UAV altitude measurement method and system based on data fusion

Abstract

The present invention relates to the field of height measurement technology, and in particular to a method and system for measuring the height of an unmanned aerial vehicle based on data fusion. The method obtains various influencing data of the unmanned aerial vehicle at each moment; obtains the abnormality of each influencing data according to the distribution of the influencing data, and then screens out the effective influencing data; obtains the reliability based on the effective influencing data and the distribution of the abnormality of the effective influencing data; corrects the fixed weight of each height measuring instrument by the reliability of each influencing data and the abnormality of each influencing data at the end of a preset time period, obtains the corrected fixed weight, and then obtains the true height of the unmanned aerial vehicle. The present invention analyzes each influencing data, accurately determines the modified fixed weight of each height measuring instrument, and then accurately obtains the true height of the unmanned aerial vehicle according to the corrected fixed weight of each height measuring instrument, and accurately analyzes the flight status of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of altitude measurement, and in particular to a method and system for measuring altitude of an unmanned aerial vehicle based on data fusion. Background Art

[0002] With the development of science and technology, the application scenarios of drones are becoming more and more complex. They are widely used in traffic management, environmental testing, public services and entertainment. In order to make drones meet the personalized needs of users stably and efficiently, it is necessary to accurately monitor the flight status of drones to ensure their safe operation. Among them, the flight altitude of drones is the key factor in judging whether the flight of drones is safe. Therefore, it is necessary to accurately measure the altitude of drones.

[0003] In the existing method, multiple altimeters are set to measure the altitude of the UAV at the same time, and then the altitudes of the UAV monitored by the multiple altimeters are fused through fixed weights to accurately obtain the altitude of the UAV. However, in actual situations, environmental factors and the UAV's own posture will affect the accuracy of each altimeter, resulting in inaccurate altitude of the UAV monitored by each altimeter. Continuing to use the fixed weight corresponding to each altimeter to fuse the altitudes of the UAV monitored by each altimeter will result in inaccurate altitude of the UAV finally obtained, and thus it is impossible to accurately analyze the flight status of the UAV. Summary of the invention

[0004] In order to solve the technical problem that environmental factors and the drone's own posture will affect the accuracy of each altimeter, resulting in inaccurate drone height monitored by each altimeter, and continuing to use the fixed weight corresponding to each altimeter to fuse the drone height monitored by each altimeter will result in inaccurate drone height finally obtained, the purpose of the present invention is to provide a drone height measurement method and system based on data fusion, and the technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for measuring the height of an unmanned aerial vehicle based on data fusion, the method comprising the following steps:

[0006] Obtain various impact data of the drone at each moment within the preset time period;

[0007] According to the distribution of each influencing data within a preset time period, the abnormality degree of each influencing data is obtained; and the effective influencing data is screened out based on the abnormality degree;

[0008] Based on the distribution of effective influence data in each type of influence data and the abnormality of the effective influence data, the reliability of each type of influence data is obtained;

[0009] The fixed weight of each altimeter is corrected according to the reliability of each influencing data and the abnormality of each influencing data at the end of a preset time period, so as to obtain a corrected fixed weight of each altimeter;

[0010] Based on the corrected fixed weight, get the real altitude of the drone.

[0011] Furthermore, the method for obtaining the abnormality degree is:

[0012] For any type of impact data, the impact data is clustered using the ISODATA algorithm to obtain a cluster of the impact data;

[0013] For any cluster of the influencing data, the normality of the cluster is obtained according to the difference between the central data of the cluster and the sample central data of the influencing data, and the fluctuation of the influencing data in the cluster;

[0014] For any influencing data in any cluster, the abnormality degree of the influencing data is obtained according to the difference between the influencing data and other influencing data in the cluster and the normality degree of the cluster.

[0015] Furthermore, the method for obtaining the effective impact data is:

[0016] When the abnormality level meets the preset normal level range, the corresponding impact data is effective impact data.

[0017] Furthermore, the method for obtaining the reliability is:

[0018] For any type of impact data, obtain the proportion of valid impact data in the impact data as a first reference value of the impact data;

[0019] Obtaining the mean value of the abnormality degree of the effective influencing data in each cluster of the influencing data as the first abnormality degree of the corresponding cluster;

[0020] The mean value of the first abnormality level is used as the target abnormality level of the affected data;

[0021] Obtain the result of accumulating the difference between the first abnormality degree and the target abnormality degree of each cluster of the influencing data as the reference fluctuation degree of the influencing data;

[0022] Obtain the result of accumulating the difference between the center data of each cluster of the influence data and the sample center data of the influence data as the target result of the influence data;

[0023] The reliability of the influencing data is obtained according to the first reference value, the target abnormality level, the reference fluctuation level and the target result; wherein the first reference value is positively correlated with the reliability level, and the target abnormality level, the reference fluctuation level and the target result are negatively correlated with the reliability level.

[0024] Furthermore, the method for obtaining the modified fixed weight is:

[0025] According to the reliability of each type of impact data and the difference in reliability with other types of impact data, obtain the reference level of each type of impact data;

[0026] Based on the reference degree of each influencing data and the abnormal degree of each influencing data at the end time of the preset time period, the accuracy of each influencing data is obtained; wherein the reference degree is positively correlated with the accuracy of the data itself, and the abnormal degree is negatively correlated with the accuracy of the data itself;

[0027] The fixed weight of each altimeter is corrected according to the accuracy of each influencing data to obtain the corrected fixed weight of each altimeter.

[0028] Furthermore, the reference degree is obtained by:

[0029] For any type of impact data, the mean reliability of all other types of impact data except this type of impact data is taken as the target reliability;

[0030] Obtaining the difference between the reliability of the impact data and the target reliability as the second difference of the impact data;

[0031] According to the reliability and the second difference of the impact data, a reference degree of the impact data is obtained; wherein the reliability and the second difference are both positively correlated with the reference degree.

[0032] Furthermore, the method for obtaining the modified fixed weight is:

[0033] The result of normalizing the accumulated results of all types of factors affecting the accuracy of the data is used as the target weight;

[0034] The product of the target weight and the fixed weight of each altimeter is taken as the corrected fixed weight of each altimeter.

[0035] Furthermore, the method for obtaining the real height of the drone is:

[0036] For any altimeter, the product of the height of the altimeter at the end of the preset time period and the corrected fixed weight of the altimeter is used as the fusion height of the altimeter;

[0037] The accumulated result of the fused heights of all altimeters is taken as the true height of the UAV.

[0038] Further, the influencing data includes: temperature data, air pressure data, wind speed data, drone roll angle data, drone pitch angle data and drone yaw angle data;

[0039] The altimeter includes: an ultra-long-range laser altimeter, a long-range laser altimeter, a medium-range laser altimeter, a millimeter-wave radar and a laser radar.

[0040] In the second aspect, another embodiment of the present invention provides a UAV height measurement system based on data fusion, the system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0041] The present invention has the following beneficial effects:

[0042] The present invention first obtains the abnormality of each influencing data according to the distribution of each influencing data within a preset time period, accurately reflects the abnormality of each influencing data, and then screens out effective influencing data based on the abnormality, which is conducive to the subsequent accurate analysis of the impact of each influencing data on the altimeter; in order to analyze the distribution of each influencing data itself, it is conducive to the subsequent determination of the participation degree of each influencing data, and then improves the accuracy of adjusting the fixed weight of the altimeter, further based on the effective influencing data in each influencing data and the distribution of the abnormality of the effective influencing data, the reliability of each influencing data is obtained, accurately reflects the reliability and accuracy of each influencing data itself, and indirectly determines the proportion of each influencing data participating in adjusting the fixed weight of the altimeter; and then corrects the fixed weight of each altimeter according to the reliability of each influencing data and the abnormality of each influencing data at the end time of the preset time period, obtains the corrected fixed weight of each altimeter, improves the accuracy of obtaining the real height of the unmanned aerial vehicle according to the monitoring height of each altimeter; and then based on the corrected fixed weight, the monitoring height of the altimeter is accurately weighted and fused, and the real height of the unmanned aerial vehicle is accurately obtained, which is conducive to accurate analysis of the flight state of the unmanned aerial vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic flow chart of a method for measuring the height of an unmanned aerial vehicle based on data fusion provided by an embodiment of the present invention;

[0045] Figure 2 A flow chart of a method for obtaining an abnormality degree provided by an embodiment of the present invention;

[0046] Figure 3 A flow chart of a method for obtaining a modified fixed weight provided by an embodiment of the present invention;

[0047] Figure 4 A structural diagram of a UAV altitude measurement system based on data fusion provided by one embodiment of the present invention;

[0048] Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation, structure, features and effects of the drone altitude measurement method and system based on data fusion proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0050] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0051] The specific scheme of the UAV height measurement method and system based on data fusion provided by the present invention is described in detail below with reference to the accompanying drawings.

[0052] Embodiment 1:

[0053] The specific scenario of this embodiment is as follows: in order to accurately obtain the height of the drone in the existing method, multiple altimeters are simultaneously loaded on the drone, wherein all altimeters are in the same position, and then a fixed weight is set for each altimeter, and the monitoring height of each altimeter at the same time is weighted and fused based on the fixed weight of each altimeter, so as to obtain the height of the drone at the corresponding time. However, in actual situations, the flight environment and the posture of the drone will affect the monitoring results of the altimeter, so this embodiment analyzes the data related to the flight environment and the posture of the drone, determines the influence of the flight environment and the posture of the drone on the altimeter, and then adjusts the fixed weight of each altimeter, obtains the corrected fixed weight of each altimeter, and according to the corrected fixed weight of each altimeter, the height of the drone monitored by each altimeter at the same time is weighted and fused, accurately obtains the height of the drone at the specified time, improves the accuracy of the drone height detection, and then accurately analyzes the flight status of the drone.

[0054] This embodiment proposes a method for measuring the height of a UAV based on data fusion. Figure 1 , which shows a schematic flow chart of a method for measuring the height of an unmanned aerial vehicle based on data fusion provided by an embodiment of the present invention, the method comprising the following steps:

[0055] Step S1: Obtain various impact data of the UAV at each moment within a preset time period.

[0056] Specifically, the flight environment and posture of the drone will affect the monitoring results of the airborne altimeter, which will lead to inaccurate drone height monitored by the altimeter, and thus the real height of the drone cannot be accurately analyzed. Therefore, in order to analyze the impact of the drone's flight environment and its own posture on the altimeter, this embodiment installs a temperature sensor, an air pressure sensor and a wind speed sensor on the drone to obtain the temperature data, air pressure data and wind speed data of the drone at each moment. At the same time, according to the drone's own monitoring system, the drone roll angle data, drone pitch angle data and drone yaw angle data at each moment are obtained. For the convenience of description, this embodiment uses temperature data, air pressure data, wind speed data, drone roll angle data, drone pitch angle data and drone yaw angle data as impact data. This embodiment collects each impact data at the same time, where it is collected once every 1 second. The implementer can set the size of the data collection interval according to the actual situation, which is not limited here.

[0057] In this embodiment, the time period corresponding to a climbing process of the drone is used as a preset time period, wherein the initial moment of the preset time period is the start moment of the climb, and the end moment is the hovering moment of the drone. The implementer can set the size of the preset time period according to the actual situation, which is not limited here. In order to obtain the real height of the drone at the end moment of the preset time period, and then obtain the monitoring height of each altimeter at the end moment of the preset time period, wherein the fixed weight of each altimeter is known. In order to analyze the influence of various influencing data on the altimeter, and then obtain various influencing data of the drone at each moment in the preset time period. It should be noted that the altimeter of this embodiment includes: ultra-long-range laser altimeter, long-range laser altimeter, medium-range laser altimeter, millimeter wave radar and laser radar, and the types of altimeter and influencing data are not limited here.

[0058] Step S2: according to the distribution of each influencing data within a preset time period, the abnormality degree of each influencing data is obtained; and valid influencing data is screened out based on the abnormality degree.

[0059] Specifically, there may be interference factors in the process of climbing of the drone, which may cause abnormality in certain influencing data at a certain moment. For example, when magnetic field interference or air flow occurs, the drone's posture may be unstable, and there may be shaking or shaking, etc., which may cause errors in the drone's roll angle data, the drone's pitch angle data, or the drone's yaw angle data, resulting in the inability to accurately adjust the weight of the height measuring instrument through the influencing data. Therefore, it is necessary to exclude the abnormal data in each influencing data to improve the accuracy of the analysis of each influencing data. When abnormal data appears in certain influencing data due to external interference within a preset time period, the corresponding abnormal data will obviously deviate from the normal change trend of the influencing data. Therefore, this embodiment obtains the abnormality degree of each influencing data according to the distribution of each influencing data within the preset time period, and then selects the effective influencing data based on the abnormality degree, and excludes the abnormal data in each influencing data.

[0060] Preferably, in one possible implementation of this embodiment, the method for obtaining the abnormality degree is as follows: Figure 2 , which shows a flow chart of a method for obtaining the abnormality degree provided in this embodiment, the method comprising the following steps:

[0061] Step S201: for any type of impact data, cluster the impact data using the ISODATA algorithm to obtain a cluster of the impact data.

[0062] The change of each type of impact data is continuous within the preset time period, and the change rules of each type of impact data are different. Therefore, this embodiment analyzes each type of impact data separately. The size of the same type of impact data is similar in the local time period. Then, this embodiment clusters each type of impact data within the preset time period through the iterative self-organizing clustering (ISODATA) algorithm to obtain a clustering cluster of each type of impact data. The impact data in each cluster must be similar.

[0063] Among them, the iterative self-organizing clustering (ISODATA) algorithm is a well-known technology and will not be described in detail.

[0064] Step S202: for any cluster of the influencing data, the normality of the cluster is obtained according to the difference between the central data of the cluster and the sample central data of the influencing data, and the fluctuation of the influencing data in the cluster.

[0065] For any type of influencing data, when clustering the influencing data, there is a possibility that the abnormal data in the influencing data are divided into the same cluster. In order to analyze whether this situation exists and avoid the inaccurate subsequent abnormal data screening of the influencing data, this embodiment first analyzes each cluster corresponding to the influencing data to determine the normality of each cluster. For any cluster of the influencing data, when the center data of the cluster is equal to the sample center data of the influencing data, it means that the influencing data in the cluster is more normal, that is, the cluster is more normal; at the same time, when the distribution of the influencing data in the cluster is more uniform, it also means that the cluster is more normal. Therefore, this embodiment obtains the normality of the cluster according to the difference between the center data of the cluster and the sample center data of the influencing data, and the fluctuation of the influencing data in the cluster. The greater the normality, the less likely the influencing data in the cluster is abnormal. Among them, the method for obtaining the sample center data is a well-known technology and will not be repeated.

[0066] In order to accurately represent the normality of each cluster, this embodiment specifically quantifies the normality into a normality value. The larger the normality value, the more normal the impact data in the corresponding cluster. The calculation formula of the normality value is: ; In the formula, is the normality value of the jth cluster corresponding to the ath type of impact data; is the center data of the jth cluster corresponding to the ath type of influence data; is the sample center data of the ath type of influencing data; is the total number of influencing data in the jth cluster corresponding to the ath type of influencing data; is the nth influential data in the jth cluster corresponding to the ath type of influential data; is the mean of all the influencing data in the jth cluster corresponding to the ath influencing data; is the absolute value function; It is the first preset constant, which is greater than 0.

[0067] This embodiment will Set to 0.1 to avoid the denominator being 0. The implementer can set it according to the situation. The size is not limited here.

[0068] It should be noted that The smaller it is, the closer the impact data in the jth cluster corresponding to the ath impact data is to the sample center data of the ath impact data, which indirectly reflects that the impact data in the jth cluster corresponding to the ath impact data is more normal. The bigger; The smaller it is, the more evenly the distribution of the influencing data in the jth cluster corresponding to the ath influencing data is, and the smaller the possibility of abnormal data existing in the jth cluster corresponding to the ath influencing data is. The larger the The larger it is, the more normal the influence data in the jth cluster corresponding to the ath influence data is.

[0069] At this point, the normality value of each cluster that affects the data is obtained.

[0070] Step S203: for any influencing data in any cluster, the abnormality degree of the influencing data is obtained according to the difference between the influencing data and other influencing data in the cluster and the normality degree of the cluster.

[0071] In order to accurately filter out abnormal data in each type of influencing data, this embodiment analyzes each influencing data in each cluster. The greater the difference between a certain influencing data and other influencing data in the cluster, the greater the possibility that the influencing data is abnormal. At the same time, the smaller the normality of the cluster in which the influencing data is located, the greater the possibility that the influencing data is abnormal. Therefore, for any influencing data in any cluster, this embodiment obtains the abnormality of the influencing data based on the difference between the influencing data and other influencing data in the cluster and the normality of the cluster. The greater the abnormality, the more likely the corresponding influencing data is to be abnormal data.

[0072] In order to accurately represent the abnormality degree of each influencing data, this embodiment specifically quantifies the abnormality degree into an abnormality degree value. The larger the abnormality degree value, the more likely the corresponding influencing data is abnormal data. The calculation formula of the abnormality degree value is: ; In the formula, is the abnormality value of the i-th influencing data in the j-th cluster corresponding to the a-th influencing data; is the i-th influence data in the j-th cluster corresponding to the a-th influence data; is the mean of all the influencing data in the jth cluster corresponding to the ath influencing data; is the absolute value function; is the normality value of the jth cluster corresponding to the ath type of impact data; norm is the normalization function.

[0073] At this point, the abnormality level value of each affecting data is obtained.

[0074] It is known that the larger the abnormality value, the more likely the corresponding impact data is abnormal data, and the smaller the abnormality value, the more likely the corresponding impact data is normal effective impact data. Therefore, this embodiment sets the preset abnormality threshold value to 0.5, and the implementer can set the preset abnormality threshold value according to the actual situation, which is not limited here. When the abnormality value is less than the preset abnormality threshold value, the corresponding impact data is effective impact data; when the abnormality value is greater than or equal to the preset abnormality threshold value, the corresponding impact data is abnormal data.

[0075] At this point, the effective impact data of each impact data is obtained.

[0076] Step S3: Based on the distribution of the effective influence data in each type of influence data and the abnormality degree of the effective influence data, the reliability of each type of influence data is obtained.

[0077] Specifically, during the climbing process of the drone within the preset time period, various impact data will have anomalies and fluctuations. When the number of anomalies of a certain impact data is less, that is, the number of effective impact data is more, it means that the accuracy of the impact data within the preset time period is higher and the impact data is more reliable. At the same time, when the degree of anomaly of the effective impact data in the impact data is smaller, it also means that the impact data is more reliable. Therefore, this embodiment obtains the reliability of each impact data based on the distribution of the effective impact data in each impact data and the degree of anomaly of the effective impact data. The greater the reliability, the more accurate the corresponding type of impact data is within the preset time period.

[0078] Preferably, in a method that can be implemented in this embodiment, the method for obtaining the reliability is: for any type of influence data, obtain the proportion of effective influence data in the influence data as the first reference value of the influence data; the larger the first reference value, the more accurate and stable the influence data; obtain the mean value of the abnormality of the effective influence data in each cluster of the influence data as the first abnormality of the corresponding cluster; wherein, the smaller the first abnormality, the more stable the distribution of the influence data in the corresponding cluster. In order to analyze the overall abnormality of the influence data, the embodiment further obtains the mean value of the first abnormality as the target abnormality of the influence data. The smaller the target abnormality, the more reliable the influence data. When the difference in the first abnormality between the clusters corresponding to the influence data is smaller, it means that the distribution of the abnormality of the influence data is more stable, which indirectly indicates that the distribution of the influence data in the preset time period is more stable. Therefore, the embodiment obtains the difference between the first abnormality of each cluster of the influence data and the target abnormality as the reference fluctuation of the influence data. The smaller the reference fluctuation, the more accurate and reliable the influence data is in the preset time period. In order to more accurately analyze the reliability of the impact data, the difference between the center data of each cluster of the impact data and the sample center data of the impact data is further obtained and the cumulative result is used as the target result of the impact data; when the target result is smaller, it means that the distribution of the impact data in the preset time period is more stable and reliable. Therefore, this embodiment obtains the reliability of the impact data according to the first reference value, the target abnormality, the reference fluctuation degree and the target result; wherein the first reference value is positively correlated with the reliability, and the target abnormality, the reference fluctuation degree and the target result are all negatively correlated with the reliability.

[0079] In order to accurately represent the reliability of each type of impact data, this implementation specifically quantifies the reliability as a reliability value. The larger the reliability value, the more stable and reliable the corresponding type of impact data is within a preset time period. The calculation formula for the reliability value is: ; In the formula, is the reliability value of the a-th type of impact data; is the number of effective impact data in the a-th type of impact data; is the number of type a impact data within the preset time period; is the target abnormality value of the a-th type of impact data; is the number of clusters corresponding to the a-th type of impact data; is the first abnormality value of the kth cluster corresponding to the ath type of impact data; is the center data of the kth cluster corresponding to the ath type of influence data; is the sample center data of the ath type of influencing data; is the second preset constant, which is greater than 0; is the absolute value function; is the first reference value; is the reference volatility value; For the target result.

[0080] This embodiment will Set to 0.1 to avoid the denominator being 0. The implementer can set it according to the actual situation. The size is not limited here.

[0081] It should be noted that the first reference value The larger the value is, the more accurate and stable the impact data of type a is within the preset time period, and the more reliable the impact data of type a is. The bigger; The smaller it is, the more reliable the impact data of type a is within the preset time period. The larger the value, the greater the reference fluctuation The smaller it is, the more stable the distribution of the a-th impact data within the preset time period is, which indirectly reflects that the a-th impact data within the preset time period is more reliable. The bigger the target result The smaller it is, the more stable and reliable the distribution of the a-th impact data within the preset time period is. The larger the The larger it is, the more reliable the type a impact data is within the preset time period.

[0082] At this point, the reliability value of each influencing data is obtained.

[0083] Step S4: Correct the fixed weight of each altimeter according to the reliability of each influencing data and the abnormality of each influencing data at the end of the preset time period to obtain the corrected fixed weight of each altimeter.

[0084] Specifically, when the reliability of a certain influencing data is greater, it means that the influencing data is more accurate within the preset time period. When analyzing the influence of the influencing data on the altimeter, the reference degree of the influencing data is higher, so that the fixed weight adjustment of the altimeter is more accurate. Considering that the final detected drone height is the drone height at the end of the preset time period, and then analyzing the abnormality of each influencing data at the end of the preset time period, when the abnormality of a certain influencing data at the end of the preset time period is greater, the influence of the influencing data on the altimeter at the end of the preset time period should be smaller, so as to avoid inaccurate analysis of the influence on the altimeter, resulting in the inability to accurately weighted fuse the height monitored by the altimeter at the end of the preset time period, so that the true height of the drone cannot be accurately obtained. Therefore, this embodiment corrects the fixed weight of each altimeter by the reliability of each influencing data and the abnormality of each influencing data at the end of the preset time period, and obtains the corrected fixed weight of each altimeter. It is conducive to the accurate weighted fusion of the height monitored by the altimeter in the future, and accurately obtains the true height of the drone at the end of the preset time period.

[0085] Preferably, in one possible implementation of this embodiment, the method for obtaining the modified fixed weights can be found in Figure 3 , which shows a flow chart of a method for obtaining a modified fixed weight provided in this embodiment, the method comprising the following steps:

[0086] Step S401: Obtain a reference level for each type of impact data according to the reliability level of each type of impact data and the difference in reliability levels with other types of impact data.

[0087] When the reliability of a certain type of influencing data is greater and the difference with the reliability of other types of influencing data is greater, it means that the type of influencing data is more accurate in the preset time period, and the degree to which the type of influencing data can be referred when analyzing the impact on the altimeter is greater. Therefore, this embodiment obtains the reference degree of each type of influencing data according to the reliability of each type of influencing data and the difference with the reliability of other types of influencing data. The greater the reference degree, the greater the proportion of the corresponding type of influencing data in correcting the fixed weight of the altimeter.

[0088] Preferably, in a method that can be implemented in this embodiment, the method for obtaining the reference degree is: for any type of influence data, the average of the reliability levels of all other types of influence data except this type of influence data is used as the target reliability level; the difference between the reliability level of this type of influence data and the target reliability level is obtained as the second difference of this type of influence data; the larger the second difference is, the more accurate and reliable this type of influence data is, which indirectly indicates that the reliability level of this type of influence data is greater. Therefore, this embodiment obtains the reference degree of this type of influence data based on the reliability level and the second difference of this type of influence data; wherein, the reliability level and the second difference are both positively correlated with the reference level.

[0089] In order to accurately represent the reference degree of each type of influencing data, this embodiment specifically quantifies the reference degree into a reference degree value. The larger the reference degree value, the greater the proportion of the corresponding type of influencing data participating in adjusting the fixed weight of the height measuring instrument. The calculation formula of the reference degree value is: ; In the formula, is the reference level value of the a-th type of impact data; is the reliability value of the a-th type of impact data; is the target reliability value; is the second difference; norm is the normalization function.

[0090] At this point, the reference degree value of each impact data is obtained.

[0091] Step S402: Based on the reference degree of each influencing data and the abnormality degree of each influencing data at the end time of the preset time period, the accuracy degree of each influencing data is obtained; wherein the reference degree is positively correlated with the accuracy degree, and the abnormality degree is negatively correlated with the accuracy degree.

[0092] It is known that the greater the reference degree, the more accurate the corresponding type of influencing data is within the preset time period; at the same time, when the degree of abnormality of a certain type of influencing data at the end of the preset time period is smaller, the more accurate the type of influencing data is at the end of the preset time period, and the greater the proportion of the type of influencing data participating in adjusting the fixed weight of the altimeter should be, so as to reduce the error of adjusting the fixed weight of the altimeter, and make the result of weighted fusion of the height monitored by the altimeter more accurate. Therefore, this embodiment obtains the self-accuracy of each type of influencing data based on the reference degree of each type of influencing data and the degree of abnormality of each type of influencing data at the end of the preset time period; wherein the reference degree is positively correlated with the self-accuracy, and the degree of abnormality is negatively correlated with the self-accuracy. The greater the self-accuracy, the more accurate the corresponding type of influencing data is within the preset time period, and the greater the proportion of the corresponding type of influencing data participating in adjusting the fixed weight of the altimeter.

[0093] In order to accurately represent the accuracy of each type of influencing data, this embodiment specifically quantifies the accuracy as an accuracy value. The larger the accuracy value, the more accurate the corresponding type of influencing data is within the preset time period, and the smaller the impact on the height measuring instrument. The calculation formula of the accuracy value is: ; In the formula, is the accuracy value of the a-th type of influencing data itself; is the reference level value of the a-th type of impact data; is the abnormality level value of the ath type of impact data at the end time of the preset time period; It is the third preset constant, which is greater than 0.

[0094] This embodiment will Set to 1 to avoid the denominator being 0. Implementers can set it according to actual conditions. The size is not limited here.

[0095] At this point, the accuracy value of each type of impact data is obtained.

[0096] Step S403: the fixed weight of each altimeter is corrected according to the accuracy of each influencing data, so as to obtain the corrected fixed weight of each altimeter.

[0097] Each altimeter will be affected by each type of influencing data. When the influencing data is more accurate, the altimeter will be less affected by the inaccuracy, and the degree of correction of the fixed weight of the altimeter will be smaller. Therefore, this embodiment corrects the fixed weight of each altimeter according to the accuracy of each type of influencing data to obtain the corrected fixed weight of each altimeter.

[0098] Preferably, in a manner that can be implemented in this embodiment, the method for obtaining the corrected fixed weight is: normalizing the accumulated results of the accuracy of all types of influencing data themselves as the target weight; the larger the target weight, the more accurate the influencing data within the preset time period, which indirectly means that the influence of the influencing data on the altimeter is smaller, and the degree to which the fixed weight of each altimeter needs to be adjusted is smaller, and then the product of the target weight and the fixed weight of each altimeter is used as the corrected fixed weight of each altimeter.

[0099] At this point, the corrected fixed weight of each height measuring instrument at the end time of the preset time period is obtained.

[0100] Step S5: Based on the corrected fixed weight, the real height of the UAV is obtained.

[0101] The monitoring height of each altimeter at the end of the preset time period is weighted and fused according to the corresponding corrected fixed weight to obtain the actual height of the UAV at the end of the preset time period.

[0102] Specifically, the method for obtaining the true height of the UAV at the end of the preset time period is as follows: for any altimeter, the product of the height of the altimeter at the end of the preset time period and the corrected fixed weight of the altimeter is used as the fusion height of the altimeter; the cumulative result of the fusion heights of all altimeters is used as the true height of the UAV at the end of the preset time period, thereby improving the accuracy of obtaining the true height of the UAV at the end of the preset time period, and then accurately analyzing the flight status of the UAV based on the true height of the UAV.

[0103] In summary, this embodiment obtains various influencing data of the drone at each moment; obtains the abnormality of each influencing data according to the distribution of the influencing data, and then screens out the effective influencing data; obtains the reliability based on the effective influencing data and the distribution of the abnormality of the effective influencing data; corrects the fixed weight of each altimeter by the reliability of each influencing data and the abnormality of each influencing data at the end of the preset time period, obtains the corrected fixed weight, and then obtains the true height of the drone. The present invention analyzes each influencing data to accurately determine the modified fixed weight of each altimeter, and then accurately obtains the true height of the drone according to the corrected fixed weight of each altimeter, and accurately analyzes the flight status of the drone.

[0104] Embodiment 2:

[0105] The present invention also proposes a UAV altitude measurement system based on data fusion, please refer to Figure 4 , which shows a structure diagram of a UAV height measurement system based on data fusion provided by an embodiment of the present invention, the system includes: a data acquisition module 10, an effective impact data acquisition module 20, a reliability acquisition module 30, a corrected fixed weight acquisition module 40 and a data processing module 50.

[0106] The data acquisition module 10 is used to acquire various impact data of the UAV at each moment within a preset time period.

[0107] The effective impact data acquisition module 20 is used to acquire the abnormality degree of each impact data according to the distribution of each impact data within a preset time period; and filter out the effective impact data based on the abnormality degree.

[0108] The reliability acquisition module 30 is used to acquire the reliability of each type of influence data based on the distribution of the effective influence data in each type of influence data and the abnormality degree of the effective influence data.

[0109] The corrected fixed weight acquisition module 40 is used to correct the fixed weight of each altimeter according to the reliability of each influencing data and the abnormality of each influencing data at the end of a preset time period, so as to obtain the corrected fixed weight of each altimeter.

[0110] The data processing module 50 is used to obtain the real height of the drone based on the modified fixed weight.

[0111] It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the above embodiment provides a drone altitude measurement system based on data fusion and a drone altitude measurement method based on data fusion, which belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0112] Embodiment 3:

[0113] The present invention also proposes a drone altitude measurement device based on data fusion, the device includes a memory and a processor, wherein the memory stores an executable program code, and the processor is used to call and execute the executable program code to execute a drone altitude measurement method based on data fusion provided in an embodiment of the present application. The device can be a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute a drone altitude measurement method based on data fusion provided in the above embodiment.

[0114] In addition, the present application embodiment also protects a computer device, see Figure 5 The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402, wherein when the processor 402 executes the computer program 403, the computer device can execute any of the above-mentioned drone altitude measurement methods based on data fusion.

[0115] Embodiment 4:

[0116] This embodiment also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is executed on a computer, the computer executes the above-mentioned related method steps to implement a drone height measurement method based on data fusion provided in the above embodiment.

[0117] Embodiment 5:

[0118] This embodiment also provides a computer program product. When the computer program product runs on a computer, it enables the computer to execute the above-mentioned related steps to implement a drone altitude measurement method based on data fusion provided in the above embodiment.

[0119] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment is used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be repeated here.

[0120] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for measuring the height of an unmanned aerial vehicle based on data fusion, characterized in that: The method comprises the following steps: Obtain various impact data of the drone at each moment within the preset time period; According to the distribution of each influencing data within a preset time period, the abnormality degree of each influencing data is obtained; and the effective influencing data is screened out based on the abnormality degree; Based on the distribution of effective influence data in each type of influence data and the abnormality of the effective influence data, the reliability of each type of influence data is obtained; The fixed weight of each altimeter is corrected according to the reliability of each influencing data and the abnormality of each influencing data at the end of a preset time period, so as to obtain a corrected fixed weight of each altimeter; Based on the corrected fixed weight, the real altitude of the drone is obtained; The influencing data include: temperature data, air pressure data, wind speed data, drone roll angle data, drone pitch angle data and drone yaw angle data; The altimeter includes: an ultra-long-range laser altimeter, a long-range laser altimeter, a medium-range laser altimeter, a millimeter-wave radar and a laser radar.

2. A method for measuring the height of an unmanned aerial vehicle based on data fusion as claimed in claim 1, characterized in that: The method for obtaining the abnormality degree is: For any type of impact data, the impact data is clustered using the ISODATA algorithm to obtain a cluster of the impact data; For any cluster of the influencing data, the normality of the cluster is obtained according to the difference between the central data of the cluster and the sample central data of the influencing data, and the fluctuation of the influencing data in the cluster; For any influencing data in any cluster, the abnormality degree of the influencing data is obtained according to the difference between the influencing data and other influencing data in the cluster and the normality degree of the cluster.

3. The method for measuring the altitude of an unmanned aerial vehicle based on data fusion according to claim 1, characterized in that: The method for obtaining the effective impact data is: When the abnormality level meets the preset normal level range, the corresponding impact data is effective impact data.

4. A method for measuring the height of an unmanned aerial vehicle based on data fusion as claimed in claim 2, characterized in that: The method for obtaining the reliability is: For any type of impact data, obtain the proportion of valid impact data in the impact data as a first reference value of the impact data; Obtaining the mean value of the abnormality degree of the effective influencing data in each cluster of the influencing data as the first abnormality degree of the corresponding cluster; The mean value of the first abnormality level is used as the target abnormality level of the affected data; Obtain the result of accumulating the difference between the first abnormality degree and the target abnormality degree of each cluster of the influencing data as the reference fluctuation degree of the influencing data; Obtain the result of accumulating the difference between the center data of each cluster of the influence data and the sample center data of the influence data as the target result of the influence data; The reliability of the influencing data is obtained according to the first reference value, the target abnormality level, the reference fluctuation level and the target result; wherein the first reference value is positively correlated with the reliability level, and the target abnormality level, the reference fluctuation level and the target result are negatively correlated with the reliability level.

5. The method for measuring the altitude of an unmanned aerial vehicle based on data fusion according to claim 1, characterized in that: The method for obtaining the modified fixed weight is: According to the reliability of each type of impact data and the difference in reliability with other types of impact data, obtain the reference level of each type of impact data; Based on the reference degree of each influencing data and the abnormal degree of each influencing data at the end time of the preset time period, the accuracy of each influencing data is obtained; wherein the reference degree is positively correlated with the accuracy of the data itself, and the abnormal degree is negatively correlated with the accuracy of the data itself; The fixed weight of each altimeter is corrected according to the accuracy of each influencing data to obtain the corrected fixed weight of each altimeter.

6. A method for measuring the height of an unmanned aerial vehicle based on data fusion as claimed in claim 5, characterized in that: The method for obtaining the reference degree is: For any type of impact data, the mean reliability of all other types of impact data except this type of impact data is taken as the target reliability; Obtaining the difference between the reliability of the impact data and the target reliability as the second difference of the impact data; According to the reliability and the second difference of the impact data, a reference degree of the impact data is obtained; wherein the reliability and the second difference are both positively correlated with the reference degree.

7. The method for measuring the altitude of an unmanned aerial vehicle based on data fusion as claimed in claim 5, characterized in that: The method for obtaining the modified fixed weight is: The result of normalizing the accumulated results of all types of factors affecting the accuracy of the data is used as the target weight; The product of the target weight and the fixed weight of each altimeter is taken as the corrected fixed weight of each altimeter.

8. The method for measuring the altitude of an unmanned aerial vehicle based on data fusion according to claim 1, characterized in that: The method for obtaining the real height of the drone is: For any altimeter, the product of the height of the altimeter at the end of the preset time period and the corrected fixed weight of the altimeter is used as the fusion height of the altimeter; The accumulated result of the fused heights of all altimeters is taken as the true height of the UAV.

9. A data fusion-based UAV altitude measurement system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When executing the computer program, the processor implements the steps of a method for measuring the altitude of an unmanned aerial vehicle based on data fusion as described in any one of claims 1 to 8.

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