Task processing method and device based on assessment level task network, and storage medium

Abstract

The invention discloses a task processing method and device based on an evaluation hierarchy task network and a storage medium, and relates to the technical field of computer-aided intelligent information processing.The method comprises the steps that an initial evaluation hierarchy task network is generated according to a target task, and the initial evaluation hierarchy task network comprises a hierarchy task network; when the execution of the target node is completed, determining evaluation information of the target node; determining a preorder task sequence of an associated node of the target node according to the evaluation information; and determining an optimization evaluation level task network according to the preorder task sequence, and completing the target task according to the optimization evaluation level task network. On the basis of a hierarchical task network, a target node in an initial evaluation hierarchical task network is evaluated to obtain evaluation information, a preorder task sequence of an associated node of the target node is adjusted according to the evaluation information, and then the initial evaluation hierarchical task network is optimized according to the evaluation information of the target node. And an optimized evaluation level task network is obtained, and the execution efficiency of the target task is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-aided intelligent information processing, and in particular to a task processing method, device and storage medium based on an evaluation hierarchical task network. Background Art

[0002] As the intelligence of equipment continues to increase, the equipment is gradually able to complete more and more complex tasks. At present, when equipment performs complex tasks, it is necessary to provide the equipment with specific parameters of tasks and actions through computer-aided technologies such as Hierarchical Task Network (HTN). However, the execution efficiency of tasks using the Hierarchical Task Network is currently low. Summary of the invention

[0003] The present invention provides a task processing method, device and storage medium based on an evaluation hierarchical task network to solve the problem of low task execution efficiency when current devices execute tasks through a hierarchical task network.

[0004] According to one aspect of the present invention, there is provided a task processing method based on an evaluation hierarchical task network, comprising:

[0005] Generate an initial evaluation hierarchical task network according to the target task, wherein the initial evaluation hierarchical task network includes a hierarchical task network;

[0006] When the target node is executed, determining the evaluation information of the target node, the target node being any task node in the evaluation hierarchical task network;

[0007] Determine the preceding task sequence of the associated nodes of the target node according to the evaluation information; determine the optimized evaluation hierarchical task network according to the preceding task sequence, and complete the target task according to the optimized evaluation hierarchical task network.

[0008] According to another aspect of the present invention, there is provided a task processing device based on an evaluation hierarchical task network, comprising:

[0009] An initialization module, used to generate an initial evaluation hierarchical task network according to a target task, wherein the initial evaluation hierarchical task network includes a hierarchical task network;

[0010] An evaluation module, used for determining evaluation information of the target node when the target node is executed, wherein the target node is any task node in the evaluation hierarchical task network;

[0011] The optimization module is used to determine the predecessor task sequence of the associated nodes of the target node according to the evaluation information; determine the optimized evaluation hierarchical task network according to the predecessor task sequence, and complete the target task according to the optimized evaluation hierarchical task network.

[0012] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the task processing method based on the evaluation hierarchical task network described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the task processing method based on the evaluation hierarchical task network described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present invention generates an initial evaluation hierarchical task network according to the target task, the initial evaluation hierarchical task network includes a hierarchical task network; when the target node is executed, the evaluation information of the target node is determined, the target node is any task node in the evaluation hierarchical task network; the predecessor task sequence of the associated node of the target node is determined according to the evaluation information; the optimized evaluation hierarchical task network is determined according to the predecessor task sequence, and the target task is completed according to the optimized evaluation hierarchical task network. On the basis of the hierarchical task network, the target node in the initial evaluation hierarchical task network is evaluated to obtain the evaluation information, the predecessor task sequence of the associated node of the target node is adjusted according to the evaluation information, and then the initial evaluation hierarchical task network is optimized according to the evaluation information of the target node to obtain the optimized evaluation hierarchical task network, thereby improving the execution efficiency of the target task.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 It is a flowchart of a task processing method based on an evaluation hierarchical task network provided by an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of process data involved in the weight score provided in an embodiment of the present invention;

[0022] Figure 3 It is a flowchart of another task processing method based on an evaluation hierarchical task network provided by an embodiment of the present invention;

[0023] Figure 4 It is a structural schematic diagram of a task processing device based on an evaluation hierarchical task network provided by an embodiment of the present invention;

[0024] Figure 5 It is a structural schematic diagram of an electronic device for implementing the task processing method based on an evaluation hierarchical task network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] As the intelligence of equipment continues to increase, the equipment is gradually able to complete more and more complex tasks. At present, when equipment performs complex tasks, it is necessary to provide the equipment with specific parameters of tasks and actions through computer-aided technologies such as hierarchical task networks. However, the execution efficiency of tasks is low when using hierarchical task networks to perform tasks. Specifically, the hierarchical task network is currently separated from the task evaluation, that is, after completing the target task based on the hierarchical task network, the overall completion of the target task is evaluated. And it is impossible to optimize the hierarchical task network based on the evaluation, resulting in low execution efficiency of the target task.

[0028] The task processing method based on the evaluation hierarchical task network provided by the embodiment of the present invention can evaluate according to the execution result of any task node, that is, the target node, during the execution of the target task, and obtain evaluation information. The initial evaluation hierarchical task network is optimized according to the evaluation information, so that during the execution of the target task, the subsequent execution process of the target task is adjusted according to the execution result of a certain node in the execution process, that is, the preceding task sequence of the associated node of the target node is adjusted. The target task can dynamically adjust the task flow according to the evaluation information, thereby improving the execution efficiency of the target task.

[0029] Figure 1 It is a flowchart of a task processing method based on an evaluation hierarchy task network provided by an embodiment of the present invention. This embodiment can be applied to situations where task processing is performed through computer-assisted equipment. Computer assistance is used to provide specific actions for execution for intelligent devices. Intelligent devices include but are not limited to drones, intelligent robots, etc. Among them, the intelligent robot can be a machine with a mechanical arm or with equipment required to perform tasks. Such as a sweeping robot with a roller brush, etc. The method can be executed by a task processing device based on an evaluation hierarchy task network. The task processing device based on the evaluation hierarchy task network can be implemented in the form of hardware and / or software. The task processing device based on the evaluation hierarchy task network can be configured in electronic devices such as intelligent devices, personal computers or servers. Figure 1 As shown, the method includes:

[0030] Step S101: Generate an initial evaluation hierarchical task network according to a target task, wherein the initial evaluation hierarchical task network includes a hierarchical task network.

[0031] The target task is the task that the device needs to complete. By splitting the target task, the subtasks required to complete the target task and the actions contained in the subtasks are obtained. The target task can be divided into multiple subtasks, multiple actions, or a combination of multiple actions and subtasks. An action can be the smallest task unit that the device can execute. A subtask can consist of multiple actions. Subtasks can be in a parent-child relationship or a sibling relationship.

[0032] For example, when the device is a sweeping robot, the target task may be to clean the floors of four rooms. The target task includes four subtasks corresponding to the floor cleaning of each room. Each subtask may include its own subtasks, which are used to generate targeted subtasks or actions for different terrains of each room.

[0033] Application scenarios include intelligent or multi-agent work, such as remote sensing photography, drone performances, smart delivery, and automated guided vehicle (AGV) scheduling. Specifically, when the intelligent agent is a drone cluster, the target task can be to complete the entire performance, and each drone has its own subtasks, such as flying along the designed route and avoiding obstacles.

[0034] The initial evaluation hierarchical task network is constructed according to the subtasks included in the target task. The initial evaluation hierarchical task network includes a hierarchical task network. The hierarchical task network can be constructed according to the construction method of the hierarchical task network. The nodes of the hierarchical task network include hierarchical task network parameters, such as ,in, Represents a task node Task information, Represents a task node Output information, Represents a task node Input information.

[0035] The difference between the hierarchical task network in the prior art and the initial evaluation hierarchical task network provided by the embodiment of the present invention is that, on the basis of the hierarchical task network, relevant parameters are further evaluated in each node. The target node is any task node in the evaluation hierarchical task network. The target node of the evaluation hierarchical task network includes hierarchical task network parameters and evaluation-related parameters. The evaluation-related parameters include a combination of one or more of the following parameters: task subject, evaluation information, preceding task, operation target, evaluation mode, task time or task feedback information.

[0036] Exemplarily, the node structure of the initial evaluation hierarchical task network provided by the embodiment of the present invention can be: , based on the hierarchical task network nodes, it also includes evaluation related parameters, specifically: task body , Assessment Information , Predecessor Tasks , Operation target , Evaluation Model , Task time , task feedback information .

[0037] Among them, the task subject Indicates the subject that executes the task, which can be a single user, a group, or an independent user in a group. The task subject can be a natural person or a robot that executes the target node. Evaluation information Represents the weight of the target node's task in the overall task performance evaluation. Indicates a task that needs to be completed before this task. Indicates the target node's task execution target (last node) or the target used for performance evaluation. If the target node is the last node that executes the target task, the operation target is the final target that the target task needs to achieve. Otherwise, if the target node is not the last node to execute the target task, the target node is The goal of performance evaluation. Evaluation model Indicates the evaluation method applicable to the target node. The evaluation mode is divided into task-level evaluation and action-level evaluation, or a combination of the two. Task time It is optional. For some tasks that are strongly related to time, the task execution time needs to be recorded for evaluation. Task feedback information There are two types: one is the result that is fed back to the operator in real time, and the other is a score used to evaluate overall effectiveness. The final score is calculated after the task is completed.

[0038] Step S102: When the target node is executed, the evaluation information of the target node is determined, and the target node is any task node in the evaluation hierarchical task network.

[0039] After the target node is executed, the target node is evaluated to obtain evaluation information. The target node can be evaluated in the following ways.

[0040] Optionally, determining the evaluation information of the target node may be implemented in the following manner:

[0041] If the target node is a subtask node, the evaluation information is determined according to a combination of one or more of the following evaluation methods: azimuth distance evaluation, indirect feedback evaluation, numerical comparison evaluation or affiliation evaluation.

[0042] The azimuth distance assessment is used to assess whether the operation target has reached the specified location or direction according to the operation target of the target node. It can be converted to any score between 0 and 1 within the specified accuracy according to business needs, and the conversion matrix can be transformed linearly or nonlinearly according to business needs.

[0043] Indirect feedback evaluation is used in scenarios where the evaluation results of the operation target cannot be obtained directly. Other evaluation entities can perform evaluations based on business needs. Indirect feedback is used to evaluate the target node based on the information provided by other entities.

[0044] Numerical comparison evaluation is used in scenarios with high numerical requirements. If the value meets the requirement within the error range, it is 1, otherwise it is 0. There is no other evaluation status in this process.

[0045] The dependency evaluation is used to determine the status between two task targets. If the two task targets reach this status, it is 1, otherwise it is 0. There is no other evaluation status in this process.

[0046] The above implementation manner can evaluate the subtask node, and a combination of one or more evaluation methods is selected according to the needs to obtain the evaluation information of the subtask node.

[0047] Optionally, determining the evaluation information of the target node may be implemented in the following manner:

[0048] If the target node is an action-level node, the evaluation information is determined according to a combination of one or more of the following evaluation methods: dynamic rule evaluation, time series evaluation, or state change evaluation.

[0049] Action-level evaluation is mainly aimed at abnormal state evaluation and warning during task execution. Since the failure of each task starts from every small error, to complete the task-level abnormality evaluation, it is necessary to start with action-level evaluation. Action-level evaluation methods are mainly divided into three types: dynamic rule evaluation, time series evaluation, and state change evaluation. The above three action-level evaluation methods correspond to the output of no abnormality, no impact of failure, and unable to work normally. The specific content corresponding to dynamic rule evaluation, time series evaluation, and state change evaluation can be configured according to needs.

[0050] The above implementation manner can evaluate the action-level node, and a combination of one or more evaluation methods is selected according to the requirements to obtain evaluation information of the action-level node.

[0051] Optionally, determining the evaluation information of the target node may also be implemented in the following manner:

[0052] Step 1: Determine input features according to the graph density of the initial evaluation hierarchical task network.

[0053] Specifically, the input feature is determined according to the graph density of the initial evaluation hierarchical task network, which can be implemented as follows:

[0054] Determine a graph structure based on the initial evaluation hierarchical task network; determine a graph intimacy matrix of the graph structure; obtain a context node for each task node; determine original features based on the context nodes; determine absolute role features based on position information of the target node in the graph structure and graph embedding information of the context nodes; determine relative position features of the target node based on the position information of the target node in the graph structure; and determine input features based on the original features, the absolute role features, and the relative position features.

[0055] Exemplarily, step S1: converting the task model into a graph structure G.

[0056] Figure 2 A schematic diagram of process data involved in the weight score provided in an embodiment of the present invention, Figure 2 The red node is the target node.

[0057] Step S2: Calculate the graph intimacy matrix of the graph structure G ,in for The hyperparameters between are usually (0.15), represents the column-normalized adjacency matrix, is the adjacency matrix of the graph, is the corresponding diagonal matrix. Represents the identity matrix.

[0058] Step S3: Get the context node of each node.

[0059] Assume that the target node is , the context node of the target node is represented as , .

[0060] The context node is a node whose intimacy is greater than the intimacy threshold determined by the graph intimacy matrix. The intimacy threshold is .

[0061] Step S4: Calculate the original features of the node .

[0062] in, Represents a graph embedding algorithm.

[0063] Step S5: Calculate the absolute role characteristics of the node

[0064]

[0065] in It is a method for extracting the kernel vector of a graph.

[0066] It is the location information of the target node in the graph structure. Represents the dimension of node features, Indicates the length of the graph.

[0067] Step S6: Calculate node relative position features: .

[0068] The context node for the target node.

[0069] Step S7: Aggregate original features, absolute role features, and relative position features to obtain LSTM input .

[0070] Step 2: Input the input features into the time series neural network to obtain output information.

[0071] The optional time series neural network can be a Long Short-Term Memory (LSTM) network or a Recurrent Neural Network (GRU) network.

[0072] Optionally, aggregate node features Input the tasks into the LSTM model in order to get .right Perform convolution and normalize to get the score.

[0073] Step three: normalize the output information of multiple target nodes to obtain a weight score for each target node, and use the weight score as evaluation information.

[0074] The above implementation can calculate the weight score of the target node as evaluation information through a time series neural network such as LSTM, thereby improving the accuracy of the evaluation information.

[0075] Step S103, determining a preceding task sequence of a node associated with the target node according to the evaluation information; determining an optimized evaluation hierarchical task network according to the preceding task sequence, and completing the target task according to the optimized evaluation hierarchical task network.

[0076] Optionally, determining the preceding task sequence of the associated node of the target node according to the evaluation information may be implemented in the following manner:

[0077] If the initial evaluation hierarchical task network is a fixed-state task model, determining a preceding task sequence of an associated node of the target node according to the evaluation information and a rule base, wherein the associated node is a child node or a sibling node of the target node;

[0078] If the initial evaluation hierarchical task network is a task model in a non-fixed state, a key node is determined according to a preceding task of a node associated with the target node, and re-verification based on the evaluation information is started according to the key node.

[0079] After the task evaluation is completed, the task sequence can be regenerated based on the task evaluation results using the rule base method or the key node re-verification method to carry out the task. The rule base method is mainly used for task models with fixed task evaluation, as shown in the following table. The main input of the rule base is the status of the relevant subtasks and the status of the predecessor task, and the output is the task to be carried out; the key node re-verification method is mainly used for tasks with unfixed task evaluation (expressed by scores). It is necessary to set the task failure threshold (failure if the score is lower than the threshold) and the key node score threshold (task weight is higher than the threshold, it is a key node task) before the task starts. During the task, the subtasks with failed tasks can be found based on the threshold, and the key node tasks in the predecessor tasks of the task can be found in the task hierarchy network, and re-verification can be started from the key node tasks.

[0080] Exemplarily, the status of the predecessor task is shown in Table 1. The current task status can be determined according to the evaluation information of the target node. The evaluation information of the associated node is determined according to the predecessor task of the target node, and then the status of the predecessor task is determined. The task to be performed is determined according to the current task status, the predecessor task status and the subtask status, and then the predecessor task of the target node is adjusted.

[0081] Table 1

[0082]

[0083] The task processing method based on the evaluation hierarchical task network provided by the embodiment of the present invention generates an initial evaluation hierarchical task network according to the target task, wherein the initial evaluation hierarchical task network includes a hierarchical task network; when the target node is executed, the evaluation information of the target node is determined, wherein the target node is any task node in the evaluation hierarchical task network; the preceding task sequence of the associated node of the target node is determined according to the evaluation information; the optimized evaluation hierarchical task network is determined according to the preceding task sequence, and the target task is completed according to the optimized evaluation hierarchical task network. On the basis of the hierarchical task network, the target node in the initial evaluation hierarchical task network is evaluated to obtain the evaluation information, the preceding task sequence of the associated node of the target node is adjusted according to the evaluation information, and then the initial evaluation hierarchical task network is optimized according to the evaluation information of the target node to obtain the optimized evaluation hierarchical task network, thereby improving the execution efficiency of the target task.

[0084] Figure 3A schematic diagram of a task processing method based on an evaluation hierarchical task network provided by an embodiment of the present invention includes:

[0085] Step S201: Generate an initial evaluation hierarchical task network according to the target task.

[0086] The initial evaluation hierarchical task network includes a hierarchical task network. The target node of the evaluation hierarchical task network includes hierarchical task network parameters and evaluation related parameters. The evaluation related parameters include a combination of one or more of the following parameters: task subject, evaluation information, preceding task, operation target, evaluation mode, task time or task feedback information.

[0087] Step S202: When the target node is executed, the evaluation information of the target node is determined, and the target node is any task node in the evaluation hierarchical task network.

[0088] Optionally, if the target node is a subtask node, the evaluation information is determined according to a combination of one or more of the following evaluation methods: azimuth distance evaluation, indirect feedback evaluation, numerical comparison evaluation or affiliation evaluation.

[0089] Optionally, if the target node is an action-level node, the evaluation information is determined according to a combination of one or more of the following evaluation methods: dynamic rule evaluation, time series evaluation, or state change evaluation.

[0090] Optionally, the evaluation information of the target node is calculated in the following way:

[0091] Step 1, determining the graph structure according to the initial evaluation hierarchical task network;

[0092] Step 2: Determine the graph intimacy matrix of the graph structure;

[0093] Step 3: Get the context node of each task node;

[0094] Step 4: determining original features according to the context node;

[0095] Step 5: determining the absolute role feature according to the position information of the target node in the graph structure and the graph embedding information of the context node;

[0096] Step 6: Determine the relative position feature of the target node according to the position information of the target node in the graph structure;

[0097] Step 7: Determine input features according to the original features, the absolute role features and the relative position features.

[0098] Step 8: Input the input features into the time series neural network to obtain output information;

[0099] Step 9: Normalize the output information of multiple target nodes to obtain a weight score for each target node, and use the weight score as evaluation information.

[0100] Step S203: If the initial evaluation hierarchical task network is a fixed-state task model, determine the preceding task sequence of the associated node of the target node according to the evaluation information and the rule base, wherein the associated node is a child node or a sibling node of the target node.

[0101] Step S204: If the initial evaluation hierarchical task network is a task model in a non-fixed state, a key node is determined according to a preceding task of a node associated with the target node, and re-verification is started based on the evaluation information according to the key node.

[0102] Step S205: determining an optimized evaluation hierarchical task network according to the preceding task sequence, and completing the target task according to the optimized evaluation hierarchical task network.

[0103] Figure 4 This is a structural diagram of a task processing device based on an evaluation hierarchical task network provided by an embodiment of the present invention. This embodiment can be applied to situations where task processing is performed through computer-assisted equipment. Computer assistance is used to provide specific actions for execution for intelligent devices, and intelligent devices include but are not limited to drones, intelligent robots, etc. Among them, intelligent robots can be machines with mechanical arms or equipment required to perform tasks. The task processing device based on the evaluation hierarchical task network can be implemented in the form of hardware and / or software, and the task processing device based on the evaluation hierarchical task network can be configured in electronic devices such as intelligent devices, personal computers or servers. Figure 4 As shown, the device includes: an initialization module 31, an evaluation module 32 and an optimization module 33.

[0104] An initialization module 31 is used to generate an initial evaluation hierarchical task network according to a target task, wherein the initial evaluation hierarchical task network includes a hierarchical task network;

[0105] An evaluation module 32, used for determining evaluation information of the target node when the target node is executed, the target node being any task node in the evaluation hierarchical task network;

[0106] The optimization module 33 is used to determine the predecessor task sequence of the associated nodes of the target node according to the evaluation information; determine the optimized evaluation hierarchical task network according to the predecessor task sequence, and complete the target task according to the optimized evaluation hierarchical task network.

[0107] Based on the above embodiment, optionally, the target node of the evaluation hierarchical task network includes hierarchical task network parameters and evaluation related parameters;

[0108] The evaluation-related parameters include a combination of one or more of the following parameters: task subject, evaluation information, preceding task, operation target, evaluation mode, task time or task feedback information.

[0109] Based on the above embodiment, optionally, the evaluation module 32 is used to:

[0110] If the target node is a subtask node, the evaluation information is determined according to a combination of one or more of the following evaluation methods:

[0111] Position distance assessment, indirect feedback assessment, numerical comparison assessment or affiliation assessment.

[0112] Based on the above embodiment, optionally, the evaluation module 32 is used to:

[0113] If the target node is an action-level node, the evaluation information is determined according to a combination of one or more of the following evaluation methods:

[0114] Dynamic rule evaluation, time series evaluation, or state change evaluation.

[0115] Based on the above embodiment, optionally, the evaluation module 32 is used to:

[0116] Determining input features according to the graph density of the initial evaluation hierarchical task network;

[0117] Inputting the input features into a time series neural network to obtain output information;

[0118] Normalization is performed based on the output information of multiple target nodes to obtain a weight score for each target node, and the weight score is used as evaluation information.

[0119] Based on the above embodiment, optionally, the evaluation module 32 is used to determine the input feature according to the graph density of the initial evaluation hierarchical task network, including:

[0120] determining a graph structure according to the initial evaluation hierarchical task network;

[0121] Determine a graph intimacy matrix of the graph structure;

[0122] Get the context node of each task node;

[0123] Determining original features according to the context node;

[0124] Determining an absolute role feature according to the position information of the target node in the graph structure and the graph embedding information of the context node;

[0125] Determining a relative position feature of the target node according to the position information of the target node in the graph structure;

[0126] An input feature is determined according to the original feature, the absolute role feature and the relative position feature.

[0127] Based on the above embodiment, optionally, the optimization module 33 is used to:

[0128] If the initial evaluation hierarchical task network is a fixed-state task model, determining a preceding task sequence of an associated node of the target node according to the evaluation information and a rule base, wherein the associated node is a child node or a sibling node of the target node;

[0129] If the initial evaluation hierarchical task network is a task model in a non-fixed state, a key node is determined according to a preceding task of a node associated with the target node, and re-verification based on the evaluation information is started according to the key node.

[0130] The task processing device based on the evaluation hierarchical task network provided by the embodiment of the present invention comprises an initialization module 31, which is used to generate an initial evaluation hierarchical task network according to a target task, wherein the initial evaluation hierarchical task network comprises a hierarchical task network; an evaluation module 32, which is used to determine the evaluation information of the target node when the target node is executed, wherein the target node is any task node in the evaluation hierarchical task network; an optimization module 33, which is used to determine the preceding task sequence of the associated node of the target node according to the evaluation information; determine the optimized evaluation hierarchical task network according to the preceding task sequence, and complete the target task according to the optimized evaluation hierarchical task network. On the basis of the hierarchical task network, the target node in the initial evaluation hierarchical task network is evaluated to obtain the evaluation information, the preceding task sequence of the associated node of the target node is adjusted according to the evaluation information, and then the initial evaluation hierarchical task network is optimized according to the evaluation information of the target node to obtain the optimized evaluation hierarchical task network, thereby improving the execution efficiency of the target task.

[0131] The task processing device based on the evaluation hierarchical task network provided by the embodiment of the present invention can execute the task processing method based on the evaluation hierarchical task network provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0132] Figure 51 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0133] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0134] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0135] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a task processing method based on an evaluation hierarchical task network.

[0136] In some embodiments, the task processing method based on the evaluation hierarchical task network can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the task processing method based on the evaluation hierarchical task network described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the task processing method based on the evaluation hierarchical task network by any other appropriate means (for example, by means of firmware).

[0137] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] The computer program for implementing the task processing method based on the evaluation hierarchical task network of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or the block diagram are implemented. The computer program can be executed completely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or completely on a remote machine or server.

[0139] The embodiment of the invention further provides a computer-readable storage medium, the computer-readable storage medium storing computer instructions, the computer instructions being used to cause a processor to execute a task processing method based on an evaluation hierarchical task network, the method comprising:

[0140] Generate an initial evaluation hierarchical task network according to the target task, wherein the initial evaluation hierarchical task network includes a hierarchical task network;

[0141] When the target node is executed, determining the evaluation information of the target node, the target node being any task node in the evaluation hierarchical task network;

[0142] Determine the preceding task sequence of the associated nodes of the target node according to the evaluation information; determine the optimized evaluation hierarchical task network according to the preceding task sequence, and complete the target task according to the optimized evaluation hierarchical task network.

[0143] Based on the above embodiment, optionally, the target node of the evaluation hierarchical task network includes hierarchical task network parameters and evaluation related parameters;

[0144] The evaluation-related parameters include a combination of one or more of the following parameters: task subject, evaluation information, preceding task, operation target, evaluation mode, task time or task feedback information.

[0145] Based on the above embodiment, optionally, determining the evaluation information of the target node includes:

[0146] If the target node is a subtask node, the evaluation information is determined according to a combination of one or more of the following evaluation methods:

[0147] Position distance assessment, indirect feedback assessment, numerical comparison assessment or affiliation assessment.

[0148] Based on the above embodiment, optionally, determining the evaluation information of the target node includes:

[0149] If the target node is an action-level node, the evaluation information is determined according to a combination of one or more of the following evaluation methods:

[0150] Dynamic rule evaluation, time series evaluation, or state change evaluation.

[0151] Based on the above embodiment, optionally, determining the evaluation information of the target node includes:

[0152] Determining input features according to the graph density of the initial evaluation hierarchical task network;

[0153] Inputting the input features into a time series neural network to obtain output information;

[0154] Normalization is performed based on the output information of multiple target nodes to obtain a weight score for each target node, and the weight score is used as evaluation information.

[0155] Based on the above embodiment, optionally, determining the input feature according to the graph density of the initial evaluation hierarchical task network includes:

[0156] determining a graph structure according to the initial evaluation hierarchical task network;

[0157] Determine a graph intimacy matrix of the graph structure;

[0158] Get the context node of each task node;

[0159] Determine original features according to the context node;

[0160] Determining an absolute role feature according to the position information of the target node in the graph structure and the graph embedding information of the context node;

[0161] Determining a relative position feature of the target node according to the position information of the target node in the graph structure;

[0162] An input feature is determined according to the original feature, the absolute role feature and the relative position feature.

[0163] Based on the above embodiment, optionally, determining the preceding task sequence of the associated node of the target node according to the evaluation information includes:

[0164] If the initial evaluation hierarchical task network is a fixed-state task model, determining a preceding task sequence of an associated node of the target node according to the evaluation information and a rule base, wherein the associated node is a child node or a sibling node of the target node;

[0165] If the initial evaluation hierarchical task network is a task model in a non-fixed state, a key node is determined according to a preceding task of a node associated with the target node, and re-verification based on the evaluation information is started according to the key node.

[0166] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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), an 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.

[0167] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0168] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0169] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0170] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0171] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A task processing method based on an evaluation hierarchical task network, characterized in that: include: Generate an initial evaluation hierarchical task network according to the target task, wherein the initial evaluation hierarchical task network includes a hierarchical task network; When the target node is executed, determining the evaluation information of the target node, including: if the target node is a subtask node, determining the evaluation information according to a combination of one or more of the following evaluation methods: azimuth distance evaluation, indirect feedback evaluation, numerical comparison evaluation or affiliation evaluation; the target node is any task node in the evaluation hierarchical task network; Determine the preceding task sequence of the associated nodes of the target node according to the evaluation information; determine the optimized evaluation hierarchical task network according to the preceding task sequence, and complete the target task according to the optimized evaluation hierarchical task network.

2. The method according to claim 1, characterized in that The target node of the evaluation hierarchical task network includes hierarchical task network parameters and evaluation related parameters; The evaluation-related parameters include a combination of one or more of the following parameters: task subject, evaluation information, preceding task, operation target, evaluation mode, task time or task feedback information.

3. The method according to claim 1, characterized in that Determine the evaluation information of the target node, including: If the target node is an action-level node, the evaluation information is determined according to a combination of one or more of the following evaluation methods: Dynamic rule evaluation, time series evaluation, or state change evaluation.

4. The method according to claim 1, characterized in that: Determining the evaluation information of the target node includes: Determining input features according to the graph density of the initial evaluation hierarchical task network; Inputting the input features into a time series neural network to obtain output information; Normalization is performed based on the output information of multiple target nodes to obtain a weight score for each target node, and the weight score is used as evaluation information.

5. The method according to claim 4, characterized in that Determining input features according to the graph density of the initial evaluation hierarchical task network includes: determining a graph structure according to the initial evaluation hierarchical task network; Determine a graph intimacy matrix of the graph structure; Get the context node of each task node; Determine original features according to the context node; Determining an absolute role feature according to the position information of the target node in the graph structure and the graph embedding information of the context node; Determining a relative position feature of the target node according to the position information of the target node in the graph structure; An input feature is determined according to the original feature, the absolute role feature and the relative position feature.

6. The method according to claim 1, characterized in that Determining a preceding task sequence of a node associated with the target node according to the evaluation information includes: If the initial evaluation hierarchical task network is a fixed-state task model, determining a preceding task sequence of an associated node of the target node according to the evaluation information and a rule base, wherein the associated node is a child node or a sibling node of the target node; If the initial evaluation hierarchical task network is a task model in a non-fixed state, a key node is determined according to a preceding task of a node associated with the target node, and re-verification based on the evaluation information is started according to the key node.

7. A task processing device based on an evaluation hierarchical task network, characterized in that: include: An initialization module, used to generate an initial evaluation hierarchical task network according to a target task, wherein the initial evaluation hierarchical task network includes a hierarchical task network; An evaluation module, used for determining evaluation information of the target node when the target node is executed, including: if the target node is a subtask node, determining the evaluation information according to a combination of one or more of the following evaluation methods: azimuth distance evaluation, indirect feedback evaluation, numerical comparison evaluation or affiliation evaluation; the target node is any task node in the evaluation hierarchical task network; The optimization module is used to determine the predecessor task sequence of the associated nodes of the target node according to the evaluation information; determine the optimized evaluation hierarchical task network according to the predecessor task sequence, and complete the target task according to the optimized evaluation hierarchical task network.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the task processing method based on an evaluation hierarchical task network according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the task processing method based on an evaluation hierarchical task network according to any one of claims 1 to 6 when executed.

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