A general unmanned aerial vehicle control instruction execution effect diagnosis method

Abstract

The application specifically relates to a general unmanned aerial vehicle control instruction execution effect diagnosis method, which comprises the following steps: a diagnosis configuration file corresponding to a model of an unmanned aerial vehicle is constructed in advance, and diagnosis models are set in the configuration file according to flight task stages; each diagnosis model comprises a control instruction, a post-state element, a delay judgment time and a judgment rule. During diagnosis, a corresponding configuration file is loaded according to the model of a to-be-diagnosed unmanned aerial vehicle, and a diagnosis instance is created in combination with the model; after a target control instruction is sent by a ground control vehicle, a target diagnosis model is matched in combination with a current operation stage of the to-be-diagnosed unmanned aerial vehicle, then execution effect judgment is performed on feedback state data of the unmanned aerial vehicle, and an execution effect diagnosis result is generated. The diagnosis logic of the control instruction execution effect is separated from instruction data of a specific model unmanned aerial vehicle, manual comparison work of a telemetry state by an operator is reduced, and the diagnosis automation degree and expansion capability of a general ground control vehicle in a multi-model unmanned aerial vehicle interoperation scene are improved.

Description

Technical Field

[0001] This invention relates to the field of UAV ground monitoring technology, and specifically to a general method for diagnosing the execution effect of UAV control commands. Background Technology

[0002] With the demands of modern warfare, the unmanned aerial vehicle (UAV) industry has experienced explosive growth, with various types of UAVs capable of performing reconnaissance, surveillance, and strike missions emerging in large numbers. However, due to differences in manufacturers and technological systems, information exchange between different UAV systems has become a significant problem. To address the interoperability issues of different UAV systems, general-purpose ground control stations have become a hot research topic. Compared to dedicated stations, general-purpose stations can achieve interoperability among multiple UAV systems, providing core functions such as mission planning, health management, flight monitoring, payload monitoring, and intelligence processing.

[0003] Unmanned aerial vehicle (UAV) flight monitoring is one of the core functions of a ground station. Operators send remote control commands via a ground control vehicle. Upon receiving the commands, the UAV executes different actions based on the specific instructions, and then feeds back telemetry data to the ground control vehicle. Operators must observe the UAV's status data to determine if the commands were executed correctly. With the development of universal ground stations, a single station can interoperate with different UAV models. From the operator's perspective, this requires mastering the execution status of various remote control commands for different UAV models, resulting in tens of thousands of data points and significantly increasing operational workload. There is an urgent need for a universal method that uses software to determine the execution status of control commands, allowing the system to simply feed the results back to the operator, greatly reducing their burden.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a general method for diagnosing the execution effect of unmanned aerial vehicle (UAV) control commands, a computer-readable storage medium, and a computer program product, which can effectively overcome the defects existing in the prior art.

[0006] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to a first aspect of the present invention, a method for diagnosing the execution effect of general unmanned aerial vehicle (UAV) control commands is provided, the method comprising: A diagnostic configuration file is pre-built; the diagnostic configuration file includes: the UAV model, the diagnostic model for each flight mission phase, and each diagnostic model includes: control commands, post-state elements, delay determination time and determination rules, and the post-state elements are the UAV's state data items; Load the corresponding target diagnostic configuration file according to the model of the drone to be diagnosed; Based on the target control commands sent from the ground control vehicle to the UAV under diagnosis, and combined with the UAV model and current operating stage, the target diagnosis model corresponding to the target control commands is matched from the target diagnosis configuration file. The target diagnostic model is used to determine the execution effect of the feedback state data of the UAV under diagnosis and to generate the execution effect diagnostic results corresponding to the target control commands.

[0008] In some exemplary embodiments, the pre-built diagnostic profile includes: Build corresponding diagnostic configuration files according to the drone model; The diagnostic configuration file for each UAV model is divided according to the flight mission stage to obtain the diagnostic configuration layer corresponding to each flight mission stage. At least one diagnostic model is configured under each diagnostic configuration layer; wherein, each diagnostic model includes: the instruction ID of the control instruction, a list of post-state elements, the delay judgment time corresponding to each post-state element, and the judgment rule corresponding to each post-state element. The correspondence between various UAV models, flight mission phases, and diagnostic models is stored in a structured format to obtain a diagnostic configuration file.

[0009] In some exemplary embodiments, the step of matching the target control command sent from the ground control vehicle to the UAV under diagnosis, combined with the UAV's model and current operating stage, with the target diagnostic model corresponding to the target control command from the target diagnostic configuration file includes: Listen to the target control commands sent from the ground control vehicle to the UAV under diagnosis, and obtain the command ID corresponding to the target control commands; Based on the model of the drone to be diagnosed, determine the loaded target diagnostic configuration file; Based on the current operating stage of the UAV to be diagnosed, determine the diagnostic configuration layer corresponding to the current operating stage in the target diagnostic configuration file; Based on the instruction ID, a search is performed among the diagnostic models included in the diagnostic configuration layer, and the diagnostic model that matches the instruction ID is identified as the target diagnostic model.

[0010] In some exemplary embodiments, the step of using the target diagnostic model to determine the execution effect of the feedback state data of the UAV to be diagnosed and generating the execution effect diagnostic result corresponding to the target control command includes: Obtain the list of post-state elements corresponding to the target control command from the target diagnostic model, as well as the delay judgment time and judgment rule corresponding to each post-state element; Taking the time of receiving the target control command as the starting time, the determination time of each subsequent state element is determined according to the delay determination time corresponding to each subsequent state element. Parallel state monitoring tasks are established for each post-state element, and the corresponding state values ​​are obtained from the feedback state data of the UAV to be diagnosed based on each post-state element. At the decision time corresponding to each subsequent state element, the obtained state value is judged by the corresponding decision rule to obtain the state decision result corresponding to each subsequent state element. Based on the state determination results corresponding to each post-state element, the execution effect diagnosis results corresponding to the target control instruction are generated.

[0011] In some exemplary embodiments, generating the execution effect diagnostic result corresponding to the target control instruction based on the state determination result corresponding to each subsequent state element includes: If the status judgment results of all subsequent state elements corresponding to the target control instruction are normal, then an execution effect diagnostic result is generated to characterize the successful execution of the target control instruction. If the state determination result of any post-state element corresponding to the target control instruction is abnormal, an execution effect diagnosis result is generated to characterize the execution abnormality of the target control instruction, and the abnormal post-state element is recorded in the execution effect diagnosis result. The results of the performance evaluation are output to the ground control vehicle.

[0012] In some exemplary embodiments, before using the target diagnostic model to determine the execution effect of the feedback state data of the UAV to be diagnosed, the method further includes: Obtain the current diagnostic model corresponding to the control command currently being diagnosed; Compare the post-state elements in the target diagnostic model with the post-state elements in the current diagnostic model; If the target diagnostic model and the current diagnostic model have the same post-state elements, it is determined that there is a conflict between the target control command and the control command currently being diagnosed. If an instruction conflict is detected, the diagnostic process corresponding to the currently being diagnosed control instruction is terminated, and the currently being diagnosed control instruction is marked as interrupted due to conflict.

[0013] In some exemplary embodiments, the determination rule includes: The numerical range comparison rule is used to determine whether the state value corresponding to the subsequent state element is within the preset numerical range, or whether it meets the error condition between the value and the target value in the diagnostic command. The state enumeration matching rule is used to determine whether the state value corresponding to the subsequent state element is consistent with the preset enumeration state. The trend judgment rule is used to determine whether the trend of the state value corresponding to the subsequent state element after the target control command is executed meets the preset trend condition. Logical combination rules are used to perform logical combination judgments on the judgment results corresponding to one or more subsequent state elements.

[0014] According to a second aspect of the present invention, a general-purpose unmanned aerial vehicle (UAV) control command execution effect diagnostic system is provided, the system comprising: The diagnostic configuration file construction module is used to pre-build diagnostic configuration files. The diagnostic configuration file includes: the UAV model, the diagnostic model for each flight mission phase, and each diagnostic model includes: control commands, post-state elements, delay determination time and determination rules. The post-state elements are the UAV's state data items. The target diagnostic configuration file loading module is used to load the corresponding target diagnostic configuration file according to the model of the UAV to be diagnosed. The target diagnostic model matching module is used to match the target diagnostic model corresponding to the target control command sent from the ground control vehicle to the UAV under diagnosis, and in combination with the model and current operating stage of the UAV under diagnosis, to the target diagnostic configuration file. The execution effect judgment module is used to judge the execution effect of the feedback status data of the UAV under diagnosis using the target diagnostic model, and generate the execution effect diagnostic results corresponding to the target control commands.

[0015] According to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, the device where the storage medium is located executes the above-described method for diagnosing the execution effect of a general unmanned aerial vehicle control command.

[0016] According to a third aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the above-described method for diagnosing the execution effect of general unmanned aerial vehicle control commands.

[0017] According to a fourth aspect of the present invention, an electronic device is provided, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to implement the aforementioned general unmanned aerial vehicle (UAV) control instruction execution effect diagnosis method by executing the executable instructions.

[0018] The present invention provides a method for diagnosing the execution effect of general unmanned aerial vehicle (UAV) control commands. This method pre-constructs diagnostic configuration files corresponding to UAV models and flight mission phases, structurally configuring post-state elements, delay judgment times, and judgment rules for different control commands. During the diagnosis process, the corresponding configuration file is loaded according to the UAV model to be diagnosed, and a target diagnostic model is matched with the target control command and the current operating phase. Subsequently, the UAV feedback state data is automatically monitored and rule-determined based on the target diagnostic model to generate a diagnostic result for the execution effect of the control commands. Simultaneously, the solution introduces a command conflict judgment mechanism based on post-state elements before diagnosis. When new and old control commands involve the same state data items, the diagnostic process affected by the conflict is automatically interrupted. This achieves the separation of the diagnostic logic for the execution effect of control commands from the command data of specific UAV models, enabling the same diagnostic program to adapt to multiple UAV models by loading different configuration files. This improves the automation, accuracy, and scalability of general ground control vehicles in multi-UAV interoperability scenarios. Furthermore, it solves the problems of low diagnostic efficiency, difficulty in reusing diagnostic logic, and high maintenance costs for adapting to new UAV models when general ground control vehicles interoperate with multiple UAV models in the prior art.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0021] Figure 1 This schematically illustrates a flowchart of a general-purpose unmanned aerial vehicle (UAV) control command execution effect diagnosis method, an exemplary embodiment of the present invention. Figure 2 This schematic diagram illustrates a diagnostic configuration file of an exemplary embodiment of the present invention. Figure 3 The illustration shows a flowchart of a general unmanned aerial vehicle (UAV) control command execution effect diagnosis method according to an exemplary embodiment of the present invention; Figure 4 This schematic diagram illustrates a structural block diagram of a general-purpose unmanned aerial vehicle (UAV) control command execution effect diagnostic system, an exemplary embodiment of the present invention. Figure 5 This schematic diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0023] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0024] To address the shortcomings and deficiencies of existing technologies, this example embodiment provides a general method for diagnosing the execution effect of UAV control commands. (Reference) Figure 1 As shown, it can specifically include: Step S10: Pre-build a diagnostic configuration file; wherein, the diagnostic configuration file includes: the model of the UAV, the diagnostic model for each flight mission stage, and each diagnostic model includes: control commands, post-state elements, delay determination time and determination rules, and the post-state elements are the state data items of the UAV. Step S12: Load the corresponding target diagnostic configuration file according to the model of the UAV to be diagnosed; Step S14: Based on the target control command sent from the ground control vehicle to the UAV to be diagnosed, and combined with the model of the UAV to be diagnosed and its current operating stage, match the target diagnosis model corresponding to the target control command from the target diagnosis configuration file. Step S16: Use the target diagnostic model to determine the execution effect of the feedback state data of the UAV to be diagnosed, and generate the execution effect diagnostic result corresponding to the target control command.

[0025] The following will describe in more detail each step of a general UAV control command execution effect diagnosis method in this example embodiment, with reference to the accompanying drawings and embodiments.

[0026] For example, in step 10, the pre-built diagnostic profile includes: Step 101: Build corresponding diagnostic configuration files according to the drone model; Step 102: Divide the diagnostic configuration file corresponding to each UAV model according to the flight mission stage to obtain the diagnostic configuration layer corresponding to each flight mission stage. Step 103: Configure at least one diagnostic model under each diagnostic configuration layer; wherein, each diagnostic model includes: the instruction ID of the control instruction, a list of post-state elements, the delay judgment time corresponding to each post-state element, and the judgment rule corresponding to each post-state element. Step 104: Store the correspondence between each UAV model, each flight mission stage, and each diagnostic model in a structured format to obtain the diagnostic configuration file.

[0027] Specifically, before diagnosing the execution effect of control commands, the models of each UAV that need to be connected to the general ground control vehicle are first obtained, and a corresponding diagnostic configuration file is created for each UAV model. Since different UAV models may differ in control command encoding, status data names, telemetry data sources, and criteria for judging execution effects, the diagnostic configuration files are built and stored independently according to the UAV model.

[0028] For example, corresponding diagnostic configuration files can be established for reconnaissance UAVs, strike UAVs, and communication relay UAVs. Each diagnostic configuration file describes the control commands, associated status data, and diagnostic basis for the execution effect of the corresponding UAV model. Therefore, in subsequent diagnostic processes, the corresponding diagnostic configuration file can be directly loaded based on the model of the UAV to be diagnosed, without needing to embed the command diagnostic logic for different UAV models into the diagnostic program.

[0029] Secondly, the diagnostic configuration file for each UAV model is divided into layers according to the flight mission phase in which the UAV performs the mission. The flight mission phase includes, but is not limited to, one or more of the following: ground preparation phase, takeoff phase, air flight phase, cruise phase, mission execution phase, return phase, and landing phase.

[0030] Because the same control command may have different diagnostic conditions in different flight mission phases, or certain control commands may only be executed in specific flight mission phases, different diagnostic configuration layers are set up in the diagnostic configuration file according to the flight mission phase. Each diagnostic configuration layer is used to store the diagnostic model applicable to that flight mission phase.

[0031] For example, during the ground preparation phase, diagnostic models can be configured for control commands such as engine start, self-test, and payload power-on; during the flight phase, diagnostic models can be configured for control commands such as altitude change, heading adjustment, speed control, and payload activation. By dividing the diagnostic configuration layers according to the flight mission phases, the mixing of diagnostic logic across different phases can be avoided, improving the accuracy of diagnostic model matching.

[0032] Then, under the diagnostic configuration layer corresponding to each flight mission phase, at least one diagnostic model is configured according to the control commands that are allowed to be executed or require diagnosis in that phase. Each diagnostic model corresponds to one control command and is used to describe the diagnostic basis for the effect of executing that control command.

[0033] Each diagnostic model includes the instruction ID of the control command, a list of post-state elements, the delay judgment time corresponding to each post-state element, and the judgment rule corresponding to each post-state element.

[0034] The command ID is used to uniquely identify the control command and corresponds to the control command code sent by the ground control vehicle. The post-state element list records the UAV status data items that need to be monitored after the target control command is executed. Post-state elements include, but are not limited to: flight altitude, heading angle, flight speed, vertical speed, engine status, payload power status, and payload operating status.

[0035] The delay determination time is used to characterize the time allowed from the time the target control command is received or executed until the corresponding subsequent state element reaches the expected state. For the same control command, different delay determination times can be configured for different subsequent state elements.

[0036] The judgment rules describe the criteria for determining whether the state value of a corresponding subsequent state element meets the requirements for executing the control command. Judgment rules can take the form of numerical range comparison, target value error comparison, state enumeration matching, trend judgment, or logical combination judgment. Specifically, the numerical range comparison rule determines whether the state value corresponding to the subsequent state element is within a preset numerical range or whether it meets the error condition between the value and the target value in the diagnostic command; the state enumeration matching rule determines whether the state value corresponding to the subsequent state element is consistent with the preset enumerated states; the trend judgment rule determines whether the trend of the state value corresponding to the subsequent state element after the execution of the target control command meets the preset trend condition; and the logical combination rule performs a logical combination judgment on the judgment results corresponding to one or more subsequent state elements.

[0037] For example, for an altitude change command, its post-state elements can include flight altitude and vertical speed. The determination rule for flight altitude can be "the difference between the current altitude and the target altitude is not greater than a preset threshold," and the determination rule for vertical speed can be "the current vertical speed is within a preset speed range." When both of the above post-state elements satisfy their corresponding determination rules, the altitude change command can be considered to have executed normally.

[0038] Finally, after configuring each UAV model, each flight mission phase, and each diagnostic model, these are organized and stored according to a hierarchical data structure to form a diagnostic configuration file. The data structure in the diagnostic configuration file can sequentially include a UAV model layer, a flight mission phase layer, and a diagnostic model layer.

[0039] The diagnostic configuration file is divided into three layers: the UAV model layer, which identifies the UAV model to which the configuration file is adapted; the flight mission phase layer, which distinguishes the diagnostic configuration content under different operating phases; and the diagnostic model layer, which stores the post-state elements, delay judgment time and judgment rules corresponding to each control command.

[0040] The structured format can be JSON, XML, or other structured file formats that can express hierarchical data relationships. Storing diagnostic configuration files in a structured format improves readability, editability, and extensibility. When adding new drone models, flight mission phases, or control commands, adaptation can be achieved by adding or modifying the diagnostic configuration file without requiring modifications to the core diagnostic program.

[0041] Through the above steps, the diagnostic configuration file forms a hierarchical organizational structure of "UAV model - flight mission phase - diagnostic model". During operation, the diagnostic system can load the corresponding diagnostic configuration file based on the UAV model to be diagnosed, and quickly match the corresponding target diagnostic model from the diagnostic configuration file according to the current flight mission phase and target control commands of the UAV, providing a data foundation for subsequent performance evaluation.

[0042] Further, refer to Figure 2 As shown, Figure 2 This is a schematic diagram of the diagnostic configuration file. The diagnostic configuration file is configured independently for each drone model. Figure 2 Let's take the configuration file corresponding to model A as an example. This configuration file adopts a hierarchical structure. The first layer is the configuration file corresponding to the UAV model, the second layer is the configuration file for different flight mission stages, the third layer is the control commands for each flight mission stage, and the fourth layer is the post-state elements and diagnostic conditions corresponding to each control command.

[0043] In the configuration file corresponding to Model A, the flight mission phases include ground preparation, takeoff, cruise, mission execution, return, and landing. Each flight mission phase is configured with at least one diagnostic model corresponding to a control command. For example, in the ground preparation phase, diagnostic models corresponding to control commands such as Command ID_001 and Command ID_002 are configured.

[0044] For each control command, its diagnostic model includes at least one post-state element. Each post-state element corresponds to a configuration delay time and a decision rule. For example, the post-state elements corresponding to command ID_001 include engine status and navigation system status; wherein, the delay time for engine status is 2s, and the decision rule is that the status value is equal to "ON"; the delay time for navigation system status is 5s, and the decision rule is that the status value is equal to "READY".

[0045] Furthermore, in application scenarios where multiple UAV models are integrated into a general-purpose ground control vehicle, the control command encoding, telemetry status data items, task phase divisions, and execution effect judgment rules of different UAV models often differ. If these differentiated diagnostic logics are directly embedded in the diagnostic program code, when a new UAV model or control command is added, the diagnostic program needs to be modified, tested, and deployed again, resulting in high system maintenance costs and long expansion cycles, making it difficult to meet the general-purpose ground control vehicle's need for rapid adaptation to multiple UAV models. This solution configures diagnostic criteria such as control commands, post-state elements, delay judgment times, and judgment rules, allowing the diagnostic program to handle only general matching, monitoring, and judgment processes, while diagnostic data for specific UAV models is provided by configuration files. Therefore, without modifying the core diagnostic code, automatic diagnosis of the execution effect of control commands for newly developed UAV models can be achieved by adding or updating the corresponding model's diagnostic configuration file, thereby improving the system's scalability and maintenance convenience.

[0046] Therefore, the diagnostic configuration file can hierarchically organize diagnostic conditions for different UAV models, flight mission stages, control commands, and status data items. During the diagnostic process, the system can load the corresponding configuration file according to the model of the UAV to be diagnosed, and, in conjunction with the current flight mission stage and target control commands, quickly determine the corresponding post-state elements, delay judgment times, and judgment rules, providing a basis for judging the effect of control command execution.

[0047] For example, in step 12, the model information of the drone to be diagnosed is obtained, and the model information is matched with a pre-built diagnostic configuration file to determine the diagnostic configuration file corresponding to the model information. The matched diagnostic configuration file is then used as the target diagnostic configuration file and loaded into the diagnostic system.

[0048] The target diagnostic configuration file contains diagnostic models for this UAV model under different flight mission phases. Each diagnostic model describes the diagnostic basis for the execution effect of the corresponding control command, including the command ID, post-state elements, delay determination time, and determination rules of the control command.

[0049] In this way, the diagnostic system can automatically call the corresponding diagnostic configuration file according to the model of the drone to be diagnosed, so that the diagnostic logic of the control commands of different drone models does not need to be fixed in the diagnostic program, thereby improving the adaptability of the diagnostic method to multiple drone models.

[0050] For example, in step 14, the target control command sent from the ground control vehicle to the UAV under diagnosis, combined with the UAV's model and current operating stage, matches the target diagnostic model corresponding to the target control command from the target diagnostic configuration file, including: Step 141: Listen to the target control commands sent by the ground control vehicle to the UAV to be diagnosed, and obtain the command ID corresponding to the target control commands; Step 142: Determine the loaded target diagnostic configuration file based on the model of the drone to be diagnosed; Step 143: Based on the current operating stage of the UAV to be diagnosed, determine the diagnostic configuration layer corresponding to the current operating stage in the target diagnostic configuration file; Step 144: Based on the instruction ID, search among the diagnostic models included in the diagnostic configuration layer, and determine the diagnostic model that matches the instruction ID as the target diagnostic model.

[0051] Specifically, during operation, the diagnostic system continuously monitors the control commands sent from the ground control vehicle to the UAV under diagnosis. When a new control command is detected from the ground control vehicle, it takes that control command as the target control command, parses the target control command, and obtains the command ID corresponding to the target control command.

[0052] The instruction ID is used to uniquely identify the target control instruction and corresponds to the control instruction identifier pre-configured in the target diagnostic configuration file. For example, when the target control instruction is a height change instruction, its instruction ID can be CMD_ALTITUDE_CHANGE; when the target control instruction is a load activation instruction, its instruction ID can be CMD_CAMERA_ON.

[0053] Furthermore, when acquiring the instruction ID corresponding to the target control instruction, the reception time of the target control instruction can also be recorded simultaneously. The reception time can be used as the starting point for subsequently determining the judgment time based on the delay judgment time.

[0054] After acquiring the target control commands, the diagnostic system determines the currently loaded target diagnostic profile based on the model of the UAV to be diagnosed. Since different UAV models may have different control command codes, telemetry status data, and diagnostic rules, different UAV models correspond to different diagnostic profiles. The diagnostic system has already loaded the target diagnostic profile based on the UAV model in the previous steps. The diagnostic system further confirms that the current target control commands should be matched in the target diagnostic profile corresponding to that UAV model.

[0055] For example, when the drone to be diagnosed is model ReconDrone-1, the diagnostic system determines that the current matching object is the target diagnostic profile corresponding to model ReconDrone-1, and will not search for diagnostic profiles of other drone models. This avoids the mixing of diagnostic models between different drone models.

[0056] The diagnostic system obtains the current operating stage of the UAV under diagnosis and searches for the corresponding diagnostic configuration layer in the target diagnostic configuration file. Since the same control command may have different diagnostic logic in different operating stages, or some control commands may only be applicable to specific operating stages, it is necessary to first determine the corresponding diagnostic configuration layer based on the current operating stage, and then retrieve the target diagnostic model in that diagnostic configuration layer.

[0057] The current operational phase can include one of the following: ground preparation phase, takeoff phase, in-flight phase, cruise phase, mission execution phase, return phase, and landing phase. Target diagnostic configuration files are stored hierarchically according to operational phases, with each configuration layer corresponding to one operational phase and storing the diagnostic models available for that phase.

[0058] For example, when the UAV to be diagnosed is in the air flight phase, the diagnostic system determines the diagnostic configuration layer corresponding to "air flight" from the target diagnostic configuration file; when the UAV to be diagnosed is in the ground preparation phase, the diagnostic system determines the diagnostic configuration layer corresponding to "ground preparation".

[0059] Specifically, after determining the diagnostic configuration layer corresponding to the current operational phase, the diagnostic system searches among the diagnostic models contained in that configuration layer using the instruction ID of the target control instruction as the retrieval condition. When the control instruction identifier in a diagnostic model matches the instruction ID of the target control instruction, that diagnostic model is identified as the target diagnostic model corresponding to the target control instruction. The target diagnostic model includes the post-state elements corresponding to the target control instruction, the delay determination time corresponding to each post-state element, and the determination rules corresponding to each post-state element.

[0060] For example, if the target control command's command ID is CMD_ALTITUDE_CHANGE, and the current operational phase is "flight in the air," the diagnostic system searches for the diagnostic model with command ID CMD_ALTITUDE_CHANGE in the "flight in the air" diagnostic configuration layer. If a corresponding diagnostic model is found, it is used as the target diagnostic model. This target diagnostic model may include post-state elements such as "flight altitude" and "vertical speed," as well as the corresponding delay time and decision rules for each post-state element.

[0061] Through the above steps, the diagnostic system can accurately determine the target diagnostic model corresponding to the target control command from the target diagnostic configuration file according to the matching order of "UAV model - current operating stage - command ID". This matching method can ensure that the diagnostic logic between different UAV models, different operating stages, and different control commands is mutually distinguishable, thereby improving the accuracy of target diagnostic model matching and the scalability of the diagnostic process.

[0062] For example, in step 16, the step of using the target diagnostic model to determine the execution effect of the feedback state data of the UAV to be diagnosed and generating the execution effect diagnostic result corresponding to the target control command includes: Step 161: Obtain the list of post-state elements corresponding to the target control command from the target diagnostic model, as well as the delay judgment time and judgment rule corresponding to each post-state element. Step 162: Taking the time of receiving the target control command as the starting time, determine the judgment time of each subsequent state element according to the delay judgment time corresponding to each subsequent state element. Step 163: Establish parallel state monitoring tasks for each post-state element, and obtain the corresponding state value from the feedback state data of the UAV to be diagnosed based on each post-state element. Step 164: At the judgment time corresponding to each subsequent state element, the obtained state value is judged using the corresponding judgment rule to obtain the state judgment result corresponding to each subsequent state element. Step 165: Generate the execution effect diagnosis result corresponding to the target control instruction based on the state judgment result corresponding to each post-state element.

[0063] Specifically, after determining the target diagnostic model corresponding to the target control command, the diagnostic system reads the diagnostic data from the target diagnostic model. The target diagnostic model includes a list of post-state elements that require status monitoring after the target control command is executed, as well as the delay determination time and determination rules corresponding to each post-state element.

[0064] The subsequent state elements are UAV state data items associated with the execution effect of the target control command. For example, for an altitude change command, the subsequent state elements may include flight altitude and vertical speed; for a payload activation command, the subsequent state elements may include payload power status. The delay determination time is used to determine the timing of the determination of the corresponding subsequent state element, and the determination rule is used to determine whether the state value of the corresponding subsequent state element meets the execution requirements of the target control command.

[0065] When the diagnostic system detects a target control command, it records the time of receipt of the command and uses this time as the starting point. For each subsequent state element in the target diagnostic model, the starting point is added to the corresponding delay decision time to obtain the decision time for that subsequent state element.

[0066] For example, if the reception time of the target control command is T0, and the delay decision time of a certain post-state element in the target diagnostic model is 10 seconds, then the decision time of that post-state element is T0+10 seconds. If the delay decision time of another post-state element is 15 seconds, then the decision time of that post-state element is T0+15 seconds. Therefore, different decision timings can be set for different post-state elements.

[0067] The diagnostic system establishes a state monitoring task for each post-state element in the target diagnostic model. These state monitoring tasks can be executed in parallel to monitor the feedback state data corresponding to different post-state elements.

[0068] During status monitoring, the diagnostic system retrieves the corresponding status values ​​from the telemetry or status data fed back by the UAV under diagnosis, based on the data source or status identifier corresponding to each subsequent status element. For example, when the subsequent status element is flight altitude, the system retrieves the altitude value fed back by the UAV; when the subsequent status element is vertical velocity, the system retrieves the vertical velocity value fed back by the UAV; and when the subsequent status element is payload power status, the system retrieves the payload power status value fed back by the UAV.

[0069] By establishing parallel state monitoring tasks for each subsequent state element, multiple state data items corresponding to the same target control command can be processed according to their respective delay judgment time and judgment rules.

[0070] Specifically, when any subsequent state element reaches its corresponding judgment time, the diagnostic system obtains the state value corresponding to that judgment time, and calls the judgment rule corresponding to that subsequent state element to make a judgment, thereby obtaining the state judgment result corresponding to that subsequent state element.

[0071] The judgment rules can be set according to the characteristics of different state data items. For example, for numerical state data, it can be determined whether the current state value is within a preset range, or whether the difference between the current state value and the instruction target value meets a preset error condition; for state data, it can be determined whether the current state value is consistent with a preset state.

[0072] For example, for altitude change commands, if the judgment rule for flight altitude is "the difference between the current altitude and the target altitude is not greater than a preset threshold," then the diagnostic system obtains the current altitude value at the judgment time corresponding to the flight altitude and determines whether the flight altitude is normal according to the judgment rule. For vertical speed, if the judgment rule is "the current vertical speed is within a preset speed range," then the diagnostic system obtains the current vertical speed value at the judgment time corresponding to the vertical speed and determines whether it meets the corresponding range requirements.

[0073] After all the post-state elements corresponding to the target control command have been determined, the diagnostic system summarizes the state determination results corresponding to each post-state element and generates the execution effect diagnostic results corresponding to the target control command.

[0074] Through the above steps, the diagnostic system can automatically determine the feedback status data of the UAV to be diagnosed based on the pre-configured post-state elements, delay judgment time and judgment rules in the target diagnostic model, and summarize the judgment results of multiple post-state elements into the overall execution effect diagnostic result of the target control command.

[0075] For example, in step 165, generating the execution effect diagnosis result corresponding to the target control instruction based on the state determination result corresponding to each subsequent state element includes: Step 1651: If the status judgment results of all subsequent state elements corresponding to the target control instruction are normal, then generate an execution effect diagnostic result to characterize the successful execution of the target control instruction. Step 1652: If the state determination result of any post-state element corresponding to the target control instruction is abnormal, then generate an execution effect diagnosis result to characterize the execution abnormality of the target control instruction, and record the abnormal post-state element in the execution effect diagnosis result. Step 1653: Output the execution effect diagnosis results to the ground control vehicle.

[0076] Specifically, the diagnostic system summarizes the status judgment results of each post-state element corresponding to the target control command. When each post-state element meets its corresponding judgment rule, it indicates that the UAV's feedback status data after the target control command is executed meets the corresponding diagnostic requirements, and at this time, a successful execution effect diagnostic result is generated.

[0077] For example, if the target control command is an altitude change command, and the status judgment results of the two post-state elements, "flight altitude" and "vertical speed", are both normal, then the altitude change command is determined to have been executed successfully.

[0078] If any post-state element corresponding to the target control instruction fails to meet the corresponding judgment rule, it indicates that after the target control instruction is executed, at least one state data item related to the execution effect fails to meet the diagnostic requirements. In this case, the diagnostic system generates an execution effect diagnostic result indicating an execution anomaly.

[0079] Furthermore, the diagnostic system records the post-state elements with abnormal status judgment results in the execution effect diagnostic results, so that operators can quickly locate the source of the anomaly. For example, when the "flight altitude" does not meet the altitude error requirement, the abnormal post-state element "flight altitude" can be recorded in the execution effect diagnostic results; when the "load power status" does not reach the expected state, the abnormal post-state element "load power status" can be recorded in the execution effect diagnostic results.

[0080] The diagnostic system sends the generated execution effect diagnostic results to the ground control vehicle, enabling the ground control vehicle to display the execution status of the target control command on the operating interface. The execution effect diagnostic results may include the command ID of the target control command, the determination result of successful or abnormal execution, and information such as abnormal post-state elements.

[0081] Furthermore, the ground control vehicle can display, prompt, or record the results of the execution effect diagnosis. For example, when the target control command is executed successfully, a success message can be displayed on the operation interface; when the target control command is executed abnormally, an error message can be displayed on the operation interface, and the corresponding log can be recorded.

[0082] In this way, the diagnostic system can summarize the status judgment results of multiple post-state elements into the overall execution effect diagnostic result of the target control command, and feed the result back to the ground control vehicle, thereby helping the operator to quickly grasp the actual execution status of the control command.

[0083] For example, in step 15, before using the target diagnostic model to determine the execution effect of the feedback state data of the UAV to be diagnosed, the method further includes: Step 151: Obtain the current diagnostic model corresponding to the control command currently being diagnosed; Step 152: Compare the post-state elements in the target diagnostic model with the post-state elements in the current diagnostic model; Step 153: If the target diagnostic model and the current diagnostic model have the same post-state elements, then it is determined that there is a conflict between the target control command and the control command currently being diagnosed. Step 154: If an instruction conflict is determined, terminate the diagnostic process corresponding to the currently being diagnosed control instruction and mark the currently being diagnosed control instruction as interrupted due to conflict.

[0084] Specifically, after the diagnostic system receives the target control command sent by the ground control vehicle, it determines whether the UAV to be diagnosed has any control commands that have not yet been diagnosed. If there is a control command currently being diagnosed, the system obtains the current diagnostic model corresponding to that control command.

[0085] The current diagnostic model includes the control command currently being diagnosed, the corresponding subsequent state elements, the delay time, and the judgment rules. By obtaining the current diagnostic model, the state data items associated with the control command currently being diagnosed can be determined, providing a basis for subsequent judgment on whether there is a conflict between the target control command and the current one.

[0086] Specifically, the diagnostic system obtains the list of subsequent state elements from the target diagnostic model and the list of subsequent state elements from the current diagnostic model. Then, it compares the state data items in the two lists to determine whether the target control command and the currently being diagnosed control command involve the same UAV state data items.

[0087] The post-state elements are UAV state data items associated with the effect of control command execution. If the same post-state element exists in two diagnostic models, it means that both the target control command and the control command currently being diagnosed need to monitor and determine the same state data item.

[0088] Specifically, when any post-state element in the target diagnostic model is the same as any post-state element in the current diagnostic model, the diagnostic system determines that there is a conflict between the target control command and the control command currently being diagnosed.

[0089] For example, if the post-state element of the control command currently being diagnosed includes "engine status," and the post-state element of the target control command also includes "engine status," then both control commands involve diagnosing "engine status." In this case, if the diagnostic process for both control commands continues to be executed simultaneously, the effect of the previous control command may be affected by the subsequent control command; therefore, a command conflict is determined.

[0090] Specifically, after determining that there is a conflict between the target control command and the control command currently being diagnosed, the diagnostic system terminates the status monitoring task and rule determination process corresponding to the control command currently being diagnosed, and marks the diagnostic status of the control command as "interrupted by conflict".

[0091] Furthermore, the diagnostic system can record conflict resolution information. This conflict resolution information includes at least one of the following: the control command currently being diagnosed, the target control command, the subsequent state element where the conflict occurred, and the time of the conflict occurrence. After completing conflict resolution, the diagnostic system continues to determine the execution effect of the target control command based on the target diagnostic model.

[0092] Through the above steps, the diagnostic system can automatically identify whether there is a conflict between a new target control command and the currently being diagnosed control command based on the post-state elements when the new target control command arrives, and interrupt the diagnostic process affected by the conflict, thereby improving the accuracy of diagnostic results in scenarios where multiple commands are executed continuously or concurrently.

[0093] The method provided in the embodiments of the present invention is referred to Figure 3 As shown, Figure 3 This is a flowchart illustrating a general method for diagnosing the execution effect of UAV control commands. The flowchart describes the overall execution process of this method, and its main steps are as follows: First, after the diagnostic system starts, it enters the diagnostic process and loads the configuration file corresponding to the model of the drone to be diagnosed. The configuration file pre-stores diagnostic models for different flight mission phases, including control commands, post-state elements, delay judgment time, and judgment rules.

[0094] Subsequently, the system creates a corresponding diagnostic instance for the drone to be diagnosed, which is used to independently manage the drone's control commands, feedback status data, and diagnostic results. After the diagnostic instance is created, the system continuously monitors the control commands sent from the ground control vehicle to the drone.

[0095] Upon receiving a new control command, the system first determines whether the command conflicts with the currently being diagnosed command. If a conflict exists, the current diagnostic process is terminated, and the conflict information is recorded. If no conflict exists, the system records the time the control command was received and retrieves the corresponding diagnostic model from the configuration file based on the control command ID.

[0096] After obtaining the diagnostic model, the system initiates the corresponding status monitoring task based on the configured post-state elements in the diagnostic model, and waits for the corresponding judgment time according to the delay judgment time corresponding to each post-state element. After the judgment time is reached, the system collects the status data fed back by the UAV, and calculates and judges the status data according to the judgment rules in the diagnostic model.

[0097] If the state data of a certain post-state element conforms to the corresponding judgment rule, the element is marked as normal; otherwise, it is marked as abnormal. Subsequently, the system determines whether all post-state elements corresponding to the target control command have been judged.

[0098] Once all subsequent state elements have been evaluated, the system generates an overall diagnostic result based on the evaluation results of each element and outputs the diagnostic result to the ground station interface. After outputting the result, the system returns to the control command listening state and continues to wait for and process new control commands.

[0099] Specifically, this embodiment provides an implementation scenario to illustrate the present invention in detail, as follows: Step 1: Setting up the implementation scenario Suppose a general-purpose ground control station is simultaneously controlling two different types of drones to perform missions: UAV A: Model "Reconnaissance UAV-1", currently in the air flight phase.

[0100] Drone B: Model "Strike Drone-1", currently in the ground preparation phase.

[0101] The ground station operator needs to send control commands to the two drones, and the diagnostic system of this invention will automatically determine the effect of the command execution.

[0102] Step 2: Configuration file definition example Taking the "Reconnaissance UAV-1" as an example, its configuration file (such as ReconDrone-1.json) contains the following definition: { "Drone Model": "ReconDrone-1" "Working Phase": { Ground preparation: [...] "Airborne Flight": [ { "Command ID": "CMD_ALTITUDE_CHANGE", "Post-state element": [ { Name: Flight Altitude Data source: "telemeter.altitude", "Delay judgment time": "10", "Decision rule": "abs(current value - target value of instruction) <= 5" }, { Name: Vertical Velocity Data source: "telemeter.vspeed", "Delay determination time": "15", Judgment Rule: "Current value >= 0 and current value <= 2" } ] }, { "Command ID": "CMD_CAMERA_ON", "Post-state element": [ { Name: Load Power Status "Data source": "payload.camera_power", "Delay determination time": "3", "Judgment Rule": "Current value == 'ON'" } ] } ] } } Step 3: Example of the diagnostic process Scenario: The operator sends an altitude change command CMD_ALTITUDE_CHANGE to drone A (in the air flight phase), with a target altitude of 100 meters.

[0103] (1) Command listening and matching: The diagnostic system captures the command and the receiving time T0, and finds the corresponding diagnostic model in the configuration of the "flying in the air" stage according to the command ID.

[0104] (2) Parallel monitoring: The system creates two monitoring threads, corresponding to the "flight altitude" and "vertical speed" status elements respectively.

[0105] (3) Status acquisition and determination: At T0+10 seconds, check the "Flight Altitude" value. If the telemetry altitude is 98 meters, the system calculates |98-100|=2, which meets the rule (≤5), and the element is judged to be normal.

[0106] At T0+15 seconds, check the "Vertical Velocity" value. If the telemetry shows a vertical velocity of 1.5 m / s and it is within the range [0,2], the element is considered normal.

[0107] (4) Output results: Both post-state elements are judged to be normal. The system displays "Command CMD_ALTITUDE_CHANGE executed successfully" on the operation interface and records the log.

[0108] Furthermore, this embodiment provides an example of instruction conflict handling to illustrate the present invention in detail below: Scenario: The operator sends two instructions to drone B (ground preparation phase): CMD_ENGINE_START (Start the engine) CMD_SELF_TEST (System self-check, engine must be shut down) (1) After the first instruction CMD_ENGINE_START is sent, the diagnostic system begins to monitor its subsequent status elements (such as "engine speed").

[0109] (2) Before the first instruction reached the delay time, the operator sent the second instruction CMD_SELF_TEST.

[0110] (3) Conflict detection: The system retrieves the diagnostic model of the two instructions and finds that a post-state element of CMD_SELF_TEST is "engine state" and requires its value to be "OFF". This directly conflicts with the expected state "ON" of CMD_ENGINE_START, and both of them act on the same state element "engine state".

[0111] (4) Conflict handling: The system determines that the two instructions conflict, automatically terminates the diagnostic process of CMD_ENGINE_START, and pops up a prompt "Instruction conflict, the new instruction has interrupted the previous instruction: CMD_ENGINE_START", and then begins to diagnose CMD_SELF_TEST.

[0112] This example implementation provides a general-purpose unmanned aerial vehicle (UAV) control command execution performance diagnostic system. (Reference) Figure 4 As shown, it can specifically include: The diagnostic configuration file construction module 401 is used to pre-build the diagnostic configuration file; wherein, the diagnostic configuration file includes: the UAV model, the diagnostic model for each flight mission stage, and each diagnostic model includes: control commands, post-state elements, delay determination time and determination rules, and the post-state elements are the UAV's state data items; The target diagnostic configuration file loading module 402 is used to load the corresponding target diagnostic configuration file according to the model of the UAV to be diagnosed. The target diagnostic model matching module 403 is used to match the target diagnostic model corresponding to the target control command sent from the ground control vehicle to the UAV under diagnosis, and in combination with the model and current operating stage of the UAV under diagnosis, to the target diagnostic configuration file. The execution effect judgment module 404 is used to judge the execution effect of the feedback state data of the UAV to be diagnosed using the target diagnostic model, and generate the execution effect diagnostic results corresponding to the target control commands.

[0113] The beneficial effects of this invention are as follows: (1) Improve the automation level of control command execution effect diagnosis: The present invention automatically completes the judgment of control command execution status through software, reducing the operator's manual comparison of telemetry status data and reducing the operational pressure in multi-command and multi-state monitoring scenarios. (2) Improve the universality of diagnostic methods for multiple UAV models: This invention models the diagnostic process of control command execution effect in a unified manner, so that a single diagnostic program can be applied to multiple UAV models, avoiding the need to repeatedly develop diagnostic code for different UAV models.

[0114] (3) Improve system scalability and maintenance convenience: The present invention describes the control commands, status data and judgment criteria of specific UAV models through configuration files. Without modifying the core diagnostic code, new UAV models or new control commands can be adapted by adding or updating configuration files.

[0115] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0116] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0117] Figure 5 A schematic diagram of an electronic device suitable for implementing embodiments of the present invention is shown.

[0118] It should be noted that, Figure 5 The electronic device 1000 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0119] like Figure 5As shown, the electronic device 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from storage section 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004. Furthermore, the electronic device 1000 also includes an FPGA device and a System-on-a-Chip (SoC) device.

[0120] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0121] In particular, according to embodiments of the present invention, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0122] Specifically, the aforementioned electronic devices can be airborne intelligent electronic devices.

[0123] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0124] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0125] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0126] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps of the method shown.

[0127] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0128] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0129] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0130] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A general UAV control instruction execution effect diagnosis method, characterized in that, The method includes: A diagnostic configuration file is pre-built; the diagnostic configuration file includes: the UAV model, the diagnostic model for each flight mission phase, and each diagnostic model includes: control commands, post-state elements, delay determination time and determination rules, and the post-state elements are the UAV's state data items; Load the corresponding target diagnostic configuration file according to the model of the drone to be diagnosed; Based on the target control commands sent from the ground control vehicle to the UAV under diagnosis, and combined with the UAV model and current operating stage, the target diagnosis model corresponding to the target control commands is matched from the target diagnosis configuration file. The target diagnostic model is used to determine the execution effect of the feedback state data of the UAV under diagnosis and to generate the execution effect diagnostic results corresponding to the target control commands.

2. The method according to claim 1, characterized in that, The pre-built diagnostic configuration file includes: Build corresponding diagnostic configuration files according to the drone model; The diagnostic configuration file for each UAV model is divided according to the flight mission stage to obtain the diagnostic configuration layer corresponding to each flight mission stage. At least one diagnostic model is configured under each diagnostic configuration layer; wherein, each diagnostic model includes: the instruction ID of the control instruction, a list of post-state elements, the delay judgment time corresponding to each post-state element, and the judgment rule corresponding to each post-state element. The correspondence between various UAV models, flight mission phases, and diagnostic models is stored in a structured format to obtain a diagnostic configuration file.

3. The method according to claim 1, characterized in that, The target control commands sent from the ground control vehicle to the UAV under diagnosis, combined with the UAV's model and current operational stage, match the target diagnostic model corresponding to the target control commands from the target diagnostic configuration file, including: Listen to the target control commands sent from the ground control vehicle to the UAV under diagnosis, and obtain the command ID corresponding to the target control commands; Based on the model of the drone to be diagnosed, determine the loaded target diagnostic configuration file; Based on the current operating stage of the UAV to be diagnosed, determine the diagnostic configuration layer corresponding to the current operating stage in the target diagnostic configuration file; Based on the instruction ID, a search is performed among the diagnostic models included in the diagnostic configuration layer, and the diagnostic model that matches the instruction ID is identified as the target diagnostic model.

4. The method according to claim 2, characterized in that, The step of using a target diagnostic model to determine the execution effect of the feedback state data of the UAV under diagnosis and generating execution effect diagnostic results corresponding to the target control commands includes: Obtain the list of post-state elements corresponding to the target control command from the target diagnostic model, as well as the delay judgment time and judgment rule corresponding to each post-state element; Taking the time of receiving the target control command as the starting time, the determination time of each subsequent state element is determined according to the delay determination time corresponding to each subsequent state element. Parallel state monitoring tasks are established for each post-state element, and the corresponding state values ​​are obtained from the feedback state data of the UAV to be diagnosed based on each post-state element. At the decision time corresponding to each subsequent state element, the obtained state value is judged by the corresponding decision rule to obtain the state decision result corresponding to each subsequent state element. Based on the state determination results corresponding to each post-state element, the execution effect diagnosis results corresponding to the target control instruction are generated.

5. The method according to claim 4, characterized in that, The step of generating the execution effect diagnosis result corresponding to the target control instruction based on the state determination result corresponding to each subsequent state element includes: If the status judgment results of all subsequent state elements corresponding to the target control instruction are normal, then an execution effect diagnostic result is generated to characterize the successful execution of the target control instruction. If the state determination result of any post-state element corresponding to the target control instruction is abnormal, an execution effect diagnosis result is generated to characterize the execution abnormality of the target control instruction, and the abnormal post-state element is recorded in the execution effect diagnosis result. The results of the performance evaluation are output to the ground control vehicle.

6. The method according to claim 1, characterized in that, Before using the target diagnostic model to determine the execution effect of the feedback state data of the UAV to be diagnosed, the method further includes: Obtain the current diagnostic model corresponding to the control command currently being diagnosed; Compare the post-state elements in the target diagnostic model with the post-state elements in the current diagnostic model; If the target diagnostic model and the current diagnostic model have the same post-state elements, it is determined that there is a conflict between the target control command and the control command currently being diagnosed. If an instruction conflict is detected, the diagnostic process corresponding to the currently being diagnosed control instruction is terminated, and the currently being diagnosed control instruction is marked as interrupted due to conflict.

7. The method according to claim 1, characterized in that, The determination rules include: The numerical range comparison rule is used to determine whether the state value corresponding to the subsequent state element is within the preset numerical range, or whether it meets the error condition between the value and the target value in the diagnostic command. The state enumeration matching rule is used to determine whether the state value corresponding to the subsequent state element is consistent with the preset enumeration state. The trend judgment rule is used to determine whether the trend of the state value corresponding to the subsequent state element after the target control command is executed meets the preset trend condition. Logical combination rules are used to perform logical combination judgments on the judgment results corresponding to one or more subsequent state elements.

8. A general-purpose unmanned aerial vehicle (UAV) control command execution effect diagnostic system, characterized in that, The system includes: The diagnostic configuration file construction module is used to pre-build diagnostic configuration files. The diagnostic configuration file includes: the UAV model, the diagnostic model for each flight mission phase, and each diagnostic model includes: control commands, post-state elements, delay determination time and determination rules. The post-state elements are the UAV's state data items. The target diagnostic configuration file loading module is used to load the corresponding target diagnostic configuration file according to the model of the UAV to be diagnosed. The target diagnostic model matching module is used to match the target diagnostic model corresponding to the target control command sent from the ground control vehicle to the UAV under diagnosis, and in combination with the model and current operating stage of the UAV under diagnosis, to the target diagnostic configuration file. The execution effect judgment module is used to judge the execution effect of the feedback status data of the UAV under diagnosis using the target diagnostic model, and generate the execution effect diagnostic results corresponding to the target control commands.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

Images