Unmanned aerial vehicle Beidou differential positioning data quality evaluation method and system

Abstract

The invention discloses an unmanned aerial vehicle Beidou differential positioning data quality evaluation method and system, and the method comprises the steps: evaluating each quality index of Beidou differential data and unmanned aerial vehicle local observation data, and calculating a comprehensive quality index based on an evaluation result; the method comprises the following steps: carrying out unmanned aerial vehicle RTK calculation, if a calculation result is not fixed, carrying out ionosphere weighting model RTK calculation, when the calculation result of the unmanned aerial vehicle RTK calculation or the calculation result of the ionosphere weighting model RTK calculation is fixed and a fixed rate reaches a set threshold value, judging that the current data has a high-precision RTK positioning condition, and if the calculation result of the ionosphere weighting model RTK calculation cannot be fixed, judging that the current data has a high-precision RTK positioning condition, and if the calculation result of the ionosphere weighting model RTK calculation cannot be fixed, judging that the current data has a high-precision RTK positioning condition. And if not, determining that the current data does not have the high-precision RTK positioning condition. The method can dynamically judge the RTK resolving availability, guarantees the positioning precision and stability of the unmanned aerial vehicle in a complex environment, and is suitable for safety control and performance optimization of unmanned aerial vehicle high-precision application scenes such as surveying and mapping, monitoring and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of centimeter-level RTK high-precision positioning performance evaluation for unmanned aerial vehicles (UAVs), and in particular to a method and system for evaluating the quality of Beidou differential positioning data for UAVs. Background Art

[0002] The navigation module in drones primarily uses satellite navigation technology for absolute positioning. Due to the need for precise positioning in areas such as precision navigation and mapping, the application of high-precision RTK positioning technology has become increasingly widespread. By accessing ground-based augmentation services, drones can achieve centimeter-level positioning, making their flight paths more accurate, enabling more efficient and precise aerial photography missions and improving the accuracy and reliability of data collection. High-precision positioning also ensures the accuracy and stability of the drone's position while it is hovering, allowing it to more stably and accurately record changes in target objects and their location during aerial photography or monitoring. Furthermore, high-precision positioning plays a significant role and significance in the safe control and real-time supervision of drones.

[0003] During the process of real-time RTK high-precision positioning, drones may be affected by many factors, including: (1) the impact of Beidou differential data quality: Beidou differential data is a prerequisite for the implementation of RTK positioning technology, and the quality of its data determines the performance of terminal positioning. The sources of Beidou differential data generally include two methods: self-built short baseline base stations and access to ground-based augmentation system services. For the latter, the more base stations there are and the more evenly distributed they are, the higher the positioning accuracy of the system will be. It is also affected by factors such as the performance of the ground-based augmentation service software and the space atmospheric environment; (2) signal obstruction: During the flight of drones, it is easy for the surrounding buildings, mountains and other landforms to block the signal, resulting in reduced signal quality, thereby affecting the RTK positioning performance; (3) electromagnetic interference: drone RTK systems that communicate, measure distance and navigate by modulating radio waves are easily affected by surrounding electromagnetic interference, resulting in reduced signal quality and reduced system accuracy.

[0004] The above analysis shows that RTK positioning performed by drones is affected by numerous factors. Evaluating the real-time, high-precision positioning performance of drones is crucial to their safety and reliability. This is especially true when drones are unable to perform reliable, high-precision RTK positioning. Clarifying the mechanisms of these influencing factors can also effectively assess the Beidou differential data source, drone terminal signal reception and resolution, and the operating environment, thereby providing strong support and promotion for the development of high-precision drone applications. Summary of the Invention

[0005] In order to solve the above problems, the present invention proposes a method and system for evaluating the quality of Beidou differential positioning data for unmanned aerial vehicles. By quantifying the indicators of key factors such as Beidou differential data quality and signal obstruction, combining single and comprehensive quality evaluation, fixed rate judgment and ionospheric weighting, the RTK solution availability is dynamically judged to ensure the positioning accuracy and stability of unmanned aerial vehicles in complex environments.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0007] The present invention provides a method for evaluating the quality of Beidou differential positioning data of an unmanned aerial vehicle, comprising:

[0008] Evaluate the quality indicators of BeiDou differential data and UAV local observation data, and calculate the comprehensive quality index based on the evaluation results;

[0009] When the comprehensive quality index reaches the set judgment threshold, the UAV RTK solution is performed. If the solution result is not fixed, the ionospheric weighted model RTK solution is performed; when the solution result of the UAV RTK solution or the ionospheric weighted model RTK solution is fixed and the fixation rate reaches the set threshold, it is determined that the current Beidou differential data and the UAV local observation data meet the high-precision RTK positioning conditions; if the solution result of the ionospheric weighted model RTK solution cannot be fixed, it is determined that the current Beidou differential data and the UAV local observation data do not meet the high-precision RTK positioning conditions.

[0010] A further improvement of the present invention is that the various quality indicators include observation data integrity rate, cycle slip ratio, multipath effect, pseudorange observation noise, carrier observation noise and ionospheric variation, and the calculation expression is:

[0011] Observation data completeness rate: (1); (2);

[0012] in, Indicates the completeness rate of single-frequency observation data; Indicates the completeness rate of single system observation data; Indicates the total number of satellites observed during the observation period; Indicates that during the observation period, The total number of actual observation epochs of a satellite at a certain frequency; Indicates that during the observation period, The total number of theoretical epochs of a satellite at a certain frequency point; Indicates that during the observation period, The number of epochs in which all frequencies of the satellite have valid observation data; Indicates that during the observation period, The theoretical total number of epochs for satellites;

[0013] Cycle slip ratio: (3);

[0014] in, represents the cycle slip ratio; is the total number of observation epochs; is the epoch number where the cycle slip occurs;

[0015] Multipath error: (4);

[0016] in: Indicates the number of epochs of the sliding window; Indicates the epoch number of the sliding window; Indicates frequency; Indicates the moment; Indicates that the satellite is The estimated value of multipath error in frequency; express Satellite at all times The computational complexity of the frequency includes multipath error and integer ambiguity information;

[0017] Pseudorange and carrier observation noise: (5); (6);

[0018] in, and are pseudorange observation noise and carrier observation noise respectively, It indicates the number of triple differences of the satellite's carrier phase observations at adjacent epochs at a certain frequency point; represents the epoch; and express Pseudo-range phase observations and carrier phase observations of the satellite at a certain frequency point at any moment; represents the cubic difference factor;

[0019] Ionospheric change rate: (7);

[0020] in, Indicates that the satellite is The rate of change of ionospheric delay with frequency, in m / s; 、 Represents satellites at 、 Always The amount of calculation on frequency.

[0021] A further improvement of the present invention is that the calculation process of the comprehensive quality index includes:

[0022] Carry out trend processing and normalization of various quality indicators: (8);

[0023] in: Indicates the The quality index is The dimensionless value under the observation sample; Represents the original quality indicator after trending; and represents the normalization constant;

[0024] The entropy method is used to determine the weight of each quality indicator;

[0025] Entropy: (9);

[0026] Coefficient of variation: (10);

[0027] Weight coefficient: (11);

[0028] in: Indicates the The entropy value of each quality indicator; represents the number of samples; Indicates the total number of quality indicators; Indicates the Normalized weights of quality indicators; Indicates the The coefficient of variation of the quality indicators;

[0029] Calculate the comprehensive quality index: (12);

[0030] in: Indicates the The comprehensive evaluation value of the samples; Indicates the The ideal value of a quality indicator.

[0031] A further improvement of the present invention is that the ionospheric weighted model expression is: (13);

[0032] in: represents the double difference operator; express Carrier phase observation of frequency; express Pseudorange observations of frequency; express the wavelength of the frequency; Indicates the Frequency ambiguity; express Ionospheric amplification factor of frequency; represents the direction cosine coefficient matrix; Represents the position parameter to be estimated; represents the ionospheric pseudo-observation value; represents the ionospheric delay parameter to be estimated;

[0033] The measurement noise variance of the ionospheric pseudo-observation of the ionospheric weighted model is: (14);

[0034] in: represents the constraint strength, represents the measurement noise variance of the ionospheric pseudo-observation.

[0035] The present invention provides a UAV BeiDou differential positioning data quality evaluation system, comprising:

[0036] Single quality indicator evaluation module, used to evaluate the quality indicators of Beidou differential data and UAV local observation data;

[0037] Comprehensive quality index evaluation module, used to calculate the comprehensive quality index of Beidou differential data and the comprehensive quality index of UAV local observation data;

[0038] The RTK solution module is used to perform UAV RTK solution when the comprehensive quality index reaches the set judgment threshold. If the solution result is not fixed, the ionosphere-weighted model RTK solution is performed. When the solution result of the UAV RTK solution or the ionosphere-weighted model RTK solution is fixed and the fixation rate reaches the set threshold, it is determined that the current Beidou differential data and the UAV local observation data meet the high-precision RTK positioning conditions. If the solution result of the ionosphere-weighted model RTK solution cannot be fixed, it is determined that the current Beidou differential data and the UAV local observation data do not meet the high-precision RTK positioning conditions.

[0039] A further improvement of the present invention is that the various quality indicators include observation data integrity rate, cycle slip ratio, multipath effect, pseudorange and carrier observation noise, and ionospheric change, and the calculation expression is:

[0040] Observation data completeness rate: (1); (2);

[0041] in, Indicates the completeness rate of single-frequency observation data; Indicates the completeness rate of single system observation data; Indicates the total number of satellites observed during the observation period; Indicates that during the observation period, The total number of actual observation epochs of a satellite at a certain frequency; Indicates that during the observation period, The total number of theoretical epochs of a satellite at a certain frequency point; Indicates that during the observation period, The number of epochs in which all frequencies of the satellite have valid observation data; Indicates that during the observation period, The theoretical total number of epochs for satellites;

[0042] Cycle slip ratio: (3);

[0043] in, represents the cycle slip ratio; is the total number of observation epochs; is the epoch number where the cycle slip occurs;

[0044] Multipath error: (4);

[0045] in: Indicates the number of epochs of the sliding window; Indicates the epoch number of the sliding window; Indicates frequency; Indicates the moment; Indicates that the satellite is The estimated value of multipath error in frequency; Indicates Satellite at all times The computational complexity of the frequency includes multipath error and integer ambiguity information;

[0046] Pseudorange and carrier observation noise: (5); (6);

[0047] in, and are pseudorange observation noise and carrier observation noise respectively; It indicates the number of triple differences of the satellite's carrier phase observations at adjacent epochs at a certain frequency point; represents the epoch; and express Pseudo-range phase observations and carrier phase observations of the satellite at a certain frequency point at any moment; represents the cubic difference factor;

[0048] Ionospheric change rate: (7);

[0049] in, Indicates that the satellite is The rate of change of ionospheric delay with frequency, in m / s; 、 Represents satellites at 、 Always The amount of calculation on frequency.

[0050] A further improvement of the present invention is that the calculation process of the comprehensive quality index includes:

[0051] Carry out trend processing and normalization of various quality indicators: (8);

[0052] in: Indicates the The quality index is The dimensionless value under observation samples; Represents the original quality indicator after trending; and represents the normalization constant;

[0053] The entropy method is used to determine the weight of each quality indicator;

[0054] Entropy: (9);

[0055] Coefficient of variation: (10);

[0056] Weight coefficient: (11);

[0057] in: Indicates the The entropy value of each quality indicator; represents the number of samples; Indicates the total number of quality indicators; Indicates the Normalized weights of quality indicators; Indicates the The coefficient of variation of the quality indicators;

[0058] Calculate the comprehensive quality index: (12);

[0059] in: Indicates the The comprehensive evaluation value of the samples; Indicates the The ideal value of a quality indicator.

[0060] A further improvement of the present invention is that the ionospheric weighted model expression is: (13);

[0061] in: represents the double difference operator; express Carrier phase observation of frequency; express Pseudorange observations of frequency; express the wavelength of the frequency; express Frequency ambiguity; express Ionospheric amplification factor of frequency; represents the direction cosine coefficient matrix; Represents the position parameter to be estimated; represents the ionospheric pseudo-observation value; represents the ionospheric delay parameter to be estimated;

[0062] The measurement noise variance of the ionospheric pseudo-observation of the ionospheric weighted model is: (14);

[0063] in: represents the constraint strength, represents the measurement noise variance of the ionospheric pseudo-observation.

[0064] The electronic device of the present invention includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for evaluating the quality of Beidou differential positioning data of a UAV are implemented.

[0065] The computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for evaluating the quality of Beidou differential positioning data of a drone are implemented.

[0066] The beneficial effects of the present invention are as follows: the present invention quantifies the indicators of key factors such as Beidou differential data quality and signal obstruction, combines single and comprehensive quality assessment, fixed rate judgment and ionospheric weighted modeling and other steps, dynamically judges the availability of RTK solution, ensures the positioning accuracy and stability of drones in complex environments, and is suitable for safety management and performance optimization of high-precision drone application scenarios such as surveying and monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a flow chart of a method in an embodiment of the present invention; Figure 2 is an implementation flow chart in an embodiment of the present invention; Figure 3 is the multipath error in the open observation environment of the UAV in the embodiment of the present invention; Figure 4 is the multipath error in the complex observation environment of the UAV in the embodiment of the present invention; Figure 5 is the data availability and cycle slip ratio in the open observation environment of the UAV in the embodiment of the present invention; Figure 6 is the data availability and cycle slip ratio in the complex observation environment of the UAV in the embodiment of the present invention; Figure 7 1 is a schematic diagram of a positioning error sequence using an ionospheric fixed model in a complex scenario in an embodiment of the present invention; Figure 8 It is a schematic diagram of a positioning error sequence using an ionospheric floating-point model in a complex scenario in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0069] like Figure 1 and Figure 2As shown, this embodiment discloses a method for evaluating the quality of BeiDou differential positioning data for UAVs. The UAV locally uploads positioning results in GPGGA format and requests BeiDou differential data from a Continuously Operating Reference Station (CORS) center. The CORS center generates virtual observations (BeiDou differential data) based on the approximate position uploaded by the UAV and broadcasts them to the UAV via the network for local use. BeiDou differential data includes pseudorange observations and carrier observations. Quality indicators are calculated for the UAV's local observation data and BeiDou differential data. If any individual quality indicator fails to meet a set condition, the current data quality is determined to be abnormal and unusable for high-precision positioning solutions for the UAV. If all quality indicators meet the set conditions, a comprehensive quality indicator for the BeiDou differential data and the UAV's local observation data is calculated to further determine whether the quality of the current data (including the BeiDou differential data and the UAV's local observation data) is acceptable. If the current data quality is deemed acceptable, UAV RTK solutions are performed. If the solution result is stable and the fixation rate meets a set threshold, this further indicates that not only is the current data quality acceptable, but residual ionospheric errors and other errors after the differential analysis are also insignificant. If the solution cannot be fixed, switch to the ionospheric weighted model. After switching to the ionospheric weighted model, the solution is performed using ionospheric weighted constraint strengths (10cm, 15cm, 20cm) from small to large. When the fixation rate meets the requirements, it indicates that the ionospheric error is the main factor affecting the fixation performance. If fixation is still not possible, it is generally determined that the current data cannot be used for high-precision positioning of drones. The specific implementation steps are as follows:

[0070] Step 1: Evaluate various quality indicators of Beidou differential data and local drone observation data, including observation data integrity, cycle slip ratio, multipath effect, pseudorange and carrier observation noise, and ionospheric change rate. If any indicator fails to meet the set threshold, the current data quality is considered abnormal and cannot be used for high-precision drone positioning. Local drone observation data refers to the local observation data received by the GNSS positioning module onboard the drone. Local observation data includes pseudorange observation data and carrier observation data.

[0071] The calculation of various quality indicators specifically includes:

[0072] (1) Completeness of observation data:

[0073] Completeness rate of single-frequency observation data: (1);

[0074] Single system observation data completeness rate: (2);

[0075] in, Indicates the completeness rate of single-frequency observation data; Indicates the completeness rate of single system observation data; Indicates the total number of satellites observed during the observation period; Indicates that during the observation period, The total number of actual observation epochs of a satellite at a certain frequency; Indicates that during the observation period, The total number of theoretical epochs of a satellite at a certain frequency point; Indicates that during the observation period, The number of epochs in which all frequencies of the satellite have valid observation data; Indicates that during the observation period, The theoretical total number of epochs for satellites.

[0076] Judgment threshold: Based on engineering application experience, data is considered abnormal when the completeness rate is < 90%.

[0077] (2) Cycle slip ratio

[0078] The main steps for calculating the cycle slip ratio are as follows:

[0079] (a) Read BeiDou differential data and UAV local observation data, and count the actual epoch data of BeiDou differential data and UAV local observation data respectively;

[0080] (b) Combine the gross error detection method, cycle slip detection method, and receiver clock slip detection method to determine the epoch where the cycle slip occurs and calculate the data quantity of the cycle slip epoch;

[0081] (c) Calculate the evaluation value according to the definition of cycle slip ratio.

[0082] (3);

[0083] in, represents the cycle slip ratio; is the total number of observation epochs; is the epoch number at which the cycle slip occurs.

[0084] Judgment threshold: When the average cycle slip ratio is less than or equal to 200, the current data is determined to be unreliable.

[0085] (3) Multipath error

[0086] Calculating pseudorange multipath error requires dual-frequency observation data. The pseudorange observation equation and the carrier phase observation equation are combined to eliminate the effects of tropospheric and ionospheric delays, and the calculation is performed as follows.

[0087] (4);

[0088] in: and Represent the multipath errors of frequency point 1 and frequency point 2 respectively, that is, frequency, Frequency multipath error, 、 express frequency, Frequency pseudorange observation, in meters (m), frequency, The frequency is the drone navigation signal; 、 express frequency, Frequency: The frequency of the carrier wave, in MHz; 、 express frequency, Frequency carrier phase observation, in meters (m).

[0089] For the same satellite, the combined ambiguity parameters do not change when continuously observed without cycle slips. The multipath error is obtained by calculating the following formula between multiple epochs without cycle slips.

[0090] (5);

[0091] in: Indicates the number of epochs in the sliding window, the default is 50; Indicates the epoch number of the sliding window; Indicates frequency; Indicates the moment; Indicates that the satellite is The estimated value of multipath error in frequency; express Satellite at all times The computational cost of frequency includes multipath error and integer ambiguity information.

[0092] Judgment threshold: When >0.5m (meter), it is judged that the multipath error is large.

[0093] (4) Pseudorange and carrier observation noise

[0094] It is calculated by third-order difference and the expression is: (6); (7);

[0095] in, and are pseudorange observation noise and carrier observation noise respectively, It represents the number of triple differences of the satellite's carrier phase observations at adjacent epochs at a certain frequency point. represents the epoch, and express The pseudo-range phase observation and carrier phase observation of the satellite at a certain frequency point at this moment, represents the cubic difference factor;

[0096] Judgment threshold: If the pseudorange observation noise is greater than 1.5m, the current data quality is poor. If the carrier observation noise is greater than 0.005m, the current data noise is high.

[0097] (5) Ionospheric change rate

[0098] The calculation expression is: (8);

[0099] in, Indicates that the satellite is The rate of change of ionospheric delay with frequency, in meters per second (m / s); 、 Represents satellites at 、 Always The amount of calculation on the frequency includes ionospheric delay, multipath and integer ambiguity information.

[0100] Decision threshold: When the ionospheric change rate is greater than 0.07m / s, it is determined that an ionospheric jump has occurred and the current data is not used for high-precision positioning.

[0101] In this embodiment, the multipath error of the UAV in an open, non-interference scene and a complex, interference scene is as follows: Figure 3 and Figure 4 As shown in the figure, the data integrity rate and cycle slip of the drone in open interference-free scenes and complex interference scenes are as follows: Figure 5 and Figure 6 As shown in Table 1, various quality indicators of the two sets of data are statistically analyzed.

[0102] Table 1: Statistical results of data quality indicators in open and complex observation environments of UAVs

[0103] Step 2: If all individual quality indicators meet the corresponding judgment thresholds, the comprehensive quality indicator is further calculated. This embodiment combines normalization processing, entropy weight method and Euclidean distance to calculate the comprehensive quality indicator.

[0104] (1) Trending and dimensionless

[0105] In order to facilitate the unified comparison of indicators of different dimensions, each individual quality indicator is first Perform same trend processing and normalization: (9);

[0106] in: Indicates the The quality index is The dimensionless value under observation samples; Represents the original quality indicator after trending; and is a normalization constant, and in this embodiment, c=0 and d=1.

[0107] (2) Determining indicator weights using entropy method

[0108] Entropy: (10);

[0109] Coefficient of variation: (11);

[0110] Weight coefficient: (12);

[0111] in: Indicates the The entropy value of each quality indicator; represents the number of samples; Indicates the total number of quality indicators; Indicates the Normalized weights of quality indicators; Indicates the The coefficient of variation of the quality indicators.

[0112] (3) Calculation of comprehensive quality indicators

[0113] Use the Euclidean distance method to calculate the The comprehensive evaluation value of the samples : (13);

[0114] in: Indicates the The ideal value of a quality indicator; Indicates the The comprehensive evaluation value of the samples, The smaller the value, the better the overall data quality.

[0115] (4) Judgment threshold setting

[0116] Reference standard experience and engineering practice: set the experience threshold. In this embodiment, the judgment threshold is set to 0.3. If the current data quality is qualified, the drone RTK solution is performed, that is, conventional RTK model positioning.

[0117] If the solution is fixable and the fixation rate is above the threshold, the residual error has little impact on positioning performance. If fixation is not possible, the system switches to the ionospheric weighted model and dynamically adjusts the constraint strength to further determine the impact of ionospheric error. Finally, if fixation is still not possible, the current data is determined to be unsuitable for high-precision RTK positioning.

[0118] Step 3: Perform ionospheric weighted solution using different constraint strengths. In this embodiment, the constraint strengths are 10 cm, 15 cm, and 20 cm.

[0119] The ionospheric weighting is as follows: (14);

[0120] in: represents the double difference operator; express Carrier phase observation of frequency; express Pseudorange observations of frequency; express the wavelength of the frequency; express Frequency ambiguity; express Ionospheric amplification factor of frequency; represents the direction cosine coefficient matrix; Represents the position parameter to be estimated; Indicates the ionospheric pseudo observation value. In this embodiment ; Represents the parameter to be estimated for the ionospheric delay. The measurement noise variance of the ionospheric pseudo-observation constrains the fluctuation amplitude of the ionospheric residual error. When the measurement noise variance is zero, the ionospheric residual error is zero and completely eliminated, equivalent to the ionospheric fixed model. When the measurement noise variance is infinite, the ionospheric residual error is not constrained and is estimated jointly from carrier observations and pseudorange observations, equivalent to the ionospheric floating-point model. Compared to the fixed ionospheric model, the ionospheric floating-point model adds an estimated parameter per satellite, with the same number of observations, resulting in a weakened model. Therefore, with a small number of satellites, the ionospheric floating-point model will have difficulty converging to a fixed solution. The ionospheric weighted model, on the other hand, adds an ionospheric pseudo-observation per satellite to constrain the ionospheric delay, resulting in a slightly stronger model. The ionospheric floating-point model and the ionospheric fixed model can be considered special cases of the ionospheric weighted model.

[0121] The measurement noise variance of the ionospheric pseudo-observation of the ionospheric weighted model is: (15);

[0122] in: represents the constraint strength, represents the measurement noise variance of the ionospheric pseudo-observation.

[0123] In this embodiment, the positioning error of the ionosphere fixed model and the positioning error of the ionosphere floating point model in the complex environment of the UAV are as follows: Figure 7 and Figure 8 As shown in the figure, AVE represents the mean, STD represents the standard deviation, RMS represents the root mean square error, Fixed represents the fixed solution, Float represents the floating-point solution, and Fix represents the fixation rate. If the fixation rate meets the requirements, it indicates that the ionospheric error is the main factor affecting the fixation solution. If fixation is still not possible, the current data does not meet the conditions for high-precision RTK positioning.

[0124] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined similarly as herein, will not be interpreted in an idealized or overly formal sense.

[0125] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the quality of BeiDou differential positioning data for unmanned aerial vehicles, characterized by: include: Evaluate the quality indicators of Beidou differential data and UAV local observation data, and calculate the corresponding comprehensive quality indicators based on the evaluation results; When the comprehensive quality index reaches the set judgment threshold, the UAV RTK solution is performed. If the solution result is not fixed, the ionospheric weighted model RTK solution is performed; when the solution result of the UAV RTK solution or the ionospheric weighted model RTK solution is fixed and the fixation rate reaches the set threshold, it is determined that the current Beidou differential data and the UAV local observation data meet the high-precision RTK positioning conditions; if the solution result of the ionospheric weighted model RTK solution cannot be fixed, it is determined that the current Beidou differential data and the UAV local observation data do not meet the high-precision RTK positioning conditions.

2. The method for evaluating the quality of BeiDou differential positioning data of an unmanned aerial vehicle according to claim 1, wherein: The quality indicators include observation data integrity rate, cycle slip ratio, multipath effect, pseudorange observation noise, carrier observation noise and ionospheric variation; Observation data completeness rate: （1）； （2）； in, Indicates the completeness rate of single-frequency observation data; Indicates the completeness rate of single system observation data; Indicates the total number of satellites observed during the observation period; Indicates that during the observation period, The total number of actual observation epochs of a satellite at a certain frequency; Indicates that during the observation period, The total number of theoretical epochs of a satellite at a certain frequency point; Indicates that during the observation period, The number of epochs in which all frequencies of the satellite have valid observation data; Indicates that during the observation period, The theoretical total number of epochs for satellites; Cycle slip ratio: （3）； in, represents the cycle slip ratio; is the total number of observation epochs; is the epoch number where the cycle slip occurs; Multipath error: （4）； in: Indicates the number of epochs of the sliding window; Indicates the epoch number of the sliding window; Indicates frequency; Indicates the moment; Indicates that the satellite is The estimated value of multipath error in frequency; Indicates Satellite at all times The computational complexity of the frequency includes multipath error and integer ambiguity information; Pseudorange and carrier observation noise: （5）； （6）； in, and are pseudorange observation noise and carrier observation noise respectively; It indicates the number of triple differences of the satellite's carrier phase observations at adjacent epochs at a certain frequency point; represents the epoch; and express Pseudo-range phase observations and carrier phase observations of the satellite at a certain frequency point at any moment; represents the cubic difference factor; Ionospheric change rate: （7）； in, Indicates that the satellite is The rate of change of ionospheric delay with frequency; 、 Represents satellites at 、 Always The amount of calculation on frequency.

3. The method for evaluating the quality of BeiDou differential positioning data of an unmanned aerial vehicle according to claim 1, wherein: The calculation process of the comprehensive quality index includes: Normalize each quality indicator: （8）； in: Indicates the The quality index is The dimensionless value under observation samples; Represents the original quality indicator after trending; and represents the normalization constant; The entropy method is used to determine the weight of each quality indicator; Entropy: （9）； Coefficient of variation: （10）； Weight coefficient: （11）； in: Indicates the The entropy value of each quality indicator; represents the number of samples; Indicates the total number of quality indicators; Indicates the Normalized weights of quality indicators; Indicates the The coefficient of variation of the quality indicators; Calculate the comprehensive quality index: （12）； in: Indicates the The comprehensive evaluation value of the samples; Indicates the The ideal value of a quality indicator.

4. The method for evaluating the quality of BeiDou differential positioning data for unmanned aerial vehicles according to claim 1, wherein: The ionospheric weighted model expression is: （13）； in: represents the double difference operator; express Carrier phase observation of frequency; express Pseudorange observations of frequency; express the wavelength of the frequency; express Frequency ambiguity; express Ionospheric amplification factor of frequency; represents the direction cosine coefficient matrix; Represents the position parameter to be estimated; represents the ionospheric pseudo-observation value; represents the ionospheric delay parameter to be estimated; The measurement noise variance of the ionospheric pseudo-observation of the ionospheric weighted model is: （14）； in: represents the constraint strength, represents the measurement noise variance of the ionospheric pseudo-observation.

5. A UAV BeiDou differential positioning data quality assessment system, characterized by: include: Single quality indicator evaluation module, used to evaluate the quality indicators of Beidou differential data and UAV local observation data; Comprehensive quality index evaluation module, used to calculate the comprehensive quality index of Beidou differential data and the comprehensive quality index of UAV local observation data; The RTK solution module is used to perform UAV RTK solution when the comprehensive quality index reaches the set judgment threshold. If the solution result is not fixed, the ionosphere-weighted model RTK solution is performed. When the solution result of the UAV RTK solution or the ionosphere-weighted model RTK solution is fixed and the fixation rate reaches the set threshold, it is determined that the current Beidou differential data and the UAV local observation data meet the high-precision RTK positioning conditions. If the solution result of the ionosphere-weighted model RTK solution cannot be fixed, it is determined that the current Beidou differential data and the UAV local observation data do not meet the high-precision RTK positioning conditions.

6. The UAV BeiDou differential positioning data quality evaluation system according to claim 5, characterized in that: The quality indicators include observation data integrity rate, cycle slip ratio, multipath effect, pseudorange observation noise, carrier observation noise and ionospheric variation; Observation data completeness rate: （1）； （2）； in, Indicates the completeness rate of single-frequency observation data; Indicates the completeness rate of single system observation data; Indicates the total number of satellites observed during the observation period; Indicates that during the observation period, The total number of actual observation epochs of a satellite at a certain frequency; Indicates that during the observation period, The total number of theoretical epochs of a satellite at a certain frequency point; Indicates that during the observation period, The number of epochs in which all frequencies of the satellite have valid observation data; Indicates that during the observation period, The theoretical total number of epochs for satellites; Cycle slip ratio: （3）； in, represents the cycle slip ratio, is the total number of observation epochs, is the epoch number where the cycle slip occurs; Multipath error: （4）； in: Indicates the number of epochs of the sliding window; Indicates the epoch number of the sliding window; Indicates frequency; Indicates the moment; Indicates that the satellite is The estimated value of multipath error in frequency; express Satellite at all times The computational complexity of the frequency includes multipath error and integer ambiguity information; Pseudorange and carrier observation noise: （5）； （6）； in, and are pseudorange observation noise and carrier observation noise respectively; It indicates the number of triple differences of the satellite's carrier phase observations at adjacent epochs at a certain frequency point; represents the epoch; and express Pseudo-range phase observations and carrier phase observations of the satellite at a certain frequency point at any moment; represents the cubic difference factor; Ionospheric change rate: （7）； in, Indicates that the satellite is The rate of change of ionospheric delay with frequency; 、 Represents satellites at 、 Always The amount of calculation on frequency.

7. The UAV BeiDou differential positioning data quality evaluation system according to claim 5, characterized in that: The calculation process of the comprehensive quality index includes: Normalize each quality indicator: （8）； in: Indicates the The quality index is The dimensionless value under observation samples; Represents the original quality indicator after trending; and represents the normalization constant; The entropy method is used to determine the weight of each quality indicator; Entropy: （9）； Coefficient of variation: （10）； Weight coefficient: （11）； in: Indicates the The entropy value of each quality indicator; represents the number of samples; Indicates the total number of quality indicators; Indicates the Normalized weights of quality indicators; Indicates the The coefficient of variation of the quality indicators; Calculate the comprehensive quality index: （12）； in: Indicates the The comprehensive evaluation value of the samples; Indicates the The ideal value of a quality indicator.

8. The UAV BeiDou differential positioning data quality evaluation system according to claim 5, characterized in that: The ionospheric weighted model expression is: （13）； in: represents the double difference operator; express Carrier phase observation of frequency; express Pseudorange observations of frequency; express the wavelength of the frequency; express Frequency ambiguity; express Ionospheric amplification factor of frequency; represents the direction cosine coefficient matrix; Represents the position parameter to be estimated; represents the ionospheric pseudo-observation value; represents the ionospheric delay parameter to be estimated; The measurement noise variance of the ionospheric pseudo-observation of the ionospheric weighted model is: （14）； in: represents the constraint strength, represents the measurement noise variance of the ionospheric pseudo-observation.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for evaluating the quality of Beidou differential positioning data of a drone as described in any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium storing a computer program, wherein: When the computer program is executed by a processor, the steps of a method for evaluating the quality of Beidou differential positioning data of a UAV as described in any one of claims 1 to 4 are implemented.

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