations based on total time consumption, thus improving planning efficiency. During the evaluation, factors such as flight distance, the number of delays at mission data collection points, and total delay time are considered to meet mission time requirements from multiple dimensions. The evaluation value Ra is calculated using preset weighting factors, and the optimal path is selected based on the comprehensive evaluation value. This achieves a balance between time feasibility, flight efficiency, and the accuracy of mission data collection point times, making it more suitable for overall mission requirements than the path selection based on a single optimal indicator. Communication effect analysis: When the UAV arrives at a certain task data collection point during its flight along the set optimal flight path, the communication impact parameters of the UAV at the current task data collection point are comprehensively analyzed to determine the communication impact index Ma of the environment where the UAV is located at the corresponding task data collection point; the communication impact parameters include illumination, communication signal strength and electromagnetic interference; The parameter acquisition process is as follows: The analysis process for each communication-affecting parameter is as follows: S1: Set the evaluation time window during the UAV's visual communication process, extract the illumination intensity of the UAV's environment at each time point within the evaluation time window, denoted by Ki, where i represents the number of each time point; preset the reference illumination intensity corresponding to the illumination intensity, denoted by... This indicates; a preliminary optimal light intensity setting; through The illumination deviation value Ei at each time point is obtained. The average value of the illumination deviation value of the environment where the UAV is located at each time point within the evaluation time window is calculated to obtain the illumination evaluation value L1 of the environment where the UAV is located. S2: Use radio frequency sensors pre-deployed on the drone to obtain signal strength values ​​at different distances between the drone and the ground control station in the current environment, and preset the acceptable signal strength between the drone and the ground control station, wherein the acceptable signal strength is measured by technicians; Extract the signal strength values ​​of the UAV at each time point within the current environment evaluation time window, and take the average value as the mean signal strength of the UAV within the evaluation time window, denoted as Sp. S3: Obtain the anti-interference parameters of the UAV tested in an interference-free environment, and extract the bit error rate and signal-to-noise ratio (SNR) of the UAV under the interference-free test environment. Calculate the ratio using the bit error rate as the numerator and the SNR as the denominator to obtain the reference anti-interference benchmark value for the UAV. express; An evaluation time window is set for the UAV visual communication process. The bit error rate and signal-to-noise ratio of the environment where the UAV is located are extracted at each time point within the evaluation time window. After the ratio is calculated, the anti-interference performance value at each time point is obtained. The average value of the anti-interference performance value at each time point is calculated to obtain the anti-interference estimate of the environment where the UAV is located within the evaluation time window, which is denoted as Fp. Additional explanation: The average anti-interference parameter of the UAV is calculated by using the bit error rate as the numerator and the signal-to-noise ratio as the denominator. The smaller the result, the stronger the anti-interference capability of the UAV. When the average anti-interference parameter of the UAV is used as the numerator and the anti-interference benchmark parameter of the UAV is used as the denominator, the anti-interference estimate Fp of the UAV is obtained. The larger the result, the better. According to the formula The communication impact index Ma of the drone in the current environment is obtained by weighting the illumination assessment value, signal strength assessment value, and anti-interference assessment value of the drone in the current environment. , as well as These represent the preset allowable illumination assessment value, signal strength pass value, and anti-interference estimate reference value corresponding to the UAV illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp, respectively. c1, c2, and c3 are the preset influence weight factors corresponding to the illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp, respectively. Additional notes: The smaller the illumination assessment value L1, the better the illumination intensity in the current environment; the larger the signal strength assessment value Sp and the anti-interference estimate Fp, the better the signal strength and the stronger the anti-interference capability in the current environment. By quantitatively analyzing and evaluating the illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp within the evaluation time window, and finally integrating the three-dimensional parameters of illumination, signal strength, and anti-interference, the communication impact index Ma is obtained using a formula containing preset weighting factors and reference values. This can flexibly adapt to the emphasis requirements of different scenarios for each factor. Compared with the single-dimensional or non-quantitative evaluation of existing technologies, it realizes a multi-dimensional and quantitative comprehensive evaluation of communication impact, improving the comprehensiveness and accuracy of the evaluation. Transmission strategy selection: The communication impact index Ma of the UAV in the environment of the corresponding task data collection point is compared with the set threshold index. If it is less than the set threshold index, the communication optimization signaling is triggered and the corresponding steps are executed to determine the optimal optimization strategy of the UAV in the environment of the corresponding task data collection point. Ma is parsed, and the communication impact parameters and data transmission strategies applied during each flight of the UAV are stored and integrated into flight cases. A case database is built based on each set of flight cases. Obtain the UAV's illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp under the current environment, as well as the weighting factors c1, c2, and c3 of the communication impact index Ma and the baseline values ​​of each parameter. Extract each flight case from the case database, where each flight case includes a communication impact index Ma and the applied optimization strategy. Analyze the communication impact index Ma for each flight case to obtain the corresponding illumination assessment value, signal strength assessment value, and anti-interference estimate. Then, calculate the differences between these differences and the UAV's illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp under the current environment, and the pre-stored optimization cases in the database. These differences are then labeled as follows: Through formula The comprehensive difference value of each flight case is calculated and labeled as Ve, where z1, z2, and z3 are the corresponding preset weighting factors. In addition, by comparing the communication impact index Ma with the threshold, a flight case database containing communication parameters and transmission strategies is constructed, providing historical references for current environment optimization and breaking through the limitations of optimization in a single environment. By calculating the comprehensive difference value Ve between the current environment and the case database in terms of illumination, signal strength, and anti-interference, accurate matching of multiple parameter dimensions is achieved, which can more efficiently determine the most suitable optimization strategy and improve the pertinence and adaptability of UAV communication optimization in different mission data collection environments. The expected comprehensive difference value corresponding to the preset comprehensive difference value is selected. Flight cases with a comprehensive difference value less than the comprehensive difference value are selected as reference cases. The comprehensive difference value Ve with the smallest value is selected from the reference cases, and the applied optimization strategy is extracted as the most suitable optimization strategy for the current UAV in the environment of the corresponding task data collection point. In addition, this method differs from existing technologies in the selection of UAV communication optimization strategies, achieving a more accurate and efficient strategy matching effect: by pre-setting the expected comprehensive difference value, reference cases that meet the requirements are screened out first, and invalid cases with excessive differences are eliminated to ensure the effectiveness of the reference range; on this basis, the optimization strategy applied by the case with the smallest comprehensive difference value Ve is selected as the current optimal strategy. Using the quantified minimum difference as the standard, compared with the existing technology which may lack clear screening criteria or only use fuzzy matching, this method significantly improves the matching accuracy between the optimization strategy and the current environment, making the strategy selection more targeted and scientific, and further ensuring the effectiveness of UAV communication optimization in the mission data collection environment; When Ve is greater than the expected aggregate difference value, the following strategy will be used: Get all maintenance personnel who are currently working and mark them as candidates; get the number of times each candidate has formulated optimization strategies before the current time, denoted as i; Extract the time spent formulating optimization strategies for each candidate and take the average value to obtain the average strategy formulation time, denoted as p; For each candidate's optimization strategy, a pre-evaluation score for data transmission effectiveness is obtained. The data transmission effectiveness score is given by technical personnel based on the final display effect of the transmitted data after each application of the optimization strategy, and the score range is set from 1 to 10. The higher the score, the better the optimization strategy is. The data transmission performance scores of each candidate for each optimization strategy are compared with the set passing score. Optimization strategies with scores higher than the passing score are recorded as excellent strategies. Obtain the number of times each candidate has a good strategy and the total number of times they have formulated an optimization strategy in history, and calculate the ratio. The success rate of each candidate's historical optimization strategy before the current time point is recorded as 0. Among them, the number of times the optimization strategy was formulated in history i, the average time to formulate the strategy p, and the success rate of the optimization strategy in history o should be as high as possible; Through formula The comprehensive ability value Xt of each candidate is obtained by weighting the number of times i, the average time p, and the success rate o of the historical optimization strategy formulation for each candidate. Here, s1, s2, and s3 correspond to the weighting factors of the number of times i, the average time p, and the success rate o of the historical optimization strategy formulation, respectively. These factors can be dynamically adjusted according to the actual situation. In addition, when Ve is greater than the expected comprehensive difference value, this method selects personnel to formulate optimization strategies by comprehensively considering the number of times candidate maintenance personnel have formulated strategies in the past, the average duration and the success rate, and using a formula with dynamic weight factors to calculate the comprehensive capability value. This is different from the personnel selection method in the existing technology that may be based solely on experience or efficiency. It realizes a multi-dimensional quantitative evaluation of the maintenance personnel's capabilities, and the weights can be flexibly adjusted to adapt to actual needs. It can more scientifically and accurately select the best personnel, ensure the efficiency and quality of optimization strategy formulation when the automatic matching strategy fails, and improve the reliability of communication optimization in extreme cases. Based on the comprehensive ability value Xt of each candidate, the candidate with the highest comprehensive ability value Xt is selected as the maintenance personnel for the UAV. Additional explanation: A comprehensive capability threshold is set for each candidate. Each candidate's comprehensive capability value Xt is compared to this threshold, and candidates with Xt values ​​less than the threshold are eliminated. The remaining candidates' comprehensive capability values ​​Xt are then sorted. If a candidate has the highest comprehensive capability value, that candidate determines the optimal optimization strategy for the UAV in the corresponding task data collection point environment, and the key parameters for the current task are simultaneously pushed to them. If multiple candidates have the same comprehensive capability value Xt, the candidate with the highest task success rate is prioritized to determine the optimal optimization strategy for the UAV in the corresponding task data collection point environment. If the task success rates are the same, the candidate with the longest work experience is selected to determine the optimal optimization strategy for the UAV in the corresponding task data collection point environment. Ground control and navigation: After optimization based on the selected optimal strategy, the flight data collected by the UAV at the corresponding task data collection point is transmitted in real time.

[0018] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A visual communication and navigation method for unmanned aerial vehicles (UAVs), characterized in that, include: Path planning: Based on the current UAV starting point and mission requirements, generate various pre-selected flight paths, comprehensively analyze the flight impact parameters of each pre-selected flight path, select the best flight path based on the analysis results, and control the UAV to start the flight mission; among which the flight impact parameters include flight distance k1, number of data collection points required to mark the delayed mission k2, and total delay time k3; Communication effect analysis: When the UAV arrives at a certain task data collection point during its flight along the set optimal flight path, the communication impact parameters of the UAV at the current task data collection point are comprehensively analyzed to determine the communication impact index Ma of the environment where the UAV is located at the corresponding task data collection point; the communication impact parameters include illumination, communication signal strength and electromagnetic interference; Transmission strategy selection: The communication impact index Ma of the UAV in the environment of the corresponding task data collection point is compared with the set threshold index. If it is less than the set threshold index, the communication optimization signaling is triggered and the corresponding steps are executed to determine the optimal optimization strategy of the UAV in the environment of the corresponding task data collection point. Ground control and navigation: After optimization based on the selected optimal strategy, the flight data collected by the UAV at the corresponding task data collection point is transmitted in real time.

2. The UAV visual communication and navigation method according to claim 1, characterized in that, The comprehensive analysis of the flight impact parameters of each pre-selected flight path is as follows: Obtain the estimated total time for different pre-selected flight paths, and use the required completion time marked in the mission requirements as the ideal total time for the UAV; The estimated total time for different pre-selected flight paths is compared with the ideal total time. Pre-selected flight paths with estimated total time higher than ideal total time are eliminated, and the remaining pre-selected flight paths are marked as candidate paths. The flight distance of different candidate paths is extracted and denoted as k1. Identify the location of the data collection points required for each group of drone tasks on different candidate paths, and obtain the estimated time period for the drone to reach the data collection points required for each group of tasks on different candidate paths, starting from the set task start time. Based on the task requirements, the ideal data collection time period for the collection points required for each group of tasks is set; the estimated time period for the UAV to reach the collection points required for each group of tasks from different candidate paths is compared with the set ideal data collection time period. If the earliest estimated time in the estimated time period for a certain task's collection point is later than the latest ideal time in the corresponding ideal data collection time period, it is marked as a delay. The number of data collection points required for the delayed tasks in different candidate paths is counted and denoted as k2. For the number of data collection points required for the delayed tasks, the highest estimated time and the latest ideal time are extracted and the difference is calculated to obtain the delay duration. The delay durations calculated for each group in different candidate paths are summed to obtain the total delay duration corresponding to different candidate paths, denoted as k3.

3. The UAV visual communication and navigation method according to claim 2, characterized in that, The selection of the optimal flight path based on the analysis results is specifically as follows: Through formula The flight path evaluation values ​​corresponding to different candidate paths are obtained and labeled as Ra, where a, b, and c represent the preset weight factors corresponding to k1, k2, and k3, respectively. Based on the flight path evaluation value Ra corresponding to different candidate paths, the candidate path with the highest flight path evaluation value Ra is selected as the best flight path for the UAV.

4. The UAV visual communication and navigation method according to claim 3, characterized in that, The comprehensive analysis of the communication impact parameters of the UAV at its current mission data collection point is as follows: Set an evaluation time window in the process of UAV visual communication, and extract the light intensity of the environment where the UAV is located at each time point within the evaluation time window, denoted by Ki, where i represents the number of each time point; The reference light intensity corresponding to the preset light intensity is used This indicates; a preliminary optimal light intensity setting; through The illumination deviation value Ei at each time point is obtained. The average value of the illumination deviation value of the environment where the UAV is located at each time point within the evaluation time window is calculated to obtain the illumination evaluation value L1 of the environment where the UAV is located. Obtain the signal strength values ​​between the UAV and the ground control station at different equal distances in the current environment, and preset the acceptable signal strength between the UAV and the ground control station; extract the signal strength values ​​of the UAV at each time point within the evaluation time window in the current environment, and take the average value as the average signal strength of the UAV within the evaluation time window, denoted by Sp. Obtain the anti-interference parameters of the UAV tested in an interference-free environment, and extract the bit error rate (BER) and signal-to-noise ratio (SNR) of the UAV under the interference-free test environment. Calculate the ratio using BER as the numerator and SNR as the denominator to obtain the reference anti-interference benchmark value for the UAV. express; An evaluation time window is set for the UAV visual communication process. The bit error rate and signal-to-noise ratio of the environment where the UAV is located are extracted at each time point within the evaluation time window. The ratio is calculated to obtain the anti-interference performance value at each time point. The average value of the anti-interference performance value at each time point is calculated to obtain the anti-interference estimate of the environment where the UAV is located within the evaluation time window, which is denoted as Fp.

5. The UAV visual communication and navigation method according to claim 4, characterized in that, The determination of the communication impact index of the environment where the UAV is located at the corresponding task data collection point is specifically as follows: According to the formula The communication impact index Ma of the drone in the current environment is obtained by weighting the illumination assessment value, signal strength assessment value, and anti-interference assessment value of the drone in the current environment. , as well as c1, c2, and c3 represent the preset allowable illumination assessment value, signal strength pass value, and anti-interference benchmark value corresponding to the UAV illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp, respectively. c1, c2, and c3 are the preset influence weight factors corresponding to the illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp, respectively.

6. The UAV visual communication and navigation method according to claim 5, characterized in that, The specific steps for triggering communication optimization signaling and executing corresponding procedures are as follows: The communication impact index Ma of the UAV in the environment of the corresponding task data collection point is compared with the set threshold index. If it is less than the set threshold index, the communication optimization signaling is triggered and the corresponding steps are executed to determine the optimal optimization strategy of the UAV in the environment of the corresponding task data collection point. Ma is analyzed to obtain the UAV's illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp in the current environment, as well as the weighting factors c1, c2, and c3 of the communication impact index Ma and the baseline values ​​of each parameter. Extract each flight case from the case database, where each flight case includes a communication impact index Ma and the applied optimization strategy. Analyze the communication impact index Ma for each flight case to obtain the corresponding illumination assessment value, signal strength assessment value, and anti-interference estimate. Then, calculate the differences between these differences and the UAV's illumination assessment value L1, signal strength assessment value Sp, and anti-interference estimate Fp under the current environment, and the pre-stored optimization cases in the database. These differences are then labeled as follows: Through formula The comprehensive difference value of each flight case is calculated and denoted as Ve, where z1, z2, and z3 are the corresponding preset weighting factors.

7. The UAV visual communication and navigation method according to claim 6, characterized in that, The optimal strategy for determining the UAV's location in the corresponding task data collection point is as follows: The expected comprehensive difference value corresponding to the preset comprehensive difference value is selected. Flight cases with a comprehensive difference value less than the comprehensive difference value are selected as reference cases. The case with the smallest comprehensive difference value Ve is selected from the reference cases, and the applied optimization strategy is extracted as the most suitable optimization strategy for the current UAV in the environment of the corresponding task data collection point.

8. The UAV visual communication and navigation method according to claim 7, characterized in that, If the overall difference value Ve of each group of flight cases is higher than the expected overall difference value, then the following will be executed: Get all maintenance personnel who are currently working and mark them as candidates; get the number of times each candidate has formulated optimization strategies before the current time, denoted as i. Extract the time spent formulating optimization strategies for each candidate and take the average value to obtain the average strategy formulation time, denoted as p; For each candidate's corresponding optimization strategy, obtain the pre-evaluated data transmission effect score; The data transmission performance score is determined by technical personnel based on the final display effect of the transmitted data after each application of the optimization strategy. The data transmission performance scores of each candidate for each optimization strategy are compared with the set passing score, and the optimization strategies with scores higher than the passing score are recorded as excellent strategies. Obtain the number of times each candidate has a good strategy and the total number of times they have formulated an optimization strategy in history, and calculate the ratio. The success rate of each candidate's historical optimization strategy before the current time point is recorded as 0. Through formula The comprehensive ability value Xt of each candidate is obtained by weighting the number of times i, the average time p, and the success rate o of the historical optimization strategy formulation for each candidate. Here, s1, s2, and s3 correspond to the weighting factors of the number of times i, the average time p, and the success rate o of the historical optimization strategy formulation, respectively. Based on the comprehensive ability value Xt of each candidate, the candidate with the highest comprehensive ability value Xt is selected as the maintenance personnel for the UAV.

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