Robot task state monitoring method and apparatus and related device

Abstract

A robot (103) task state monitoring method and apparatus and a related device. The robot (103) task state monitoring method comprises: acquiring task information of a task instance to be executed of a robot (103); processing the task information of said task instance, and on the basis of the task information, determining an action sequence of the robot (103); on the basis of the action sequence of the robot (103), determining first key elements correspondingly associated with said task instance; searching a pre-established ontology knowledge base for first associated elements having an association relationship with the first key elements; on the basis of the first key elements and the first associated elements, determining first attribute information of said task instance of the robot (103); and on the basis of the first attribute information of said task instance of the robot (103), determining task state information of said task instance of the robot (103).

Description

Robot task status monitoring method, device and related equipment

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure claims priority to Chinese patent application number 202311021508.5, filed on August 14, 2023, entitled “Robot Task State Monitoring Method, Device and Related Equipment”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to the field of Internet of Things technology, and in particular to a robot task status monitoring method, apparatus, and related equipment. Background Art

[0004] Smart spaces are a prime example of environments for studying the principles and technologies of harmonious human-computer interaction. Within smart spaces, users can interact with a variety of information sources, including devices and data. With the advancement of artificial intelligence (AI), the integration of humans, machines, and objects within these spaces will become increasingly close.

[0005] In smart spaces, robots are capable of intelligently interacting with other devices and objects, enabling them to perform complex tasks. However, due to the multitude of elements and the complex and ever-changing environment within these spaces, the execution of these tasks depends not only on the status of the robot and the target object itself, but also on numerous other factors, particularly environmental emergencies. Consequently, in related technologies, sudden environmental changes during task execution can prevent the system from adjusting the robot's task status in a timely manner.

[0006] 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 prior art known to ordinary technicians in the field.

[0007] Summary of the Invention

[0008] The present disclosure provides a robot task status monitoring method, apparatus and related equipment, which at least to a certain extent overcome the problem in related technologies that the changes in element attributes in the task cannot be monitored in a timely manner due to sudden changes in the task execution environment.

[0009] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0010] According to one aspect of the present disclosure, a robot task status monitoring method is provided, which includes: 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: multiple 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; and 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.

[0011] 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.

[0012] 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.

[0013] 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 based on 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.

[0014] 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.

[0015] 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 task instance to be executed is inconsistent with the target attribute information, the task status information of the robot task instance to be executed is determined to be an abnormal state, including: when the first attribute information of the robot task instance to be executed is inconsistent with the target attribute information, the task status information of the robot task instance to be executed is determined to be an interruption state; when the task status information of the robot task instance to be executed is an interruption state, repeatedly perform the following steps until a preset stop condition is met, and determine that the task status information of the robot 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 task instance to be executed is an interruption state, determine the interruption position of the robot in the interruption state; update the robot task instance to be executed according to the interruption position task information of the instance; determine the updated action sequence of the robot according to the updated task information; determine the second key element corresponding to the updated task instance to be executed according to the updated action sequence of the robot; 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 status information of the task instance to be executed after the robot is updated is normal; if the second attribute information of the task instance to be executed after the robot is updated is inconsistent with the target attribute information, redetermine 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.

[0016] 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 further 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.

[0017] According to another aspect of the present disclosure, a robot task status monitoring device is also provided, which includes: a task information acquisition module, configured to acquire the task information of the robot's task instance to be performed; an action sequence processing module, configured to process the task information of the task instance to be performed, and determine the action sequence of the robot based on the task information; a key element determination module, configured to determine a first key element corresponding to the task instance to be performed based on the robot's action sequence; an associated element determination module, configured 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, configured to determine the first attribute information of the robot's task instance to be performed based on the first key element and the first associated element; a task status determination module, configured to determine the task status information of the robot's task instance to be performed based on the first attribute information of the robot's task instance to be performed.

[0018] According to another aspect of the present disclosure, an electronic device is provided, 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.

[0019] 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.

[0020] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements any one of the above-mentioned robot task status monitoring methods when executed by a processor.

[0021] The robot task status monitoring method, apparatus and related equipment provided in the embodiments of the present disclosure first obtain task information of the robot's task instance to be performed; secondly, process the task information of the task instance to be performed, 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 performed according to the robot's action sequence; thereafter, find the first associated element having an associated 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 performed 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 performed according to the first attribute information of the robot's task instance to be performed.

[0022] 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 status due to sudden changes in the environment, resulting in the robot being unable to adjust the task status in time. The embodiment of the present disclosure 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 monitoring the attribute information of the robot's task instance to be executed in real time, the task status 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-timeness and task integrity encountered in task execution, and make the intelligent service robot in the intelligent space more efficient and intelligent when performing tasks.

[0023] 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 disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are incorporated into 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 drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0025] FIG1 is a schematic diagram showing a system architecture of a method for monitoring robot task status according to an embodiment of the present disclosure;

[0026] FIG2 is a schematic diagram showing a method for monitoring a robot task state according to an embodiment of the present disclosure;

[0027] FIG3 is a schematic diagram showing a method for obtaining key elements corresponding to a task instance to be executed according to an embodiment of the present disclosure;

[0028] FIG4 shows a schematic flow chart of a robot task status monitoring system according to an embodiment of the present disclosure;

[0029] FIG5 shows a schematic diagram of adjusting task planning according to task status information in an embodiment of the present disclosure;

[0030] FIG6 shows a schematic diagram of a robot task status monitoring device according to an embodiment of the present disclosure;

[0031] FIG7 shows a schematic diagram of an electronic device to which a method for monitoring robot task status is applied in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will 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.

[0033] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. 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 can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present disclosure.

[0034] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0035] FIG1 shows a schematic diagram of an exemplary application system architecture to which the robot task status monitoring method according to an embodiment of the present disclosure can be applied. As shown in FIG1 , the system architecture may include a monitoring device 101 , a network 102 , and a robot 103 .

[0036] 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.

[0037] Optionally, the above-mentioned wireless network or wired network uses standard communication technologies and / or protocols. The network is typically the Internet, but 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 private 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 over 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), etc. can 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-mentioned data communication technologies.

[0038] The monitoring device 101 may be any electronic device, including but not limited to a smartphone, a tablet computer, a laptop computer, a desktop computer, a wearable device, an augmented reality device, a virtual reality device, and the like.

[0039] 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 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 phone client, a PC client, etc.

[0040] The robot 103 may be a robot that provides various services, such as a background management robot that provides support for the user's operation of the monitoring device 101. The background management robot may analyze and process the received request data and feed back the processing results to the monitoring device.

[0041] 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 these. The monitoring device and the robot can be directly or indirectly connected through wired or wireless communication, which is not limited in this application.

[0042] Those skilled in the art will appreciate that the number of monitoring devices 101, networks 102, and robots 103 in FIG1 is merely illustrative, and any number of monitoring devices, networks, and robots may be provided based on actual needs, which is not limited in the present disclosure.

[0043] Under the above system architecture, an embodiment of the present disclosure provides a method for monitoring robot task status, which can be executed by any electronic device with computing and processing capabilities.

[0044] 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.

[0045] Below, each step of the robot task status monitoring method in this example implementation will be described in more detail with reference to the accompanying drawings and embodiments.

[0046] First, the embodiments of the present disclosure provide a control method that can be applied but is not limited to monitoring the task status of a robot in an intelligent space. In the related art, during the actual task execution process of the robot, due to the large number of elements in the intelligent space and the complex and changeable environment, the task execution status not only depends on the status of the robot and the target object itself, but is also affected by 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 element attributes in the task cannot be monitored in time when the task execution environment changes.

[0047] FIG2 shows a schematic diagram of a robot task status monitoring method according to an embodiment of the present disclosure. As shown in FIG2 , the robot task status monitoring method provided in an embodiment of the present disclosure includes the following steps:

[0048] S201, obtaining task information of a task instance to be executed by the robot.

[0049] In some embodiments, the task information of the robot's task instance to be performed obtained in the embodiments of the present disclosure may be the task information generated by receiving a task instruction from the user end 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, and 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."

[0050] S202: Process the task information of the task instance to be executed, and determine the action sequence of the robot according to the task information.

[0051] In some embodiments, the robot in the disclosed embodiment receives a task instruction 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 embodiment processes 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.

[0052] S203: Determine a first key element corresponding to the task instance to be executed according to the action sequence of the robot.

[0053] 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 sequence "walk through the living room", "walk through the corridor", "reach the kitchen", "find the table where the cup of water is placed", etc., the first key elements such as "living room", "corridor", "kitchen", "table", "cup of water" and so on corresponding to the task instance to be executed are determined.

[0054] S204: Find a first associated element associated with the key element in a pre-established ontology knowledge base, where the ontology knowledge base includes: multiple key elements and associated elements associated with each key element.

[0055] In some embodiments, the embodiment of the present disclosure obtains the task information obtained by the robot and processes it, thereby obtaining a first key element corresponding to the task instance to be executed. In addition to the first key element that affects the execution of the task, the remaining symbiotic elements associated with the key elements in the spatial environment will also affect the execution of the task. Therefore, the embodiment of the present disclosure matches the obtained first key element with a pre-established ontology knowledge base including multiple key elements and associated elements associated with each key element, thereby determining a first associated element associated 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 open and close status of the study door can affect the robot's range of activity, and the items on the desk can affect the taking of the water cup, etc. Therefore, the first associated element associated 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.

[0056] S205: Determine first attribute information of the task instance to be performed by the robot based on the first key element and the first associated element.

[0057] 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 element. For example, the "obstacles" in the corridor, the "door" in the study, and the "items" on the desk are 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.

[0058] S206: Determine task status information of the robot's task instance based on the first attribute information of the robot's task instance.

[0059] 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.

[0060] The robot task status monitoring method in the embodiment of the present disclosure first obtains 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.

[0061] 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 status due to sudden changes in the environment, resulting in the robot being unable to adjust the task status in time. The embodiment of the present disclosure 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 monitoring the attribute information of the robot's task instance to be executed in real time, the task status 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-timeness and task integrity encountered in task execution, and make the intelligent service robot in the intelligent space more efficient and intelligent when performing tasks.

[0062] In some embodiments, as shown in FIG3 , the method for obtaining the first key element according to an embodiment of the present disclosure includes the following steps:

[0063] S301, generating an action sequence corresponding to the task instance to be executed based on the task information of the task instance to be executed, wherein the action sequence includes multiple actions to be executed by the robot;

[0064] 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.

[0065] 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 disassemble the task information of the task instance to be executed according to a pre-built action extraction model to 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.

[0066] In some embodiments, the first key element corresponding to the task instance to be executed is determined based on 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.

[0067] In more detail, the element processing model in the embodiment of the present disclosure can be but is not limited to a semantic model. Taking the semantic model as an example, the embodiment of the present disclosure combines the action sequence corresponding to the task instance to be executed with the action extraction model, and then extracts the first key element involved in 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" involved in the above action sequence that are consistent with the robot's task instance to be executed are extracted.

[0068] 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 embodiment of the present disclosure also includes: obtaining target attribute information of all corresponding associated elements in the robot's task instance to be performed.

[0069] In some embodiments, the robot task status monitoring method in the embodiment of the present disclosure can be completed through a pre-built task monitoring model. As shown in Figure 4, 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. Among them, the task monitoring management layer mainly tracks, monitors and manages the current task instance, including decomposing the task instance into an action sequence, binding the element attributes related to the task instance, performing element attribute evaluation, and updating the task status in real time when the element attributes change.

[0070] 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 of the current space (for example, a semantic model); 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 performed 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 performed to the associated attribute monitoring layer to complete the monitoring of the first attribute information of the robot's task instance to be performed; the associated attribute extraction layer outputs the first attribute information of the robot's task instance to be performed 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 performed to the task monitoring management layer; the task monitoring management layer determines the task status information of the robot's task instance to be performed based on the first attribute information of the current robot's task instance to be performed.

[0071] In some embodiments, the present embodiment disassembles the task information of the task instance to be performed by the robot to obtain an action sequence, and obtains the first associated element having an associated relationship with each first key element through the first key element involved in the task instance, and records the first attribute information that can complete the task instance as target attribute information. For example, the present embodiment can record the door of the study in an open state as target attribute information, etc.

[0072] In some embodiments, the task status information in the embodiments of the present disclosure includes: normal state and 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.

[0073] In some embodiments, the disclosed embodiments compare the determined first attribute information of the robot's task instance to be performed with the target attribute information to determine whether the acquired first attribute information of the robot's task instance to be performed can complete the robot's task instance to be performed. If the first attribute information of the robot's task instance to be performed is inconsistent with the target attribute information, it is determined that the element attributes in the robot's task instance to be performed 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 performed 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 performed.

[0074] It should be noted that the target attribute information in the embodiment of the present disclosure is a preset range generated by combining the key elements determined based on the robot's action sequence and the corresponding associated elements found in the ontology knowledge base. For example, based on the 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° to 90°)" is generated. The first attribute information of the robot's to-be-performed task instance collected on-site is compared with the target attribute information. If the first attribute information of the robot's to-be-performed task instance collected on-site is within the range of the target attribute information, it is determined that the robot can normally complete the to-be-performed task instance. If the first attribute information of the robot's to-be-performed task instance collected on-site is not within the range of the target attribute information, it is determined that the robot cannot normally complete the to-be-performed task instance. For example, if the first attribute information of the robot's to-be-performed task 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 to-be-performed task instance.

[0075] 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 is inconsistent with the target attribute information, the task status information of the robot's task instance is determined to be an abnormal state, including: when the first attribute information of the robot's task instance is inconsistent with the target attribute information, the task status information of the robot's task instance is determined to be an interruption state; when the task status information of the robot's task instance is in an interruption state, repeatedly perform the following steps until the preset stop condition is met, and determine that the task status information of the robot's task instance is in 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 is in an interruption state, determine the interruption position of the robot in the interruption state; update the robot's task instance according to the interruption position The method comprises the following steps: determining the task information of the task instance to be executed after the robot is updated; 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 is updated 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 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 status information of the task instance to be executed after the robot is updated is normal; 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-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 is updated with the target attribute information to obtain a comparison result.

[0076] In more detail, if the first attribute information of the robot's task instance to be executed in the embodiment of the present invention is inconsistent with the target attribute information, it means that the robot cannot complete the task instance to be executed normally. 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 re-determined at the interrupted position, and the robot's action sequence is updated according to the updated task information, and 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 used to determine whether the second attribute information is consistent with the target attribute information. If the second attribute information of the updated robot's task instance is consistent with the target attribute information, the task status information of the updated robot's task instance is determined to be normal, and the task instance continues to be executed; if the second attribute information of the updated robot's task instance is inconsistent with the target attribute information, the interruption position of the robot in the interruption state is repeatedly determined, and the second attribute information of the updated robot's task instance 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 the preset threshold, the task status information of the robot's task instance is determined to be a failed state.

[0077] 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.

[0078] More specifically, as shown in Figure 5, the task status information in the embodiment of the present disclosure 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 then 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, the task status information is determined to be a failed state; if the monitored task status information is a failed state, all corresponding 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.

[0079] Based on the same inventive concept, the present disclosure also provides a robot task state monitoring device, as shown in 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.

[0080] FIG6 shows a schematic diagram of a robot task status monitoring device according to an embodiment of the present disclosure. As shown in FIG6 , the device includes:

[0081] The task information acquisition module 601 is used to obtain the task information of the task instance to be performed by the robot;

[0082] 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;

[0083] A key element determination module 603 is configured to determine a first key element associated with the task instance to be executed according to the robot's action sequence;

[0084] The associated element determination module 604 is configured to search a pre-established ontology knowledge base for a first associated element associated with the first key element, wherein the ontology knowledge base includes: a plurality of key elements and associated elements associated with each key element;

[0085] An attribute information determination module 605 is configured to determine first attribute information of a task instance to be performed by the robot based on the first key element and the first associated element;

[0086] The task status determination module 606 is configured to determine 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.

[0087] The robot task status monitoring device provided in the embodiments of the present disclosure obtains the task information of the robot's to-be-executed task instance through a task information acquisition module; processes the task information of the to-be-executed task instance 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 to-be-executed task instance according to the robot's action sequence through a key element determination module; finds 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 to-be-executed task instance 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 to-be-executed task instance according to the first attribute information of the robot's to-be-executed task instance through a task status determination module.

[0088] 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 status due to sudden changes in the environment, resulting in the robot being unable to adjust the task status in time. The embodiment of the present disclosure 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 monitoring the attribute information of the robot's task instance to be executed in real time, the task status 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-timeness and task integrity encountered in task execution, and make the intelligent service robot in the intelligent space more efficient and intelligent when performing tasks.

[0089] In some embodiments, the action sequence processing module in the embodiment of the present disclosure is further 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.

[0090] 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 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.

[0091] In some embodiments, the robot task status monitoring device in the embodiment of the present disclosure further includes, 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: a target attribute information acquisition module, configured to obtain the target attribute information of all corresponding associated elements in the robot's task instance to be performed.

[0092] 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; and to determine that the task status information of the robot's task instance to be executed is an abnormal state when the first attribute information of the robot's task instance to be executed is inconsistent with the target attribute information.

[0093] 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, determining the second key element corresponding to the updated task instance to be executed; 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 is updated 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 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 status 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-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 is updated with the target attribute information to obtain a comparison result.

[0094] In some embodiments, in the embodiments of the present disclosure, after determining the task status information of the robot's task instance to be performed based on the first attribute information of the robot's task instance to be performed, the device also includes: a first monitoring module, configured to generate a first monitoring instruction when the task status information of the robot's task instance to be performed 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 performed; a second monitoring module, configured to generate a second monitoring instruction when the task status information of the robot's task instance to be performed is in an interrupted state, and the second monitoring instruction is used to monitor the second attribute information of the robot's task instance to be performed after the update.

[0095] It should be noted that the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above method embodiments. It should be noted that the above modules as part of the device can be executed in a computer system such as a set of computer executable instructions.

[0096] 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 implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits," "modules," or "systems."

[0097] The electronic device 700 according to this embodiment of the present disclosure is described below with reference to Figure 7. The electronic device 700 shown in Figure 7 is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0098] As shown in Figure 7, electronic device 700 is implemented as a general-purpose computing device. Components of electronic device 700 may include, but are not limited to, the aforementioned at least one processing unit 701, the aforementioned at least one storage unit 702, and a bus 703 connecting various system components (including storage unit 702 and processing unit 701).

[0099] The storage unit stores program codes, which can be executed by the processing unit 701, so that the processing unit 701 executes the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification.

[0100] In some embodiments, when the electronic device is used to control, for example, the knowledge graph-based question-answering method disclosed above, the processing unit 701 may perform the following steps of the above method embodiment:

[0101] Get the task information of the robot's pending task instance.

[0102] The task information of the task instance to be executed is processed and the action sequence of the robot is determined based on the task information.

[0103] A first key element corresponding to the task instance to be executed is determined according to the action sequence of the robot.

[0104] A first associated element having an associated relationship with the first key element is searched 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.

[0105] Determine first attribute information of the robot's task instance to be performed based on the first key element and the first associated element.

[0106] Determine task status information of the robot's task instance according to the first attribute information of the robot's task instance.

[0107] The storage unit 702 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 7021 and / or a cache memory unit 7022 , and may further include a read-only memory unit (ROM) 7023 .

[0108] 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.

[0109] 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.

[0110] The electronic device 700 may also communicate with one or more external devices 704 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 700, and / or any device that enables the electronic device 700 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 705. Furthermore, the electronic device 700 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 706. As shown, the network adapter 706 communicates with other modules of the electronic device 700 via 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.

[0111] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments 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, and includes 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 embodiments of the present disclosure.

[0112] 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 executed by a processor.

[0113] 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 program code. When the program product is run on a monitoring device, the program code is used to cause the monitoring device to perform the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Method" section above of this specification.

[0114] 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 fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0115] In the present disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0116] 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 thereof.

[0117] 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++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's 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's computing device via 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., via the Internet using an Internet service provider).

[0118] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, 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 concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0119] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all 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.

[0120] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments 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, and includes 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 embodiments of the present disclosure.

[0121] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims

1. A robot task status monitoring method, comprising: 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 the task instance to be performed according to 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; Determining first attribute information of the robot's task instance to be performed according to the first key element and the first associated element; 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.

2. The robot task status monitoring method according to claim 1, processing the task information of the task instance to be executed, and determining the action sequence of the robot according to the task information, comprising: 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 state monitoring method according to claim 1, determining the first key element corresponding to the task instance to be executed according to the action sequence of the robot, comprising: 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 state monitoring method according to claim 1, before determining the first attribute information of the robot task instance to be performed according to the first key element and the first associated element, the method further comprises: 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 4, wherein the task status information comprises: Normal state and abnormal state, determining the 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, including: When the first attribute information of the robot's task instance to be performed is consistent with the target attribute information, determining that the task state information of the robot's task instance to be performed is a normal state; When the first attribute information of the robot's task instance to be performed is inconsistent with the target attribute information, it is determined that the task state information of the robot's task instance to be performed is in an abnormal state.

6. The robot task status monitoring method according to claim 5, wherein the abnormal state of the task status information comprises: Interruption state or failure state, when the first attribute information of the robot task instance to be executed is inconsistent with the target attribute information, determining that the task state information of the robot task instance to be executed is 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; Determining, according to the updated action sequence of the robot, a second key element corresponding to the updated task instance 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; 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.

7. The robot task state monitoring method according to claim 6, after determining the task state information of the robot task instance to be performed according to the first attribute information of the robot task instance to be performed, the method further comprises: 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.

8. A robot task status monitoring device, comprising: A task information acquisition module, configured to acquire task information of a task instance to be performed by the robot; an action sequence processing module, configured 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, configured to determine a first key element corresponding to the task instance to be performed according to the action sequence of the robot; An associated element determination module is configured to find an 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; 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; The task status determination module is configured to determine 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.

9. An electronic device, comprising: 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 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the robot task state monitoring method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the robot task status monitoring method according to any one of claims 1 to 7 is implemented.

Images