A Data Processing Method and System for Real-Time Hot Backup of Unmanned Aerial Vehicles

Abstract

This application relates to the technical field of data processing, and in particular, to a data processing method and system for real-time hot backup of unmanned aerial vehicles. The method includes: when collecting target monitoring information for a target area, receiving initial position information at different times sent by the positioning system of the unmanned aerial vehicle; determining a first parameter value of the unmanned aerial vehicle at a target time according to the initial position information of the unmanned aerial vehicle within a preset time period where the target time is located, so as to determine a second parameter value of the unmanned aerial vehicle at the target time; adjusting the initial position information of the unmanned aerial vehicle at the target time according to the second parameter value of the unmanned aerial vehicle within the preset time period where the target time is located to obtain target position information, 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 on the matching result. Through the above technical solutions, hot backup data including more accurate monitoring information can be obtained.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly relates to a data processing method and system for real-time hot backup of unmanned aerial vehicles (UAVs). Background Art

[0002] The real-time hot backup of a UAV refers to, without interrupting the normal operation of the UAV, sending target monitoring information such as image information, temperature information, humidity information, and altitude information collected by the UAV to a 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 the publication number CN109857034A, a real-time hot backup integrated data processing system is provided, which includes two servers: integrated processing unit A and integrated processing unit B; the integrated processing unit is configured with 4 network interfaces, 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 through a serial port; the network interfaces A and B on the external network of each integrated processing unit are backup to each other, the network interfaces A and B on the internal network are backup to each other, and the integrated processing unit A and the integrated processing unit B are backup to each other; the downlink data processed by the integrated data processing system includes: telemetry data, service data, and image data; the uplink data includes remote control data.

[0004] When the UAV collects monitoring information, the position information of the UAV may be affected by the strength of the positioning signal, thereby sending position information that does not match the actual position of the UAV to the server, making the monitoring information received by the server not match the actual position of the UAV, and thus unable to obtain hot backup data including accurate monitoring information. Summary of the Invention

[0005] To overcome the problem in the related art that hot backup data including accurate monitoring information cannot be obtained, this application provides a data processing method and system for real-time hot backup of UAVs.

[0006] According to the first aspect of the embodiments of the present application, a data processing method for real-time hot backup of an unmanned aerial vehicle is provided, including: when controlling the unmanned aerial vehicle to collect target monitoring information of a target area according to a planned path, receiving initial position information at different times sent by the positioning system of the unmanned aerial vehicle; determining a first parameter value of the unmanned aerial vehicle at a target time according to the initial position information of the unmanned aerial vehicle 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 unmanned aerial vehicle at adjacent times within the preset time period; taking the degree of difference value of the first parameter value of the unmanned aerial vehicle at the target time and at a first time as the second parameter value of the unmanned aerial vehicle at the target time; the first time is the time corresponding to other traveled positions near the initial position information of the unmanned aerial vehicle at the target time; adjusting the initial position information of the unmanned aerial vehicle at the target time according to the second parameter value of the unmanned aerial vehicle within the preset time period where the target time is located to obtain target position information; receiving the target monitoring information of the target area sent by the unmanned aerial vehicle at the target time, 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 on the matching result.

[0007] In this way, by receiving the position information sent by the unmanned aerial vehicle, comparing the initial position information of the unmanned aerial vehicle within the preset time period where the target time is located to obtain the first parameter value of the unmanned aerial vehicle at the target time, the first parameter value can reflect the flight state of the unmanned aerial vehicle, comparing the first parameter value of the unmanned aerial vehicle at the target time with the first parameter value of other traveled positions near the initial position information of the unmanned aerial vehicle at the target time to obtain the second parameter value, the second parameter value can reflect whether there is an abnormality according to the position information of the unmanned aerial vehicle, and adjusting the initial position information of the unmanned aerial vehicle at the target time according to the second parameter value can obtain more accurate position information, 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 unmanned aerial vehicle at the target time is determined by the following method: , where Z is the first parameter value of the unmanned aerial vehicle at the target time, norm is a normalization processing function, X is the average value of the acceleration magnitudes of the unmanned aerial vehicle within the preset time period where the target time is located, r is the number of times within the preset time period, and are the acceleration magnitudes at the j-th and the (j + 1)-th times within the preset time period respectively, and are the flight speeds at the j-th and the (j + 1)-th times within the preset time period respectively.

[0009] In this way, since the flight speed and acceleration magnitude of the UAV at different moments within a preset time period are determined based on the position information sent by the UAV, comparing the flight speeds of the UAV at adjacent moments within the preset time period and comparing the acceleration magnitudes of the UAV at adjacent moments within the preset time period to obtain the first parameter value of the UAV at the target moment helps to determine whether there is an abnormality in the position information of the UAV at the target moment.

[0010] Optionally, the difference degree value is determined by the following method: , where W is the difference degree value of the first parameter value of the UAV at the target moment and the first moment, norm is the normalization processing function, A is the number of the first moments corresponding to the target moment, is the first parameter value of the UAV at the target moment, 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 magnitude 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, and the UAV passes through the same local space area at the target moment and multiple first moments, the obtained difference degree value can better characterize the difference degree of the first parameter value of the UAV at the target moment and the first moment, so as to determine whether there is an abnormality in the position information of the UAV at the target moment.

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

[0013] In this way, since the second parameter value of the UAV at the target moment can characterize the probability of the abnormal positioning of the UAV at the target moment, comparing the second parameter value of the target moment with the second parameter values of other moments within the same preset time period can find more subtle abnormalities in the positioning of the UAV. Therefore, the abnormalities existing in the UAV at the target moment can be determined more accurately.

[0014] Optionally, the anomaly coefficient of the UAV's position information at the target moment is determined as follows: , where is the anomaly coefficient of the UAV at the target moment, norm is the normalization function, r is the duration of the preset time period, is the predicted flight speed value of the UAV at the target moment, is the predicted flight speed value of the UAV at the a-th first moment within the preset time period, is the second parameter value of the UAV at the target moment, is the second parameter value of the a-th first moment corresponding to the target moment.

[0015] In this way, by comparing the predicted flight speed values of the UAV at other moments within the preset time period with the predicted flight speed value of the UAV at the target moment, and by comparing the second parameter value of the UAV at the target moment with the second parameter values of the UAV at other moments within the preset time period, the probability of abnormal positioning of the UAV at the target moment can be more accurately characterized by the obtained anomaly coefficient.

[0016] Optionally, the target position information of the UAV at the target moment is determined as follows: when the anomaly coefficient of the UAV at the target moment is greater than or equal to the preset threshold, multiple second moments with anomaly coefficients less than the preset threshold are determined from different moments within the 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 as follows: when the anomaly coefficient of the UAV at the target moment is greater than or equal to the preset threshold, multiple reference position information located on the planned path is determined from different initial position information within the preset time period; based on the multiple reference position information corresponding to the target moment of the UAV, 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 the second aspect of the embodiments of the present application, a data processing system for real-time hot backup of a UAV is provided, including: a processor and a memory, where 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 the UAV 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 the first parameter value of the drone at the target moment, the first parameter value can reflect the flight state of the drone, comparing the first parameter value of the drone at the target moment with the first parameter values of other traveled positions near the initial position information at the target moment to obtain the 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 can obtain more accurate position information, so that after matching with the target monitoring information, hot backup data including more accurate monitoring information can be obtained.

[0020] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. Brief Description of the Drawings

[0021] The drawings here are incorporated into the specification and form a part of this specification, showing the embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0022] Figure 1 is a flowchart of a data processing method for real-time hot backup of an unmanned aerial vehicle according to an exemplary embodiment;

[0023] Figure 2 is a schematic structural diagram of a data processing system for real-time hot backup of an unmanned aerial vehicle according to an exemplary embodiment. Detailed Embodiments

[0024] Here, the exemplary embodiments will be described in detail, and the examples are shown in the 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 embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application.

[0025] First, a brief introduction to the application scenario of the embodiments 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 the planned path, the drone can achieve its own positioning through the positioning system and send the position information through the wireless connection established with the server. The drone can also send the collected monitoring information to the server. The server can achieve the matching of the position information and the monitoring information through the position information and the timestamp information corresponding to the monitoring information, so as to achieve the hot backup of the monitoring information.

[0026] For example, by matching the position information and the monitoring information, the monitoring information of different position points in the spatial area monitored by the drone can be determined. The monitoring information can be, for example, temperature information, humidity information, image information, etc.

[0027] However, the acquisition of position information by the drone may be affected by the positioning signal, making it difficult for the drone to obtain relatively accurate position information, and thus sending position information inconsistent with the actual position to the server. For example, due to the poor positioning signal of the drone, the position information of the drone is not obtained for a period of time, and after a period of time, the position information of the drone shows that it reaches the second position from the first position in a short time, and the drone cannot reach the second position from the first position in a short time at its own flight speed.

[0028] Since the position information obtained by the server from the drone may not reflect the actual position of the drone, it is difficult for the server to accurately match the position information and the monitoring information when matching the position information and the monitoring information, and thus it is difficult to obtain the hot backup data including accurate monitoring information.

[0029] 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 drones, which can be applied to a server, and the server can be used to process the data sent by the drones. Figure 1 It is a flowchart of a data processing method for real-time hot backup of drones shown according to an exemplary embodiment, as Figure 1 shown, and the method includes the following steps.

[0030] In step S101, when controlling the drone to collect target monitoring information for the target area according to the planned path, receive the initial position information at different times sent by the positioning system of the drone.

[0031] The collection of the monitoring information for the target area can be realized by setting sensors on the drone. The sensors can be image sensors, temperature sensors, humidity sensors, altitude sensors, etc., to realize the collection of target monitoring information such as image information, temperature information, humidity information, and altitude information.

[0032] The drone can realize the search for people in the target area, the search for other animals except humans, map construction, and the monitoring of temperature changes, etc. by collecting the target monitoring information for the target area, which will not be elaborated here.

[0033] For example, a drone can obtain its own position 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 position information to the server via a wireless signal.

[0034] As the recipient of the drone's position information, the position information received by the server from the drone may be inaccurate. Based on the position information received from the drone, the server may determine that the flight speed of the drone fluctuates frequently, or the drone has a large flight acceleration. However, for the drone to facilitate the collection of monitoring information, the actual flight speed of the drone may not have such frequent or large fluctuations.

[0035] To facilitate the drone to collect target monitoring information in the target area, path planning can be performed on the drone before it departs, or adaptive path planning can be performed according to the actual situation of the environment where the drone is located during flight. The content related to the path planning of the drone will not be elaborated here.

[0036] By receiving the initial position information at different times sent by the drone's positioning system, the server can help determine the position of the drone in the target area to determine whether the position information of the drone may be abnormal, so as to more accurately match the monitoring information and the position information; the target area can be, for example, a farmland, mountain area, building to be detected, or an area where personnel search is to be carried out.

[0037] In step S102, according to the initial position information of the drone within the preset time period at the target moment, the first parameter value of the drone at the target moment is determined.

[0038] The first parameter value is used to characterize the degree of difference between the initial position information of the drone at adjacent moments within the preset time period; if, based on the position information sent by the drone at different times, it is determined that the flight speed of the drone fluctuates greatly or frequently within a period of time, and in order to effectively collect monitoring information, a relatively stable flight speed is usually used for the collection of monitoring information. Therefore, when the flight speed of the drone fluctuates greatly or frequently within a period of time, there may be position information in the position information sent by the drone that does not match the actual position of the drone.

[0039] By comparing the initial position information of the drone 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 drone 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 drone at the target moment based on the first parameter value at the target moment.

[0040] The target moment can be any one of the multiple moments corresponding to the multiple position information sent by the drone; within the preset time period where the target moment is located, it can refer to a time period with a certain time interval from the target moment at both ends. For example, it can refer to a time period with a preset duration centered on the target moment, and the preset duration can be set according to actual needs. For example, the preset duration can be between 10 seconds and 20 seconds.

[0041] In one embodiment, the first parameter value of the drone at the target moment is determined in the following manner: , where Z is the first parameter value of the drone at the target moment, norm is the normalization processing function, X is the average value of the acceleration magnitudes of the drone within the preset time period where the target moment is located, r is the number of moments within the preset time period, and are the acceleration magnitudes at the j-th and the (j + 1)-th moments within the preset time period respectively, and are the flight speeds at the j-th and the (j + 1)-th moments within the preset time period respectively.

[0042] The acceleration magnitudes and flight speeds of the drone at different moments within the preset time period where the target moment is located can be determined based on the position information at different moments received from the server. For example, based on the position information at different moments received from the server, the flight direction and flight speed of the drone at different moments within the preset time period can be determined, so as to determine the acceleration magnitude of the drone within the preset time period based on the flight direction and flight speed of the drone at different moments within the preset time period.

[0043] Since the drone is in the working state of collecting target monitoring information, the drone usually does not accelerate or decelerate frequently, or the drone usually does not accelerate or decelerate significantly. If the change amount of the acceleration magnitude determined based on the position information sent by the drone within adjacent moments is large, or the change amount of the flight speed determined based on the position information sent by the drone within adjacent moments is large, it indicates that there is a high probability that the position information of the drone is deviated, and there is a high probability that the position information sent by the drone to the server does not match the actual position of the drone.

[0044] In this way, since the flight speed and acceleration magnitude of the UAV at different moments within the preset time period are determined based on the position information sent by the UAV, comparing the flight speeds of the UAV at adjacent moments within the preset time period and comparing the acceleration magnitudes of the UAV at adjacent moments within the preset time period to obtain the first parameter value of the UAV at the target moment helps to determine whether there is an abnormality in the position information of the UAV at the target moment.

[0045] In step S103, the difference degree value of the UAV at the target moment and the first moment in the first parameter value is used as the second parameter value of the UAV at the target moment.

[0046] The first moment is the moment corresponding to other traveled positions near the initial position information of the UAV at the target moment; for example, if the position coordinates of the initial position information of the UAV at the target moment in the space coordinate system are (1, 1, 1), the moment when the UAV passes through other coordinates near the coordinate (1, 1, 1) in the space coordinate line can be used as the first moment corresponding to the UAV at the target moment.

[0047] Here, since the UAV is in the same local space region 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 states of the UAV at the first moment and the target moment usually have similar performances. If there are significant differences in the flight states of the UAV at the first moment and the target moment determined according to the position information sent by the UAV, it indicates that there may be an abnormality in the position information of the UAV at the target moment.

[0048] Since the first parameter value of the UAV is determined based on the flight speed and flight acceleration magnitude of the UAV over a period of time, the first parameter value of the UAV can reflect the flight situation of the UAV over a period of time. If there are significant differences in the first parameter values of the UAV when passing through the same local space region, it indicates that the probability of an abnormality in the position information of the UAV at the target moment is relatively high.

[0049] In one embodiment, the difference degree value is determined in the following manner: , where W is the difference degree value of the UAV at the target moment and the first moment in the first parameter value, norm is the normalization processing function, A is the number of the first moments corresponding to the target moment, is the first parameter value of the UAV at the target moment, 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 magnitude of the UAV at the target moment.

[0050] 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, and the UAV has passed through the same local spatial area at the target moment and multiple first moments, the degree of difference value obtained can 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 an abnormality in the position information of the UAV at the target moment.

[0051] The larger the degree of difference value corresponding to the target moment, the greater the degree of difference in the first parameter value of the UAV at the target moment and the first moment, and the greater the degree of difference in the flight situation of the UAV when passing through the same local spatial area as the already traveled position at the target moment. When the UAV passes through the same local spatial area to perform the acquisition task of the target monitoring information, there will not be a large difference in the flight situation of the UAV. This large difference between the first parameter value of the UAV at the target moment and the first parameter value of the first moment is very likely caused by a 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.

[0052] In step S104, the initial position information of the UAV at the target moment is adjusted according to the second parameter value within the preset time period when the UAV is at the target moment to obtain the target position information.

[0053] Since the second parameter value of the UAV at the target moment can reflect the probability of abnormal 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 of abnormal positioning signal of the UAV at the target moment, and the greater the impact of the abnormal positioning signal of the UAV at the target moment on the matching of the target monitoring information of the UAV.

[0054] On the contrary, the smaller the second parameter value of the UAV at the target moment, the smaller the probability of abnormal positioning signal of the UAV at the target moment, and the server can achieve a more accurate matching between the position information at the target moment and the target monitoring information.

[0055] Therefore, according to the second parameter value within the preset time period when the UAV is 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 obtain a more accurate target position information of the UAV at the target moment after adjustment. The target position information of the UAV at the target moment can better reflect the actual position where the UAV is located, and can facilitate the UAV to achieve the matching between the target monitoring information at the target moment and the actual position.

[0056] In one embodiment, adjusting the initial position information of the unmanned aerial vehicle (UAV) at the target moment according to the second parameter value within a preset time period when the UAV is located at the target moment to obtain the target position information includes: determining an anomaly coefficient of the position information of the UAV at the target moment according to the second parameter value within the preset time period when the UAV is located at the target moment; the anomaly coefficient is used to characterize the degree of difference in the second parameter value between the UAV at the target moment and other moments within the preset time period; adjusting the initial position information of the UAV at the target moment according to the anomaly coefficients of different moments within the preset time period and the initial position information to obtain the target position information.

[0057] In this way, since the second parameter value of the UAV at the target moment can characterize the probability that the positioning of the UAV is abnormal at the target moment, comparing the second parameter value at the target moment with the second parameter values of other moments within the same preset time period can detect more subtle anomalies in the positioning of the UAV. Therefore, the anomalies existing in the UAV at the target moment can be determined more accurately.

[0058] The greater the anomaly coefficient of the position information of the UAV at the target moment, the greater the degree of difference in the second parameter value between the UAV at the target moment and other moments within the preset time period, and the higher the probability that the positioning of the UAV at the target moment is abnormal; it is more necessary to adjust the initial position information of the UAV at the target moment.

[0059] Adjusting the initial position information of the UAV at the target moment according to the anomaly coefficients of different moments within the preset time period and the initial position information to obtain the target position information can obtain the target position information that can better reflect the actual position where the UAV is located, so that the server can better achieve the matching between the position information and the target monitoring information.

[0060] In one embodiment, the anomaly coefficient of the position information of the UAV at the target moment is determined by the following method: , where is the anomaly coefficient of the UAV at the target moment, norm is the normalization processing function, r is the duration of the preset time period, is the predicted flight speed value of the UAV at the target moment, is the predicted flight speed value of the UAV at the a-th first moment within the preset time period, is the second parameter value of the UAV at the target moment, is the second parameter value corresponding to the a-th first moment of the target moment.

[0061] When a drone is performing a task of collecting target monitoring information, in order to better achieve the collection of target monitoring information, a relatively stable flight mode is usually adopted. Therefore, the predicted flight speed of the drone at the target moment can be determined according to the flight speeds of the drone at at least one moment before the target moment. With reference to the predicted flight speed of the drone at the target moment, the predicted flight speeds of other moments within the time period where the target moment is located can be determined.

[0062] Among them, the predicted flight speed of the drone at the target moment can be obtained by performing a moving average on the flight speeds of the drone at at least one moment before the target moment. Or, the time interval from the target moment can be used as a weight to perform a weighted moving average on the flight speeds of the drone at at least one moment before the target moment.

[0063] The larger the anomaly coefficient of the drone at the target moment, at least it indicates that the difference degree between the second parameter value of the drone at the target moment and the second parameter values of the drone at other moments within the preset time period is larger. And by comparing the predicted flight speeds of the drone at other moments within the preset time period with the predicted flight speed of the drone at the target moment, the consistency of the predicted flight speeds of the drone within the preset time period can be reflected to determine whether there is an anomaly in the positioning of the drone.

[0064] In this way, by comparing the predicted flight speeds of the drone at other moments within the preset time period with the predicted flight speed of the drone at the target moment, and by comparing the second parameter value of the drone at the target moment with the second parameter values of the drone at other moments within the preset time period, the probability of an anomaly in the positioning of the drone at the target moment can be more accurately characterized by the obtained anomaly coefficient.

[0065] In one embodiment, the target position information of the drone at the target moment is determined in the following manner: when the anomaly coefficient of the drone at the target moment is greater than or equal to a preset threshold, multiple second moments with anomaly coefficients less than the preset threshold are determined from different moments within the preset time period; according to the initial position information of the drone at the multiple second moments, the position information of the drone at the target moment is predicted, and the predicted position information is used as the target position information.

[0066] Since the anomaly coefficient of the drone at the target moment can relatively accurately characterize the probability of an anomaly in the positioning of the drone at the target moment, when the anomaly coefficient of the drone at the target moment is greater than or equal to the preset threshold, it indicates that the probability of an anomaly in the positioning of the drone at the target moment is relatively large, and it is necessary to adjust the initial position information of the drone at the target moment.

[0067] The preset threshold can be set according to the actual situation. For example, when the value range of the anomaly coefficient is between 0 and 1, the preset threshold can be set between 0.3 and 0.5.

[0068] When the anomaly coefficient of the UAV at the target moment is less than the preset threshold, it indicates that the positioning of the UAV at the target moment is relatively accurate. 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.

[0069] In this way, by determining multiple second moments with anomaly coefficients less than the preset threshold from different moments within the preset time period, the position information of other moments with a relatively low probability of abnormal positioning can be used as a reference to adjust the initial position information of the UAV at the target moment, thereby obtaining a more accurate target position information of the UAV at the target moment.

[0070] Predicting the position information of the UAV at the 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.

[0071] For example, when there are second moments on both sides of the target moment, the position corresponding to the target moment on the first flight path can be used as the target position information of the UAV at the target moment; or when there is only a second moment on one side of the target moment, the position corresponding to the target moment on the extension line of the first flight path can be used as the target position information of the UAV at the target moment.

[0072] Or when there is only a second moment on one side of the target moment, the midpoint of the line connecting the position corresponding to the target moment on the extension line of the first flight path 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.

[0073] When there is no moment with an anomaly coefficient less than or equal to the preset threshold 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, it can refer 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 within the preset time period, which will not be elaborated here.

[0074] In another embodiment, the target position information of the UAV at the target moment is determined as follows: when the anomaly 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.

[0075] 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 adjustment of the initial position information of the UAV at the target moment can be better realized to obtain more accurate target position information at the target moment.

[0076] Predicting the position information of the UAV at the target moment based on the multiple reference position information corresponding to the UAV at the target moment and using the predicted position information as the target position information may include: generating a continuous second flight path based on the multiple reference position information corresponding to the UAV at the target moment, where 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 UAV at the target moment can be predicted based on the obtained second flight path.

[0077] For example, when there is reference position information on both sides of the initial position information at the target moment, the position corresponding to the target moment on the second travel path can be used as the target position information of the UAV at the target moment; or, when there is only reference position information on one side of the initial position information at the target moment, the position corresponding to the target moment on the extension line of the second travel path can be used as the target position information of the UAV at the target moment.

[0078] Or, when there is only reference position information on one side of the initial position information at the target moment, the midpoint of the line connecting the position corresponding to the target moment on the extension line of the second travel path 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.

[0079] When there are no multiple reference position information located 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, it can refer 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 elaborated here.

[0080] In step S105, receive the target monitoring information of the target area at the target moment sent by the drone, and match the target monitoring information at the target moment and the target location information to obtain a matching result, so as to perform real-time hot backup on the matching result.

[0081] 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 positioning signal of the drone, when the signal strength of the positioning signal of the drone 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 at different moments collected, so that the server can implement the matching between the location information and the target monitoring information according to the timestamp information.

[0082] Alternatively, when the signal strength of the wireless signal is poor, the drone can store the target monitoring information collected within a period of time and assign timestamp information to these target monitoring information corresponding to different moments, so that the server can implement the matching between the location information and the target monitoring information according to the timestamp information; the drone can periodically determine the signal strength of the wireless signal with the server, and when it is determined that the signal strength of the wireless signal has recovered, the drone can send the stored target monitoring information with timestamp information to the server.

[0083] Matching the target monitoring information at the target moment and the target location information to obtain a matching result, so as to perform real-time hot backup on the matching result, includes: constructing a data pair corresponding to the timestamp information according to the timestamp information at the target moment; the data pair includes the target monitoring information and the target location information corresponding to the same timestamp information. According to the data pairs corresponding to different timestamp information, determine the corresponding relationship between different position points of the target area in the spatial coordinate system and different target monitoring information; different position points correspond to different target location information; hot backup the corresponding relationship to the target database.

[0084] For example, the server can construct a map of the target area according to the image data received from the drone; or, it can share the location information of the personnel searched by the drone; or, it can determine the temperature distribution of the target area according to the temperature information collected by the drone.

[0085] Through the data processing method for real-time hot backup of an unmanned aerial vehicle (UAV) provided by the embodiments of the present application, the position information sent by the UAV is received, and the initial position information of the UAV within a preset time period at the target moment is compared to obtain a first parameter value of the UAV at the target moment. The first parameter value can reflect the flight state of the UAV. The first parameter value of the UAV at the target moment is compared with the first parameter values of other traveled positions 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 UAV. The initial position information of the UAV at the target moment is adjusted according to the second parameter value, and a more accurate position information can be obtained. Thus, after matching with the target monitoring information, hot backup data including more accurate monitoring information can be obtained.

[0086] Figure 2 FIG. 4 is a schematic structural diagram of a data processing system 1000 for real-time hot backup of an unmanned aerial vehicle according to an exemplary embodiment. It can be applied to a server, and the server can process the monitoring information sent by the UAV. Refer to Figure 2 As shown in FIG. 4, the data processing system 1000 for real-time hot backup of an unmanned aerial vehicle includes: a processor 1100 and a memory 1200. The memory 1200 stores computer program instructions. 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 unmanned aerial vehicle in the present application are implemented.

[0087] It should be understood that unless otherwise specifically stated, the features of some embodiments of the present application described herein can be combined with each other.

[0088] 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. On the contrary, 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, the first component, part, region, layer, or section mentioned in the examples described herein can also be referred to as the second component, part, region, layer, or section.

[0089] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of these features. In the description herein, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0090] In addition, as used herein, the word "exemplary" is used to mean serving as an example, instance, or illustration. Any aspect or design described herein as "exemplary" is not necessarily to be construed as advantageous over other aspects or designs. Instead, the word exemplary is intended to present concepts in a concrete fashion. As used herein, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or".

[0091] 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 drawings. In particular with respect to the various functions performed by the above-described components (e.g., elements, resources, etc.), unless otherwise noted, the terms used to describe such components are intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if not structurally equivalent to the disclosed structure.

[0092] In addition, although particular features of the present application may have been disclosed with respect to only one of several implementations, such features may, as may be desired and advantageous for any given or particular application, be combined with one or more other features of other implementations.

[0093] Other embodiments of the present application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include known common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are to be considered exemplary only.

[0094] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes may be made without departing from its scope.

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 UAV, 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; , 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.

2. 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 moment.

3. 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.

4. The data processing method for real-time hot backup of a drone according to claim 3 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 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.

5. The data processing method for real-time hot backup of a drone according to claim 3 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.

6. The data processing method for real-time hot backup of a drone according to claim 3 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.

7. 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 6 is implemented.

Images