Robot task status monitoring method, device and related equipment

Abstract

The present disclosure provides a robot task status monitoring method, device and related equipment, which relate to the field of Internet of Things technology. The method includes: obtaining task information of a robot task instance to be executed; processing the task information of the task instance to be executed, and determining the action sequence of the robot according to the task information; determining the first key element corresponding to the task instance to be executed according to the action sequence of the robot; finding the first associated element having an associated relationship with the first key element in a pre-established ontology knowledge base; determining the first attribute information of the robot task instance to be executed according to the first key element and the first associated element; determining the task status information of the robot task instance to be executed according to the first attribute information of the robot task instance to be executed. The present disclosure can overcome the problem that the related technology cannot timely monitor the changes in the element attributes in the task due to the sudden change of the task execution environment.

Description

Background Art

[0002] Smart space is a typical environment for studying the principles and technologies of harmonious human-computer interaction. In smart space, users can interact with various information sources (including devices and data). With the development of artificial intelligence technology, the integration of man, machine and object in space will become closer and closer.

[0003] In smart spaces, robots have the ability to interact intelligently with other devices and objects and can perform complex tasks. In the actual task execution process, due to the numerous elements in smart spaces and the complex and changeable environment, the task execution status depends not only on the status of the robot and the target object itself, but also on many factors, especially sudden situations from the environment. Therefore, in related technologies, when the robot is performing a task, the system cannot adjust the robot's task status in time due to sudden changes in the environment.

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

[0005] The present disclosure provides a robot task status monitoring method, device and related equipment, which at least to a certain extent overcome the problem that related technologies cannot timely monitor changes in element attributes in a task due to sudden changes in the task execution environment.

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

[0007] According to one aspect of the present disclosure, a robot task status monitoring method is provided, the method comprising: obtaining task information of the robot's task instance to be performed; processing the task information of the task instance to be performed, and determining the robot's action sequence according to the task information; determining a first key element corresponding to the task instance to be performed according to the robot's action sequence; searching for a first associated element having an associated relationship with the key element in a pre-established ontology knowledge base, the ontology knowledge base comprising: a plurality of key elements and associated elements having an associated relationship with each key element; determining first attribute information of the robot's task instance to be performed according to the first key element and the first associated element; determining task status information of the robot's task instance to be performed according to the first attribute information of the robot's task instance to be performed.

[0008] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, the task information of the task instance to be executed is processed, and the action sequence of the robot is determined according to the task information, including: generating an action sequence corresponding to the task instance to be executed according to the task information of the task instance to be executed, wherein the action sequence includes multiple actions to be executed by the robot.

[0009] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, the key elements corresponding to the task instance to be executed are determined according to the action sequence of the robot, including: inputting multiple action sequences to be executed by the robot into a pre-trained element processing model, and outputting the key elements corresponding to the task instance to be executed, wherein the element processing model is used to analyze the action sequence to be executed by the robot and extract the key elements corresponding to the task instance to be executed.

[0010] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, before determining the first attribute information of the robot's task instance to be performed according to the first key element and the first associated element, the method also includes: obtaining target attribute information of all corresponding associated elements in the robot's task instance to be performed.

[0011] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, the task status information includes: a normal state and an abnormal state, and the task status information of the robot task instance to be performed is determined according to the first attribute information of the robot task instance to be performed, including: when the first attribute information of the robot task instance to be performed is consistent with the target attribute information, determining that the task status information of the robot task instance to be performed is a normal state; when the first attribute information of the robot task instance to be performed is inconsistent with the target attribute information, determining that the task status information of the robot task instance to be performed is an abnormal state.

[0012] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, the abnormal state of the task status information includes: an interruption state or a failure state. When the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information, the task status information of the robot's task instance to be executed is determined to be an abnormal state, including: when the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information, the task status information of the robot's task instance to be executed is determined to be an interruption state; when the task status information of the robot's task instance to be executed is an interruption state, repeatedly execute the following steps until a preset stop condition is met, and determine that the task status information of the robot's task instance to be executed is a failure state, wherein the preset stop condition is that the second attribute information is inconsistent with the target attribute information and the preset stop condition is that the number of repetitions is greater than a preset threshold; when the task status information of the robot's task instance to be executed is an interruption state, determine the interruption position of the robot in the interruption state; update the robot's task instance to be executed according to the interruption position The task information of the instance is determined; according to the updated task information, the updated action sequence of the robot is determined; according to the updated action sequence of the robot, the second key element corresponding to the updated task instance to be executed is determined; the second associated element having an associated relationship with the second key element is found in the ontology knowledge base; according to the second key element and the second associated element, the second attribute information of the task instance to be executed after the robot is updated is determined; it is determined whether the second attribute information of the task instance to be executed after the robot is updated is consistent with the target attribute information; if the second attribute information of the task instance to be executed after the robot is updated is consistent with the target attribute information, the task state information of the task instance to be executed after the robot is updated is determined to be a normal state; if the second attribute information of the task instance to be executed after the robot is updated is inconsistent with the target attribute information, the interruption position of the robot in the interruption state is re-determined, and the second attribute information of the task instance to be executed after the robot is updated is compared with the target attribute information to obtain a comparison result.

[0013] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, after determining the task status information of the robot's task instance to be performed according to the first attribute information of the robot's task instance to be performed, the method also includes: when the task status information of the robot's task instance to be performed is in a normal state, generating a first monitoring instruction, the first monitoring instruction is used to continue monitoring the first attribute information of the robot's task instance to be performed; when the task status information of the robot's task instance to be performed is in an interrupted state, generating a second monitoring instruction, the second monitoring instruction is used to monitor the second attribute information of the robot's updated task instance to be performed.

[0014] According to another aspect of the present disclosure, a robot task status monitoring device is also provided, which includes: a task information acquisition module, used to acquire the task information of the robot's task instance to be executed; an action sequence processing module, used to process the task information of the task instance to be executed, and determine the action sequence of the robot according to the task information; a key element determination module, used to determine the first key element corresponding to the task instance to be executed according to the action sequence of the robot; an associated element determination module, used to find an associated element having an associated relationship with the key element in a pre-established ontology knowledge base, the ontology knowledge base containing: multiple key elements and a first associated element having an associated relationship with each key element; an attribute information determination module, used to determine the first attribute information of the robot's task instance to be executed according to the first key element and the first associated element; a task status determination module, used to determine the task status information of the robot's task instance to be executed according to the first attribute information of the robot's task instance to be executed.

[0015] According to another aspect of the present disclosure, there is also provided an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned robot task status monitoring methods by executing the executable instructions.

[0016] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any one of the above-mentioned robot task status monitoring methods is implemented.

[0017] The robot task status monitoring method, device and related equipment provided in the embodiments of the present invention first obtain the task information of the robot's task instance to be executed; secondly, process the task information of the task instance to be executed, and determine the robot's action sequence according to the task information; then, determine the first key element corresponding to the task instance to be executed according to the robot's action sequence; then, find out the first associated element that has an association relationship with the first key element in a pre-established ontology knowledge base; then, determine the first attribute information of the robot's task instance to be executed according to the first key element and the first associated element; finally, determine the task status information of the robot's task instance to be executed according to the first attribute information of the robot's task instance to be executed.

[0018] Compared with the related art, in the actual task execution process, the robot cannot timely monitor the changes in the element attributes in the task state due to sudden changes in the environment, resulting in the robot being unable to adjust the task state in time. The disclosed embodiment monitors the attribute information of the robot in the task execution, and determines the attribute information of the robot's task instance to be executed based on the first key element and the first associated element that has an association relationship with the robot's task instance to be executed. By real-time monitoring the attribute information of the robot's task instance to be executed, the task state information of the robot's task instance to be executed is determined, so as to timely correct and improve the task execution decision, improve the defects of insufficient intelligence, real-time and task integrity encountered in task execution, and make the intelligent service robot in the intelligent space more efficient and intelligent when performing tasks.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0021] Figure 1 A schematic diagram of a system architecture of a method for monitoring robot task status according to an embodiment of the present disclosure is shown;

[0022] Figure 2 A schematic diagram of a robot task status monitoring method according to an embodiment of the present disclosure is shown;

[0023] Figure 3 A schematic diagram showing a method for obtaining key elements corresponding to and associated with a task instance to be executed in an embodiment of the present disclosure;

[0024] Figure 4 A schematic diagram of a robot task status monitoring system process in an embodiment of the present disclosure is shown;

[0025] Figure 5 A schematic diagram of adjusting task planning according to task status information in an embodiment of the present disclosure is shown;

[0026] Figure 6 A schematic diagram of a robot task status monitoring device in an embodiment of the present disclosure is shown;

[0027] Figure 7 A schematic diagram of an electronic device using a robot task status monitoring method in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0028] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example 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.

[0029] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0030] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0031] Figure 1 FIG. 2 shows an exemplary application system architecture diagram to which the robot task state monitoring method in the embodiment of the present disclosure can be applied. Figure 1 As shown, the system architecture may include a monitoring device 101 , a network 102 , and a robot 103 .

[0032] The network 102 is a medium for providing a communication link between the monitoring device 101 and the robot 103, and can be a wired network or a wireless network.

[0033] Optionally, the wireless network or wired network described above uses standard communication technology and / or protocol. The network is usually the Internet, but it can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a dedicated network or any combination of a virtual private network). In some embodiments, technologies and / or formats including Hyper Text Mark-up Language (HTML), Extensible Markup Language (XML), etc. are used to represent data exchanged through the network. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPsec) can also be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above data communication technologies.

[0034] The monitoring device 101 may be various electronic devices, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, wearable devices, augmented reality devices, virtual reality devices, etc.

[0035] Optionally, the client of the application installed in different monitoring devices 101 is the same, or the client of the same type of application based on different operating systems. Based on the different terminal platforms, the specific form of the client of the application can also be different, for example, the application client can be a mobile client, a PC client, etc.

[0036] The robot 103 may be a robot that provides various services, such as a background management robot that provides support for the device operated by the user using the monitoring device 101. The background management robot may analyze and process the received request and other data, and feed back the processing results to the monitoring device.

[0037] Optionally, the robot can be an independent physical robot, or a robot cluster or distributed system composed of multiple physical robots, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The monitoring device can be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited to this. The monitoring device and the robot can be directly or indirectly connected via wired or wireless communication, which is not limited in this application.

[0038] Those skilled in the art will know that Figure 1 The number of monitoring devices 101, networks 102 and robots 103 is only illustrative, and any number of monitoring devices, networks and robots may be provided according to actual needs. This is not limited in the embodiments of the present disclosure.

[0039] Under the above system architecture, a robot task status monitoring method is provided in an embodiment of the present disclosure, and the method can be executed by any electronic device with computing and processing capabilities.

[0040] In some embodiments, the robot task status monitoring method provided in the embodiments of the present disclosure can be executed by the monitoring device of the above-mentioned system architecture; in other embodiments, the robot task status monitoring method provided in the embodiments of the present disclosure can be executed by the robot in the above-mentioned system architecture; in other embodiments, the robot task status monitoring method provided in the embodiments of the present disclosure can be implemented by the monitoring device and the robot in the above-mentioned system architecture through interaction.

[0041] Below, each step of the robot task state monitoring method in this example implementation will be described in more detail with reference to the accompanying drawings and embodiments.

[0042] First, a control method is provided in the disclosed embodiments for monitoring the task status of a robot in an intelligent space, which can be applied but is not limited to. In the related art, during the actual task execution of the robot, due to the large number of elements in the intelligent space and the complex and changeable environment, the task execution status depends not only on the status of the robot and the target object itself, but also on many factors, especially emergencies from the environment. The following method of the present application can effectively avoid the problem in the related art that changes in the attributes of the elements in the task cannot be monitored in time when the task execution environment changes.

[0043] Figure 2 A schematic diagram of a robot task status monitoring method in an embodiment of the present disclosure is shown. Figure 2 As shown, the robot task state monitoring method provided in the embodiment of the present disclosure includes the following steps:

[0044] S201, obtaining task information of a task instance to be executed by the robot.

[0045] In some embodiments, the task information of the robot's task instance to be performed acquired in the embodiments of the present disclosure may be the task information generated by receiving a task instruction from a user terminal and then querying the relevant static knowledge defined by the current task instance in the knowledge base according to the received task instruction, wherein the static knowledge is constructed through common sense knowledge and task background, and is used to describe concepts such as abstract categories, inherent attributes, and relationships between categories, as well as relationships between concepts. For example, the task information of the robot's task instance to be performed is "get a glass of water from the kitchen."

[0046] S202, processing the task information of the task instance to be executed, and determining the action sequence of the robot according to the task information.

[0047] In some embodiments, the robot in the disclosed embodiments receives task instructions from the user end, and generates task information by querying the relevant static knowledge defined in the current task instance in the knowledge base. The disclosed embodiments process the generated task information to obtain the robot's action sequence. For example, the task information "get a glass of water from the kitchen" is processed to obtain the action sequence "walk through the living room", "walk through the corridor", "find the table where the glass of water is placed", etc.

[0048] S203: Determine a first key element corresponding to the task instance to be executed according to the action sequence of the robot.

[0049] It should be noted that the first key element in the embodiment of the present disclosure can be a noun in any action sequence. For example, according to the action sequences "walk through the living room", "walk through the corridor", "reach the kitchen", "find the table for the cup of water", etc., determine the first key elements such as "living room", "corridor", "kitchen", "table", "cup of water" corresponding to the task instance to be executed.

[0050] S204, searching for a first associated element associated with the key element in a pre-established ontology knowledge base, wherein the ontology knowledge base includes: a plurality of key elements and associated elements associated with each key element.

[0051] In some embodiments, the disclosed embodiment obtains the first key element corresponding to the task instance to be executed by processing the task information obtained by the robot. In addition to the first key element that affects the task execution, the other symbiotic elements associated with the key element in the spatial environment will also affect the task execution. Therefore, the disclosed embodiment matches the obtained first key element with a pre-established ontology knowledge base including multiple key elements and associated elements having an associated relationship with each key element, so as to determine the first associated element having an associated relationship with the first key element. For example, whether there are obstacles in the corridor that prevent the robot from continuing to execute the task instance, the switch state of the study door can affect the robot's range of activities, and the items on the desk can affect the taking of the water cup, etc. Therefore, the first associated element having an associated relationship with the first key element is determined to be the "obstacle" in the corridor, the "door" in the study, the "item" on the desk, etc.

[0052] S205: Determine first attribute information of the task instance to be performed by the robot according to the first key element and the first associated element.

[0053] In some embodiments, the first attribute information in the embodiments of the present disclosure may be the attribute information of any first key element and / or any first associated element. Specifically, the embodiments of the present disclosure may input the first key element and the first associated element into a pre-built neural network model, and output the attribute information of the key element associated elements. For example, "obstacles" in the corridor, "doors" in the study, and "items" on the desk may be input into the neural network model to determine whether the obstacles in the corridor hinder the robot's movement, the "switch status" of the study door, and whether the items on the desk hinder the water cup, etc.

[0054] S206: Determine task state information of the task instance to be performed by the robot according to the first attribute information of the task instance to be performed by the robot.

[0055] In some embodiments, due to the large number of elements in the smart space and the complex and changeable environment, in order to ensure the execution of the task, it is necessary to monitor the first attribute information of the robot's task instance to be executed in real time, so that when the first attribute information of the robot's task instance to be executed changes, the task execution can be changed or optimized. For example, if it is determined that the door of the study is closed, or if an obstacle appears during the robot's navigation process, the navigation route needs to be changed. If a cup is suddenly hit the ground and broken during the execution of the task, the task can be stopped.

[0056] The robot task status monitoring method in the disclosed embodiment first obtains the task information of the robot's task instance to be executed; secondly, processes the task information of the task instance to be executed, and determines the robot's action sequence according to the task information; then, determines the first key element corresponding to the task instance to be executed according to the robot's action sequence; thereafter, finds the first associated element having an associated relationship with the first key element in a pre-established ontology knowledge base; then, determines the first attribute information of the robot's task instance to be executed according to the first key element and the first associated element; finally, determines the task status information of the robot's task instance to be executed according to the first attribute information of the robot's task instance to be executed.

[0057] Compared with the related art, in the actual task execution process, the robot cannot timely monitor the changes in the element attributes in the task state due to sudden changes in the environment, resulting in the robot being unable to adjust the task state in time. The disclosed embodiment monitors the attribute information of the robot in the task execution, and determines the attribute information of the robot's task instance to be executed based on the first key element and the first associated element that has an association relationship with the robot's task instance to be executed. By real-time monitoring the attribute information of the robot's task instance to be executed, the task state information of the robot's task instance to be executed is determined, so as to timely correct and improve the task execution decision, improve the defects of insufficient intelligence, real-time and task integrity encountered in task execution, and make the intelligent service robot in the intelligent space more efficient and intelligent when performing tasks.

[0058] In some embodiments, Figure 3 As shown, the method for obtaining the first key element in the embodiment of the present disclosure includes the following steps:

[0059] S301, generating an action sequence corresponding to the task instance to be executed according to the task information of the task instance to be executed, wherein the action sequence includes multiple actions to be executed by the robot;

[0060] S302, input multiple action sequences to be executed by the robot into a pre-trained element processing model, and output key elements corresponding to the task instances to be executed, wherein the element processing model is used to analyze the action sequences to be executed by the robot and extract the first key element corresponding to the task instances to be executed.

[0061] In some embodiments, the first key element in the embodiment of the present disclosure is some spatial elements that the robot may encounter during the execution of the task. Specifically, the embodiment of the present disclosure can decompose the task information of the task instance to be executed according to a pre-built action extraction model, and obtain the action sequence corresponding to the task instance to be executed. For example, the action sequence corresponding to the task instance to be executed in the embodiment of the present disclosure can be: starting from the living room, crossing the corridor, entering the study, picking up the water cup on the desk, and returning to the living room. According to the above action sequence, the action sequence of the above robot is input into the pre-trained element processing model, and the key elements corresponding to the task instance to be executed are output, and the key elements "living room", "corridor", "desk" and "water cup" corresponding to the task instance to be executed are obtained.

[0062] In some embodiments, the first key element corresponding to the task instance to be executed is determined according to the robot's action sequence, including: inputting multiple action sequences to be executed by the robot into a pre-trained element processing model, and outputting the key elements corresponding to the task instance to be executed, wherein the element processing model is used to analyze the action sequence to be executed by the robot and extract the first key element corresponding to the task instance to be executed.

[0063] In more detail, the element processing model in the embodiments of the present disclosure can be but is not limited to a semantic model. The embodiments of the present disclosure take the semantic model as an example, combine the action sequence corresponding to the task instance to be executed with the action extraction model, and then extract the first key element that is consistent with the task instance to be executed. For example, the action sequence: starting from the living room, crossing the corridor, entering the study, picking up the water cup on the desk, and returning to the living room is input into the pre-trained semantic model, and combined with the semantics, the key elements "living room", "corridor", "desk" and "water cup" in the above action sequence that are consistent with the robot's task instance to be executed are extracted.

[0064] In some embodiments, before determining the first attribute information of the robot's task instance to be performed based on the first key element and the first associated element, the robot task status monitoring method in the disclosed embodiment also includes: obtaining target attribute information of all corresponding associated elements in the robot's task instance to be performed.

[0065] In some embodiments, the robot task status monitoring method in the disclosed embodiments can be completed by a pre-built task monitoring model, such as Figure 4As shown, the task monitoring model includes: a task monitoring management layer, a key element extraction layer, an associated element analysis layer, an associated attribute extraction layer and an associated attribute monitoring layer. Specifically, after obtaining the task information, the embodiment of the present disclosure outputs the created task instance to the task monitoring management layer for monitoring and management, wherein the task monitoring management layer mainly tracks, monitors and manages the current task instance, including decomposing the task instance into an action sequence, binding element attributes related to the task instance, performing element attribute evaluation, and updating the task status in real time when the element attributes change.

[0066] Specifically, the task monitoring management layer in the embodiment of the present disclosure receives a task instance and preliminarily decomposes the task into an action sequence; the key element extraction layer extracts the first key element involved in completing the task instance based on the task action sequence and in combination with the element processing model (for example, the semantic model) of the current space; the associated element analysis layer extracts the first associated element having an associated relationship with the task instance based on the first key element and in combination with the ontology knowledge base of the current space; and the associated attribute extraction layer determines the first attribute information of the robot's task instance to be executed based on the input first key element and the first associated element; the associated attribute extraction layer outputs the first attribute information of the robot's task instance to be executed to the associated attribute monitoring layer to complete the monitoring of the first attribute information of the robot's task instance to be executed; the associated attribute extraction layer outputs the first attribute information of the robot's task instance to be executed to the task monitoring management layer to complete the binding of the task instance with the action attribute information of multiple elements; the associated attribute monitoring layer outputs the monitoring result of the first attribute information of the robot's task instance to be executed to the task monitoring management layer; the task monitoring management layer determines the task status information of the robot's task instance to be executed based on the first attribute information of the current robot's task instance to be executed.

[0067] In some embodiments, the disclosed embodiments decompose the task information of the task instance to be performed by the robot to obtain an action sequence, and obtain the first associated element having an associated relationship with each first key element through the first key element involved in the task instance, and record the first attribute information that can complete the task instance as target attribute information. For example, the disclosed embodiments can record the door of the study in an open state as target attribute information, etc.

[0068] In some embodiments, the task status information in the embodiments of the present disclosure includes: a normal state and an abnormal state. The task status information of the robot's task instance to be executed is determined based on the first attribute information of the robot's task instance to be executed, including: when the first attribute information of the robot's task instance to be executed is consistent with the target attribute information, determining that the task status information of the robot's task instance to be executed is a normal state; when the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information, determining that the task status information of the robot's task instance to be executed is an abnormal state.

[0069] In some embodiments, the disclosed embodiments compare the determined first attribute information of the robot's task instance to be executed with the target attribute information to determine whether the acquired first attribute information of the robot's task instance to be executed can complete the robot's task instance to be executed. If the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information, it is determined that the element attributes in the robot's task instance to be executed have changed and the task instance cannot be completed. It is determined that the task status information of the robot's task instance to be executed is in an abnormal state, and the task plan can be adjusted in time according to the task status information of the task instance to be executed.

[0070] It should be noted that the target attribute information in the embodiment of the present disclosure is a preset range generated by the key elements determined according to the action sequence of the robot and the corresponding associated elements found in the ontology knowledge base. For example, according to the above-mentioned key element "study" and the associated element "door", a range of "the angle range of the door opening when the robot can pass through the study (45°~90°)" is generated, and the first attribute information of the robot's task to be performed instance collected on site is compared with the above-mentioned target attribute information. If the first attribute information of the robot's task to be performed instance collected on site is within the range of the target attribute information, it is determined that the robot can normally complete the task instance to be performed. If the first attribute information of the robot's task to be performed instance collected on site is not within the range of the target attribute information, it is determined that the robot cannot normally complete the task instance to be performed. For example, the first attribute information of the robot's task to be performed instance collected on site is "the door opening angle is 0 (the door is closed)", the first attribute information (0°) is not between 45° and 90°, and the robot cannot normally complete the task instance to be performed.

[0071] In some embodiments, the abnormal state of the task status information in the embodiments of the present disclosure includes: an interruption state and a failure state. When the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information, the task status information of the robot's task instance to be executed is determined to be an abnormal state, including: when the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information, the task status information of the robot's task instance to be executed is determined to be an interruption state; when the task status information of the robot's task instance to be executed is an interruption state, repeatedly execute the following steps until the preset stop condition is met, and determine that the task status information of the robot's task instance to be executed is a failure state, wherein the preset stop condition is that the second attribute information is inconsistent with the target attribute information and the preset stop condition is that the number of repetitions is greater than a preset threshold; when the task status information of the robot's task instance to be executed is an interruption state, determine the interruption position of the robot in the interruption state; update the robot's task instance to be executed according to the interruption position The method comprises the steps of: determining the task information of the task instance to be executed after the robot updates the task information of the task instance; determining the updated action sequence of the robot according to the updated task information; determining the second key element corresponding to the updated task instance to be executed according to the updated action sequence of the robot; finding the second associated element having an associated relationship with the second key element in the ontology knowledge base; determining the second attribute information of the task instance to be executed after the robot updates the task information according to the second key element and the second associated element; judging whether the second attribute information of the task instance to be executed after the robot updates the task information is consistent with the target attribute information; if the second attribute information of the task instance to be executed after the robot updates the task information is consistent with the target attribute information, determining that the task state information of the task instance to be executed after the robot updates the task status information is a normal state; if the second attribute information of the task instance to be executed after the robot updates the task information is inconsistent with the target attribute information, re-determining the interruption position of the robot in the interruption state, and comparing the second attribute information of the task instance to be executed after the robot updates the task information with the target attribute information to obtain a comparison result.

[0072] In more detail, if the first attribute information of the robot's task instance to be executed in the embodiment of the present disclosure is inconsistent with the target attribute information, it means that the robot cannot normally complete the task instance to be executed. Therefore, when the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information, the task state information of the robot's task instance to be executed is determined to be an interrupted state, and the interruption position of the robot in the interrupted state is determined, and the task information of the robot's task instance to be executed is redetermined at the interrupted position, the robot's action sequence is updated according to the updated task information, the second key element and the second associated element are re-determined according to the updated work sequence, and the updated task to be executed is determined according to the updated second key element and the second associated element. The second attribute information of the instance is determined, and whether the second attribute information is consistent with the target attribute information. If the second attribute information of the updated robot task instance to be executed is consistent with the target attribute information, then the task status information of the updated robot task instance to be executed is determined to be in a normal state, and the task instance continues to be executed; if the second attribute information of the updated robot task instance to be executed is inconsistent with the target attribute information, then the interruption position of the robot in the interruption state is repeatedly determined, and the second attribute information of the updated robot task instance to be executed is compared with the target attribute information to obtain a comparison result. If the second attribute information is inconsistent with the target attribute information and the number of repetitions is greater than a preset threshold, then the task status information of the robot task instance to be executed is determined to be in a failed state.

[0073] In some embodiments, after determining the task status information of the robot's task instance to be executed based on the status attribute information of the associated elements, the method also includes: when the task status information of the robot's task instance to be executed is in a normal state, generating a first monitoring instruction, the first monitoring instruction is used to continue monitoring the status attribute information of the associated elements; when the task status information of the robot's task instance to be executed is in an interrupted state, generating a second monitoring instruction, the second monitoring instruction is used to monitor the second attribute information of the robot's updated task instance to be executed.

[0074] More specifically, Figure 5As shown, the task status information in the disclosed embodiment includes a normal state, an interrupted state and a failed state. If the monitored task status information is a normal state, the robot will continue to execute the task instance; if the monitored task status information is an interrupted state, the robot will first suspend the execution of the above task instance, re-plan the action sequence of the task and issue a request to resume the execution of the above task instance. If the number of repetitions is within the preset threshold and it is judged that the updated second attribute information is consistent with the target attribute information, it can be determined that the action sequence of the re-planned task can execute the task normally. The robot continues to execute the task according to the re-planned action sequence, and monitors the updated second attribute information of the task instance to be executed in real time. If the number of recovery requests exceeds the preset threshold, it is determined that the task status information is a failed state; if the monitored task status information is a failed state, all correspondingly associated elements in the robot's task instance to be executed are unbound from their corresponding target attribute information, and the monitoring resources are released to control the robot to stop executing the task instance.

[0075] Based on the same inventive concept, the present disclosure also provides a robot task state monitoring device, such as the following embodiment. Since the principle of solving the problem in the device embodiment is similar to that in the above method embodiment, the implementation of the device embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be repeated.

[0076] Figure 6 A schematic diagram of a robot task status monitoring device in an embodiment of the present disclosure is shown. Figure 6 As shown, the device comprises:

[0077] The task information acquisition module 601 is used to acquire the task information of the task instance to be performed by the robot;

[0078] An action sequence processing module 602 is used to process the task information of the task instance to be executed and determine the action sequence of the robot according to the task information;

[0079] A key element determination module 603 is used to determine a first key element corresponding to the task instance to be executed according to the action sequence of the robot;

[0080] The associated element determination module 604 is used to find out the first associated element associated with the first key element in a pre-established ontology knowledge base, wherein the ontology knowledge base includes: a plurality of key elements and associated elements associated with each key element;

[0081] An attribute information determination module 605 is used to determine first attribute information of the robot's task instance to be performed according to the first key element and the first associated element;

[0082] The task status determination module 606 is used to determine the task status information of the task instance to be performed by the robot according to the first attribute information of the task instance to be performed by the robot.

[0083] The robot task status monitoring device provided in the embodiments of the present disclosure obtains the task information of the robot's task instance to be executed through a task information acquisition module; processes the task information of the task instance to be executed through an action sequence processing module, and determines the robot's action sequence according to the task information; determines the first key element corresponding to the task instance to be executed according to the robot's action sequence through a key element determination module; finds out the first associated element having an associated relationship with the first key element in a pre-established ontology knowledge base through an associated element determination module; determines the first attribute information of the robot's task instance to be executed according to the first key element and the first associated element through an attribute information determination module; and determines the task status information of the robot's task instance to be executed according to the first attribute information of the robot's task instance to be executed through a task status determination module.

[0084] Compared with the related art, in the actual task execution process, the robot cannot timely monitor the changes in the element attributes in the task state due to sudden changes in the environment, resulting in the robot being unable to adjust the task state in time. The disclosed embodiment monitors the attribute information of the robot in the task execution, and determines the attribute information of the robot's task instance to be executed based on the first key element and the first associated element that has an association relationship with the robot's task instance to be executed. By real-time monitoring the attribute information of the robot's task instance to be executed, the task state information of the robot's task instance to be executed is determined, so as to timely correct and improve the task execution decision, improve the defects of insufficient intelligence, real-time and task integrity encountered in task execution, and make the intelligent service robot in the intelligent space more efficient and intelligent when performing tasks.

[0085] In some embodiments, the action sequence processing module in the embodiments of the present disclosure is also used to generate an action sequence corresponding to the task instance to be executed based on the task information of the task instance to be executed, and the action sequence includes multiple actions to be executed by the robot.

[0086] In some embodiments, the key element determination module in the embodiments of the present disclosure is also used to input multiple action sequences to be executed by the robot into a pre-trained element processing model, and output key elements corresponding to the task instances to be executed, wherein the element processing model is used to analyze the action sequences to be executed by the robot and extract the first key element corresponding to the task instances to be executed.

[0087] In some embodiments, the robot task status monitoring device in the embodiment of the present disclosure further includes: a target attribute information acquisition module for acquiring target attribute information of all corresponding associated elements in the robot task instance to be executed before determining the first attribute information of the robot task instance to be executed based on the first key element and the first associated element.

[0088] In some embodiments, the task status information in the embodiments of the present disclosure includes: normal state and abnormal state. The above-mentioned task status determination module is also used to determine that the task status information of the robot's task instance to be executed is a normal state when the first attribute information of the robot's task instance to be executed is consistent with the target attribute information; when the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information, determine that the task status information of the robot's task instance to be executed is an abnormal state.

[0089] In some embodiments, the abnormal states of the task status information in the embodiments of the present disclosure include: an interruption state and a failure state. The above-mentioned task status determination module is also used to determine that the task status information of the robot's task instance to be executed is an interruption state when the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information; when the task status information of the robot's task instance to be executed is an interruption state, repeatedly execute the following steps until the preset stop condition is met, and determine that the task status information of the robot's task instance to be executed is a failure state, wherein the preset stop condition is that the second attribute information is inconsistent with the target attribute information and the preset stop condition is that the number of repetitions is greater than a preset threshold; when the task status information of the robot's task instance to be executed is an interruption state, determine the interruption position of the robot in the interruption state; update the task information of the robot's task instance to be executed according to the interruption position; determine the robot according to the updated task information An updated action sequence; according to the updated action sequence of the robot, determine the second key element corresponding to the updated task instance to be executed; find out the second associated element with the association relationship with the second key element in the ontology knowledge base; determine the second attribute information of the task instance to be executed after the robot is updated according to the second key element and the second associated element; judge whether the second attribute information of the task instance to be executed after the robot is updated is consistent with the target attribute information; if the second attribute information of the task instance to be executed after the robot is updated is consistent with the target attribute information, determine that the task state information of the task instance to be executed after the robot is updated is a normal state; if the second attribute information of the task instance to be executed after the robot is updated is inconsistent with the target attribute information, re-determine the interruption position of the robot in the interruption state, and compare the second attribute information of the task instance to be executed after the robot is updated with the target attribute information to obtain a comparison result.

[0090] In some embodiments, in the embodiments of the present disclosure, after determining the task status information of the robot's task instance to be executed based on the first attribute information of the robot's task instance to be executed, the device also includes: a first monitoring module, which is used to generate a first monitoring instruction when the task status information of the robot's task instance to be executed is in a normal state, and the first monitoring instruction is used to continue monitoring the first attribute information of the robot's task instance to be executed; a second monitoring module, which is used to generate a second monitoring instruction when the task status information of the robot's task instance to be executed is in an interrupted state, and the second monitoring instruction is used to monitor the second attribute information of the robot's updated task instance to be executed.

[0091] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0092] Refer to the following Figure 7 An electronic device 700 according to this embodiment of the present disclosure is described. Figure 7 The electronic device 700 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0093] like Figure 7 As shown, the electronic device 700 is in the form of a general computing device. The components of the electronic device 700 may include but are not limited to: at least one processing unit 701, at least one storage unit 702, and a bus 703 connecting different system components (including the storage unit 702 and the processing unit 701).

[0094] The storage unit stores program codes, which can be executed by the processing unit 701, so that the processing unit 701 executes the steps described in the above “exemplary method” section of this specification according to various exemplary embodiments of the present disclosure.

[0095] In some embodiments, when the electronic device is used to control, for example, the question-answering method based on the knowledge graph disclosed above, the processing unit 701 may perform the following steps of the above method embodiment:

[0096] Get the task information of the robot's task instance to be executed.

[0097] The task information of the task instance to be executed is processed, and the action sequence of the robot is determined according to the task information.

[0098] A first key element corresponding to the task instance to be executed is determined according to the action sequence of the robot.

[0099] A first associated element having an associated relationship with the first key element is found in a pre-established ontology knowledge base, wherein the ontology knowledge base includes: a plurality of key elements and associated elements having an associated relationship with each key element.

[0100] Determine first attribute information of the task instance to be performed by the robot according to the first key element and the first associated element.

[0101] According to the first attribute information of the task instance to be performed by the robot, task state information of the task instance to be performed by the robot is determined.

[0102] The storage unit 702 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 7021 and / or a cache storage unit 7022 , and may further include a read-only storage unit (ROM) 7023 .

[0103] The storage unit 702 may also include a program / utility 7024 having a set (at least one) of program modules 7025, such program modules 7025 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0104] Bus 703 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0105] The electronic device 700 may also communicate with one or more external devices 704 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 700, and / or may communicate with any device that enables the electronic device 700 to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface 705. In addition, the electronic device 700 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 706. As shown, the network adapter 706 communicates with other modules of the electronic device 700 through a bus 703. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0106] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0107] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart may be implemented as a computer program product, which includes: a computer program, which implements the above-mentioned robot task state monitoring method when the computer program is executed by a processor.

[0108] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. A program product capable of implementing the above-mentioned method of the present disclosure is stored thereon. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a monitoring device, the program code is used to cause the monitoring device to execute the steps according to various exemplary implementations of the present disclosure described in the above-mentioned "Exemplary Method" section of this specification.

[0109] More specific examples of computer-readable storage media in the present disclosure may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0110] In the present disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein a readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0111] Alternatively, the program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0112] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0113] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0114] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0115] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0116] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A robot task status monitoring method, characterized in that: include: Obtaining task information of the task instance to be performed by the robot; Processing the task information of the task instance to be executed, and determining the action sequence of the robot according to the task information; Determining a first key element corresponding to and associated with the task instance to be performed according to the action sequence of the robot, wherein the first key element includes a noun in the action sequence of the robot; Finding a first associated element associated with the first key element in a pre-established ontology knowledge base, wherein the ontology knowledge base includes: a plurality of key elements and associated elements associated with each key element; Determine first attribute information of the task instance to be performed by the robot according to the first key element and the first associated element, wherein the first attribute information includes attribute information of the first key element and / or the first associated element; Determining task state information of the robot's task instance to be performed according to the first attribute information of the robot's task instance to be performed; Among them, the task status information includes: normal state and abnormal state. When the first attribute information of the robot's task instance to be executed is consistent with the target attribute information, the task status information of the robot's task instance to be executed is determined to be a normal state; when the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information, the task status information of the robot's task instance to be executed is determined to be an abnormal state.

2. The robot task status monitoring method according to claim 1, characterized in that: Processing the task information of the task instance to be executed and determining the action sequence of the robot according to the task information includes: An action sequence corresponding to the task instance to be executed is generated according to the task information of the task instance to be executed, wherein the action sequence includes a plurality of actions to be executed by the robot.

3. The robot task status monitoring method according to claim 1, characterized in that: Determining a first key element corresponding to the task instance to be performed according to the action sequence of the robot includes: Input multiple action sequences to be performed by the robot into a pre-trained element processing model, and output key elements corresponding to the task instances to be performed, wherein the element processing model is used to analyze the action sequences to be performed by the robot and extract the first key element corresponding to the task instances to be performed.

4. The robot task status monitoring method according to claim 1, characterized in that: Before determining the first attribute information of the robot's task instance to be performed according to the first key element and the first associated element, the method further includes: Obtain target attribute information of all elements corresponding to the task instance to be performed by the robot.

5. The robot task status monitoring method according to claim 1, characterized in that: The abnormal state of the task status information includes: an interruption state or a failure state. When the first attribute information of the task instance to be executed by the robot is inconsistent with the target attribute information, determining that the task status information of the task instance to be executed by the robot is in an abnormal state includes: When the first attribute information of the robot's task instance to be performed is inconsistent with the target attribute information, determining that the task state information of the robot's task instance to be performed is an interrupt state; When the task status information of the task instance to be executed by the robot is in an interrupted state, repeatedly executing the following steps until a preset stop condition is met, and determining that the task status information of the task instance to be executed by the robot is in a failed state, wherein the preset stop condition is that the second attribute information is inconsistent with the target attribute information and the preset stop condition is that the number of repetitions is greater than a preset threshold; When the task state information of the task instance to be executed by the robot is an interruption state, determining an interruption position of the robot in the interruption state; Update the task information of the robot's task instance to be executed according to the interruption position; Determine an updated action sequence of the robot according to the updated task information; Determine, according to the updated action sequence of the robot, a second key element corresponding to the updated instance of the task to be performed, wherein the second key element includes a noun in the updated action sequence of the task to be performed; Searching for a second associated element having an associated relationship with the second key element in the ontology knowledge base; Determining second attribute information of the updated task instance to be executed by the robot according to the second key element and the second associated element, wherein the second attribute information includes attribute information of the second key element and / or the second associated element; Determining whether the second attribute information of the task instance to be executed after the robot is updated is consistent with the target attribute information; If the second attribute information of the task instance to be executed after the robot is updated is consistent with the target attribute information, determining that the task state information of the task instance to be executed after the robot is updated is a normal state; If the second attribute information of the task instance to be executed after the robot is updated is inconsistent with the target attribute information, the interruption position of the robot in the interruption state is re-determined, and the second attribute information of the task instance to be executed after the robot is updated is compared with the target attribute information to obtain a comparison result.

6. The robot task status monitoring method according to claim 5, characterized in that: After determining the task state information of the robot's task instance to be performed according to the first attribute information of the robot's task instance to be performed, the method further includes: When the task status information of the task instance to be performed by the robot is in a normal state, generating a first monitoring instruction, wherein the first monitoring instruction is used to continue monitoring the first attribute information of the task instance to be performed by the robot; When the task status information of the task instance to be executed by the robot is in an interrupted state, a second monitoring instruction is generated, where the second monitoring instruction is used to monitor the second attribute information of the task instance to be executed after being updated by the robot.

7. A robot task status monitoring device, characterized in that: include: A task information acquisition module, used to acquire task information of the task instance to be performed by the robot; An action sequence processing module, used for processing the task information of the task instance to be executed, and determining the action sequence of the robot according to the task information; A key element determination module, configured to determine a first key element corresponding to and associated with the task instance to be executed according to the action sequence of the robot, wherein the first key element includes a noun in the action sequence of the robot; An associated element determination module is used to find a first associated element associated with the first key element in a pre-established ontology knowledge base, wherein the ontology knowledge base includes: a plurality of key elements and associated elements associated with each key element; an attribute information determination module, configured to determine first attribute information of the robot's task instance to be performed according to the first key element and the first associated element, wherein the first attribute information includes attribute information of the first key element and / or the first associated element; A task status determination module, used to determine the task status information of the task instance to be performed by the robot according to the first attribute information of the task instance to be performed by the robot; Among them, the task status information includes: normal state and abnormal state. The task status determination module is also used to determine that the task status information of the robot's task instance to be executed is a normal state when the first attribute information of the robot's task instance to be executed is consistent with the target attribute information; when the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information, determine that the task status information of the robot's task instance to be executed is an abnormal state.

8. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the robot task state monitoring method described in any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the robot task status monitoring method according to any one of claims 1 to 6 is implemented.

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