A navigation and positioning method based on multi-source data fusion

Abstract

The present invention relates to a navigation and positioning method based on multi-source data fusion, aiming to improve the positioning accuracy and stability of mobile devices such as unmanned aerial vehicles (UAVs). First, a cylindrical preset configuration centered on the UAV is constructed to screen high-quality base station data. Subsequently, by deeply analyzing the relationship between the included angle, distance, and the quality and quantity of adjustments, a model is established to dynamically evaluate and update the optimal adjustment quantity. Further, the method fuses various measurement data to generate comprehensive navigation information and sends it to the flight command center to control the flight of the UAV. In addition, the concepts of the minimum credible region and the dynamic continuous optimal configuration are introduced. By combining multiple credible regions and preferentially matching the data of the previous time period, more accurate and stable positioning is achieved. This method comprehensively considers angles, distances, credible regions, and environmental characteristics, can dynamically construct and optimize the optimal configuration of the UAV, significantly improve its positioning performance in complex environments, and provides strong support for the autonomous flight and intelligent navigation of the UAV.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular, to a navigation and positioning method based on multi-source data fusion. Background Art

[0002] Today, with the increasing development of unmanned aerial vehicle technology, the positioning accuracy and flight control of unmanned aerial vehicles have become key technical issues. Traditional unmanned aerial vehicle positioning systems mainly rely on satellite navigation systems such as GPS. However, in some complex environments, such as mountainous areas, densely built-up urban areas, or areas with severe electromagnetic interference, GPS signals may be blocked or interfered with, resulting in a decrease in positioning accuracy or even inability to position. This not only affects the flight safety of unmanned aerial vehicles but also restricts the application of unmanned aerial vehicles in more fields.

[0003] In order to improve the positioning accuracy of unmanned aerial vehicles and their ability to adapt to complex environments, various auxiliary positioning methods have emerged in the prior art. However, these methods often have some problems. For example, some methods rely on ground base stations or beacons for positioning, but the deployment cost of base stations or beacons is relatively high, and they may not be covered in some areas; other methods attempt to perform positioning through the sensors of the unmanned aerial vehicle itself, but due to the limitations of the accuracy and stability of the sensors, the positioning effect is not ideal.

[0004] In addition, the flight control of unmanned aerial vehicles also faces challenges. In a complex flight environment, an unmanned aerial vehicle needs to be able to sense its own position, speed, attitude, and other information in real time and accurately, and make correct flight decisions based on this information. However, due to the positioning accuracy problem, it is often difficult for an unmanned aerial vehicle to obtain accurate position information, resulting in deviations in flight control and even possible flight accidents.

[0005] Therefore, there is an urgent need for an unmanned aerial vehicle positioning and flight control method that can improve the positioning accuracy of unmanned aerial vehicles, adapt to complex environments, and achieve precise flight control. This method needs to be able to fuse a variety of measurement data, including the sensor data of the unmanned aerial vehicle itself, the data of ground base stations or beacons, and other available positioning information, to improve the reliability and accuracy of positioning. At the same time, this method also needs to be able to dynamically adjust positioning parameters and flight control strategies according to the flight mission and environmental characteristics of the unmanned aerial vehicle to ensure that the unmanned aerial vehicle can fly safely and stably in a complex environment. Summary of the Invention

[0006] In view of the technical problems existing in the prior art, the present invention provides a navigation and positioning method based on multi-source data fusion. To solve the above problems, a navigation and positioning method based on multi-source data fusion includes: collecting the position measurement information of an unmanned aerial vehicle (UAV), constructing a cylindrical first configuration with the UAV position as the center point, and collecting the coordinate information and distance adjustment numbers of surrounding base stations to correct the measured distance between the UAV and the base stations. At the same time, the method also calculates the relationship between the included angle, the base station distance, and the optimal adjustment number to obtain the quality of the adjustment number, sets the quality value threshold range of the adjustment number, forms a comprehensive distance adjustment number, and calculates the current position information of the UAV. Finally, the method fuses various measurement data to generate comprehensive navigation information and sends the relevant information to the flight command center to achieve precise flight control of the UAV.

[0007] The technical solution of the present invention to solve the above technical problems is as follows: A navigation and positioning method based on multi-source data fusion includes:

[0008] S101, collecting and receiving the position measurement information of the UAV, and presetting a virtual first configuration with the UAV position as the positioning point;

[0009] S102, sending information to surrounding base stations during the startup stage of the UAV, and collecting the coordinate information of each base station within the first configuration;

[0010] S103, collecting the distance adjustment numbers provided by each base station, calculating the optimal adjustment number, and dynamically updating it according to a preset time period;

[0011] S104, calculating the relationship between the included angle, the base station distance, and the optimal adjustment number to obtain the quality of the adjustment number;

[0012] S105, setting the quality value threshold range of the adjustment number, forming a comprehensive distance adjustment number, and calculating the current position information of the UAV;

[0013] S106, fusing various measurement data to generate comprehensive navigation information;

[0014] S107, sending the collected UAV flight parameters, comprehensive navigation information, the current position information of the UAV, and the comprehensive distance adjustment number to the flight command center;

[0015] S108, the flight command center confirms the UAV position based on the UAV flight parameters and flight navigation information, and controls the UAV flight.

[0016] Preferably, in S101, the first configuration includes:

[0017] The first configuration means constructing a cylinder with the UAV position as the center point;

[0018] The radius R of the cylinder is the maximum horizontal distance of the UAV signal transmission;

[0019] The height H of the cylinder is the vertical maximum distance for the UAV signal transmission;

[0020] The radius R and height H of the cylinder are greater than or equal to the maximum transmission distance D of the UAV signal, i.e., R = H = max(D).

[0021] Preferably, in step S103, the preset duration is dynamically updated, including:

[0022] The preset duration is defined based on the flight mission of the UAV and the requirements of the flight environment characteristics. When performing high-precision positioning tasks such as precise mapping and target tracking, it is defined to update the optimal adjustment number every 5 seconds;

[0023] When performing general positioning tasks such as simple patrol and monitoring, the optimal adjustment number is updated every minute;

[0024] In the mountainous complex environment with more signal occlusion and larger environmental changes, it is defined to update the optimal adjustment number every 10 seconds;

[0025] In an open environment, it is defined to update the optimal adjustment number every 3 seconds.

[0026] Preferably, step S104 includes:

[0027] Define the included angle as the lower included angle between the connection line of the UAV and the base station and the preset first configuration axis;

[0028] At the same time, measure the actual distance between the UAV and the base station; by analyzing the relationship between the included angle, the base station distance and the quality of the adjustment number, establish a calculation model for evaluating the reliability of the distance adjustment numbers provided by different base stations;

[0029] Due to the relationship between the change of the included angle and the difference between the value sent by the same base station and the optimal adjustment number, the following calculation formula is defined:

[0030]

[0031] Where the included angle is , the base station distance is , and are the weights of the included angle and the base station distance respectively, satisfying ;

[0032] is a function of the included angle, defined by the cosine function, indicating that the included angle and the function value are inversely proportional. When , the function obtains the maximum value of 1:

[0033]

[0034] is a function of the base station distance, and is defined by an inverse proportional function, indicating that the base station distance and the function value are inversely proportional. When , the function reaches the maximum value of 1:

[0035]

[0036] Adjust the quality Q calculation formula:

[0037]

[0038] Substitute the adjustment number, optimal adjustment number of each base station and the reliability score of the adjustment number of each base station into the above formula to obtain the numerical value Q for evaluating the quality of the adjustment number.

[0039] Preferably, the S105 includes:

[0040] During the dynamic adjustment period of the optimal adjustment number, if the distance adjustment number below the threshold is not used, and if the distance adjustment number above the threshold, calculate the weight and perform weighted average to obtain the comprehensive distance adjustment number, and use the comprehensive distance adjustment number as the final adjustment number. The comprehensive distance adjustment number calculation formula is as follows:

[0041] Comprehensive distance adjustment number =

[0042] where is the i-th high-quality distance adjustment number, is its corresponding weight, and n is the number of high-quality distance adjustment numbers selected.

[0043] When performing a high-precision positioning task, the quality value is a normalized value, ranging from 0 to 1, and the quality value threshold is set between 0.95 and 1.0;

[0044] When performing a general positioning task, the quality value threshold is between 0.85 and 0.95;

[0045] When performing a complex environment task, the quality value threshold is between 0.7 and 0.95;

[0046] When performing an open environment task, the quality value threshold is set between 0.95 and 0.99.

[0047] Preferably, the S105 further includes:

[0048] Starting from the sector to be recycled, traverse its adjacent sectors;

[0049] S201, collect the included angle and distance data between all available base stations and the drone at the current moment, and calculate the reliability score;

[0050] S202. Set the reliability score threshold St, consider the base stations with scores higher than St as trusted base stations, and determine the relative position relationship of the straight-line distance between the trusted base stations and the drone.

[0051] S203. According to the positions of the trusted base stations, draw the minimum trusted area centered on the drone.

[0052] S204. Combine the minimum trusted areas according to their position relationships.

[0053] S205. For each base station in the combined area, assign weights according to its reliability score Si and its position relationship in the combined area to obtain the final comprehensive adjustment number.

[0054] Preferably, in S203, the minimum trusted area includes:

[0055] The minimum trusted area represents connecting the positions of the trusted base stations centered on the drone.

[0056] According to the positions of the trusted base stations, draw cylindrical ring sector intervals with different radii and heights, and these intervals are the minimum trusted areas.

[0057] Preferably, S204 includes:

[0058] S301. Obtain the motion state information of the drone at the current moment, and collect the angle and distance data between all available base stations and the drone at the current moment.

[0059] S302. Predict the position of the drone at the next moment according to the motion model and the current state information of the drone.

[0060] S303. Combine the predicted position and the base station data at the current moment, recalculate the reliability score of each base station, and select the base stations with scores higher than the set threshold St as trusted base stations according to the reliability scores.

[0061] S304. Draw the optimal trusted area at the next moment centered on the predicted position of the drone according to the positions of the trusted base stations.

[0062] S306. Identify the minimum trusted area closest to the position of the drone between the previous moment and the current moment.

[0063] S306. Identify the minimum trusted area closest to the drone's position between the previous moment and the current moment.

[0064] S307. Set the distance adjustment numbers sent by the base stations in at least n minimum trusted areas, and compare them with the optimal trusted area calculated at the previous moment to obtain the comprehensive distance adjustment number.

[0065] Preferably, the S305 includes:

[0066] S401, during the continuous movement of the drone, continuously predict the position of the drone at the next moment to obtain the optimal interval configuration;

[0067] S402, repeat step S401 to form an optimal configuration of a dynamic continuous point angle combination;

[0068] S403, preferentially obtain the base station data at the matching position of the combined form.

[0069] Preferably, the S403 includes:

[0070] When the drone moves to the next actual position, start collecting the data of all base stations in the area;

[0071] Analyze the base station data at the current moment and the dynamic continuous optimal configuration constructed at the previous moment, calculate the angle between the connection line of each base station and the drone, and compare it with the angle combination form at the previous moment;

[0072] At the same time, consider the distance between the base station and the drone to ensure that the matching data is also close in distance;

[0073] Set the matching error threshold for the angle and distance;

[0074] Select the data of no less than n base stations from the matching data;

[0075] Each base station will send a distance adjustment number, indicating the deviation between the measured distance of the drone by the base station and its actual distance;

[0076] Combine the distance adjustment number sent by the base station and the positional relationship in the optimal credible area to calculate the comprehensive distance adjustment number.

[0077] The beneficial effects of the present invention are:

[0078] 1. By constructing a cylindrical preset configuration centered on the drone with the maximum signal transmission distance as the radius and height, an accurate and efficient communication range is defined for the drone positioning. Within this range, the drone can establish a stable connection with base stations with good signal quality and appropriate positions, thereby ensuring that the base station data participating in the positioning calculation is both comprehensive and accurate. This design is particularly important in a multi-base station environment because it effectively excludes those base stations that are too far away, have unstable signals, or may interfere with the positioning, significantly improving the positioning accuracy and efficiency.

[0079] 2. Deeply analyze the complex relationship among the included angle, base station distance, and the quality of adjustment numbers, and construct a set of scientific and accurate calculation models. This model can comprehensively consider the spatial position between the UAV and the base station, the signal transmission distance, and the quality of adjustment numbers, so as to achieve a comprehensive evaluation of the distance adjustment numbers provided by each base station. This evaluation mechanism not only ensures the accuracy of the adjustment numbers, but also enables the UAV to adjust the value of the adjustment numbers in real time according to the flight mission and environmental changes through a dynamically updated optimal adjustment number strategy. In a multi-base station environment, this mechanism can quickly screen out the most reliable and accurate adjustment numbers, effectively avoiding interference and improving the stability and accuracy of positioning.

[0080] 3. Construct cylindrical ring sector intervals with different radii and heights. This technical method significantly reduces the source range of distance adjustment numbers. This refined zoning method can more accurately locate the base stations that have an important impact on UAV positioning, thereby effectively excluding redundant data that has little impact on the results or may introduce noise. This not only improves the efficiency of data processing, but also greatly enhances the stability and accuracy of UAV positioning. Because by restricting the source of adjustment numbers, the positioning deviation caused by data fluctuations or outliers can be reduced, making the position information of the UAV more reliable and accurate.

[0081] 4. Combine multiple minimum credible regions and further calculate the distance adjustment number at the current moment to obtain a comprehensive adjustment number. This step has significant advantages in technology. By combining minimum credible regions of different sizes and shapes, it is possible to more comprehensively cover all credible base stations that contribute to UAV positioning, ensuring that the information of each base station is fully utilized. At the same time, this combination method also takes into account the relative position and reliability score between base stations, and reflects their importance to the final adjustment number by assigning different weights. This weighted average calculation method can more accurately reflect the actual impact of each base station on the UAV's position, thereby obtaining a more accurate and stable comprehensive adjustment number. This not only improves the accuracy and robustness of UAV positioning, but also provides more reliable data support for subsequent navigation and control.

[0082] 5. The embodiment of this application dynamically and continuously predicts the position of the UAV at the next moment, and combines the base station data at the current moment to accurately calculate the optimal credible region at the next moment. This process not only ensures that the UAV can update its positioning information in real time during flight, but also provides a solid foundation for the accurate navigation and positioning of the UAV by continuously optimizing the range and accuracy of the credible region. The combination of this dynamic prediction and real-time correction greatly improves the positioning accuracy and stability of the UAV in a complex environment, enabling it to better meet the requirements of various flight missions.

[0083] 6. The embodiment of the present application also sets a quantity threshold to screen the base station data within no less than n minimum credible regions by combining the distance adjustment numbers sent by the base stations within the optimal credible region obtained at the previous moment and the minimum credible region that is closest in distance between the previous moment and the current moment, further improving the accuracy and robustness of the UAV positioning. This method not only makes full use of the redundant information provided by multiple base stations, but also effectively suppresses the influence of incorrect data on the positioning result through intelligent screening and weight allocation. At the same time, through the calculation of the comprehensive distance adjustment number, the fine correction of the UAV position is realized, thus ensuring the high-precision positioning of the UAV during flight and providing a strong guarantee for the safe flight and mission execution of the UAV.

[0084] 7. By comprehensively considering the angle, distance, size of the credible region and its distribution law, this technical solution can dynamically construct and optimize the optimal configuration of the UAV. This configuration is centered on the UAV, and a specific combination form of point angle is formed by the connection line between the center point and the center point of the credible region, which not only improves the accuracy of UAV positioning, but also enhances its adaptability to complex environments. The UAV can adjust its positioning strategy in real time according to the changes in the surrounding environment to ensure high-precision positioning during the movement process.

[0085] 8. When obtaining the global base station data in the next time period, this technical solution preferentially obtains the base station data that matches the combination form constructed in the previous time period. This strategy significantly reduces the data processing volume, improves the positioning efficiency, and at the same time ensures the accuracy and relevance of the data used. By preferentially processing the data that matches the current optimal configuration, the UAV can respond to environmental changes faster, achieve more stable and reliable positioning, and thus provide a solid technical guarantee for its execution of various tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 is a flowchart of a navigation and positioning method based on multi-source data fusion according to the present invention;

[0087] Figure 2 is a first configuration diagram of the maximum distance of UAV signal transmission according to the present invention;

[0088] Figure 3 is an example diagram of the minimum credible region according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0089] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0090] In the description of the present application, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present application, "a plurality of" means two or more, unless otherwise specifically defined.

[0091] In the description of the present application, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present application is not necessarily construed as being more preferred or having more advantages than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present application.

[0092] Embodiment 1:

[0093] As Figure 1 shown, a navigation and positioning method based on multi-source data fusion includes the following steps:

[0094] S101, collect the position measurement information of the receiving unmanned aerial vehicle (UAV), and preset a virtual first configuration with the position of the UAV for positioning.

[0095] Wherein, the first configuration means constructing a cylinder with the position of the UAV as the center point. The radius R of the cylinder is the maximum horizontal distance of the UAV signal transmission, and the height H of the cylinder is the maximum vertical distance of the UAV signal transmission. The radius R and height H of the cylinder are greater than or equal to the maximum transmission distance D of the UAV signal, that is, R = H = max(D).

[0096] S102, send information to the surrounding base stations during the startup stage of the UAV, and collect the coordinate information of each base station within the first configuration.

[0097] S103, collect the distance adjustment numbers provided by each base station, calculate the optimal adjustment number, and dynamically update it according to a preset duration.

[0098] Wherein, install a GPS detection receiver on the base station at a precise known position. This receiver can measure and calculate the difference between the actual distance and the measured distance between the base station and the GPS Beidou satellite, that is, the distance adjustment number;

[0099] The distance adjustment number is used to correct the measured distance between the UAV and the base station to improve the positioning accuracy.

[0100] The optimal adjustment number is calculated by using the existing weighted least squares method. In the scenario of this embodiment, assuming that there are n base stations providing distance adjustment numbers, which are respectively denoted as , ,......, , and the corresponding weights are , ,......, , and satisfy , then the optimal adjustment number can be calculated by the following formula:

[0101]

[0102] Since the weights have been normalized, the sum of the weights of all base stations must be equal to 1, that is , so the denominator can be omitted; where represents the optimal adjustment number, n is the number of base stations, is the distance adjustment number provided by the i-th base station, is the weight corresponding to the i-th base station.

[0103] The preset duration is defined based on the flight mission of the UAV and the requirements of the flight environment characteristics. When performing high-precision positioning tasks such as precision mapping and target tracking, the optimal adjustment number is defined to be updated every 5 seconds; when performing general positioning tasks such as simple patrol and monitoring, the optimal adjustment number is updated every minute; in the mountainous complex environment with many signal occlusions and large environmental changes, the optimal adjustment number is defined to be updated every 10 seconds; in the open environment, the optimal adjustment number is defined to be updated every 3 seconds.

[0104] S104, calculate the relationship between the included angle, the base station distance and the optimal adjustment number to obtain the adjustment number quality.

[0105] Specifically, the included angle is defined as the lower included angle between the line connecting the UAV and the base station and the preset first configuration axis; at the same time, measure the actual distance between the UAV and the base station; by analyzing the relationship between the included angle, the base station distance and the adjustment number quality, establish a calculation model for evaluating the reliability of the distance adjustment numbers provided by different base stations;

[0106] Due to the relationship between the change of the included angle and the difference between the value sent by the same base station and the optimal adjustment number, the following calculation formula is defined:

[0107] Assuming there are n base stations, the included angle is , the base station distance is , define the following formula to evaluate the reliability score of the adjustment number of each base station :

[0108]

[0109] Among them, and are the weights of the included angle and the base station distance respectively, satisfying ;

[0110] is a function of the included angle, defined by the cosine function, indicating that the included angle and the function value are inversely proportional. When , the function reaches the maximum value of 1:

[0111]

[0112] is a function of the base station distance, defined by the inverse proportional function, indicating that the base station distance and the function value are inversely proportional. When , the function reaches the maximum value of 1:

[0113]

[0114] Adjust the quality Q calculation formula:

[0115]

[0116] Substitute the adjustment number, optimal adjustment number and the reliability score of each base station's adjustment number into the above formula to obtain the value Q for evaluating the quality of the adjustment number.

[0117] S105. Set the quality value threshold range of the adjustment number, form the comprehensive distance adjustment number, and calculate the current position information of the UAV.

[0118] Among them, during the dynamic adjustment period of the optimal adjustment number, if the distance adjustment number below the threshold is not used, and if the distance adjustment number above the threshold, calculate the weight and perform weighted average to obtain the comprehensive distance adjustment number. Use the comprehensive distance adjustment number as the final adjustment number. The comprehensive distance adjustment number calculation formula is as follows:

[0119] Comprehensive distance adjustment number =

[0120] Among them, is the i-th high-quality distance adjustment number, is its corresponding weight, and n is the number of high-quality distance adjustment numbers selected.

[0121] When performing high-precision positioning tasks, set the quality value threshold between 0.95 and 1.0 (assuming the quality value is a normalized value within the range of 0 to 1); when performing general positioning tasks, the quality value threshold is between 0.85 and 0.95; when performing tasks in complex environments, the quality value threshold is between 0.7 and 0.95; when performing tasks in open environments, set the quality value threshold between 0.95 and 0.99.

[0122] For example, taking high-precision positioning tasks as an example, assume that the selected high-quality distance adjustment numbers are = 10.5, , , and their quality values are respectively = 0.96, , , then:

[0123] Weight calculation , , , and the distance adjustment number = 10.5×0.2 + 10.8×0.6 + 10.2×0.4 = 10.54.

[0124] It should be noted that in practical applications, comprehensive consideration and adjustment should be made according to specific factors such as the UAV positioning system, flight mission requirements, flight environment characteristics, and algorithm performance. For other positioning tasks, the comprehensive distance adjustment number can be calculated similarly. Due to the differences in actual data distribution and quality value calculation methods, the values and weights in the above examples may not be completely accurate, but the method itself is universal.

[0125] Among them, as shown in the first configuration diagram of the maximum transmission distance of the UAV signal in Figure 2 , the maximum horizontal distance that can be collected with the UAV as the center, the diameter of the cylinder formed by UAV-R1 and UAV-R2;

[0126] The maximum vertical distance collected with the UAV as the center, the height of the cylinder formed by UAV-H1 and UAV-H2, and thus the first configuration diagram of the maximum transmission distance of the UAV is constructed;

[0127] Assume that there is a base station within the configuration, and the lower angle between the line connecting the UAV and the base station and the preset first configuration axis is the angle is, and the actual distance between the UAV and the base station is r.

[0128] S106, fuse various measurement data to generate comprehensive navigation information.

[0129] Among them, the multiple measurement data include the flight states of the UAV during flight, non-flight, and waiting for flight, the data of the UAV under different angle measurement or different azimuth measurement mechanisms, the multi-channel homogeneous measurement data of the UAV under the same measurement system, the multi-channel homogeneous measurement data of the UAV under different measurement systems, the heterogeneous measurement data of the UAV under different measurement systems, and the joint measurement data of the UAV at different flight time periods.

[0130] S107, Send the collected UAV flight parameters, integrated navigation information, the current position information of the UAV, and the integrated distance adjustment number to the flight command center.

[0131] Among them, the flight parameters of the UAV include: angular rate, acceleration, roll heading, altitude, rotor speed, voltage, and fuel usage data. The fuel usage data can be the remaining power value of the UAV fuel cell, the used power value of the UAV fuel cell, the power usage percentage of the UAV fuel cell, or the remaining power percentage of the UAV fuel cell.

[0132] S108, The flight command center confirms the position of the UAV based on the flight parameters and flight navigation information of the UAV, and controls the flight of the UAV.

[0133] Specifically, the planned flight navigation information is sent to the flight command center. After receiving this information, the flight command center will perform real-time control on the UAV based on the flight parameters and flight navigation information of the UAV. The control command will be sent to the actuator of the UAV through the flight control computer to guide the UAV to fly according to the planned flight trajectory and parameters.

[0134] The technical solutions in the embodiments of the present application described above have at least the following technical effects or advantages:

[0135] 1. By constructing a cylindrical preset configuration centered on the UAV with the maximum signal transmission distance as the radius and height, an accurate and efficient communication range is defined for UAV positioning. Within this range, the UAV can establish a stable connection with a base station with good signal quality and appropriate position, thus ensuring that the base station data participating in the positioning calculation is both comprehensive and accurate. This design is particularly important in a multi-base station environment because it effectively excludes those base stations that are too far away, have unstable signals, or may interfere with positioning, significantly improving the accuracy and efficiency of positioning.

[0136] 2. Deeply analyze the complex relationship among the included angle, the distance from the base station, and the quality of the adjustment number, and construct a set of scientific and accurate calculation models. This model can comprehensively consider the spatial position between the UAV and the base station, the signal transmission distance, and the quality of the adjustment number, so as to achieve a comprehensive evaluation of the distance adjustment numbers provided by each base station. This evaluation mechanism not only ensures the accuracy of the adjustment number, but also enables the UAV to dynamically update the optimal adjustment number strategy, so that the UAV can adjust the value of the adjustment number in real time according to the flight mission and environmental changes. In a multi-base station environment, this mechanism can quickly screen out the most reliable and accurate adjustment numbers, effectively avoid interference, and improve the stability and accuracy of positioning.

[0137] Embodiment 2:

[0138] In the above Embodiment 1, by designing a cylindrical preset configuration centered on the UAV and determining its radius and height according to the maximum distance of the UAV signal, the communication and positioning ranges are effectively limited. Secondly, comprehensively considering the included angle, the distance from the base station, and the quality of the adjustment number, the reliability of the adjustment numbers provided by the base stations is evaluated through an accurate model. Combining with the dynamic optimal adjustment number, the UAV can quickly obtain reliable distance adjustment numbers in a multi-base station environment, improving the positioning accuracy and adaptability to complex environments, and providing a solid technical guarantee for the flight mission. To further improve the positioning accuracy, reduce the interference of irrelevant base stations, and optimize the positioning efficiency, now make a further improvement to Embodiment 1. Based on the formula in step S105, obtain the minimum credible regions, combine multiple minimum credible regions, calculate the distance adjustment numbers at the current moment, and obtain the comprehensive adjustment numbers.

[0139] Now make a further improvement on the basis of step S105 in Embodiment 1, specifically:

[0140] S201. Collect the included angles and distances between all available base stations and the UAV at the current moment, and calculate the reliability scores.

[0141] S202. Set a reliability score threshold St, regard the base stations with scores higher than St as credible base stations, and determine the relative position relationship of the straight-line distances between the credible base stations and the UAV.

[0142] Among them, the setting of the reliability score threshold St can dynamically adjust the value of St according to the flight environment of the UAV, the changes in the base station layout, and the real-time requirements of the positioning accuracy. For example, in an area with dense base stations and good signal quality, St can be appropriately increased to screen out better-quality credible base stations; while in an area with sparse base stations or poor signal quality, St can be appropriately decreased to retain more base stations for positioning.

[0143] S203. Draw the minimum credible area centered on the UAV according to the positions of the credible base stations.

[0144] Among them, the minimum credible area represents a cylindrical ring sector interval with different radii and heights centered on the UAV and connecting the positions of the credible base stations. These intervals are the minimum credible area, and the radius and height of each cylindrical ring sector interval can be adjusted according to actual needs to ensure that all credible base stations are covered and the range is minimized as much as possible.

[0145] An example diagram of the minimum credible area is as Figure 3 shown, where base stations A, B, C, and D are credible base stations. The distances and heights of base stations A, B, C, and D from the UAV are different. Taking the position of the UAV as the center, the minimum credible area is obtained by connecting between the base stations.

[0146] S204. Combine the minimum credible areas according to their positional relationships.

[0147] Among them, the combined area should cover all credible base stations as much as possible and ensure that the contribution of each base station to the final adjustment number is taken into account.

[0148] S205. For each base station in the combined area, assign weights according to its reliability score Si and its positional relationship in the combined area , and obtain the final comprehensive adjustment number.

[0149] Specifically, the weight should reflect the importance of the base station to the final adjustment number. The base station with a higher reliability score and closer to the center of the UAV should be assigned a greater weight. For each combined area, use the distance information provided by the internal base stations and combine the weight to calculate the distance adjustment number of this area. If there are multiple combined areas, further perform weighted averaging or take other appropriate statistical values of the distance adjustment numbers of these areas as the final comprehensive adjustment number.

[0150] The technical solutions in the embodiments of the present application described above have at least the following technical effects or advantages:

[0151] 1. Construct cylindrical ring sector intervals with different radii and heights. This technical method significantly narrows the source range of distance adjustment numbers. This refined zoning method can more accurately locate the base stations that have an important impact on UAV positioning, effectively eliminating redundant data that has less impact on the results or may introduce noise. This not only improves the efficiency of data processing but also greatly enhances the stability and accuracy of UAV positioning. Because by restricting the source of adjustment numbers, positioning deviations caused by data fluctuations or outliers can be reduced, making the position information of the UAV more reliable and accurate.

[0152] 2. Combine multiple minimum credible regions and further calculate the distance adjustment number at the current moment to obtain a comprehensive adjustment number. This step has significant advantages technically. By combining minimum credible regions of different sizes and shapes, it is possible to more comprehensively cover all the credible base stations that contribute to UAV positioning, ensuring that the information of each base station is fully utilized. At the same time, this combination method also takes into account the relative positions and reliability scores of the base stations, and reflects their importance to the final adjustment number by assigning different weights. This weighted average calculation method can more accurately reflect the actual impact of each base station on the UAV's position, thus obtaining a more accurate and stable comprehensive adjustment number. This not only improves the accuracy and robustness of UAV positioning but also provides more reliable data support for subsequent navigation and control.

[0153] Embodiment 3:

[0154] In the above Embodiment 2, by obtaining the minimum credible region, the source range of the distance adjustment number is narrowed, redundant data is reduced, and stability is enhanced. To further improve the accuracy and stability of positioning, Embodiment 2 is now further improved. By predicting the position of the UAV at the next moment and calculating the optimal credible region, combined with multi-moment base station data and quantity thresholds, a more accurate comprehensive distance adjustment number is obtained.

[0155] Now, based on step S204 of Embodiment 2, further improvements are made, specifically as follows:

[0156] S301. Obtain the motion state information of the UAV at the current moment and collect the angle and distance data between all available base stations and the UAV at the current moment.

[0157] S302. Predict the position of the UAV at the next moment according to the motion model of the UAV and the current state information.

[0158] Among them, the motion model formula is x_next = x_current + vx_current Δt, x_next represents the position of the UAV at the next moment, x_current represents the current position of the UAV, vx_current represents the current speed of the UAV, and Δt is the time interval.

[0159] S303. Combine the predicted points and the base station data at the current moment, recalculate the reliability scores of each base station, and based on the reliability scores, select the base stations with scores higher than the set threshold St as trusted base stations.

[0160] S304. With the predicted UAV position as the center, draw the optimal trusted area at the next moment according to the positions of the trusted base stations.

[0161] Among them, the optimal trusted area at the next moment consists of cylindrical ring sector intervals with different radii and different heights.

[0162] S305. When the UAV moves to the next actual point according to the control instruction, obtain the distance adjustment numbers sent by the base stations within the optimal trusted area at the previous moment.

[0163] Specifically, when the UAV moves to the next actual point according to the control instruction, send requests to all the base stations within the optimal trusted area determined at the previous moment through the communication link, requesting to obtain the distance adjustment numbers between their respective measured UAV positions and the base station positions based on the current moment (i.e., the moment after the UAV moves to the actual point). Receive the responses from each base station, and these responses contain the distance adjustment numbers calculated by them respectively. Check and sort out the received distance adjustment numbers to ensure the accuracy and integrity of the data.

[0164] S306. Identify the smallest trusted area closest to the UAV position between the previous moment and the current moment.

[0165] Among them, between the previous moment and the current moment, identify the smallest trusted area closest to the actual UAV position. Here, "closest" can be determined by calculating the Euclidean distance between the actual UAV position and the center points of each smallest trusted area, and the one with the smallest distance is the closest smallest trusted area;

[0166] The smallest trusted area is a cylindrical ring sector interval with possible different radii and heights drawn with the UAV predicted position as the center based on the positions of the trusted base stations. Therefore, when identifying the closest smallest trusted area, it is actually comparing the distance between the actual UAV position and the center points of these cylindrical ring sector intervals.

[0167] S307. Set the distance adjustment numbers sent by the base stations within no less than n quantity thresholds of the smallest trusted area, and the optimal trusted area calculated at the previous moment, to obtain the comprehensive distance adjustment number.

[0168] Specifically, no less than n distance adjustment numbers sent by base stations are screened out from the optimal credible region determined at the previous moment. Corresponding weights are assigned to these base stations according to their reliability scores and their positional relationships in the optimal credible region (such as the distance from the center point, the angle size, etc.). The calculated comprehensive distance adjustment number is used as one of the bases for UAV position correction to improve the accuracy and stability of UAV positioning.

[0169] Among them, the specific value of n should be comprehensively considered according to factors such as the actual application scenario, base station distribution density, UAV positioning accuracy requirements, and the reliability of the communication link; in actual applications, a suitable range of n values can be determined through simulation tests or experimental verification. For example, a relatively small n value can be set for preliminary testing first, and then the value of n can be gradually increased to observe the change trend of positioning accuracy, and finally an n value that can meet the positioning accuracy requirements without overly increasing the computational complexity is found.

[0170] For example, currently the UAV is performing a forest fire detection task. The current position of the UAV (x_current) = (100, 100, 50) meters (three-dimensional coordinates, assuming the height is 50 meters), the current speed of the UAV (vx_current) = (5, 3, 0) m / s (three-dimensional velocity vector, assuming the UAV moves horizontally and the height remains unchanged), the time interval (Δt) = 1 second, and the motion model formula x_next = x_current + vx_current Δt is used for calculation:

[0171] x_next = (100, 100, 50) + (5, 3, 0) 1 = (105, 103, 50) meters;

[0172] Suppose there are 5 base stations. According to their angles, distances from the UAV, and historical data, the reliability scores of each base station are calculated. The threshold St is set to 0.8, and the base stations with scores higher than 0.8 are screened out as credible base stations. Suppose 3 credible base stations are obtained after screening, and they are respectively marked as A, B, and C;

[0173] Taking the predicted UAV position (105, 103, 50) as the center, according to the positions of the credible base stations A, B, and C, cylindrical ring sector intervals with different radii and heights are drawn as the optimal credible region;

[0174] Suppose base station A is closer to the UAV, and the corresponding cylindrical ring sector interval has a smaller radius; base stations B and C are farther away, and the corresponding cylindrical ring sector intervals have larger radii;

[0175] The drone moves to the next actual position according to the control instruction. Assuming the actual position is (104, 102, 50), it sends requests to base stations A, B, and C within the optimal credible region determined at the previous moment through the communication link, and obtains the distance adjustment numbers measured by each of them between the drone's position and the base station's position. Assuming the received distance adjustment numbers are: -1 meter (base station A), +2 meters (base station B), 0 meter (base station C);

[0176] Calculate the Euclidean distance between the actual position of the drone (104, 102, 50) and the center points of each minimum credible region. Assuming the center point of the minimum credible region corresponding to base station A is the closest to the drone, the minimum credible region corresponding to base station A is identified as the nearest minimum credible region;

[0177] Set the quantity threshold n to 2 (comprehensively considered according to factors such as the actual application scenario and base station distribution density). Screen out the distance adjustment numbers sent by 2 base stations (A and B) within the optimal credible region determined at the previous moment, and assign weights to them according to the reliability scores of the base stations and their positional relationships in the optimal credible region. Assuming the weight of base station A is 0.6 and the weight of base station B is 0.4, the comprehensive distance adjustment number = -1 0.6 + 2 0.4 = 0.2.

[0178] The technical solutions in the embodiments of the present application described above have at least the following technical effects or advantages:

[0179] 1. In the embodiment of the present application, by dynamically and continuously predicting the position of the drone at the next moment and combining the base station data at the current moment, the optimal credible region at the next moment is accurately calculated. This process not only ensures that the drone can update its positioning information in real time during flight, but also provides a solid foundation for the precise navigation and positioning of the drone by continuously optimizing the scope and accuracy of the credible region. The combination of this dynamic prediction and real-time correction greatly improves the positioning accuracy and stability of the drone in a complex environment, enabling it to better meet the requirements of various flight tasks.

[0180] 2. The embodiment of the present application also combines the distance adjustment numbers sent by the base stations within the optimal credible region obtained at the previous moment, and the minimum credible region closest to the previous moment and the current moment, and sets a quantity threshold to screen out the base station data within no less than n minimum credible regions, further improving the accuracy and robustness of the drone positioning. This method not only makes full use of the redundant information provided by multiple base stations, but also effectively suppresses the influence of incorrect data on the positioning result through intelligent screening and weight assignment. At the same time, through the calculation of the comprehensive distance adjustment number, the fine correction of the drone's position is realized, thus ensuring the high-precision positioning of the drone during flight and providing a strong guarantee for the safe flight and mission execution of the drone.

[0181] Example 4:

[0182] In the above Example 3, after predicting the next position of the drone and calculating the optimal credible area in combination with the base station data, when the drone arrives, at least n base station adjustment numbers and the nearest credible area are integrated to obtain an accurate correction number. To further solve the problem of the drone efficiently and accurately determining its position and optimizing the positioning accuracy during continuous movement, the present invention further improves Example 1. During continuous flight, the drone dynamically constructs an optimal interval, fuses angles, area sizes, and distributions to form a continuous optimal configuration. Taking the drone as the center, the connection lines with the centers of the credible areas form an angle combination. In the next time period, the base station data matching this configuration is preferentially collected.

[0183] Now, based on step S305 of Example 3, further improvements are made, specifically as follows:

[0184] S401. During the continuous movement of the drone, continuously predict the position of the drone at the next moment to obtain an optimal interval configuration.

[0185] Specifically, use the motion model x_next = x_current + vx_current Δt to calculate the position of the drone at the next moment, set x_next as the new x_current, and update vx_current (if the speed changes);

[0186] Collect the angle and distance data between all available base stations and the drone at the current moment. According to the base station data, calculate the reliability score of each base station, select the base stations with scores higher than St as credible base stations, and take the predicted position of the drone as the center point. According to the positions of the credible base stations, draw a cylindrical ring sector interval with possibly different radii and heights as the optimal credible area;

[0187] Among them, considering angles (the directions of the connection lines between the base stations and the drone), the sizes of the credible areas (radii and heights), and distribution rules (such as base station density, distribution uniformity, etc.) to optimize the configuration, specifically:

[0188] Calculate the directions (angles) of the connection lines between all credible base stations and the drone, and count the number of base stations in each direction. By adjusting the flight trajectory or predicted position of the drone, make the number of base stations in each direction as balanced as possible to avoid too many or too few base stations in a certain direction; according to the flight mission or environmental characteristics of the drone, determine some key flight directions, and when optimizing the configuration, give priority to the base stations in these key directions to ensure that they can cover the predicted position of the drone;

[0189] Dynamically adjust the radius and height of the trusted area according to the flight speed and altitude of the drone and the distribution density of the base stations. For example, when the drone is flying at a relatively high speed, the radius can be appropriately increased to cover more base stations; when the drone is flying at a relatively high altitude, the height of the trusted area should be correspondingly increased.

[0190] When drawing the trusted area, the coverage range of the base stations (such as signal strength, transmission distance, etc.) should be referred to. Ensure that the trusted area can cover the effective coverage ranges of all trusted base stations.

[0191] When selecting trusted base stations, in addition to considering their reliability scores, their distribution patterns should also be considered. Priority should be given to those base stations that are evenly distributed and can cover the surrounding area of the predicted position of the drone.

[0192] S402. Repeat step S401 to form an optimal configuration of a dynamic continuous combination of point angles.

[0193] Specifically, as the drone moves continuously, taking the drone as the center point, connecting the center point with the center point of the trusted area, calculate the angles between these connections to form a combination of point angles.

[0194] S403. Prioritize obtaining the base station data at the matching combination.

[0195] Specifically, when the drone moves to the next actual point, start collecting the data of all base stations in the area.

[0196] Analyze the base station data at the current moment and the dynamic continuous optimal configuration constructed at the previous moment, calculate the angle between the connection of each base station and the drone, and compare it with the combination of angles at the previous moment.

[0197] At the same time, consider the distance between the base station and the drone to ensure that the matching data is also close in terms of distance; set the matching error thresholds for the angle and distance, such as the angle error is less than 5 degrees and the distance error is less than 10 meters.

[0198] From the matching data, screen out the data of no less than n base stations (n is a parameter determined according to the actual application scenario, such as 5).

[0199] Each base station will send a distance adjustment number, indicating the deviation between the distance of the drone measured by the base station and its actual distance.

[0200] Combining the distance adjustment numbers sent by these base stations and their positional relationships in the optimal trusted area, calculate the comprehensive distance adjustment number.

[0201] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages:

[0202] 1. By comprehensively considering the angle, distance, size of the credible region, and its distribution pattern, this technical solution can dynamically construct and optimize the optimal configuration of the UAV. This configuration is centered around the UAV, and a specific combination of point-angle forms is formed by the connection line between the center point and the center point of the credible region, which not only improves the accuracy of UAV positioning but also enhances its adaptability to complex environments. The UAV can adjust its positioning strategy in real time according to the changes in the surrounding environment to ensure high-precision positioning during the flight process.

[0203] 2. When obtaining the data of all-region base stations in the next time period, this technical solution preferentially obtains the base station data that matches the combination form constructed in the previous time period. This strategy significantly reduces the data processing volume, improves the positioning efficiency, and ensures the accuracy and relevance of the data used. By preferentially processing the data that matches the current optimal configuration, the UAV can respond more quickly to environmental changes, achieve more stable and reliable positioning, and thus provide a solid technical guarantee for its execution of various tasks.

[0204] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0205] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0206] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0207] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the flowchart(s) Figure 1 a flowchart or flowcharts and / or block(s) Figure 1 a block or blocks.

[0208] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in the flowchart(s) Figure 1 a flowchart or flowcharts and / or block(s) Figure 1 a block or blocks.

[0209] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0210] It is apparent that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A navigation and positioning method based on multi-source data fusion, characterized in that: include: S101, collecting and receiving position measurement information of the UAV, and presetting a virtual first configuration based on the position of the UAV; S102, sending information to surrounding base stations during the startup phase of the drone to collect coordinate information of each base station in the first configuration; S103, collecting the distance adjustment number provided by each base station, calculating the optimal adjustment number and dynamically updating it according to a preset time length; S104, calculating the relationship between the angle, the base station distance and the optimal adjustment number to obtain the adjustment number quality; S105, setting a quality value threshold range of the adjustment number, forming a comprehensive distance adjustment number, and calculating the current position information of the UAV; S106, integrating various measurement data to generate comprehensive navigation information; S107, sending the collected UAV flight parameters, comprehensive navigation information, current location information of the UAV, and comprehensive distance adjustment number to the flight command center; S108, the flight command center confirms the position of the drone based on the flight parameters and flight navigation information of the drone, and controls the flight of the drone.

2. A navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that: The S101, first configuration, includes: The first configuration represents the construction of a cylinder with the drone’s position as the center point; The cylindrical radius R is the maximum lateral distance that the drone signal can be sent; The height H of the cylinder is the maximum vertical distance that the drone signal can be sent; The radius R and height H of the cylinder are greater than or equal to the maximum transmission distance D of the drone signal, that is, R=H=max(D).

3. A navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that: The step S103, dynamically updating the preset duration, includes: The preset duration is defined based on the flight mission and flight environment characteristics of the drone. When performing high-precision positioning tasks such as precision mapping and target tracking, the optimal adjustment number is updated every 5 seconds. When performing simple patrol and monitoring general positioning tasks, the optimal adjustment number is updated once a minute; In complex mountainous environments with large signal obstructions and large environmental changes, the optimal adjustment number is updated every 10 seconds; In an open environment, the optimal adjustment number is defined to be updated every 3 seconds.

4. A navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that: The S104 includes: The angle is defined as the lower angle between the line connecting the drone and the base station and the preset first configuration axis; At the same time, the actual distance between the drone and the base station is measured; by analyzing the relationship between the angle, base station distance and the quality of the adjustment number, a calculation model is established to evaluate the reliability of the distance adjustment numbers provided by different base stations; Due to the change in the angle, the difference between the value sent by the same base station and the optimal adjustment value is calculated by defining the following formula: Among them, the angle is , the base station distance is , and are the weights of the angle and base station distance, respectively, satisfying ; It is a function of the angle. It is defined by the cosine function to indicate that the angle is inversely proportional to the function value. , the function reaches its maximum value 1: It is a function of the base station distance. It is defined as an inverse proportional function, which means that the base station distance is inversely proportional to the function value. When , the function reaches the maximum value 1: Adjustment number quality Q calculation formula: The adjustment number and optimal adjustment number of each base station and the reliability score of each base station adjustment number Substituting into the above formula, we get the value Q for evaluating the quality of the adjustment number.

5. A navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that: The S105 includes: During the dynamic adjustment period of the optimal adjustment number, if the distance adjustment number is lower than the threshold, it will not be used. If the distance adjustment number is higher than the threshold, the weight is calculated and the weighted average is taken to obtain the comprehensive distance adjustment number, which is used as the final adjustment number. The calculation formula for the comprehensive distance adjustment number is as follows: Comprehensive distance adjustment number = ; in, is the i-th high-quality distance adjustment number, is its corresponding weight, n is the number of high-quality distance adjustment numbers screened out; When performing high-precision positioning tasks, the quality value is a normalized value ranging from 0 to 1, and the quality value threshold is set between 0.95 and 1.0; When performing general positioning tasks, the quality value threshold is between 0.85 and 0.95; When performing complex environment tasks, the quality value threshold is between 0.7 and 0.95; When performing open environment tasks, set the quality value threshold between 0.95 and 0.

99.

6. A navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that: The S105 further includes: Starting from the sector to be reclaimed, traverse its adjacent sectors; S201, collecting the angle and distance data between all available base stations and the drone at the current moment, and calculating the reliability score; S202, setting a reliability score threshold St, considering base stations with scores higher than St as credible base stations, and determining the relative position relationship of the straight-line distance between the credible base stations and the UAV; S203, drawing a minimum trusted area with the drone as the center according to the location of the trusted base station; S204, combining the minimum credible areas according to their positional relationships; S205: For each base station in the combined area, a weight is assigned according to its reliability score Si and its position relationship in the combined area to obtain a final comprehensive adjustment number.

7. A navigation and positioning method based on multi-source data fusion according to claim 6, characterized in that: The minimum trusted area in S203 includes: The minimum trusted area represents the location centered on the drone and connected to the trusted base station; According to the location of the trusted base station, cylindrical ring fan-shaped intervals with different radii and heights are connected and drawn, and these intervals are the minimum trusted areas.

8. A navigation and positioning method based on multi-source data fusion according to claim 6, characterized in that: The S204 includes: S301, obtaining the current UAV motion state information, and collecting the angle and distance data between all available base stations and the UAV at the current moment; S302, predicting the position of the drone at the next moment based on the motion model and current state information of the drone; S303, combining the predicted point location and the base station data at the current moment, recalculating the reliability score of each base station, and selecting base stations with scores higher than a set threshold St as credible base stations according to the reliability scores; S304, taking the predicted UAV position as the center, and drawing the optimal trusted area at the next moment according to the position of the trusted base station; S305, when the UAV moves to the next actual point according to the control command, it obtains the distance adjustment number sent by the base station in the optimal trustworthy area at the previous moment; S306, identifying the smallest credible area closest to the position of the drone between the previous moment and the current moment; S307, setting the distance adjustment number sent by the base station in the minimum credible area of ​​not less than n number thresholds and the optimal credible area calculated at the last moment to obtain a comprehensive distance adjustment number.

9. A navigation and positioning method based on multi-source data fusion according to claim 8, characterized in that: The S305 includes: S401, during the continuous movement of the UAV, the position of the UAV at the next moment is continuously predicted to obtain the optimal interval configuration; S402, repeating step S401 to form an optimal configuration of a dynamic continuous point angle combination; S403, preferentially obtaining base station data at locations where the combination forms match.

10. A navigation and positioning method based on multi-source data fusion according to claim 9, characterized in that: The S403 includes: The drone moves to the next actual point and starts collecting data from base stations across the entire region; Analyze the base station data at the current moment and the dynamic continuous optimal configuration constructed at the previous moment, calculate the angle between each base station and the drone, and compare it with the angle combination form at the previous moment; Also consider the distance between the base station and the drone to ensure that the matched data is also close in distance; Set the matching error thresholds for angle and distance; From the matched data, filter out data of no less than n base stations; Each base station sends a distance adjustment number, which represents the deviation of the drone’s distance measured by the base station from its actual distance; The comprehensive distance adjustment number is calculated by combining the distance adjustment number sent by the base station and the position relationship in the optimal trusted area.

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