Diagnostic method for a smart robot and related device

Abstract

The embodiment of the application provides a kind of intelligent robot diagnostic method and related equipment, main purpose is to solve the process in which some common failure task processing mode of intelligent robot executes task, usually adopt nearby after-sales personnel directly to investigate robot or upload failure data and then by artificial intervention judgment but in actual application scene, the problem that the efficiency of processing intelligent robot after-sales problem is low and accuracy is not high.Therein, the above method comprises: obtaining the failure task generated by the intelligent robot in actual application scene, detecting the diagnostic data of a plurality of diagnostic components associated with the failure task, obtaining diagnostic results based on the diagnostic data of a plurality of diagnostic components according to a preset diagnostic rule library.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a diagnostic method and related equipment for intelligent robots. Background Technology

[0002] With the continuous development of science and technology in my country, mobile service robots are beginning to provide services such as greeting guests and delivering goods across floors in buildings such as hotels, office buildings, and shopping malls. When robots perform tasks across floors, they need to be connected to elevators via the Internet of Things (IoT). When robots notify telephones or other smart devices in rooms, they need to be connected to these notification devices. When robots retrieve items from vending machines, they need to be connected to the vending machines via IoT. Besides the robot's own installed sensors or components malfunctioning, any problem with other IoT devices can also lead to task failures. Currently, handling robot malfunctions typically involves nearby after-sales personnel directly checking the robot or uploading failure data for manual intervention, resulting in low efficiency and accuracy in resolving after-sales issues. Summary of the Invention

[0003] In view of the above problems, this patent proposes a diagnostic method for intelligent robots. The robot and each of its interconnected devices automatically complete their own status detection and diagnosis of each core operation, which are then aggregated and centrally diagnosed. Finally, the intelligent diagnostic method outputs the diagnostic results and processing flow. The main purpose is to improve the efficiency and accuracy of handling after-sales problems of robots.

[0004] To address at least one of the aforementioned technical problems, in a first aspect, the present invention provides a diagnostic method for an intelligent robot, the method comprising: Obtain failed tasks generated by the intelligent robot in actual application scenarios; Detect diagnostic data from multiple diagnostic components associated with the failed task; Based on the diagnostic data from multiple diagnostic components, a diagnostic result is obtained according to a preset diagnostic rule base, wherein the preset diagnostic rule base includes the correlation between the combination of diagnostic data from multiple diagnostic components and the diagnostic result.

[0005] Optionally, the detection of diagnostic data from multiple diagnostic components associated with the failed task includes: Based on the failed task, the target diagnostic package information is determined, which includes the diagnostic package name, diagnostic package type, and diagnostic package category. Retrieve the target diagnostic package from the diagnostic package database based on the target diagnostic package information; Execute the diagnostic formula corresponding to the target diagnostic package to detect diagnostic data of multiple diagnostic components associated with the failed task.

[0006] Optionally, the above methods also include: The diagnostic data includes historical functional status data of the functional modules associated with the failed task within a preset time period and / or current functional status data of the functional modules associated with the failed task.

[0007] Optionally, the above methods also include: The functional modules associated with the failed task include functional modules that support the execution of the task and functional modules that conflict with the execution of the task.

[0008] Optionally, the above methods also include: The diagnostic data also includes execution environment data and execution status data associated with the task.

[0009] Optionally, the above methods also include: Based on diagnostic data from multiple diagnostic components, processing suggestions are obtained according to a preset diagnostic rule base, wherein the preset diagnostic rule base includes the association between combinations of diagnostic data from multiple diagnostic components and processing suggestions. Optionally, the above method further includes: broadcasting a short-range communication request when a diagnostic result cannot be obtained based on the diagnostic data from multiple diagnostic components according to the preset diagnostic rule base; generating an external status request upon receiving a feedback message for the short-range communication request, the external status request including an external status capture request and a screen sharing request; storing the screen sharing data if received and the intelligent robot is present in the screen sharing data; and obtaining a diagnostic result based on the diagnostic data from the multiple diagnostic components and the screen sharing data.

[0010] Secondly, embodiments of the present invention also provide an apparatus for diagnosing intelligent robots, comprising: The acquisition unit is used to acquire failed tasks generated by the intelligent robot in actual application scenarios; A detection unit is used to detect diagnostic data from multiple diagnostic components associated with the failed task; The matching unit is used to match diagnostic data from multiple diagnostic components according to a preset diagnostic rule base to obtain diagnostic results.

[0011] To achieve the above objectives, according to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein the above-described diagnostic method for an intelligent robot is implemented when the program is executed by a processor.

[0012] To achieve the above objectives, according to a fourth aspect of the present invention, an electronic device is provided, comprising at least one processor and at least one memory connected to the processor; wherein the processor is configured to invoke program instructions in the memory to execute the above-described diagnostic method for an intelligent robot.

[0013] By employing the above technical solutions, embodiments of the present invention provide a diagnostic method for intelligent robots. Addressing the current problem of intelligent robots encountering abnormalities during task execution, where existing methods rely on nearby after-sales personnel to directly inspect the robot or upload failure data for manual intervention, this invention obtains failed tasks generated by the intelligent robot in actual application scenarios, then detects diagnostic data from multiple diagnostic components associated with those failed tasks, and finally obtains diagnostic results based on the diagnostic data from these components according to a preset diagnostic rule library. This achieves the effect of enabling the robot and each of its interconnected devices to automatically complete their own state detection and diagnosis of each core operation, aggregating the data for central diagnosis, and ultimately obtaining diagnostic results through an intelligent diagnostic method.

[0014] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0015] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a diagnostic method for an intelligent robot provided by an embodiment of the present invention is shown. Figure 2 A schematic flowchart of another intelligent robot diagnostic method provided by an embodiment of the present invention is shown; Figure 3 A flowchart illustrating another diagnostic method for an intelligent robot provided by an embodiment of the present invention is shown; Figure 4 A schematic structural block diagram of a diagnostic device for an intelligent robot provided in an embodiment of the present invention is shown; Figure 5 A schematic structural block diagram of an electronic device provided by an embodiment of the present invention is shown. Detailed Implementation

[0016] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0017] To address the issue of robots encountering abnormalities during task execution, the usual practice is for nearby after-sales personnel to directly inspect the robot or upload failure data for manual intervention. This approach often results in low efficiency and accuracy in handling after-sales problems.

[0018] This invention provides a diagnostic method for intelligent robots, such as... Figure 1 As shown, the method includes: S101. Obtain failed tasks generated by the intelligent robot in actual application scenarios.

[0019] For example, the above-mentioned practical application scenarios could be that the intelligent robot performs the task of transporting goods in a hotel or that the intelligent robot makes phone calls without human intervention; there is no limitation on this. The system also retrieves failed tasks generated by the intelligent robot in the practical application scenarios. A failed task can refer to a situation where the intelligent robot fails to achieve the expected effect of a preset routine instruction when executing it.

[0020] For example, the aforementioned failed task could be, for instance, the intelligent robot executing the instruction to dial a phone in the room, but failing to dial successfully; or it could be, the intelligent robot executing the instruction to transport an item, but failing to do so within the preset time or being detected midway as not moving physically. The lack of movement could be due to a fall or a problem recognizing the surrounding environment that prevents it from following the path.

[0021] S102, Detect diagnostic data of multiple diagnostic components associated with the failed task.

[0022] The detection includes, but is not limited to, the status of all associated diagnostic components during the execution of preset routine instructions for the aforementioned failed tasks.

[0023] For example, if an intelligent robot executes a command to dial a phone number in a room but fails to make the call, it's necessary to check the components associated with the call's status. These components could include whether the network connection for the call is normal, whether the registration information is correct, whether the phone's connection status is normal, whether the robot's own network connection is normal, and whether the number of times and total duration of network outages for the robot's phone module within a set period are normal. By checking the status of a large number of related diagnostic components with high coverage, a more comprehensive and systematic approach can be taken to identify specific anomalies in the intelligent robot.

[0024] Determine if the above information is normal. If not, further determine the state of the component in the abnormal state. The associated component state can be normal, abnormal, unknown, or some undefined state. The aforementioned undefined state can be confirmed by diagnosing the components associated with the failed task in a real-world application scenario, and the actual state of the component is continuously updated in the diagnostic package database.

[0025] S103. Based on the diagnostic data of multiple diagnostic components, obtain diagnostic results according to a preset diagnostic rule base, wherein the preset diagnostic rule base includes the association between the combination of diagnostic data of multiple diagnostic components and the diagnostic results.

[0026] The diagnostic data of the aforementioned diagnostic components corresponds to a unique component code. Furthermore, based on the component code, the final diagnostic result is obtained by judging according to the preset diagnostic rules in the preset diagnostic rule library.

[0027] It should be noted that the aforementioned diagnostic rule base is a configured database. It can be used to arbitrarily combine and match component codes obtained after diagnosing failed tasks during different tasks with preset rules in the database to arrive at a diagnostic result. The database can be manually updated during use to ensure that the corresponding diagnostic formulas are updated as more diagnostic components are added. Because each diagnostic data corresponds to a unique component code, it has a one-to-one characteristic when matching diagnostic rules during the diagnostic process. In scenarios where a failed task is diagnosed, it is less likely to make incorrect diagnoses during concurrent diagnosis.

[0028] Understandably, the preset rules are obtained by summarizing and analyzing the state of related components when a failed task occurs, based on information obtained from diagnosing a large number of historical failed tasks.

[0029] In the above solution, it can be ensured that when the robot fails to perform a task under complex conditions, the failed task generated by the intelligent robot in the actual application scenario can be obtained. This facilitates the detection of diagnostic data of multiple diagnostic components associated with the failed task. Compared with the current conventional manual on-site detection or network diagnosis, which has low efficiency, the above method of the present invention can automatically complete the self-state detection and diagnosis of each core operation of the robot and each device of the Internet of Things, summarize them to the central for diagnosis, and finally achieve the effect of obtaining the diagnostic results by the intelligent diagnostic method.

[0030] In some embodiments, the step of detecting diagnostic data of multiple diagnostic components associated with the failed task in the foregoing embodiments can be implemented as follows: Figure 2 As shown, it includes: S201. Based on the failed task, determine the target diagnostic package information, which includes the diagnostic package name, diagnostic package type, and diagnostic package category.

[0031] It should be noted that the intelligent robot diagnostic method determines the category of a failed task based on its type and then obtains the corresponding diagnostic package. Each diagnostic package corresponds to a unique diagnostic package code, and each diagnostic package code, in turn, corresponds to a unique diagnostic package name, type, and category—a one-to-one relationship. This relationship is more stable in the database and reduces the likelihood of phantom reads during practical applications. Authorized administrators can re-edit the above correspondence based on actual application scenarios, demonstrating the flexibility of this method.

[0032] S202. Obtain the target diagnostic package from the diagnostic package database based on the target diagnostic package information.

[0033] The diagnostic package includes a specific and complete troubleshooting and diagnostic solution for each type of failed task. The diagnostic data of the diagnostic component needs to be obtained by executing the diagnostic rules corresponding to the diagnostic solution.

[0034] S203. Execute the diagnostic formula corresponding to the target diagnostic package to detect diagnostic data of multiple diagnostic components associated with the failed task.

[0035] The diagnostic package contains a large number of pre-defined diagnostic formulas. Based on these formulas, diagnostic data results are obtained through calculations using logical operators. The logical operators used include "!" and "&&".

[0036] For example, the diagnostic component code associated with a failed task is HO13007. A Boolean logical operation is performed on HO13007, and the formula is !HO13007. If the output result is false, then an exception is diagnosed.

[0037] If there is more than one associated component, then connect them using && before performing logical operations. For example, !HO13007&&!HO13008 is the output diagnostic result that simultaneously satisfies the condition that HO13007 is diagnosed as abnormal and HO13008 is also diagnosed as abnormal.

[0038] or HO13007&&!HO13008 is the output diagnostic result that simultaneously meets the conditions HO13007 for normal diagnosis and HO13008 for abnormal diagnosis.

[0039] Using the code corresponding to the associated component and calculating through logical operators makes it easier for the computer to understand. It eliminates the need for ASCII code escaping of Chinese characters, making it more efficient and improving the efficiency of diagnosis in the case of failed tasks.

[0040] It should be noted that the Boolean data type stores variables as 8-bit (1-byte) numerical values, but can only be True or False.

[0041] When called as a constructor (with the new operator), Boolean() will convert its argument to a boolean value and return a Boolean object containing that value.

[0042] When called as a function (without the new operator), Boolean() simply converts its arguments to a primitive boolean value and eventually returns that value.

[0043] && is a symbol used in programming; it stands for logical (conditional) AND.

[0044] For example, a&&b: if both a and b are true, the value is true; otherwise, the value is false. In some embodiments, the above method includes the following when executed: S301, The diagnostic data includes historical functional status data of the functional modules associated with the failed task within a preset time period and / or current functional status data of the functional modules associated with the failed task.

[0045] When a failed task occurs, the robot intelligent diagnosis method will diagnose the current status data and historical status data within a certain period of time based on the functional modules associated with the failed task. The method will then combine the data to further judge the functional modules according to the preset diagnosis rules in order to improve the accuracy of the judgment.

[0046] For example, if the intelligent robot fails to dial the room phone, it needs to diagnose whether the phone module network is normal when dialing, and check whether the number of times the phone module network has lost connection and the total duration in the past 7 days are normal. The preset time can be edited on the configuration page of this method, so that the implementation can be more flexible and suited to different actual application scenarios.

[0047] In some embodiments, the above method includes the following when executed: S401, The functional modules associated with the failed task include functional modules that support the execution of the task and functional modules that conflict with the execution of the task.

[0048] For example, in a real-world application scenario where a smart robot needs to transport goods across floors during a transportation task, it will interact with the elevator via IoT. However, during the elevator journey, other passengers may be present, and their actions could involve exiting, entering, or more complex scenarios. In such cases, the robot often needs to detect the environment and execute avoidance or stopping commands—a conflicting task among its functional modules. In complex scenarios, the smart robot may malfunction when concurrently executing transportation and conflicting commands, leading to task failure. Understandably, detecting both the functional modules supporting the task execution and those conflicting with it allows for a more accurate and reliable identification of the specific reasons for task failure.

[0049] In some embodiments, the diagnostic data includes historical functional status data of the functional modules associated with the failed task within a preset time period and / or current functional status data of the functional modules associated with the failed task, including: S501, the diagnostic data also includes execution environment data and execution status data associated with the task.

[0050] For example, in real-world applications, a robot's failure to execute preset instructions may not only be due to malfunctions in the associated components, but also to environmental factors, human error, or problems encountered during execution. Therefore, it is necessary to detect and diagnose the execution environment and status associated with the task and acquire relevant data.

[0051] For example, if a smart robot falls while performing a delivery task, causing the task to fail, it is necessary to diagnose whether the component motors are idling, whether the laser matching is normal, whether there were people in front or behind before the fall, the direction of the fall, the slope of the area where the fall occurred, whether the travel speed matches the slope, and whether the fall occurred near an elevator entrance. The advantage of the above method is that it can more accurately diagnose the reasons for the robot's failure in performing the task, so as to provide practical diagnostic suggestions for the future.

[0052] In some embodiments, the above method further includes, when executed: S601. Based on the diagnostic data of multiple diagnostic components, obtain processing suggestions according to a preset diagnostic rule library, wherein the preset diagnostic rule library includes the association between the combination of diagnostic data of multiple diagnostic components and processing suggestions.

[0053] For example, the above-mentioned handling suggestions could be further diagnostic recommendations based on the diagnostic data. For instance, if the diagnostic result indicates a malfunction in the robot's GPS positioning unit, checking whether the robot's environmental perception sensors are functioning correctly would allow the environmental perception sensors to continue driving the robot and completing the task. Alternatively, the above-mentioned handling suggestions could be directly sent to maintenance personnel, such as immediate repair or continued observation without further repair.

[0054] Understandably, by further establishing the correlation between the combination of diagnostic data from multiple diagnostic components and the processing suggestions, it is possible not only to intelligently identify faults through the combination of diagnostic data from these components, but also to intelligently provide processing suggestions based on a preset diagnostic rule base. This ensures the standardization and uniformity of fault handling and improves fault handling efficiency. Furthermore, due to varying levels of experience among maintenance personnel, some personnel may find it difficult to determine the appropriate course of action or may provide incorrect handling methods based solely on diagnostic results in specific situations. Therefore, by obtaining processing suggestions based on the diagnostic data from multiple diagnostic components according to a preset diagnostic rule base, it is possible to ensure that faults are handled efficiently and accurately.

[0055] In some embodiments, such as Figure 3 As shown, the above method also includes the following during execution: S701. If a diagnostic result cannot be obtained based on the diagnostic data of multiple diagnostic components according to a preset diagnostic rule library, a short-range communication request is broadcast.

[0056] For example, the aforementioned broadcast short-range communication request can be issued when diagnostic data from multiple diagnostic components cannot yield a diagnostic result based on a preset diagnostic rule base. It should be noted that even if the diagnostic rule database contains a large number of rich diagnostic rules, as the number of preset instructions within the intelligent robot increases, the combination of its associated components becomes more complex. Inevitably, some unknown errors may arise that cannot match the rules in the diagnostic rule base, thus affecting the diagnosis, making it impossible to obtain diagnostic results, and even more difficult to provide diagnostic suggestions. In this case, the intelligent robot needs to activate its own short-range communication device to broadcast the short-range communication request. For example, it can activate its own Bluetooth device and send a pairing request to a short-range communication device; or it can activate the intelligent robot's built-in NFC and attempt to pair directly with a nearby NFC-enabled communication device.

[0057] Understandably, when the diagnostic component cannot obtain diagnostic results based on the preset diagnostic rule library, broadcasting a short-range communication request can ensure the maximum utilization of device resources within the communication range, minimize manual intervention during fault handling, and improve the intelligence of fault handling.

[0058] S702. Upon receiving the feedback message of the near-range communication request, an external status request is generated, which includes an external status shooting request and a screen sharing request.

[0059] Understandably, if the intelligent robot sends a broadcast short-range communication request as described above and receives a confirmation response, successfully connecting to a device in the environment, the intelligent robot will generate a request to the connected device for taking pictures and sharing screens.

[0060] It should be noted that generating a shooting request requires obtaining image device permissions from the connected device, and a screen sharing request requires obtaining screen sharing control permissions from the connected device. This solution can leverage other communicable devices in scenarios where the intelligent robot fails to execute commands, minimizing human intervention. Without requiring manual control of the shooting, remote diagnosis of the intelligent robot can be achieved through screen sharing from connected devices.

[0061] S703. If the received screen sharing data contains the intelligent robot, store the screen sharing data.

[0062] The image acquisition device of the matched external device collects the component information associated with the failed task of the intelligent robot, stores the shared on-screen streaming media data in the memory and then uploads it to the cloud. This allows after-sales personnel to make more accurate diagnostic suggestions by remotely using the streaming media video captured by the intelligent robot when it failed to perform the task, combined with diagnostic information, through the Internet after-sales terminal.

[0063] S704. Obtain diagnostic results based on the diagnostic data of the plurality of diagnostic components and the screen sharing data.

[0064] For example, in situations where it is difficult to complete fault diagnosis and obtain accurate results using diagnostic data obtained by the intelligent robot itself, a short-range communication request can be broadcast to request more comprehensive diagnostic data from nearby users. Upon receiving requests from surrounding intelligent terminals for external status photography and screen sharing, the diagnostic result is obtained by combining the screen-shared data from nearby connectable image acquisition devices that photograph the intelligent robot with the diagnostic data from multiple diagnostic components. Furthermore, since the data is collected from the screen-shared data during the photographing of the intelligent robot, nearby users or terminal devices do not need to actually store the photographed content in their memory, or even click a record button. It only requires invoking the photographing function of the image acquisition device to display the intelligent robot within the image range of the connectable image acquisition device. This avoids occupying the terminal storage capacity of nearby users or terminal devices assisting in the diagnostic work, reduces storage and transmission steps, improves the success rate of user-assisted diagnostic work, and increases the likelihood of assistance from nearby users and IoT devices.

[0065] It should be noted that, as a response to the above... Figure 1 In addition to the implementations of the methods shown in various related embodiments, this invention also provides a diagnostic device for intelligent robots, used for the above-mentioned... Figure 1 The methods described in the above embodiments are implemented accordingly. This device embodiment corresponds to the foregoing method embodiments. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiments one by one, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiments. Figure 4 As shown, the device includes: Acquisition unit 41 is used to acquire failed tasks generated by the intelligent robot in actual application scenarios; The detection unit 42 is used to detect diagnostic data of multiple diagnostic components associated with the failed task; The matching unit 43 is used to match diagnostic data from multiple diagnostic components according to a preset diagnostic rule library to obtain diagnostic results.

[0066] By employing the above technical solution, this invention provides a diagnostic method for intelligent robots. Addressing the current problem of intelligent robots encountering abnormalities during task execution, where existing methods rely on nearby after-sales personnel to directly inspect the robot or upload failure data for manual intervention, this invention obtains failed tasks generated by the intelligent robot in actual application scenarios. It then detects diagnostic data from multiple diagnostic components associated with the failed tasks. Furthermore, based on the diagnostic data from these components and a preset diagnostic rule base, a diagnostic result is obtained. This preset diagnostic rule base includes the correlation between combinations of diagnostic data from multiple diagnostic components and diagnostic results. This solution ensures that when a robot fails to execute a task under complex conditions, the robot and each IoT device automatically complete their own status detection and diagnosis of each core operation, aggregating the data centrally for diagnosis. The intelligent diagnostic method then achieves the desired diagnostic result.

[0067] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, an automated call-based communication method can be implemented to address the problem that existing call-based communication methods cannot meet the requirements of forwarding functionality.

[0068] This invention provides a storage medium storing a program that, when executed by a processor, implements the diagnostic method for the intelligent robot.

[0069] This invention provides a processor for running a program, wherein the program executes a diagnostic method for an intelligent robot.

[0070] This invention provides a device 50, such as... Figure 5 As shown, the device includes at least one processor 51, and at least one memory 52 and bus 53 connected to the processor; wherein the processor 51 and the memory 52 communicate with each other through the bus 53; the processor 51 is used to call program instructions in the memory to execute the above-mentioned diagnostic method for the intelligent robot.

[0071] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0072] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: acquiring failed tasks generated by the intelligent robot in a real-world application scenario; detecting diagnostic data of multiple diagnostic components associated with the failed tasks; and obtaining diagnostic results based on the diagnostic data of the multiple diagnostic components according to a preset diagnostic rule base, wherein the preset diagnostic rule base includes the association between combinations of diagnostic data from the multiple diagnostic components and diagnostic results.

[0073] Furthermore, the diagnostic data of the multiple diagnostic components associated with the failed task include: Based on the failed task, the target diagnostic package information is determined, which includes the diagnostic package name, diagnostic package type, and diagnostic package category. Retrieve the target diagnostic package from the diagnostic package database based on the target diagnostic package information; Execute the diagnostic formula corresponding to the target diagnostic package to detect diagnostic data of multiple diagnostic components associated with the failed task.

[0074] Furthermore, the above methods include: The diagnostic data includes historical functional status data of the functional modules associated with the failed task within a preset time period and / or current functional status data of the functional modules associated with the failed task.

[0075] Furthermore, the above methods include: The functional modules associated with the failed task include functional modules that support the execution of the task and functional modules that conflict with the execution of the task. Furthermore, the above methods include: The diagnostic data also includes execution environment data and execution status data associated with the task.

[0076] Furthermore, the above methods also include: Based on the diagnostic data from multiple diagnostic components, processing suggestions are obtained according to a preset diagnostic rule base, wherein the preset diagnostic rule base includes the association between the combination of diagnostic data from multiple diagnostic components and the processing suggestions.

[0077] Furthermore, the above methods also include: If a diagnostic result cannot be obtained based on diagnostic data from multiple diagnostic components according to a preset diagnostic rule library, a short-range communication request is broadcast. Upon receiving a feedback message for the near-range communication request, an external status request is generated, which includes an external status capture request and a screen sharing request. If the received screen sharing data contains the intelligent robot, the screen sharing data is stored. Diagnostic results are obtained based on the diagnostic data from the plurality of diagnostic components and the screen-shared data.

[0078] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] In a typical configuration, the device includes one or more processors (CPUs), memory, and a bus. The device may also include input / output interfaces, network interfaces, etc.

[0080] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM, and memory includes at least one memory chip. Memory is an example of computer-readable media.

[0081] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0082] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0083] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A diagnostic method of an intelligent robot, characterized by, include: Obtain failed tasks generated by the intelligent robot in actual application scenarios; The diagnostic data of multiple diagnostic components associated with the failed task are detected. The detection includes the status of all associated diagnostic components during the execution of preset routine instructions by the failed task. The status of the diagnostic components is normal, abnormal, unknown, or undefined. Based on the diagnostic data from multiple diagnostic components, a diagnostic result is obtained according to a preset diagnostic rule base, wherein the preset diagnostic rule base includes the correlation between the combination of diagnostic data from multiple diagnostic components and the diagnostic result; If a diagnostic result cannot be obtained based on diagnostic data from multiple diagnostic components according to a preset diagnostic rule library, a short-range communication request is broadcast. Upon receiving a feedback message for the near-range communication request, an external status request is generated, which includes an external status capture request and a screen sharing request. If screen sharing data is received and the intelligent robot is present in the screen sharing data, the screen sharing data is stored. Diagnostic results are obtained based on the diagnostic data from the plurality of diagnostic components and the screen-shared data.

2. The method of claim 1, wherein, The diagnostic data of the multiple diagnostic components associated with the failed task includes: Based on the failed task, the target diagnostic package information is determined, which includes the diagnostic package name, diagnostic package type, and diagnostic package category. Retrieve the target diagnostic package from the diagnostic package database based on the target diagnostic package information; Execute the diagnostic formula corresponding to the target diagnostic package to detect diagnostic data of multiple diagnostic components associated with the failed task.

3. The method of claim 1, wherein, The diagnostic data includes historical functional status data of the functional modules associated with the failed task within a preset time period and / or current functional status data of the functional modules associated with the failed task.

4. The method according to claim 1, characterized in that, The functional modules associated with the failed task include functional modules that support the execution of the failed task and functional modules that conflict with the execution of the failed task.

5. The method according to claim 3, characterized in that, The diagnostic data also includes execution environment data and execution status data associated with the failed task.

6. The method according to claim 1, characterized in that, Also includes: Based on the diagnostic data from multiple diagnostic components, processing suggestions are obtained according to a preset diagnostic rule base, wherein the preset diagnostic rule base includes the association between the combination of diagnostic data from multiple diagnostic components and the processing suggestions.

7. A diagnostic device for an intelligent robot, characterized in that, include: The acquisition unit is used to acquire failed tasks generated by the intelligent robot in actual application scenarios; The detection unit is used to detect diagnostic data of multiple diagnostic components associated with the failed task. The detection includes the status of all associated diagnostic components during the execution of preset routine instructions by the failed task. The status of the diagnostic components is normal, abnormal, unknown, or undefined. A matching unit is used to match diagnostic data from multiple diagnostic components according to a preset diagnostic rule base to obtain diagnostic results, wherein the preset diagnostic rule base includes the association between combinations of diagnostic data from multiple diagnostic components and diagnostic results; If a diagnostic result cannot be obtained based on diagnostic data from multiple diagnostic components according to a preset diagnostic rule library, a short-range communication request is broadcast. Upon receiving a feedback message for the near-range communication request, an external status request is generated, which includes an external status capture request and a screen sharing request. If screen sharing data is received and the intelligent robot is present in the screen sharing data, the screen sharing data is stored. Diagnostic results are obtained based on the diagnostic data from the plurality of diagnostic components and the screen-shared data.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed by a processor, it implements the diagnostic method for the intelligent robot as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the diagnostic method for the intelligent robot as described in any one of claims 1 to 6.

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