Data processing method and system for real-time hot backup of unmanned aerial vehicle

Abstract

The invention relates to the technical field of data processing, in particular to a data processing method and system for real-time hot backup of an unmanned aerial vehicle. The method comprises the steps of receiving initial position information sent by a positioning system of an unmanned aerial vehicle at different moments under the condition that target monitoring information of a target area is collected; determining a first parameter value of the unmanned aerial vehicle at the target moment according to the initial position information of the unmanned aerial vehicle in the preset time period of the target moment so as to determine a second parameter value of the unmanned aerial vehicle at the target moment; and according to the second parameter value of the unmanned aerial vehicle in the preset time period of the target moment, adjusting the initial position information of the unmanned aerial vehicle at the target moment to obtain target position information, matching the target monitoring information at the target moment with the target position information to obtain a matching result, and performing real-time hot backup on the matching result. Through the technical scheme, the hot backup data comprising more accurate monitoring information can be obtained.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data processing method and system for real-time hot backup of an unmanned aerial vehicle. Background Art

[0002] Real-time hot backup of drones means that without interrupting the normal operation of the drone, the target monitoring information such as image information, temperature information, humidity information and altitude information collected by the drone is sent to the server or other storage devices through data transmission to ensure the security and real-time availability of the data.

[0003] For example, in the Chinese patent application document with publication number CN109857034A, a real-time hot backup integrated data processing system is provided, including two servers: integrated processing unit A and integrated processing unit B; the integrated processing unit is configured with 4 network ports, which are respectively connected to external Ethernet A, external Ethernet B, internal Ethernet A, and internal Ethernet B; the integrated processing unit A and the integrated processing unit B are directly connected via a serial port; the network ports A and B on the external network of each integrated processing unit back up each other, the network ports A and B on the internal network back up each other, and the integrated processing unit A and the integrated processing unit B back up each other; the downlink data processed by the integrated data processing system includes: telemetry data, business data, and image data; the uplink data includes remote control data.

[0004] When a drone is collecting monitoring information, the drone's location information may be affected by the strength of the positioning signal, thereby sending location information to the server that does not match the drone's actual location, causing the monitoring information received by the server to not match the drone's actual location, thereby making it impossible to obtain hot backup data including accurate monitoring information. Summary of the invention

[0005] In order to overcome the problem in the related art that hot backup data including accurate monitoring information cannot be obtained, the present application provides a data processing method and system for real-time hot backup of unmanned aerial vehicles.

[0006] According to a first aspect of an embodiment of the present application, a data processing method for real-time hot backup of a drone is provided, comprising: receiving initial position information at different times sent by a positioning system of the drone while controlling the drone to collect target monitoring information of a target area according to a planned path; determining a first parameter value of the drone at the target time according to the initial position information of the drone within a preset time period where the target time is located; the first parameter value is used to characterize the degree of difference between the initial position information of the drone at adjacent times within the preset time period; using the difference degree value of the first parameter value of the drone at the target time and the first time as the second parameter value of the drone at the target time; the first time is the time corresponding to other traveled positions located near the initial position information of the drone at the target time; adjusting the initial position information of the drone at the target time to obtain the target position information according to the second parameter value of the drone within the preset time period where the target time is located; receiving the target monitoring information of the target area sent by the drone, and matching the target monitoring information and the target position information at the target time to obtain a matching result, so as to perform real-time hot backup of the matching result.

[0007] In this way, by receiving the position information sent by the drone, the initial position information of the drone within a preset time period at the target moment is compared to obtain a first parameter value of the drone at the target moment, the first parameter value can reflect the flight status of the drone, the first parameter value of the drone at the target moment is compared with the first parameter values ​​of other traveled positions of the drone near the initial position information at the target moment to obtain a second parameter value, the second parameter value can reflect whether there is an abnormality according to the position information of the drone, and the initial position information of the drone at the target moment is adjusted according to the second parameter value, so that more accurate position information can be obtained, so that after matching with the target monitoring information, hot backup data including more accurate monitoring information can be obtained.

[0008] Optionally, the first parameter value of the drone at the target time is determined by: , where Z is the first parameter value of the drone at the target time, norm is the normalization function, X is the average value of the acceleration of the drone in the preset time period at the target time, and r is the number of moments in the preset time period. as well as are the acceleration magnitudes at the jth and j+1th moments in the preset time period, as well as are the flight speeds at the jth and j+1th moments within the preset time period respectively.

[0009] In this way, since the flight speed and acceleration of the UAV at different times within the preset time period are determined based on the position information sent by the UAV, the flight speeds of the UAV at adjacent times within the preset time period are compared, and the acceleration sizes of the UAV at adjacent times within the preset time period are compared to obtain the first parameter value of the UAV at the target time, which helps to determine whether there is any abnormality in the position information of the UAV at the target time.

[0010] Optionally, the difference degree value is determined by: , where W is the difference between the first parameter value of the drone at the target moment and the first moment, norm is the normalization function, and A is the number of the first moment corresponding to the target moment. is the first parameter value of the UAV at the target time, is the first parameter value of the i-th first moment corresponding to the target moment, is the acceleration magnitude of the i-th first moment corresponding to the target moment, is the acceleration of the UAV at the target moment.

[0011] In this way, by comparing the first parameter value of the UAV at the target moment with the first parameter values ​​of the UAV at multiple first moments, the UAV passes through the same local spatial area at the target moment and multiple first moments. The obtained difference degree value can be used to better characterize the degree of difference in the first parameter value of the UAV at the target moment and the first moment, so as to determine whether there is any abnormality in the position information of the UAV at the target moment.

[0012] Optionally, according to the second parameter value of the drone within the preset time period where the target moment is located, the initial position information of the drone at the target moment is adjusted to obtain the target position information, including: determining the abnormality coefficient of the position information of the drone at the target moment according to the second parameter value of the drone within the preset time period where the target moment is located; the abnormality coefficient is used to characterize the degree of difference between the second parameter value of the drone at the target moment and other moments within the preset time period; according to the abnormality coefficient and initial position information of the drone at different moments within the preset time period, the initial position information of the drone at the target moment is adjusted to obtain the target position information.

[0013] In this way, since the second parameter value of the drone at the target time can represent the probability that there is an abnormality in the positioning of the drone at the target time, comparing the second parameter value at the target time with the second parameter values ​​at other times within the same preset time period can discover more subtle abnormalities in the positioning of the drone. Therefore, the abnormality of the drone at the target time can be determined more accurately.

[0014] Optionally, the anomaly coefficient of the position information of the drone at the target time is determined by: ,in, is the abnormal coefficient of the drone at the target time, norm is the normalization processing function, r is the length of the preset time period, is the predicted value of the UAV’s flight speed at the target time, is the predicted value of the flight speed of the UAV at the first moment in the preset time period, is the second parameter value of the UAV at the target time, is the second parameter value of the ath first moment corresponding to the target moment.

[0015] In this way, the predicted flight speed value of the UAV at other times within the preset time period is compared with the predicted flight speed value of the UAV at the target time, and by comparing the second parameter value of the UAV at the target time with the second parameter value of the UAV at other times within the preset time period, the obtained abnormality coefficient can more accurately characterize the probability that there is an abnormality in the positioning of the UAV at the target time.

[0016] Optionally, the target position information of the UAV at the target moment is determined in the following manner: when the abnormality coefficient of the UAV at the target moment is greater than or equal to a preset threshold, multiple second moments in which the abnormality coefficient is less than the preset threshold are determined from different moments within a preset time period; based on the initial position information of the UAV at the multiple second moments, the position information of the UAV at the target moment is predicted, and the predicted position information is used as the target position information.

[0017] Optionally, the target position information of the UAV at the target moment is determined in the following manner: when the abnormality coefficient of the UAV at the target moment is greater than or equal to a preset threshold, multiple reference position information located on the planned path is determined from different initial position information within a preset time period; based on the multiple reference position information corresponding to the UAV at the target moment, the position information of the UAV at the target moment is predicted, and the predicted position information is used as the target position information.

[0018] According to a second aspect of an embodiment of the present application, a data processing system for real-time hot backup of an unmanned aerial vehicle is provided, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the steps of the data processing method for real-time hot backup of an unmanned aerial vehicle provided in the first aspect of the present application are implemented.

[0019] The technical solution provided by the embodiments of the present application may include the following beneficial effects: by receiving the position information sent by the drone, comparing the initial position information of the drone within a preset time period at the target moment to obtain a first parameter value of the drone at the target moment, the first parameter value can reflect the flight status of the drone, comparing the first parameter value of the drone at the target moment with the first parameter values ​​of other traveled positions of the drone near the initial position information at the target moment to obtain a second parameter value, the second parameter value can reflect whether there is an abnormality according to the position information of the drone, and adjusting the initial position information of the drone at the target moment according to the second parameter value, so as to obtain more accurate position information, thereby obtaining hot backup data including more accurate monitoring information after matching with the target monitoring information.

[0020] 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

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0022] Figure 1 is a flow chart of a data processing method for real-time hot backup of a drone according to an exemplary embodiment; Figure 2 The present invention is a structural diagram of a data processing system for real-time hot backup of a drone according to an exemplary embodiment. DETAILED DESCRIPTION

[0023] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0024] First, a brief introduction to the application scenario of the embodiment of the present application is given. In the application scenario of the present application, when controlling the drone to collect target monitoring information of the target area according to a planned path, the drone can realize its own positioning through the positioning system, and send the location information through the wireless connection established with the server. The drone can also send the collected monitoring information to the server. The server can match the location information and the monitoring information through the timestamp information corresponding to the location information and the monitoring information, thereby realizing hot backup of the monitoring information.

[0025] For example, by matching the location information and the monitoring information, the monitoring information of different locations in the space area monitored by the drone can be determined. The monitoring information may be, for example, temperature information, humidity information, image information, etc.

[0026] However, the acquisition of location information by the drone may be affected by the positioning signal, making it difficult for the drone to obtain more accurate location information, thereby sending location information inconsistent with the actual location to the server. For example, due to the poor positioning signal of the drone, the location information of the drone is not obtained for a period of time, and after a period of time, the location information of the drone is displayed as reaching the second location from the first location in a short time, and the drone cannot reach the second location from the first location in a short time at its own flying speed.

[0027] Since the location information obtained by the server from the drone may not reflect the actual location of the drone, it is difficult for the server to accurately match the location information with the monitoring information, making it difficult to obtain hot backup data including accurate monitoring information.

[0028] In view of the above technical problems, the embodiments of the present application provide a data processing method and system for real-time hot backup of a drone, which can be applied to a server, and the server can be used to process the data sent by the drone. Figure 1 is a flow chart of a data processing method for real-time hot backup of a drone according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps.

[0029] In step S101, while controlling the drone to collect target monitoring information of the target area according to the planned path, initial position information at different times sent by the positioning system of the drone is received.

[0030] The monitoring information of the target area can be collected by setting sensors on the drone. The sensors can be image sensors, temperature sensors, humidity sensors, altitude sensors, etc., so as to collect target monitoring information such as image information, temperature information, humidity information and altitude information.

[0031] Drones can collect target monitoring information of the target area to search for people in the target area, search for animals other than humans, build maps, and monitor temperature changes, etc., which will not be elaborated here.

[0032] For example, a drone can obtain its own location information through positioning systems such as GPS (Global Positioning System), BDS (BeiDou Navigation Satellite System) and GLONASS (Global Navigation Satellite System), so as to send the determined location information to a server via wireless signals.

[0033] As the recipient of the drone's location information, the server may receive inaccurate location information from the drone. Based on the location information received from the drone, the server may determine that the drone's flight speed has frequent fluctuations or that the drone has a large flight acceleration. However, in order to facilitate the collection of monitoring information, the drone's actual flight speed may not have such frequent or large fluctuations.

[0034] In order to facilitate the collection of target monitoring information of the target area by the UAV, a path planning can be performed for the UAV before the UAV takes off, or adaptive path planning can be performed according to the actual situation of the UAV's environment during the flight. The content about the path planning of the UAV will not be repeated here.

[0035] By receiving the initial position information sent by the drone's positioning system at different times, the server helps determine the drone's position in the target area to determine whether there may be any abnormalities in the drone's position information, thereby more accurately matching the monitoring information with the position information; the target area can be, for example, farmland to be inspected, mountainous areas, buildings, or areas to be searched for personnel.

[0036] In step S102, a first parameter value of the UAV at the target time is determined according to the initial position information of the UAV within a preset time period at the target time.

[0037] The first parameter value is used to characterize the degree of difference between the initial position information of the drone at adjacent moments within a preset time period; if it is determined based on the position information sent by the drone at different moments that the flight speed of the drone fluctuates to a large extent or with a large frequency within a period of time, and in order to achieve effective collection of monitoring information, a relatively stable flight speed is usually used to collect monitoring information. Therefore, when the flight speed of the drone fluctuates to a large extent or with a large frequency within a period of time, the position information sent by the drone may contain position information that does not match the actual position of the drone.

[0038] By comparing the initial position information of the UAV at adjacent moments within a preset time period, a first parameter value is obtained to characterize the degree of difference between the initial position information of the UAV at adjacent moments within the preset time period, which helps to determine whether there is a positioning anomaly in the position information sent by the UAV at the target moment based on the first parameter value at the target moment.

[0039] The target time can be any one of the multiple time corresponding to the multiple position information sent by the drone; the preset time period where the target time is located can refer to a time period with a certain time interval between the two ends and the target time. For example, it can refer to a time period of a preset duration centered on the target time, and the preset duration can be set according to actual needs, for example, the preset duration can be between 10 seconds and 20 seconds.

[0040] In one embodiment, the first parameter value of the drone at the target time is determined by: , where Z is the first parameter value of the drone at the target time, norm is the normalization function, X is the average value of the acceleration of the drone in the preset time period at the target time, and r is the number of moments in the preset time period. as well as are the acceleration magnitudes at the jth and j+1th moments in the preset time period, as well as are the flight speeds at the jth and j+1th moments within the preset time period respectively.

[0041] The acceleration and flight speed of the drone at different times within the preset time period where the target moment is located can be determined based on the position information at different times received from the server. For example, the flight direction and flight speed of the drone at different times within the preset time period can be determined based on the position information at different times received from the server, so as to determine the acceleration of the drone within the preset time period based on the flight direction and flight speed of the drone at different times within the preset time period.

[0042] Since the UAV is in the working state of collecting target monitoring information, the UAV usually does not accelerate or decelerate frequently, or the UAV usually does not accelerate or decelerate significantly. If the acceleration determined according to the position information sent by the UAV changes greatly in adjacent moments, or the flight speed determined according to the position information sent by the UAV changes greatly in adjacent moments, it means that there is a high probability that the position information of the UAV is biased, and there is a high probability that the position information sent by the UAV to the server does not match the actual position of the UAV.

[0043] In this way, since the flight speed and acceleration of the UAV at different times within the preset time period are determined based on the position information sent by the UAV, the flight speeds of the UAV at adjacent times within the preset time period are compared, and the acceleration sizes of the UAV at adjacent times within the preset time period are compared to obtain the first parameter value of the UAV at the target time, which helps to determine whether there is any abnormality in the position information of the UAV at the target time.

[0044] In step S103, the difference between the first parameter value of the drone at the target time and the first time is used as the second parameter value of the drone at the target time.

[0045] The first moment is the moment corresponding to other traveled positions near the initial position information of the drone at the target moment; for example, if the position coordinates of the initial position information of the drone at the target moment in the spatial coordinate system are (1, 1, 1), the moment when the drone passes through other coordinates near the coordinates (1, 1, 1) in the spatial coordinate line can be used as the first moment corresponding to the drone at the target moment.

[0046] Here, since the UAV is located in the same local spatial area at the first moment and the target moment, when the UAV performs the task of collecting target monitoring information of the target area, the flight status of the UAV at the first moment and the target moment usually has similar performance. If there is a large difference between the flight status of the UAV at the first moment and the target moment determined according to the position information sent by the UAV, it means that the position information of the UAV at the target moment may be abnormal.

[0047] Since the first parameter value of the UAV is determined based on the flight speed and flight acceleration of the UAV over a period of time, the first parameter value of the UAV can reflect the flight status of the UAV over a period of time. If there are large differences in the first parameter values ​​of the UAV when passing through the same local spatial area, it means that there is a high probability that the position information of the UAV at the target time is abnormal.

[0048] In one embodiment, the difference degree value is determined by: , where W is the difference between the first parameter value of the drone at the target moment and the first moment, norm is the normalization function, and A is the number of the first moment corresponding to the target moment. is the first parameter value of the UAV at the target time, is the first parameter value of the i-th first moment corresponding to the target moment, is the acceleration magnitude of the i-th first moment corresponding to the target moment, is the acceleration of the UAV at the target moment.

[0049] In this way, by comparing the first parameter value of the UAV at the target moment with the first parameter values ​​of the UAV at multiple first moments, the UAV passes through the same local spatial area at the target moment and multiple first moments. The obtained difference degree value can be used to better characterize the degree of difference in the first parameter value of the UAV at the target moment and the first moment, so as to determine whether there is any abnormality in the position information of the UAV at the target moment.

[0050] The larger the difference degree value corresponding to the target moment, the greater the difference degree between the first parameter value of the UAV at the target moment and the first moment, and the greater the difference degree of the flight conditions of the UAV when it passes through the same local spatial area as the driving position at the target moment. When the UAV passes through the same local spatial area to perform the target monitoring information collection task, there will not be a large difference in the flight conditions of the UAV. The large difference between the first parameter value of the UAV at the target moment and the first parameter value at the first moment is more likely to be caused by the large difference between the position information sent by the UAV and the actual position information, and there is a high probability that the positioning of the UAV at the target moment is abnormal.

[0051] In step S104, according to the second parameter value of the drone in the preset time period at the target time, the initial position information of the drone at the target time is adjusted to obtain the target position information.

[0052] Since the second parameter value of the UAV at the target moment can reflect the probability that there is an abnormality in the positioning of the UAV at the target moment, the larger the second parameter value of the UAV at the target moment, the greater the probability that there is an abnormality in the positioning signal of the UAV at the target moment, and the abnormality of the positioning signal of the UAV at the target moment has a greater impact on the matching of the target monitoring information of the UAV.

[0053] On the contrary, the smaller the second parameter value of the drone at the target time, the smaller the probability that there is an abnormality in the positioning signal of the drone at the target time, and the server can achieve a more accurate match between the position information at the target time and the target monitoring information.

[0054] Therefore, according to the second parameter value of the UAV within the preset time period at the target moment, the initial position information of the UAV at the target moment can be adjusted to obtain the target position information, so as to avoid obtaining more accurate target position information of the UAV at the target moment after the adjustment. The target position information of the UAV at the target moment can better reflect the actual position of the UAV, and can facilitate the UAV to achieve a match between the target monitoring information at the target moment and the actual position.

[0055] In one embodiment, according to the second parameter value of the drone within the preset time period where the target moment is located, the initial position information of the drone at the target moment is adjusted to obtain the target position information, including: determining the abnormality coefficient of the position information of the drone at the target moment according to the second parameter value of the drone within the preset time period where the target moment is located; the abnormality coefficient is used to characterize the degree of difference between the second parameter value of the drone at the target moment and other moments within the preset time period; according to the abnormality coefficient and initial position information of the drone at different moments within the preset time period, the initial position information of the drone at the target moment is adjusted to obtain the target position information.

[0056] In this way, since the second parameter value of the drone at the target time can represent the probability that there is an abnormality in the positioning of the drone at the target time, comparing the second parameter value at the target time with the second parameter values ​​at other times within the same preset time period can discover more subtle abnormalities in the positioning of the drone. Therefore, the abnormality of the drone at the target time can be determined more accurately.

[0057] The greater the abnormal coefficient of the drone's position information at the target time, the greater the difference in the second parameter value between the drone at the target time and other times within the preset time period, and the higher the probability that there is an abnormality in the positioning of the drone at the target time; the more necessary it is to adjust the initial position information of the drone at the target time.

[0058] According to the abnormal coefficient and initial position information of the UAV at different times within the preset time period, the initial position information of the UAV at the target time is adjusted to obtain the target position information, so that the target position information that can better reflect the actual position of the UAV can be obtained, so that the server can better achieve the matching between the position information and the target monitoring information.

[0059] In one embodiment, the abnormal coefficient of the position information of the drone at the target time is determined by: ,in, is the abnormal coefficient of the drone at the target time, norm is the normalization processing function, r is the length of the preset time period, is the predicted value of the UAV’s flight speed at the target time, is the predicted value of the flight speed of the UAV at the first moment in the preset time period, is the second parameter value of the UAV at the target time, is the second parameter value of the ath first moment corresponding to the target moment.

[0060] When a UAV is performing a target monitoring information collection task, in order to better realize the collection of target monitoring information, a relatively stable flight mode is usually adopted. Therefore, the flight speed prediction value of the UAV at the target moment can be determined based on the flight speed of the UAV at at least one moment before the target moment. With reference to the flight speed prediction value of the UAV at the target moment, the flight speed prediction values ​​at other moments in the time period of the target moment can be determined.

[0061] Among them, the predicted value of the UAV's flight speed at the target moment can be obtained by taking a moving average of the UAV's flight speed at at least one moment before the target moment, or, the time interval with the target moment can be used as a weight to take a weighted moving average of the UAV's flight speed at at least one moment before the target moment.

[0062] The larger the abnormality coefficient of the UAV at the target moment, at least it means that the difference between the second parameter value of the UAV at the target moment and the second parameter value of the UAV at other moments within the preset time period is greater. By comparing the predicted flight speed value of the UAV at other moments within the preset time period with the predicted flight speed value of the UAV at the target moment, the consistency of the predicted flight speed value of the UAV within the preset time period can be reflected to determine whether there is an abnormality in the positioning of the UAV.

[0063] In this way, the predicted flight speed value of the UAV at other times within the preset time period is compared with the predicted flight speed value of the UAV at the target time, and by comparing the second parameter value of the UAV at the target time with the second parameter value of the UAV at other times within the preset time period, the obtained abnormality coefficient can more accurately characterize the probability that there is an abnormality in the positioning of the UAV at the target time.

[0064] In one embodiment, the target position information of the UAV at the target moment is determined in the following manner: when the abnormality coefficient of the UAV at the target moment is greater than or equal to a preset threshold, multiple second moments with abnormality coefficients less than the preset threshold are determined from different moments within a preset time period; based on the initial position information of the UAV at the multiple second moments, the position information of the UAV at the target moment is predicted, and the predicted position information is used as the target position information.

[0065] Since the abnormal coefficient of the UAV at the target time can more accurately characterize the probability that the UAV's positioning is abnormal at the target time, when the abnormal coefficient of the UAV at the target time is greater than or equal to the preset threshold, it means that the probability that the UAV's positioning is abnormal at the target time is relatively high, and it is necessary to adjust the initial position information of the UAV at the target time.

[0066] The preset threshold value can be set according to actual conditions. For example, when the value range of the abnormal coefficient is between 0 and 1, the preset threshold value can be set between 0.3 and 0.5.

[0067] When the abnormal coefficient of the UAV at the target time is less than the preset threshold, it means that the positioning of the UAV at the target time is relatively accurate, and the initial position information of the UAV at the target time can be used as the target position information of the UAV at the target time.

[0068] In this way, by determining multiple second moments in which the anomaly coefficient is less than a preset threshold from different moments within a preset time period, the position information of other moments with a lower probability of positioning anomalies can be used as a reference to adjust the initial position information of the UAV at the target moment, thereby obtaining more accurate target position information of the UAV at the target moment.

[0069] Predicting the position information of the UAV at a target moment based on the initial position information of the UAV at multiple second moments, and using the predicted position information as the target position information may include: generating a continuous first flight path based on the initial position information of the UAV at multiple second moments, and predicting the target position information of the UAV at the target moment based on the obtained first flight path.

[0070] For example, when there is a second moment on both sides of the target moment, the position corresponding to the target moment on the first driving path can be used as the target position information of the UAV at the target moment; or, when there is a second moment only on one side of the target moment, the position corresponding to the target moment on the extension line of the first driving path can be used as the target position information of the UAV at the target moment.

[0071] Alternatively, when there is a second moment only on one side of the target moment, the midpoint of the line connecting the two position points, the position on the extension line of the first driving path corresponding to the target moment and the initial position information of the UAV at the target moment, can be used as the target position information of the UAV at the target moment.

[0072] When there is no moment in the preset time period where the target moment is located where the abnormality coefficient is less than or equal to the preset threshold, the target position information of the UAV at the target moment can be determined based on the initial position information of the UAV in other time periods adjacent to the preset time period. Specifically, reference can be made to the process of determining the target position information of the UAV at the target moment based on the initial position information of multiple second moments in the preset time period, which will not be repeated here.

[0073] In another embodiment, the target position information of the UAV at the target moment is determined in the following manner: when the abnormality coefficient of the UAV at the target moment is greater than or equal to a preset threshold, multiple reference position information located on the planned path is determined from different initial position information within a preset time period; based on the multiple reference position information corresponding to the UAV at the target moment, the position information of the UAV at the target moment is predicted, and the predicted position information is used as the target position information.

[0074] In this way, by determining multiple reference position information located on the planned path from different initial position information within a preset time period, the initial position information of the drone at the target time can be adjusted better to obtain more accurate target position information at the target time.

[0075] Based on multiple reference position information corresponding to the drone at the target moment, the position information of the drone at the target moment is predicted, and the predicted position information is used as the target position information. This can include: generating a continuous second flight path based on the multiple reference position information corresponding to the drone at the target moment, the second flight path passes through the position points of the multiple reference position information in the spatial coordinate system, and the target position information of the drone at the target moment can be predicted based on the obtained second flight path.

[0076] For example, when there is reference position information on both sides of the initial position information at the target moment, the position on the second driving path corresponding to the target moment can be used as the target position information of the UAV at the target moment; or, when there is reference position information only on one side of the initial position information at the target moment, the position on the extension line of the second driving path corresponding to the target moment can be used as the target position information of the UAV at the target moment.

[0077] Alternatively, when there is reference position information only on one side of the initial position information at the target moment, the midpoint of the line connecting the two position points, the position on the extension line of the second driving path corresponding to the target moment and the initial position information of the UAV at the target moment, can be used as the target position information of the UAV at the target moment.

[0078] When there are no multiple reference position information on the planned path within the preset time period where the target moment is located, the target position information of the UAV at the target moment can be determined based on the initial position information of the UAV in other time periods adjacent to the preset time period. Specifically, reference can be made to the process of determining the target position information of the UAV at the target moment based on multiple reference position information within the preset time period, which will not be repeated here.

[0079] In step S105, target monitoring information of the target area at the target time sent by the drone is received, and the target monitoring information at the target time and the target position information are matched to obtain a matching result, so as to perform real-time hot backup of the matching result.

[0080] Since the frequency band of the wireless signal for data transmission between the drone and the server is independent of the frequency band of the drone's positioning signal, when the signal strength of the drone's positioning signal is poor, the signal strength of the wireless signal for data transmission between the drone and the server may still be good. The drone can assign timestamp information to the target monitoring information collected at different times, so that the server can match the location information and target monitoring information based on the timestamp information.

[0081] Alternatively, when the signal strength of the wireless signal is poor, the drone can store the target monitoring information collected over a period of time, and assign timestamp information to the target monitoring information corresponding to different moments, so that the server can match the location information and the target monitoring information based on the timestamp information; the drone can periodically determine the signal strength of the wireless signal between the server and the server, and when it is determined that the signal strength of the wireless signal has been restored, the drone can send the stored target monitoring information carrying timestamp information to the server.

[0082] The target monitoring information and target location information at the target time are matched to obtain a matching result, so as to perform a real-time hot backup of the matching result, including: constructing a data pair corresponding to the timestamp information according to the timestamp information at the target time; the data pair includes the target monitoring information and target location information corresponding to the same timestamp information. According to the data pairs corresponding to different timestamp information, the corresponding relationship between different position points of the target area in the spatial coordinate system and different target monitoring information is determined; different position points correspond to different target location information; and the corresponding relationship is hot backed up to the target database.

[0083] For example, the server can construct a map of the target area based on the image data received from the drone; or, it can share the location information of people searched by the drone; or, it can determine the temperature distribution of the target area based on the temperature information collected by the drone.

[0084] Through the data processing method for real-time hot backup of a drone provided in an embodiment of the present application, the position information sent by the drone is received, the initial position information of the drone within a preset time period at the target moment is compared to obtain a first parameter value of the drone at the target moment, the first parameter value being able to reflect the flight status of the drone, the first parameter value of the drone at the target moment is compared with the first parameter values ​​of other traveled positions of the drone near the initial position information at the target moment to obtain a second parameter value, the second parameter value being able to reflect whether there is an abnormality according to the position information of the drone, the initial position information of the drone at the target moment is adjusted according to the second parameter value, and more accurate position information can be obtained, thereby obtaining hot backup data including more accurate monitoring information after matching with the target monitoring information.

[0085] Figure 2 FIG. 1 is a schematic diagram of a data processing system 1000 for real-time hot backup of a drone according to an exemplary embodiment. The system can be applied to a server, and the server can process the monitoring information sent by the drone. Figure 2 The data processing system 1000 for real-time hot backup of the drone 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 processing method for real-time hot backup of the drone in the present application are implemented.

[0086] It should be understood that the features of some embodiments of the various applications described herein may be combined with each other, unless specifically stated otherwise.

[0087] Although terms such as "first", "second", and "third" may be used herein to describe various components, parts, regions, layers, or sections, these components, parts, regions, layers, or sections are not limited to these terms. Instead, these terms are only used to distinguish one component, part, region, layer, or section from another component, part, region, layer, or section. Therefore, without departing from the teachings of the examples described herein, the first component, part, region, layer, or section mentioned in the examples may also be referred to as the second component, part, region, layer, or section.

[0088] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description herein, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0089] In addition, the word "exemplary" is used herein to mean serving as an example, instance, or diagram. Any aspect or design described herein as "exemplary" is not necessarily to be understood as being advantageous over other aspects or designs. Instead, the use of the word exemplary is intended to present concepts in a concrete manner. As used herein, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or".

[0090] Likewise, although the present application has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. With particular regard to the various functions performed by the components (e.g., elements, resources, etc.) described above, unless otherwise noted, the terms used to describe such components are intended to correspond to any component (functionally equivalent) that performs the specific functions of the described components, even if not structurally equivalent to the disclosed structures.

[0091] Additionally, while particular features of the present application may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations as may be desired and advantageous for any given or particular application.

[0092] 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.

[0093] 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 processing method for real-time hot backup of an unmanned aerial vehicle, characterized in that: include: When controlling the UAV to collect target monitoring information of the target area according to the planned path, receiving the initial position information at different times sent by the positioning system of the UAV; Determine a first parameter value of the UAV at the target time according to the initial position information of the UAV within a preset time period at the target time; The first parameter value is used to characterize the degree of difference between the initial position information of the drone at adjacent moments within a preset time period; The difference between the first parameter value of the UAV at the target time and the first time is used as the second parameter value of the UAV at the target time; the first time is the time corresponding to other traveled positions located near the initial position information of the UAV at the target time; According to the second parameter value of the drone in the preset time period at the target time, the initial position information of the drone at the target time is adjusted to obtain the target position information; Receive the target monitoring information of the target area at the target time sent by the drone, and match the target monitoring information at the target time and the target position information to obtain a matching result, so as to perform real-time hot backup of the matching result.

2. The data processing method for real-time hot backup of a drone according to claim 1 is characterized in that: The first parameter value of the drone at the target time is determined in the following way: , where Z is the first parameter value of the drone at the target time, norm is the normalization function, X is the average value of the acceleration of the drone in the preset time period at the target time, and r is the number of moments in the preset time period. as well as are the acceleration magnitudes at the jth and j+1th moments in the preset time period, as well as are the flight speeds at the jth and j+1th moments within the preset time period respectively.

3. The data processing method for real-time hot backup of a drone according to claim 1 is characterized in that: The difference degree value is determined in the following manner: , where W is the difference between the first parameter value of the drone at the target moment and the first moment, norm is the normalization function, and A is the number of the first moment corresponding to the target moment. is the first parameter value of the UAV at the target time, is the first parameter value of the i-th first moment corresponding to the target moment, is the acceleration magnitude of the i-th first moment corresponding to the target moment, is the acceleration of the UAV at the target time.

4. The data processing method for real-time hot backup of a drone according to claim 1 is characterized in that: According to the second parameter value of the drone in the preset time period at the target time, the initial position information of the drone at the target time is adjusted to obtain the target position information, including: Determine an abnormality coefficient of the position information of the drone at the target time according to the second parameter value of the drone within the preset time period at the target time; the abnormality coefficient is used to characterize the degree of difference between the second parameter value of the drone at the target time and other times within the preset time period; According to the abnormal coefficient and initial position information of the UAV at different times within the preset time period, the initial position information of the UAV at the target time is adjusted to obtain the target position information.

5. The data processing method for real-time hot backup of unmanned aerial vehicle according to claim 4 is characterized in that: The anomaly coefficient of the drone's position information at the target time is determined in the following way: ,in, is the abnormal coefficient of the UAV at the target time, norm is the normalization processing function, r is the length of the preset time period, is the predicted value of the UAV’s flight speed at the target time, is the predicted value of the flight speed of the UAV at the first moment in the preset time period, is the second parameter value of the UAV at the target time, is the second parameter value of the ath first moment corresponding to the target moment.

6. The data processing method for real-time hot backup of a drone according to claim 4 is characterized in that: The target position information of the drone at the target time is determined by the following method: When the abnormality coefficient of the UAV at the target time is greater than or equal to the preset threshold, a plurality of second moments at which the abnormality coefficient is less than the preset threshold are determined from different moments within the preset time period; The position information of the drone at the target moment is predicted based on the initial position information of the drone at multiple second moments, and the predicted position information is used as the target position information.

7. The data processing method for real-time hot backup of a drone according to claim 4 is characterized in that: The target position information of the drone at the target time is determined by the following method: When the abnormal coefficient of the UAV at the target time is greater than or equal to a preset threshold, a plurality of reference position information located on the planned path is determined from different initial position information within a preset time period; The position information of the UAV at the target time is predicted based on a plurality of reference position information corresponding to the UAV at the target time, and the predicted position information is used as the target position information.

8. A data processing system for real-time hot backup of unmanned aerial vehicles, 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 processing method for real-time hot backup of a drone according to any one of claims 1 to 7 is implemented.

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