Unmanned aerial vehicle fault detection method and device, electronic equipment and readable storage medium

Abstract

The application discloses a kind of unmanned plane fault detection method, device, electronic equipment and readable storage medium, the method includes: obtaining the target flight log of target unmanned plane;According to the target flight log, determine the target route of the target unmanned plane;Query the plurality of candidate unmanned plane corresponding to the target route;If the number of first abnormal log in the candidate flight log of each candidate unmanned plane is greater than or equal to the preset number threshold, then output along the way environment anomaly mark.The method can detect whether the fault of the target unmanned plane is caused by the anomaly of the environment along the way, so as to avoid determining the target unmanned plane as a component failure when the parameters of the target unmanned plane appear abnormal due to the anomaly of the environment along the way, thereby reducing unnecessary component maintenance.Furthermore, when it is detected that the fault of the target unmanned plane is caused by the anomaly of the environment along the way, the anomaly of the environment along the way is output, prompting the user to adjust the target route to avoid the impact on the subsequent task of the target unmanned plane.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) control technology, specifically to a UAV fault detection method, device, electronic equipment, and readable storage medium. Background Technology

[0002] With the rapid development of the logistics drone industry, logistics drones will achieve large-scale, routine operation. Unlike drones used in other applications, logistics drones operate at a high frequency and require high reliability. To ensure the high reliability of the logistics drone itself, it is necessary to conduct meticulous monitoring of the drone's equipment and promptly repair or replace any modules or components that have malfunctions or potential problems.

[0003] Currently, there are two ways to monitor the equipment of drones: the onboard flight control computer monitors the equipment in real time, but fine-grained monitoring will consume a lot of onboard computer resources, and the drone terminal software needs to be upgraded when new monitoring points are added. For large-scale logistics drones, software upgrades increase the difficulty of system upgrades and iterations. Manually analyzing drone flight logs to monitor drone equipment is also a common method, but logistics drones fly frequently, resulting in a massive amount of flight logs, and it is very inefficient for personnel to analyze the flight logs.

[0004] However, current methods for automatically analyzing flight logs can only detect faults in the drone's own components, and the types of faults that can be detected are limited. When external factors cause the drone's flight to malfunction, it may be misjudged as a problem with a component, resulting in low detection accuracy. Summary of the Invention

[0005] This application provides a method, device, electronic device, and readable storage medium for detecting drone faults, aiming to solve the problem that current methods for automatic analysis of flight logs can only detect faults in the drone's own components. When external factors cause the drone's flight to malfunction, it may be misjudged as a problem with a component, resulting in low detection accuracy.

[0006] Firstly, this application provides a method for detecting faults in unmanned aerial vehicles (UAVs), including:

[0007] Obtain the target drone's flight log;

[0008] Based on the target flight log, determine the target flight path of the target UAV;

[0009] Query multiple candidate drones corresponding to the target flight path;

[0010] If the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold, then output the abnormal environment identifier along the route.

[0011] In one possible implementation of this application, after outputting an abnormal environment identifier along the route if the number of first abnormal logs in the candidate flight logs of each of the candidate UAVs is greater than or equal to a preset number threshold, the method further includes:

[0012] Based on the origin and destination of the target route, select an alternative route from each preset route;

[0013] Query the flight logs corresponding to each of the alternative routes and count the percentage of second abnormal logs corresponding to each of the alternative routes.

[0014] Based on the proportion of the second abnormal logs corresponding to each alternative route and the route distance, a target alternative route is selected from the alternative routes;

[0015] Based on the target alternative flight path, plan the subsequent missions of the target UAV.

[0016] In one possible implementation of this application, determining the target flight path of the target UAV based on the target flight log includes:

[0017] Extract the generation time of the fault code from the target flight log;

[0018] The target route of the target UAV is determined by matching the encoding generation time with the flight time of each preset route.

[0019] In one possible implementation of this application, after querying multiple candidate UAVs corresponding to the target flight path, the method further includes:

[0020] If the number of first abnormal logs in the candidate flight logs of each candidate UAV is less than a preset number threshold, then the fault code in the target flight log is extracted.

[0021] Based on the fault code, the faulty component among the preset components is determined;

[0022] Output the target component anomaly identifier corresponding to the faulty component.

[0023] In one possible implementation of this application, obtaining the target flight log of the target UAV includes:

[0024] The system queries the preset flight log data table in a preset order to obtain the log index information corresponding to the first log.

[0025] If the log index information corresponding to the first log does not contain a deletion mark and an analyzed mark, then the target storage address is obtained from the information corresponding to the first log.

[0026] If the target storage address is contained in the preset database, then the target flight log is read from the preset database according to the target storage address;

[0027] If the target storage address is not contained in the preset database, the first log is marked with the deletion mark, and the preset flight log data table is queried in the preset order.

[0028] In one possible implementation of this application, after outputting an abnormal environment identifier along the route if the number of first abnormal logs in the candidate flight logs of each of the candidate UAVs is greater than or equal to a preset number threshold, the method further includes:

[0029] The location of the fault on the target flight route is determined based on the generation time of the fault code in the target flight log.

[0030] Based on the fault location and the preset search radius, determine the building that is interfering with the signal;

[0031] The first control terminal of the target UAV outputs the signal jamming building and its geographical location.

[0032] In one possible implementation of this application, the step of outputting an abnormal environment identifier along the route if the number of first abnormal logs in the candidate flight logs of each of the candidate UAVs is greater than or equal to a preset number threshold includes:

[0033] If the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold, then the abnormal environmental identifier along the route is output on the first control terminal of the target UAV and the second control terminal of each candidate UAV corresponding to the target UAV.

[0034] Secondly, this application provides a UAV fault detection device, comprising:

[0035] The acquisition module is used to acquire the target drone's flight logs;

[0036] The determination module is used to determine the target flight path of the target UAV based on the target flight log;

[0037] The query module is used to query multiple candidate drones corresponding to the target route;

[0038] The identification output module is used to output an abnormal environment identification along the way if the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold.

[0039] In one possible implementation of this application, the UAV fault detection device further includes a planning module, which is used for:

[0040] Based on the origin and destination of the target route, select an alternative route from each preset route;

[0041] Query the flight logs corresponding to each of the alternative routes and count the percentage of second abnormal logs corresponding to each of the alternative routes.

[0042] Based on the proportion of the second abnormal logs corresponding to each alternative route and the route distance, a target alternative route is selected from the alternative routes;

[0043] Based on the target alternative flight path, plan the subsequent missions of the target UAV.

[0044] In one possible implementation of this application, the determining module is further configured to:

[0045] Extract the generation time of the fault code from the target flight log;

[0046] The target route of the target UAV is determined by matching the encoding generation time with the flight time of each preset route.

[0047] In one possible implementation of this application, the identifier output module is further used for:

[0048] If the number of first abnormal logs in the candidate flight logs of each candidate UAV is less than a preset number threshold, then the fault code in the target flight log is extracted.

[0049] Based on the fault code, the faulty component among the preset components is determined;

[0050] Output the target component anomaly identifier corresponding to the faulty component.

[0051] In one possible implementation of this application, the acquisition module is further used for:

[0052] The system queries the preset flight log data table in a preset order to obtain the log index information corresponding to the first log.

[0053] If the log index information corresponding to the first log does not contain a deletion mark and an analyzed mark, then the target storage address is obtained from the information corresponding to the first log.

[0054] If the target storage address is contained in the preset database, then the target flight log is read from the preset database according to the target storage address;

[0055] If the target storage address is not contained in the preset database, the first log is marked with the deletion mark, and the preset flight log data table is queried in the preset order.

[0056] In one possible implementation of this application, the UAV fault detection device further includes a building output module, which is used for:

[0057] The location of the fault on the target flight route is determined based on the generation time of the fault code in the target flight log.

[0058] Based on the fault location and the preset search radius, determine the building that is interfering with the signal;

[0059] The first control terminal of the target UAV outputs the signal jamming building and its geographical location.

[0060] In one possible implementation of this application, the identifier output module is further used for:

[0061] If the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold, then the abnormal environmental identifier along the route is output on the first control terminal of the target UAV and the second control terminal of each candidate UAV corresponding to the target UAV.

[0062] Thirdly, this application also provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor calls the computer program in the memory, it executes the steps in any of the UAV fault detection methods provided in this application.

[0063] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps in any of the UAV fault detection methods provided in this application.

[0064] In summary, the UAV fault detection method provided in this application includes: acquiring the target flight log of the target UAV; determining the target flight path of the target UAV based on the target flight log; querying multiple candidate UAVs corresponding to the target flight path; and outputting an abnormal environmental identifier along the route if the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset threshold. It is evident that the UAV fault detection method provided in this application can detect whether the fault of the target UAV is caused by abnormalities in the along-route environment. Therefore, it can avoid misclassifying abnormal parameters of the target UAV caused by factors such as base stations and tall buildings as component faults, thereby reducing unnecessary component maintenance. Furthermore, when it is detected that the fault of the target UAV is caused by abnormalities in the along-route environment, an abnormal environmental identifier along the route is output, prompting the user to adjust the target flight path to avoid affecting the subsequent tasks of the target UAV. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a schematic diagram illustrating an application scenario of the UAV fault detection method provided in this application embodiment;

[0067] Figure 2 This is a flowchart illustrating a UAV fault detection method provided in an embodiment of this application;

[0068] Figure 3 This is a flowchart illustrating a method for replanning the subsequent tasks of a drone, as provided in an embodiment of this application.

[0069] Figure 4 This is a schematic diagram of a process for obtaining the target flight log provided in an embodiment of this application;

[0070] Figure 5 This is a flowchart illustrating the geographical location of the output signal interference building and the signal interference building provided in the embodiments of this application;

[0071] Figure 6 This is a schematic diagram of a UAV fault detection system provided in the embodiments of this application;

[0072] Figure 7 This is a schematic diagram of an embodiment of the UAV fault detection device provided in this application.

[0073] Figure 8 This is a schematic diagram of an embodiment of the electronic device provided in this application. Detailed Implementation

[0074] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0075] In the description of the embodiments of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0076] To enable any person skilled in the art to implement and use this application, the following description is provided. In this description, details are set forth for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be implemented without using these specific details. In other instances, well-known processes will not be described in detail to avoid obscuring the description of the embodiments of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in the embodiments of this application.

[0077] This application provides a method, apparatus, electronic device, and readable storage medium for detecting drone faults. The drone fault detection apparatus can be integrated into an electronic device, which may be a server or a terminal, etc.

[0078] The execution subject of the UAV fault detection method in this application embodiment can be the UAV fault detection device provided in this application embodiment, or different types of electronic devices such as server equipment, physical host, or user equipment (UE) that integrate the UAV fault detection device. The UAV fault detection device can be implemented in hardware or software. The UE can be a terminal device such as a smartphone, tablet computer, laptop computer, handheld computer, desktop computer, or personal digital assistant (PDA).

[0079] The electronic device can operate independently or as a cluster of devices.

[0080] See Figure 1 , Figure 1 This is a schematic diagram of a scenario for the drone fault detection system provided in this application embodiment. The drone fault detection system may include an electronic device 101, which integrates a drone fault detection device.

[0081] In addition, such as Figure 1 As shown, the UAV fault detection system may also include a memory 102 for storing data, such as text data.

[0082] It should be noted that, Figure 1 The schematic diagram of the UAV fault detection system shown is merely an example. The UAV fault detection system and scenario described in this application are for the purpose of more clearly illustrating the technical solutions of this application and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of UAV fault detection systems and the emergence of new business scenarios, the technical solutions provided in this invention are also applicable to similar technical problems.

[0083] The following describes the UAV fault detection method provided in this application embodiment. In this application embodiment, an electronic device is used as the execution subject. For simplification and ease of description, the execution subject will be omitted in the subsequent method embodiments. The UAV fault detection includes: obtaining the target flight log of the target UAV; determining the target route of the target UAV based on the target flight log; querying multiple candidate UAVs corresponding to the target route; if the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold, then outputting an abnormal environmental identifier along the route.

[0084] Reference Figure 2 , Figure 2 This is a flowchart illustrating a UAV fault detection method provided in an embodiment of this application. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here. Specifically, the UAV fault detection method may include the following steps 201-204, wherein:

[0085] 201. Obtain the target drone's flight log.

[0086] Unmanned aerial vehicles (UAVs) are unmanned aircraft that are controlled by radio remote control equipment and their own program control devices.

[0087] A flight log is a file that records the flight data of a drone. A single flight log may contain information such as the drone's flight speed, the operating parameters of its various components, and the date of the flight.

[0088] In this embodiment, a flight log can refer to a flight log whose generation date is the current date. For example, when the target flight log is obtained on January 10, 2022, the target flight log can refer to the flight log generated after the target drone flew on January 10, 2022. In the following text, for ease of understanding, unless otherwise specified, the flight log will be considered as the flight log whose generation date is the current date. It should be noted that the generation date of the flight log cannot be used as a limitation on the embodiments of this application. The generation date of the flight log can also be a historical date. For example, when the target flight log is obtained on January 10, 2022, the target flight log can also refer to the flight log generated after the target drone flew on a historical date such as January 9, 2022.

[0089] In some embodiments, a ground-deployed server and a target drone can be connected via a pre-defined switch to obtain the target flight logs stored on the target drone's storage device. The switch and the target drone can be connected via wireless networks such as WiFi and LTE (Long Term Evolution), or via a Raspberry Pi.

[0090] The Raspberry Pi is a microcomputer motherboard based on an ARM (Advanced RISC Machines) processor, which can reduce latency when exchanging data between the target drone and the switch compared to connecting via a wireless network.

[0091] In other embodiments, pre-stored target flight logs can also be read from a preset database. These flight logs can be categorized in the preset database according to multi-level directories, for example, by log upload date, the corresponding drone number, or the date the flight log was generated.

[0092] 202. Determine the target flight path of the target UAV based on the target flight log.

[0093] The target flight path refers to the flight path of the target drone when it experiences a flight anomaly. Flight anomalies can refer to abnormalities in flight parameters such as flight speed and flight angle, or abnormalities in the operating parameters of components within the drone.

[0094] In some embodiments, the target flight log contains only information about one flight path. In this case, the information about that flight path in the target flight log can be read to obtain the target flight path of the target UAV.

[0095] In other embodiments, the target flight log may contain information on multiple flight routes, or it may not contain flight route information at all. In this case, the target flight route can be determined based on the production time of the fault code in the target flight log. The step "determine the target flight route of the target UAV based on the target flight log" can be implemented in the following way:

[0096] (i) Extract the encoding generation time of the fault code in the target flight log.

[0097] The fault codes are used to mark faults in various devices within the UAV. For example, if the hydraulic system in the target UAV malfunctions, the target flight log will record the corresponding fault code for the hydraulic system. Similarly, if the pneumatic system in the target UAV malfunctions, the target flight log will record the corresponding fault code for the pneumatic system. Furthermore, the fault codes can also mark different faults within the same device. For instance, if the hydraulic system in the target UAV malfunctions, causing the hydraulic pressure to fall below a preset first hydraulic pressure threshold, the target flight log will record a fault code for "too low hydraulic pressure." Conversely, if the hydraulic pressure exceeds a preset second hydraulic pressure threshold, the target flight log will record a fault code for "too high hydraulic pressure."

[0098] The encoding generation time can be a single point in time or a period of time.

[0099] (ii) Match the encoding generation time with the flight time of each preset route to determine the target route of the target UAV.

[0100] If the target flight log does not contain route information, the preset route can refer to all routes on the preset flight plan. If the target flight log contains information for multiple routes, the preset route can refer to all routes contained in the target flight log. For example, if the target UAV first flies along route A during the target flight process that generates the target flight log, reaches the end of route A, and then departs from the end of route A to fly along route B, then the target flight log contains information for both route A and route B. In this case, the preset route refers to both route A and route B.

[0101] Here, "voyage time" can refer to the time period corresponding to each preset route. In this case, "target route" refers to the route among the preset routes whose corresponding flight time includes the code generation time.

[0102] Since the flight time of the preset route is fixed, the target route that the target UAV is flying when the flight abnormality occurs can be obtained based on the encoding generation time.

[0103] 203. Query multiple candidate drones corresponding to the target route.

[0104] Candidate drones refer to drones whose corresponding flight logs contain information about the target flight path. For example, a preset flight plan can be queried to obtain the flight paths of each preset drone, and then multiple candidate drones can be selected from these preset drones.

[0105] 204. If the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold, then output the abnormal environment identifier along the way.

[0106] An error log is a log that contains fault codes.

[0107] The "Environmental Anomaly Along the Route" indicator is used to alert users to environmental factors that may interfere with the drone's flight path. For example, if there are base stations, high-voltage lines, or numerous tall buildings along the drone's flight path, the indicator can be displayed to warn the user of these interfering factors.

[0108] Anomaly indicators along the route can be voice or text prompts. For example, when outputting anomaly indicators along the route, the electronic device can play a voice message such as "The target flight path needs to be changed," or it can display a text message such as "The target flight path needs to be changed" on the target terminal to inform the user that there are environmental factors in the drone's flight path that may interfere with the drone's flight.

[0109] In this embodiment, the number of first abnormal logs can be used to determine whether the cause of the target drone's malfunction is due to environmental factors interfering with its flight along the target route. If the number of first abnormal logs is greater than or equal to a preset threshold, it indicates that the drone flying along the target route is highly likely to malfunction, thus confirming the presence of environmental factors interfering with its flight along the target route.

[0110] When outputting the abnormal environment marker along the flight path, it can be done either solely on the first control terminal of the target UAV, or simultaneously on the first control terminal of the target UAV and the second control terminals of each candidate UAV. Specifically, if the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset threshold, then the abnormal environment marker along the flight path is output on the first control terminal of the target UAV and the second control terminals of each corresponding candidate UAV. This simultaneously alerts the user that environmental factors interfering with UAV flight may exist along the flight paths of each candidate UAV.

[0111] The first control terminal and the second control terminal can be a drone remote controller, control platform, etc., and this application embodiment does not limit them.

[0112] In some embodiments, if the number of first abnormal logs in the candidate flight logs of each candidate UAV is less than a preset threshold, it indicates that only a small number of UAVs malfunctioned while flying along the target flight path, and therefore it can be determined that a component in the target UAV malfunctioned. In this case, after the step "querying multiple candidate UAVs corresponding to the target flight path", the method further includes:

[0113] (1) If the number of first abnormal logs in the candidate flight logs of each candidate UAV is less than a preset number threshold, then extract the fault code in the target flight log.

[0114] (2) Determine the faulty component among the preset components based on the fault code.

[0115] (3) Output the target component abnormality identifier corresponding to the faulty component.

[0116] In some embodiments, the fault code can be matched with the code corresponding to each preset component to determine the faulty component among the preset components, and then a component fault identifier is output to indicate that the component has malfunctioned. For example, when the faulty component is the hydraulic device in the target UAV, the target component fault identifier is a component fault identifier used to indicate that the hydraulic device has malfunctioned.

[0117] The component abnormality signs are the same as the environmental abnormality signs along the route, and can be voice, text or other prompts, which will not be elaborated on in detail.

[0118] In addition to outputting anomaly identifiers, the electronic device can also store fault information such as the target flight log's log ID, fault codes in the target flight log, and the generation time of the fault codes in a preset fault data table for review and inspection. At the same time, this fault information is packaged into JSON format and uploaded to the database of the logistics drone maintenance system. The logistics drone maintenance system can generate maintenance tasks based on the fault data in the database to prompt users to adjust the target drone or target flight path.

[0119] JSON (JavaScript Object Notation) is a lightweight data exchange format.

[0120] In summary, the UAV fault detection method provided in this application includes: acquiring the target flight log of the target UAV; determining the target flight path of the target UAV based on the target flight log; querying multiple candidate UAVs corresponding to the target flight path; and outputting an abnormal environment identifier along the route if the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset threshold. It is evident that the UAV fault detection method provided in this application can detect whether the fault of the target UAV is caused by abnormalities in the environment along the route. Therefore, it can avoid misclassifying abnormal parameters of the target UAV caused by factors such as base stations and tall buildings as component faults, thereby reducing unnecessary component maintenance. Furthermore, when it is detected that the fault of the target UAV is caused by abnormalities in the environment along the route, an abnormal environment identifier along the route is output, prompting the user to adjust the target flight path to avoid affecting the subsequent tasks of the target UAV.

[0121] In some embodiments, if environmental factors interfering with the flight of the target UAV are detected and confirmed to exist along the target flight path, an alternative flight path can be selected for the target UAV to avoid affecting its subsequent missions. (Reference) Figure 3 At this point, after step "if the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold, then output an abnormal environmental identifier along the route", the method further includes:

[0122] 301. Based on the starting point and ending point of the target route, select an alternative route from the preset routes.

[0123] In this embodiment of the application, the preset route refers to all routes on the preset flight schedule.

[0124] For example, the electronic device can read the start and end points of all preset routes, select a preset route with the same start and end points as the target route, and obtain an alternative route.

[0125] 302. Query the flight logs corresponding to each of the alternative routes and count the percentage of second abnormal logs corresponding to each of the alternative routes.

[0126] In this context, the flight log to be queried for an alternative route refers to the flight log containing information about that alternative route. The electronic device can acquire all drones flying along the alternative routes, and then read the flight logs of these drones to obtain the flight log to be queried for each alternative route.

[0127] The second anomaly log refers to the flight log containing the fault code within the flight log to be queried. Therefore, the second anomaly log for an alternative route refers to the flight log containing the fault code within the flight log to be queried for that alternative route.

[0128] The percentage of second-order abnormal logs corresponding to an alternative route refers to the ratio between the number of second-order abnormal logs corresponding to that alternative route and the number of flight logs to be queried for that alternative route. For example, if the number of second-order abnormal logs corresponding to an alternative route is 100, and the number of flight logs to be queried for that alternative route is 1000, then the percentage of second-order abnormal logs corresponding to that alternative route is 10%.

[0129] 303. Select a target alternative route from the alternative routes based on the proportion of the second abnormal logs corresponding to each of the alternative routes and the route distance.

[0130] In the embodiments of this application, a target alternative route can be selected from alternative routes based on a variety of principles.

[0131] In some embodiments, a target alternative route can be selected based on the product of the proportion of the number of second abnormal logs corresponding to each of the alternative routes and the route distances of each of the alternative routes. For example, the alternative route with the smallest corresponding product can be selected as the target alternative route. The reason for this selection is that when selecting a target alternative route, the goal is to choose the route with the shortest route distance and the lowest probability of the UAV malfunctioning due to environmental factors during flight. Therefore, the product of the proportion of the number of the second abnormal logs corresponding to each of the alternative routes and the route distances of each of the alternative routes can be used as an evaluation criterion, taking into account both the cost issues caused by route distance and the maintenance costs caused by environmental factors.

[0132] In other embodiments, one of the quantity percentage and route distance can be used as the primary evaluation factor, and the other as a secondary evaluation factor, to select a target alternative route from the alternative routes. For example, route distance can be used as the primary evaluation factor, mainly considering the cost issues caused by route distance. A first alternative route with a route distance less than a preset distance threshold is selected from all alternative routes, and then the target alternative route with the smallest quantity percentage of the second abnormal logs is selected from the first alternative route. As another example, the quantity percentage of the second abnormal logs corresponding to each alternative route can be used as the primary evaluation factor, mainly considering the fault maintenance costs caused by environmental factors. A second alternative route with a quantity percentage of the second abnormal logs lower than a preset percentage threshold is selected from all alternative routes, and then the target alternative route with the smallest route distance is selected from the second alternative route.

[0133] 304. Based on the target alternative flight path, plan the subsequent missions of the target UAV.

[0134] Step 304 refers to adjusting the flight path of the target UAV to complete the subsequent tasks through the target flight path, and setting the target UAV to complete the subsequent tasks through the target alternative flight path.

[0135] In some embodiments, to facilitate querying the storage address of flight logs, the electronic device stores log index information such as the log ID and storage address of new flight logs in a preset flight log information data table when acquiring new flight logs. During flight log analysis, the log index information in the flight log information data table can be queried to accurately retrieve the required flight logs. After analysis, the corresponding flight log is deleted from the preset database, and the flight log is marked as analyzed and deleted in the flight log information data table to save space in the preset database. In this case, the electronic device can periodically check the flight logs in the flight log information data table to see if the flight logs marked as analyzed and deleted have been deleted. If not deleted, they are used as the target flight logs. (Reference) Figure 4 At this point, the step "Obtain the target drone's flight log" includes:

[0136] 401. Query the preset flight log data table in the preset order to obtain the log index information corresponding to the first log.

[0137] The default flight log data table is a default data table that stores log index information such as the log ID and storage address of the flight log.

[0138] The preset order can be determined based on the storage order of the log index information in the flight log data table. In some embodiments, the log index information can be arranged in ascending order according to the corresponding log ID, that is, the log index information corresponding to the flight log with the smaller log ID is first, and the log index information corresponding to the flight log with the larger log ID is last. Therefore, the preset order can be to query backwards from the last record of the preset flight log data table so that the most recently uploaded flight log is arranged first. In other embodiments, the log index information is arranged in descending order according to the corresponding log ID, that is, the log index information corresponding to the flight log with the larger log ID is first, and the log index information corresponding to the flight log with the smaller log ID is last. Therefore, the preset order can be to query backwards from the first record of the preset flight log data table so that the most recently uploaded flight log is arranged first. Alternatively, the preset order can be fixed to query backwards from the first record of the preset flight log data table, which will not be elaborated further.

[0139] Understandably, if the log index information is sorted in ascending order by the corresponding log ID (i.e., log index information for flight logs with smaller log IDs comes first, followed by log index information for flight logs with larger log IDs), and the preset order is to query backwards from the last record in the preset flight log data table, then the first log refers to the flight log with the largest log ID. Conversely, if the log index information is sorted in ascending order by the corresponding log ID (i.e., log index information for flight logs with smaller log IDs comes first, followed by log index information for flight logs with larger log IDs), and the preset order is to query backwards from the first record in the preset flight log data table, then the first log refers to the flight log with the smallest log ID.

[0140] 402. If the log index information corresponding to the first log does not contain a deletion mark and an analyzed mark, then the target storage address is obtained from the information corresponding to the first log.

[0141] The explanation of deletion and analyzed markers can be found above, and will not be repeated here. If the information corresponding to the first log does not contain deletion or analyzed markers, there are three possibilities:

[0142] Scenario 1: The first log has not yet been analyzed. The first log is still stored in the preset database, so the corresponding information does not include the deletion mark and the analyzed mark.

[0143] Scenario 2: The first log has been analyzed and deleted from the preset database, but it has not been marked in the preset flight log data table.

[0144] Scenario 3: The first log has not yet been analyzed, but it was mistakenly deleted from the preset database.

[0145] Different situations require different handling methods, as detailed in steps 403A and 403B.

[0146] 403A. If the target storage address is contained in the preset database, then the target flight log is read from the preset database according to the target storage address.

[0147] If the preset database contains the target storage address, which corresponds to case one, then the first log can be used as the target flight log, and the target flight log can be read from the preset database according to the target storage address.

[0148] 403B. If the target storage address is not contained in the preset database, mark the first log with the deletion mark, and continue to query the preset flight log data table in the preset order.

[0149] If the target storage address is not contained in the preset database (i.e., one of the two or three scenarios), the first log can be marked as deleted in the preset flight log data table, and the preset flight log data table can be queried in the preset order. That is, the second flight log in the preset flight log data table after the first log can be queried, and it can be determined whether the storage address of the second flight log is contained in the preset database.

[0150] In some embodiments, signal interference structures along the target flight path that affect the UAV's flight can also be identified based on the encoding generation time of the fault codes in the target flight log. (Reference) Figure 5 At this point, after step "if the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold, then output an abnormal environmental identifier along the route", the method further includes:

[0151] 501. Determine the location of the fault on the target flight path based on the generation time of the fault code in the target flight log.

[0152] For an explanation of the fault code and code generation time, please refer to the above text; further details will not be provided here.

[0153] The fault location refers to the position where the target UAV experiences a fault while traveling along the target flight path. When the code generation time refers to a single point in time, the fault location refers to a single position on the target flight path. When the code generation time refers to a period of time, the fault location can refer to multiple positions on the target flight path, that is, all positions passed by the target UAV during the time period corresponding to the code generation time.

[0154] 502. Based on the fault location and the preset search radius, determine the building that is interfering with the signal.

[0155] Based on the search radius and fault location, the search range for signal-interfering buildings can be determined.

[0156] The search range refers to the geographical area centered on the fault location. This application does not limit the shape of the search range; for example, the search range can be a circular area centered on the fault location, a square area centered on the fault location, etc. In some embodiments, the search range can refer to a geographical area on a preset electronic map.

[0157] Signal-jamming structures refer to buildings within the search range that affect the flight of drones. Examples of signal-jamming structures include base stations, tall buildings, etc.

[0158] The wireless signals emitted by the base station can interfere with the communication between the drone and the ground control terminal.

[0159] The glass curtain walls of tall buildings can reflect GPS (Global Positioning System) signals from drones, causing inaccurate or even erratic positioning. Therefore, drones flying between tall buildings may experience drifting, unstable heading, and other abnormal flight behaviors. Furthermore, tall buildings typically contain radio signals such as WiFi and LTE, which can also interfere with drone control.

[0160] For example, all buildings within the search range can be considered as signal interference buildings, or the names and building types of each building within the search range can be queried in a preset electronic map to select the signal interference buildings.

[0161] 503. Output the signal jamming building and its geographical location on the first control terminal of the target UAV.

[0162] The description of the first control terminal can be found above, and will not be repeated here.

[0163] To inform users of the specific environmental factors affecting the drone's flight path along the target route, the location of the jamming buildings and their geographical locations can be output on the target drone's first control terminal.

[0164] The geographic location can refer to the actual location of the building causing the signal interference. For example, after locating the building causing the signal interference on a preset electronic map, its map location can be converted into its actual location and then output.

[0165] This application also provides a schematic diagram of another unmanned aerial vehicle (UAV) fault detection system, see reference. Figure 6 , Figure 6 The image shows a drone fault detection system 600, which includes:

[0166] Flight log storage unit 601 is used to store flight logs to be analyzed;

[0167] The flight log upload monitoring unit 602 is used to monitor the newly added flight logs in the flight log storage unit 601 in real time, and record the log index information of the newly added flight logs to the information storage unit 603.

[0168] The information storage unit 603 stores two data tables: a preset flight log data table, which records the log index information of the flight log, and a fault data table, which records fault information such as fault codes obtained from analysis.

[0169] The flight log parsing unit 604 is used to extract the flight logs to be analyzed from the flight log storage unit 601 and transmit the extracted data to the flight log analysis unit 605 for analysis.

[0170] The flight log analysis unit 605 is used to analyze the data recorded in the flight log, output fault codes and other fault information, and store the fault information in the information storage unit 603.

[0171] The fault reporting unit 606 is used to report the information in the preset flight log data table and the fault data table in the information storage unit 603 to the logistics drone maintenance system or other systems used by logistics drone maintenance personnel.

[0172] by Figure 6 Taking the UAV fault detection system 600 as an example, the UAV fault detection method provided in this application embodiment is described in an exemplary process:

[0173] A. Read flight logs from multiple drones via a switch and Raspberry Pi, sort the log ID and other log index information of each flight log in ascending order according to the log ID, and store them in a preset flight log data table in the information storage unit 603. Then, classify each flight log according to a multi-level directory such as the log upload date, the drone number corresponding to the flight log, and the generation date of the flight log, and store them in the flight log storage unit 601.

[0174] B. Starting from the first record in the preset flight log data table, query backwards to obtain the log index information of the first log. If the log index information does not contain a deletion mark or an analyzed mark, query whether the flight log storage unit 601 contains the target storage address of the first log in the log index information of the first log. If it contains it, proceed to step C1; otherwise, proceed to step C2.

[0175] C1. The first log is taken as the target flight log. Based on the target storage address of the first log, the target flight log is extracted from the flight log storage unit 601 through the flight log parsing unit 604. The target flight log is sent to the flight log analysis unit 605. The flight log analysis unit 605 extracts the encoding generation time of the fault code in the target flight log and obtains all routes on the preset flight plan table. Then, D is specified.

[0176] C2. Mark the first log in the preset flight log data table in the information storage unit 603 as deleted.

[0177] D. Match the encoding generation time with the flight time of all routes to obtain the target route of the target UAV.

[0178] E. Query the preset flight plan table to obtain the flight routes of each preset UAV, and then select multiple candidate UAVs from each preset UAV.

[0179] F. Count the number of first abnormal logs in the candidate flight logs of each candidate UAV, and compare the number of first abnormal logs with a preset number threshold. If the number of first abnormal logs is greater than or equal to the preset number threshold, output an environmental anomaly indicator along the route to prompt the user that there are environmental factors in the target route that interfere with the flight of the UAV. Store the fault code, the generation time of the fault code, and other fault information in the target flight log in the fault data table in the information storage unit 603. At the same time, report the information in the preset flight log data table in the information storage unit 603 and the information in the fault data table to the logistics UAV maintenance system or other systems used by logistics UAV maintenance personnel through the fault reporting unit 606, and then execute G1; if the number of first abnormal logs is less than the preset number threshold, then execute G2.

[0180] G1. Based on the origin and destination of the target route, select an alternative route with the same origin and destination from all routes on the preset flight schedule, and execute H.

[0181] G2. Extract the fault codes from the target flight log, determine the faulty components in each preset component based on the fault codes, output the target component abnormality identifier corresponding to the faulty component, and store the fault codes, fault code generation time, and other fault information in the target flight log in the fault data table in the information storage unit 603. Then, report the information in the preset flight log data table in the information storage unit 603 and the information in the fault data table to the logistics drone maintenance system or other systems used by logistics drone maintenance personnel through the fault reporting unit 606.

[0182] H. The flight log parsing unit 604 extracts the flight logs to be queried for each alternative route from the flight log storage unit 601, and counts the proportion of the number of second abnormal logs corresponding to each alternative route. Based on the product between the proportion of the number of second abnormal logs corresponding to each alternative route and the route distance, the alternative route with the smallest product is selected to obtain the target alternative route. The subsequent mission of the target UAV is planned based on the target alternative route.

[0183] To better implement the UAV fault detection method in the embodiments of this application, based on the UAV fault detection method, the embodiments of this application also provide a UAV fault detection device, such as... Figure 7 The diagram shown is a schematic representation of an embodiment of the UAV fault detection device 700 in this application. The UAV fault detection device 700 includes:

[0184] Module 701 is used to acquire the target flight log of the target UAV;

[0185] The determination module 702 is used to determine the target flight path of the target UAV based on the target flight log;

[0186] The query module 703 is used to query multiple candidate drones corresponding to the target route;

[0187] The identification output module 704 is used to output an abnormal environment identification along the way if the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold.

[0188] In one possible implementation of this application, the UAV fault detection device 700 further includes a planning module 705, which is used for:

[0189] Based on the origin and destination of the target route, select an alternative route from each preset route;

[0190] Query the flight logs corresponding to each of the alternative routes and count the percentage of second abnormal logs corresponding to each of the alternative routes.

[0191] Based on the proportion of the second abnormal logs corresponding to each alternative route and the route distance, a target alternative route is selected from the alternative routes;

[0192] Based on the target alternative flight path, plan the subsequent missions of the target UAV.

[0193] In one possible implementation of this application, the determining module 702 is further configured to:

[0194] Extract the generation time of the fault code from the target flight log;

[0195] The target route of the target UAV is determined by matching the encoding generation time with the flight time of each preset route.

[0196] In one possible implementation of this application, the identifier output module 704 is further configured to:

[0197] If the number of first abnormal logs in the candidate flight logs of each candidate UAV is less than a preset number threshold, then the fault code in the target flight log is extracted.

[0198] Based on the fault code, the faulty component among the preset components is determined;

[0199] Output the target component anomaly identifier corresponding to the faulty component.

[0200] In one possible implementation of this application, the acquisition module 701 is further configured to:

[0201] The system queries the preset flight log data table in a preset order to obtain the log index information corresponding to the first log.

[0202] If the log index information corresponding to the first log does not contain a deletion mark and an analyzed mark, then the target storage address is obtained from the information corresponding to the first log.

[0203] If the target storage address is contained in the preset database, then the target flight log is read from the preset database according to the target storage address;

[0204] If the target storage address is not contained in the preset database, the first log is marked with the deletion mark, and the preset flight log data table is queried in the preset order.

[0205] In one possible implementation of this application, the UAV fault detection device 700 further includes a building output module 706, which is used for:

[0206] The location of the fault on the target flight route is determined based on the generation time of the fault code in the target flight log.

[0207] Based on the fault location and the preset search radius, determine the building that is interfering with the signal;

[0208] The first control terminal of the target UAV outputs the signal jamming building and its geographical location.

[0209] In one possible implementation of this application, the identifier output module 704 is further configured to:

[0210] If the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold, then the abnormal environmental identifier along the route is output on the first control terminal of the target UAV and the second control terminal of each candidate UAV corresponding to the target UAV.

[0211] In practice, the above modules can be implemented as independent entities or combined in any way to be implemented as the same or several entities. For the specific implementation of the above modules, please refer to the previous method implementation examples, which will not be repeated here.

[0212] Since the UAV fault detection device can execute the steps in the UAV fault detection method in any embodiment, it can achieve the beneficial effects that the UAV fault detection method in any embodiment of this application can achieve, as detailed in the preceding description, and will not be repeated here.

[0213] Furthermore, to better implement the UAV fault detection method in the embodiments of this application, based on the UAV fault detection method, the embodiments of this application also provide an electronic device, see below. Figure 8 , Figure 8 This illustration shows a structural diagram of an electronic device according to an embodiment of this application. Specifically, the electronic device provided in this embodiment includes a processor 801. The processor 801 is used to execute a computer program stored in a memory 802 to implement each step of the UAV fault detection method in any embodiment; or, the processor 801 is used to execute a computer program stored in a memory 802 to implement, for example... Figure 7 The functions of each module in the corresponding embodiment.

[0214] For example, a computer program may be divided into one or more modules / units, one or more of which are stored in memory 802 and executed by processor 801 to complete the embodiments of this application. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a computer device.

[0215] The electronic device may include, but is not limited to, processor 801 and memory 802. Those skilled in the art will understand that the illustrations are merely examples of an electronic device and do not constitute a limitation on the device. It may include more or fewer components than illustrated, or combine certain components, or use different components.

[0216] The processor 801 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting various parts of the electronic device through various interfaces and lines.

[0217] The memory 802 can be used to store computer programs and / or modules. The processor 801 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 802 and by calling the data stored in the memory 802. The memory 802 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device (such as audio data, video data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0218] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the UAV fault detection device, electronic equipment and its corresponding modules described above can be referred to the description of the UAV fault detection method in any embodiment, and will not be repeated here.

[0219] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by instructions, or by instructions controlling related hardware. These instructions can be stored in a readable storage medium and loaded and executed by a processor.

[0220] Therefore, embodiments of this application provide a readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps in the UAV fault detection method in any embodiment of this application. For specific operations, please refer to the description of the UAV fault detection method in any embodiment, which will not be repeated here.

[0221] The readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0222] Since the instructions stored in the readable storage medium can execute the steps in the UAV fault detection method in any embodiment of this application, the beneficial effects that the UAV fault detection method in any embodiment of this application can achieve can be realized, as detailed in the preceding description, and will not be repeated here.

[0223] The above provides a detailed description of a method, apparatus, storage medium, and electronic device for detecting drone faults according to embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for detecting faults in unmanned aerial vehicles (UAVs), characterized in that, include: Obtain the target drone's flight log; Based on the target flight log, determine the target flight path of the target UAV; Query multiple candidate drones corresponding to the target flight path; If the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold, then output the abnormal environment identifier along the way. Based on the origin and destination of the target route, select an alternative route from each preset route; Query the flight logs corresponding to each of the alternative routes and count the percentage of second abnormal logs corresponding to each of the alternative routes. Based on the proportion of the second abnormal logs corresponding to each alternative route and the route distance, a target alternative route is selected from the alternative routes; Based on the target alternative flight path, plan the subsequent missions of the target UAV.

2. The UAV fault detection method according to claim 1, characterized in that, Determining the target flight path of the target UAV based on the target flight log includes: Extract the generation time of the fault code from the target flight log; The target route of the target UAV is determined by matching the encoding generation time with the flight time of each preset route.

3. The UAV fault detection method according to claim 1, characterized in that, After querying multiple candidate drones corresponding to the target route, the method further includes: If the number of first abnormal logs in the candidate flight logs of each candidate UAV is less than a preset number threshold, then the fault code in the target flight log is extracted. Based on the fault code, the faulty component among the preset components is determined; Output the target component anomaly identifier corresponding to the faulty component.

4. The UAV fault detection method according to claim 1, characterized in that, The acquisition of the target drone's flight logs includes: The system queries the preset flight log data table in a preset order to obtain the log index information corresponding to the first log. If the log index information corresponding to the first log does not contain a deletion mark and an analyzed mark, then the target storage address is obtained from the information corresponding to the first log. If the target storage address is contained in the preset database, then the target flight log is read from the preset database according to the target storage address; If the target storage address is not contained in the preset database, the first log is marked with the deletion mark, and the preset flight log data table is queried in the preset order.

5. The UAV fault detection method according to claim 1, characterized in that, If the number of first abnormal logs in the candidate flight logs of each of the candidate UAVs is greater than or equal to a preset number threshold, after outputting the environmental anomaly identifier along the route, the method further includes: The location of the fault on the target flight route is determined based on the generation time of the fault code in the target flight log. Based on the fault location and the preset search radius, determine the building that is interfering with the signal; The first control terminal of the target UAV outputs the signal jamming building and its geographical location.

6. The UAV fault detection method according to any one of claims 1-5, characterized in that, If the number of first abnormal logs in the candidate flight logs of each of the candidate UAVs is greater than or equal to a preset threshold, then an abnormal environmental identifier along the route is output, including: If the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold, then the abnormal environmental identifier along the route is output on the first control terminal of the target UAV and the second control terminal of each candidate UAV corresponding to the target UAV.

7. A fault detection device for unmanned aerial vehicles (UAVs), characterized in that, include: The acquisition module is used to acquire the target drone's flight logs; The determination module is used to determine the target flight path of the target UAV based on the target flight log; The query module is used to query multiple candidate drones corresponding to the target route; The identification output module is used to output an abnormal environment identification along the way if the number of first abnormal logs in the candidate flight logs of each candidate UAV is greater than or equal to a preset number threshold. The planning module is used to select alternative routes from various preset routes based on the start and end points of the target route; query the flight logs corresponding to each alternative route; and count the percentage of second abnormal logs corresponding to each alternative route. Based on the proportion of the second abnormal logs corresponding to each of the alternative routes and the route distance, a target alternative route is selected from the alternative routes; based on the target alternative route, the subsequent mission of the target UAV is planned.

8. An electronic device, characterized in that, The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the UAV fault detection method as described in any one of claims 1 to 6.

9. A readable storage medium, characterized in that, The readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the UAV fault detection method according to any one of claims 1 to 6.

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