Data acquisition method and system for ground test platform

Abstract

The invention relates to the technical field of data processing, in particular to a data acquisition method and system for a ground test platform. The method comprises the following steps: acquiring a flight state set of an unmanned aerial vehicle associated with a ground test platform in a target time period, and merging data points in the flight state set to obtain a plurality of flight state fragments; determining a stability degree value of a target fragment in the plurality of flight state fragments in the located sub-time period, and determining a target filtering coefficient of the target fragment according to the stability degree value of the target fragment; and acquiring an initial monitoring set of the corresponding sub-time period acquired by the unmanned aerial vehicle, and performing filtering processing on the initial monitoring set by using the target filtering coefficient of the target fragment to obtain a target monitoring set of the corresponding sub-time period so as to determine a total monitoring set corresponding to the target time period. Through the above technical scheme, the ground test platform can obtain accurate monitoring information of the to-be-monitored area.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data acquisition method and system for a ground test platform. Background Art

[0002] A ground test platform is a facility used to test and evaluate various equipment, systems or components on the ground under simulated real environmental conditions.

[0003] Since drones can easily change their positions by flying in space and thus perform test tasks at different locations in space, in order to facilitate the ground test platform to test the equipment to be tested, drones can be used to monitor the network signal strength, navigation signal strength, meteorological data, images and other information in the area where the ground test platform is located, and the monitored information can be fed back to the ground test platform.

[0004] When a drone performs a monitoring task during flight, its flight state may change due to the flight execution it receives, or it may change its flight state due to interference from factors such as wind direction. When the drone's flight state changes, the monitoring information monitored by the drone will also change accordingly, making it difficult to reflect the actual monitoring information of the monitored point in the monitoring information obtained by the drone.

[0005] For example, when a UAV uses an image acquisition device to perform a scanning task on an area to be monitored, when the wind speed or wind direction changes, noise caused by the change in wind speed or wind direction may exist in the image obtained by the image acquisition device, affecting the scanning result of the area to be monitored. Therefore, the monitoring information obtained by the ground test platform from the UAV may be difficult to reflect the actual monitoring information of the area to be monitored. Summary of the invention

[0006] In order to overcome the problem in the related art that it is difficult for a ground test platform to obtain relatively accurate monitoring information of a monitored area, the present application provides a data collection method and system for a ground test platform.

[0007] According to a first aspect of an embodiment of the present application, a data collection method for a ground test platform is provided, including: obtaining a flight state set of a drone associated with the ground test platform within a target time period, and merging data points in the flight state set to obtain multiple flight state segments; the same flight state segment includes data points that are adjacent in time sequence and have similar fluctuations; for a target segment in the multiple flight state segments, determining a stability value of the target segment in the sub-time period, and determining a target filter coefficient of the target segment based on the stability value of the target segment; obtaining an initial monitoring set of the corresponding sub-time period collected by the drone, and filtering the initial monitoring set using the target filter coefficient of the target segment to obtain a target monitoring set of the corresponding sub-time period; determining a total monitoring set corresponding to the target time period based on the target monitoring sets corresponding to different sub-time periods within the target time period.

[0008] Since each flight status segment contains data points that are continuous and have similar fluctuations in the time series, it is ensured that the UAVs in the same flight status segment have consistent or similar flight status. By evaluating the stability of the flight status segment, it is convenient to filter the preliminary monitoring data set obtained by the UAV, thereby realizing adaptive filtering of the monitoring information, and enabling the ground test platform to obtain a more accurate monitoring data set.

[0009] Optionally, data points in the flight status set are merged to obtain multiple flight status segments, including: merging data points in the flight status set located in the starting time period into an initial flight status segment, and determining candidate data points in the flight status set that are located at the next moment of the initial flight status segment in time sequence; determining a merging probability value of the candidate data point relative to the initial flight status segment, wherein the merging probability value is used to characterize the similarity in fluctuation degree between the candidate data point and the initial flight status segment; merging the candidate data point into the initial flight status segment or the next flight status segment according to the merging probability value, so as to obtain multiple flight status segments with reference to the process of obtaining the initial flight status segment.

[0010] In this way, the similarity in fluctuation between the candidate data point and the initial flight state segment is represented by the merged probability value, and the candidate data point is included in the initial flight state segment or the subsequent flight state segment according to the merged probability value, so that data points that are adjacent in time series and have similar fluctuation degrees can be merged into the same flight state segment.

[0011] Optionally, the merging probability value is determined by: , where P is the combined probability value of the candidate data point relative to the initial flight state segment, N is the number of sub-data point dimensions of the candidate data point, is the standard deviation of the data points in the initial flight state segment in the jth dimension, is the standard deviation of the data points in the initial flight state segment and the candidate data points in the jth dimension, is the value of the candidate data point in the jth dimension, is the mean of the data points in the initial flight state segment in the jth dimension.

[0012] Optionally, determining the stability value of the target segment within the sub-time period includes: determining the stability value of the target segment within the sub-time period, the stability value is used to characterize the stability of the rate of change of data points in the target segment per unit time; determining the state change value of the target segment within the sub-time period, the state change value is used to characterize the difference between data points at adjacent moments in the target segment; and taking the ratio of the stability value to the state change value as the stability value.

[0013] The stability value is determined by calculating the ratio of the stability value to the state change value, which makes it easy to process the monitoring information obtained by the drone according to the stability value of the drone's flight state to obtain more accurate monitoring information.

[0014] Optionally, the stability value of the target segment in the sub-time period is determined by: ,in, is the stability value of the target segment in the sub-time period, N is the number of sub-data point dimensions of the data point, is an exponential function with a natural constant as the base, C is the number of data points in the target segment, , as well as These are the values ​​of the k+1th, kth, and k-1th data points in the target segment in the jth dimension.

[0015] In this way, by comparing every three consecutive data points in the target segment, the stability of the rate of change of the data points can be determined, so that the stability value can effectively represent the stability of the rate of change of the data points in the target segment per unit time.

[0016] Optionally, the state change degree value of the target segment in the sub-time period is determined by: ,in, is the state change degree value of the target segment in the sub-time period, N is the number of sub-data point dimensions of the data point, C is the number of data points in the target segment, as well as are the values ​​of the k+1th and kth data points in the target segment in the jth dimension, is the range of the data points in the target segment in the jth dimension, Is the multiplication operator.

[0017] Optionally, determining a target filter coefficient of the target segment according to the stability value of the target segment includes: ,in, is the target filter coefficient of the target segment, is the preset filter coefficient, and W is the stability value of the target segment.

[0018] In this way, there is a negative correlation between the target filter coefficient of the target segment and the stability value of the target segment, which can filter out the noise in the monitoring information collected by the UAV during the target time period while retaining the effective actual information.

[0019] Optionally, the flight state set includes values ​​of a target flight state parameter at different times within a target time period; the target flight state parameter is any one of the following: speed information, acceleration information, altitude information, and body vibration intensity information.

[0020] Optionally, the initial monitoring set includes the values ​​of the target monitoring information at different times within the sub-time period; the target monitoring information is any one of the following: network signal strength, navigation signal strength, image information and temperature information of the flight location of the drone.

[0021] According to a second aspect of an embodiment of the present application, a data acquisition system for a ground test platform is provided, comprising: a processor and a memory, the memory storing computer program instructions, and the computer program instructions, when executed by the processor, implement the steps of the data acquisition method for a ground test platform provided in the first aspect of the present application.

[0022] The technical solution provided by the embodiments of the present application may include the following beneficial effects: obtaining a flight status set of a UAV associated with a ground test platform within a target time period, and merging the data points in the flight status set to obtain multiple flight status segments. Since the same flight status segment includes data points that are adjacent in time sequence and have similar fluctuations, the aircraft in the same flight status segment is in the same or similar flight state. According to the stability value of the flight status segment, the initial monitoring set obtained by the UAV within the time period of the flight status segment is filtered, and the monitoring information obtained by the aircraft can be adaptively filtered according to the flight status of the aircraft. Therefore, the ground test platform can obtain a more accurate monitoring set within the target time period.

[0023] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1is a flow chart showing a data collection method for a ground test platform according to an exemplary embodiment; Figure 2 It is a structural schematic diagram of a data acquisition system for a ground test platform according to an exemplary embodiment. DETAILED DESCRIPTION

[0025] First, the application scenario of the embodiment of the present application is briefly introduced. In the application scenario of the present application, a drone can be used to obtain monitoring information of the area where a ground test platform is located. However, if the flight state of the drone changes in a short period of time, it will affect the monitoring information monitored by the drone. For example, when the drone is affected by a change in wind speed or wind direction, there may be noise data points in the image information obtained by the drone, which affects the monitoring information obtained by the drone and makes it difficult for the ground test platform to obtain more accurate monitoring information.

[0026] In view of the above technical problems, the present application provides a data collection method and system for a ground test platform. Figure 1 is a flow chart of a data collection method for a ground test platform according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps.

[0027] In step S101, a flight state set of the UAV associated with the ground test platform within a target time period is obtained, and data points in the flight state set are merged to obtain a plurality of flight state segments.

[0028] The UAV can send the acquired monitoring information and the flight status parameters of the UAV itself at different times to the ground test platform, so that the ground test platform can determine more accurate monitoring information based on the monitoring information and the flight status parameters of the UAV.

[0029] The UAV can send monitoring information and flight status parameters to the ground test platform in real time, or it can send the collected monitoring information and flight status parameters to the ground test platform after completing a single or multiple monitoring tasks.

[0030] The flight state set includes the values ​​of the target flight state parameter at different times within the target time period; the target flight state parameter is any one of the following: speed information, acceleration information, altitude information, and body vibration intensity information.

[0031] The drone may be provided with a velocity sensor, an acceleration sensor, an angular velocity sensor, an air pressure sensor, a vibration sensor and other sensors for acquiring monitoring information, so as to acquire the monitoring information and the flight status parameters of the drone itself.

[0032] Since the aircraft have the same or similar flight state performance in the same flight state, and after dividing the flight state set of the UAV in the target time period, the same flight state segment in the multiple flight state segments obtained includes data points that are adjacent in time series and have similar fluctuation degrees. Therefore, the aircraft is in the same flight state in the same flight state segment.

[0033] For example, when a drone is flying at a constant speed, the speed of the drone at different times within the same time period is similar or the same; or, when a drone is flying at an accelerated speed, the acceleration of the drone at different times within the same time period is similar or the same; or, when a drone is turning, the angular velocity of the drone at different times within the same time period is similar or the same.

[0034] The data points in the flight state set are merged to obtain multiple flight state segments, and the same flight state segment includes data points that are adjacent in time series and have similar fluctuation degrees. The UAV is in the same flight state in the same flight state segment; for example, the UAV can be in one of steady flight, climbing or descending in the same flight state segment.

[0035] Merging data points in the flight status set that are adjacent in time series and have similar fluctuations into the same flight status segment helps to filter the monitoring information of the drone according to the flight status of the drone.

[0036] In one embodiment, data points in a flight state set are merged to obtain multiple flight state segments, including: merging data points in the flight state set located in a starting time period into an initial flight state segment, and determining candidate data points in the flight state set that are located at the next moment of the initial flight state segment in terms of time sequence; determining a merging probability value of the candidate data point relative to the initial flight state segment, wherein the merging probability value is used to characterize the similarity in fluctuation degree between the candidate data point and the initial flight state segment; merging the candidate data point into the initial flight state segment or the next flight state segment according to the merging probability value, so as to obtain multiple flight state segments with reference to the process of obtaining the initial flight state segment.

[0037] The duration of the starting time period can be equal to the preset duration, and the preset duration can be set according to actual needs. For example, the preset duration can be between 5 seconds and 10 seconds. Multiple data points corresponding to the earliest 6 seconds in the flight status set can be selected to form an initial flight status segment. For the data point at the 7th second, the merging probability value of the data point at the 7th second relative to the initial flight status segment can be determined to determine whether to merge the data point at the 7th second into the initial flight status segment.

[0038] For example, if it is determined that the data point at the 7th second can be merged into the initial flight status segment, the data point at the 7th second will be merged into the initial flight status segment, and it will be further determined whether the data point at the 8th second can be merged into the initial flight status segment.

[0039] If it is determined that the data point at the 7th second cannot be merged into the initial flight status segment, the selection of the initial flight status segment is completed, and the data points from the 7th to the 12th seconds are combined into the next flight status segment of the initial status segment, and it is further determined whether the data point at the 13th second can be merged into the next flight status segment of the initial status segment.

[0040] Referring to the merging process of the initial flight state segments, a plurality of merged flight state segments can be obtained respectively, and different flight state segments correspond to different flight states of the UAV.

[0041] In this way, since the merging probability value is used to characterize the similarity in fluctuation degree between the candidate data point and the initial flight state segment, the candidate data point is merged into the initial flight state segment or the next flight state segment according to the merging probability value, and data points that are adjacent in time series and have similar fluctuation degrees can be merged into the same flight state segment.

[0042] In one embodiment, the merge probability value is determined by: , where P is the combined probability value of the candidate data point relative to the initial flight state segment, N is the number of sub-data point dimensions of the candidate data point, is the standard deviation of the data points in the initial flight state segment in the jth dimension, is the standard deviation of the data points in the initial flight state segment and the candidate data points in the jth dimension, is the value of the candidate data point in the jth dimension, is the mean of the data points in the initial flight state segment in the jth dimension.

[0043] By comparing the standard deviation of the data points in the initial flight state segment with the standard deviation of the data points in the initial flight state segment and the candidate data points, the degree of matching between the candidate data points and the initial flight state segment in terms of fluctuation can be reflected.

[0044] By comparing the mean value of the data points in the initial flight state segment with the value of the candidate data point, the matching degree between the value of the candidate data point and the value of the data point in the initial flight state segment can be reflected.

[0045] In the calculation formula of the combined probability value, The units of the numerator and denominator of are the standard deviation of the data points in the flight status set, which can eliminate the impact of the dimension on the calculation process; in the calculation formula of the combined probability value, The units of the numerator and denominator of are the values ​​of the data points in the flight state set, which can eliminate the influence of the dimension on the calculation process; It can be used to indicate the degree of deviation of the candidate data point from the average number of data points in the initial flight state segment.

[0046] For example, for an initial flight status segment in which the values ​​of the data points are 1, 2, 3, 1, 2, 3, 1, 2, and 3, respectively, the average value of the data points in the initial flight status segment is 2, and the standard deviation of the data points in the initial flight status segment is 0.8165.

[0047] If the candidate data point is 2, then the set of initial flight status segments and candidate data points is 1, 2, 3, 1, 2, 3, 1, 2, 3 and 2. The average value of the data points in the initial flight status segments and candidate data points is 2, and the standard deviation of the data points in the initial flight status segments and candidate data points is 0.775.

[0048] The difference between the standard deviation of 0.775 and the standard deviation of 0.8165 is small, and the average value of the data points in the initial flight state segment is the same as the value of the candidate data point. Therefore, the candidate data point and the initial flight state segment are relatively matched, and the candidate data point can be merged into the initial flight state segment.

[0049] For another example, if the candidate data point is 5, the set of initial flight status segments and candidate data points is 1, 2, 3, 1, 2, 3, 1, 2, 3 and 5. The average value of the data points in the initial flight status segments and candidate data points is 2.3, and the standard deviation of the data points in the initial flight status segments and candidate data points is 1.1187.

[0050] There is a certain difference between the standard deviation of 1.1187 and the standard deviation of 0.8165, and there is a certain difference between the average value of the data points in the initial flight state segment and the value of the candidate data points. Therefore, the difference between the candidate data points and the initial flight state segment is large, and the candidate data points cannot be merged into the initial flight state segment.

[0051] In this way, by comparing the standard deviation of the data points in the initial flight state segment with the standard deviation of the data points in the initial flight state segment and the candidate data points, it can reflect whether the candidate data points and the initial flight state segment belong to the same flight state, so as to determine whether to merge the candidate data points with the initial flight state segment.

[0052] In step S102, for a target segment among the plurality of flight status segments, a stability value of the target segment within the sub-time period is determined, and a target filter coefficient of the target segment is determined according to the stability value of the target segment.

[0053] The target segment can be any flight state segment among multiple flight state segments, and different flight state segments correspond to different sub-time periods of the target time period; when the UAV flies in different flight states, the stability values ​​determined according to the flight state segments of the UAV are different. For example, the stability value of the UAV when flying at a constant speed on the same horizontal plane is greater than the stability value when the UAV is ascending in altitude.

[0054] Since different flight state segments of the UAV correspond to different flight states of the UAV in different continuous time periods, determining the stability value of the target segment is helpful to determine the actual flight state of the UAV within the time period of the target segment, so as to perform adaptive filtering on the monitoring information obtained by the UAV according to the actual flight state of the UAV within the time period of the target segment.

[0055] In one embodiment, determining the stability value of the target segment within the sub-time period includes: determining the stability value of the target segment within the sub-time period, the stability value is used to characterize the stability of the rate of change of data points in the target segment per unit time; determining the state change value of the target segment within the sub-time period, the state change value is used to characterize the difference between data points at adjacent moments in the target segment; and taking the ratio of the stability value to the state change value as the stability value.

[0056] When a UAV is performing a monitoring task, the probability or degree of the UAV's monitoring task being affected is different when the UAV flies at different levels of stability. Since the values ​​of the data points in the UAV's flight state set can characterize the UAV's flight state, such as the UAV's flight speed, flight acceleration, and vibration information during flight, etc., therefore, by determining the stability value of the target segment in the sub-time period, it is helpful to process the monitoring information obtained by the UAV under various flight states, so as to avoid the UAV's monitoring information being affected by the UAV's flight state.

[0057] The greater the stability value of the target segment in the sub-time period, the higher the stability of the rate of change of the data points in the target segment per unit time, and the higher the stability of the rate of change of the flight state parameters of the UAV in different time periods.

[0058] For example, during the time period of the target segment, the change in the altitude information of the drone per unit time remains unchanged, and the drone increases its altitude at a constant speed. The probability or degree of the monitoring information obtained by the drone being affected is low.

[0059] When the flight state of the UAV may change to a certain extent within the time period of the flight state segment, for example, the altitude information of the UAV has been changing within the time period of the flight state segment, if the change rate of the altitude information of the UAV is relatively stable within the time period of the flight state segment, the UAV usually has the ability to adapt to such relatively stable changes in the altitude information, and the monitoring information obtained by the UAV is affected to a lower degree or probability.

[0060] However, when the stability of the change rate of the UAV in the time period of the flight status segment is low, the UAV is difficult to adapt to this change with a low degree of stability, which makes the monitoring information obtained by the UAV affected to a higher degree or probability. Therefore, the probability of the monitoring information obtained by the UAV in the sub-time period where the target segment is located being affected is negatively correlated with the stability value of the target segment in the sub-time period.

[0061] The higher the state change degree value of the target segment in the sub-time period, the higher the probability that the monitoring information obtained by the UAV in the sub-time period of the target segment is affected; conversely, the lower the state change degree value of the target segment in the sub-time period, the lower the probability that the monitoring information obtained by the UAV in the sub-time period of the target segment is affected.

[0062] Taking the ratio of the stability value to the state change degree value as the stability value can facilitate processing the monitoring information obtained by the UAV during the time period where the target segment is located according to the stability value of the UAV during the time period where the target segment is located, so as to obtain more accurate monitoring information corresponding to the time period where the target segment is located.

[0063] In one embodiment, the stability value of the target segment in the sub-time period is determined in the following manner: ,in, is the stability value of the target segment in the sub-time period, N is the number of sub-data point dimensions of the data point, is an exponential function with a natural constant as the base, C is the number of data points in the target segment, , as well as These are the values ​​of the k+1th, kth, and k-1th data points in the target segment in the jth dimension.

[0064] Different dimensions of data points can correspond to different flight status parameters. In the calculation formula of the stability value of the target segment in the sub-time period, the change between two adjacent data points in three adjacent data points in each dimension is determined. The closer it is to 1, the more stable the rate of change of the value of the kth data point in the jth dimension in the target segment is, making the determined stability value larger.

[0065] By performing exponential operations on negative numbers using an exponential function with a natural constant as the base, it is possible to ensure that the value of the exponential operation result is between 0 and 1.

[0066] For the target data points among the multiple data points of the target segment, the target data points can be compared with the data points on both sides adjacent to each other in time series to determine the stability of the change rate of the data points. The UAV can adapt to the relatively stable change rate of the data points. The higher the stability of the change rate, the lower the probability that the monitoring information collected by the UAV is affected. Therefore, the stability value of the target segment in the sub-time period can characterize the probability that the monitoring information collected by the UAV is not affected.

[0067] In this way, by comparing every three adjacent data points in the target segment, the stability of the rate of change of the data points in the target segment can be determined, and the obtained stability value can better characterize the stability of the rate of change of the data points in the target segment per unit time.

[0068] In one embodiment, the state change degree value of the target segment in the sub-time period is determined by: ,in, is the state change degree value of the target segment in the sub-time period, N is the number of sub-data point dimensions of the data point, C is the number of data points in the target segment, as well as are the values ​​of the k+1th and kth data points in the target segment in the jth dimension, is the range of the data points in the target segment in the jth dimension, Is the multiplication operator.

[0069] In the calculation formula of the state change degree value of the target fragment within the sub-time period, the existence of the first 1 can ensure that the various items involved in the cumulative multiplication operation are at least greater than 1, thereby avoiding the result of the cumulative multiplication operation being 0 due to at least one item involved in the cumulative multiplication operation being 0, and ensuring that the various items involved in the cumulative multiplication operation play their respective operational roles.

[0070] The denominator in is the range of the data points in the target segment, and the numerator is the difference between two adjacent data points in the same dimension. Therefore, The calculation results can avoid the influence of dimension and facilitate the comparison between variables.

[0071] It can reflect the degree of difference between two adjacent data points. The higher the degree of difference between every two adjacent data points in the target segment, the higher the probability or degree of change in the flight state of the drone during the time period of the target segment. Therefore, a higher state change degree value can be determined.

[0072] In this way, by comparing every two adjacent data points in the target segment, the obtained state change degree value can better represent the difference degree between the data points at adjacent moments in the target segment.

[0073] In one embodiment, determining a target filter coefficient of a target segment according to a stability value of the target segment includes: ,in, is the target filter coefficient of the target segment, is the preset filter coefficient, and W is the stability value of the target segment.

[0074] The higher the stability value of the target segment, the lower the probability or degree of the monitoring information collected by the drone during the time period of the target segment being affected. In order to avoid the distortion of the actual monitoring information caused by the filtering process, a smaller target filtering coefficient can be used.

[0075] On the contrary, the lower the stability value of the target segment, the higher the probability or degree of the monitoring information collected by the UAV during the time period of the target segment being affected. In order to ensure more accurate monitoring information after filtering, a larger target filter coefficient can be used.

[0076] In this way, the target filter coefficient of the target segment is negatively correlated with the stability value of the target segment, which can take into account both the filtering of noise of the monitoring information collected by the UAV during the time period of the target segment and the retention of actual information.

[0077] In step S103, an initial monitoring set of the corresponding sub-time period collected by the drone is obtained, and the initial monitoring set is filtered using the target filter coefficient of the target segment to obtain a target monitoring set of the corresponding sub-time period.

[0078] The initial monitoring set includes the values ​​of the target monitoring information at different times within the sub-time period; the target monitoring information is any one of the following: the network signal strength of the UAV's flight location, the navigation signal strength, the image information of the UAV's monitored location, and the temperature information.

[0079] The filtering coefficient when filtering the initial monitoring set within the sub-time period is determined according to the stability value of the UAV's flight state segment; compared with determining the corresponding filtering coefficient according to the flight state parameters of the UAV at each moment, the flight state of the UAV within a certain time period can more accurately reflect the actual flight state of the UAV, and can avoid the interference of occasional noise data. Therefore, the filtering of the initial monitoring set within the sub-time period can be better realized.

[0080] Furthermore, since the filter coefficient when filtering the initial monitoring set within the sub-time period is determined based on the stability value of the UAV's flight status segment, compared to determining the corresponding filter coefficient based on the UAV's flight status parameters at each moment, the number of times the filter coefficient needs to be determined can be reduced, thereby reducing the amount of calculation in the process of determining the filter coefficient.

[0081] For example, for a flight state set with 100 data points, the 100 data points may be merged into 5 flight state segments through the process of merging the data points in the flight state set to obtain multiple flight state segments; when determining the corresponding filter coefficients according to the flight state parameters of the UAV at each moment, it is necessary to execute the filter coefficient determination process for 100 data points cumulatively 100 times.

[0082] In an embodiment of the present application, since the filter coefficient of the initial monitoring set in the same flight status segment is determined according to the stability value of the entire flight status segment, for the five flight status segments obtained after the merger, it is only necessary to cumulatively execute the filter coefficient determination process for the five flight status segments five times. Compared with cumulatively executing the filter coefficient determination process 100 times, the amount of calculation of the equipment can be effectively reduced.

[0083] Since the flight status fragment can better characterize the flight status of the drone than a single data point, a more accurate filter coefficient can be determined based on the flight status fragment compared to determining the filter coefficient based on a single data point. In this way, the embodiment of the present application can not only determine a more accurate filter coefficient, but also effectively reduce the amount of calculation required for the determination process of the filter coefficient, thereby reducing the power consumption of the device.

[0084] The initial monitoring set may, for example, include monitoring information such as image information, temperature information, and air pressure information collected by the drone within a sub-time period; filtering the initial monitoring set using the target filter coefficient of the target segment to obtain the target monitoring set corresponding to the sub-time period may include: using the target filter coefficient of the target segment to filter the monitoring information in the initial monitoring set in turn, and using the set consisting of the filtered monitoring information as the target monitoring set.

[0085] For the monitoring information such as image information, temperature information, and air pressure information collected by the drone in the sub-time period, the corresponding filtering algorithm can be used for filtering processing; for example, when the acquired monitoring information is image information, it can be implemented using filtering algorithms such as mean filtering, Gaussian filtering, median filtering, bilateral filtering, and Laplace filtering.

[0086] In step S104, a total monitoring set corresponding to the target time period is determined according to the target monitoring sets corresponding to different sub-time periods within the target time period.

[0087] Since the target time period is divided into different sub-time periods, different sub-time periods correspond to different flight status fragments, and after filtering the initial monitoring set, the initial monitoring set in each sub-time period is processed separately. Therefore, the filtered monitoring information at each moment in the target time period can be obtained, thereby determining the total monitoring set corresponding to the target time period.

[0088] The total monitoring set corresponding to the target time period obtained includes monitoring information corresponding to different moments in the target time period after filtering. The monitoring information after filtering can more accurately reflect the actual information of the area to be monitored.

[0089] The filtering process of the unfiltered monitoring information is determined according to the flight state of the UAV when acquiring the monitoring information. Therefore, the monitoring information after filtering effectively avoids the adverse effects of the UAV's flight state on the monitoring information, and enables the ground test platform to better monitor the monitoring area.

[0090] Figure 2 FIG. 1 is a schematic diagram showing a structure of a data acquisition system 1000 for a ground test platform according to an exemplary embodiment. Figure 2 The data acquisition system 1000 for a ground test platform includes: a processor 1100 and a memory 1200, wherein the memory 1200 stores computer program instructions, and when the computer program instructions are executed by the processor 1100, all or part of the steps of the data acquisition method for a ground test platform in the present application are implemented.

[0091] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary technical means in the art that are not disclosed in the present application, and the specification and embodiments are only considered as exemplary.

[0092] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A data acquisition method for a ground test platform, characterized in that: include: Obtain the flight status set of the UAV associated with the ground test platform within the target time period, and merge the data points in the flight status set to obtain multiple flight status segments; the same flight status segment includes data points that are adjacent in time sequence and have similar fluctuations; For a target segment in the plurality of flight status segments, determining a stability value of the target segment in the sub-time period, and determining a target filter coefficient of the target segment according to the stability value of the target segment; Obtain an initial monitoring set of the corresponding sub-time period collected by the drone, and filter the initial monitoring set using the target filter coefficient of the target segment to obtain a target monitoring set of the corresponding sub-time period; According to the target monitoring sets corresponding to different sub-time periods within the target time period, a total monitoring set corresponding to the target time period is determined.

2. The data acquisition method for a ground test platform according to claim 1, characterized in that: The data points in the flight status set are merged to obtain multiple flight status fragments, including: Merging the data points in the flight state set that are located in the starting time period into an initial flight state segment, and determining a candidate data point in the flight state set that is located at the next moment of the initial flight state segment in terms of time sequence; Determine a combined probability value of the candidate data point relative to the initial flight state segment, wherein the combined probability value is used to characterize the similarity between the candidate data point and the initial flight state segment in terms of fluctuation degree; The candidate data points are merged into the initial flight state segment or the next flight state segment according to the merging probability value, so as to obtain multiple flight state segments with reference to the obtaining process of the initial flight state segment.

3. The data collection method for a ground test platform according to claim 2, characterized in that: The combined probability value is determined in the following manner: , where P is the combined probability value of the candidate data point relative to the initial flight state segment, N is the number of sub-data point dimensions of the candidate data point, is the standard deviation of the data points in the initial flight state segment in the jth dimension, is the standard deviation of the data points in the initial flight state segment and the candidate data points in the jth dimension, is the value of the candidate data point in the jth dimension, is the mean value of the data points in the initial flight state segment in the jth dimension.

4. The data acquisition method for a ground test platform according to claim 1, characterized in that: Determine the stability value of the target segment in the sub-time period, including: Determine a stability value of the target segment in the sub-time period, where the stability value is used to characterize the stability of the rate of change of data points in the target segment per unit time; Determine a state change degree value of the target segment within the sub-time period, where the state change degree value is used to characterize the difference degree between data points at adjacent moments within the target segment; The ratio of the stability degree value to the state change degree value is used as the stability degree value.

5. The data collection method for a ground test platform according to claim 4, characterized in that: The stability value of the target segment within the sub-time period is determined in the following way: ,in, is the stability value of the target segment in the sub-time period, N is the number of sub-data point dimensions of the data point, is an exponential function with a natural constant as the base, C is the number of data points in the target segment, , as well as These are the values ​​of the k+1th, kth, and k-1th data points in the target segment in the jth dimension.

6. The data collection method for a ground test platform according to claim 4, characterized in that: The state change degree value of the target segment in the sub-time period is determined by the following method: ,in, is the state change degree value of the target segment in the sub-time period, N is the number of sub-data point dimensions of the data point, C is the number of data points in the target segment, as well as are the values ​​of the k+1th and kth data points in the target segment in the jth dimension, is the range of the data points in the target segment in the jth dimension, Is the multiplication operator.

7. The data collection method for a ground test platform according to claim 1, characterized in that: According to the stability value of the target segment, the target filter coefficient of the target segment is determined, including: ,in, is the target filter coefficient of the target segment, is the preset filter coefficient, and W is the stability value of the target segment.

8. The data collection method for a ground test platform according to claim 1, characterized in that: The flight state set includes the values ​​of the target flight state parameter at different times within the target time period; the target flight state parameter is any one of the following: speed information, acceleration information, altitude information, and body vibration intensity information.

9. The data collection method for a ground test platform according to claim 1, characterized in that: The initial monitoring set includes the values ​​of the target monitoring information at different times within the sub-time period; the target monitoring information is any one of the following: the network signal strength of the UAV's flight location, the navigation signal strength, the image information of the UAV's monitored location, and the temperature information.

10. A data acquisition system for a ground test platform, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the data acquisition method for a ground test platform according to any one of claims 1 to 9 is implemented.

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