CN115713216A - Robot scheduling method and related equipment - Google Patents
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Abstract
The embodiment of the invention discloses a robot scheduling method and related equipment, wherein the method comprises the following steps: acquiring a user service request, wherein the service request comprises service scene information and task information; constructing a basic behavior map knowledge base based on the service scene information; matching a preset task execution flow in a preset template library according to the scene information and the task information; and evaluating the matched preset task execution flow according to the basic behavior map knowledge base and a preset analysis decision model, acquiring a scheduling execution strategy, and finishing the scheduling of the robot based on the scheduling execution strategy. By constructing the template base, the experience of technicians is effectively converted into a knowledge data body which can be identified, calculated, inferred and decided by a computer program, a basic framework can be quickly constructed aiming at services under different scenes, and the scheduling efficiency of the robot is improved.
Description
Technical Field
The invention relates to the technical field of robot scheduling management, in particular to a robot scheduling method and related equipment.
Background
In a service scene scheduled by a plurality of robots, tasks are decomposed into small tasks, the tasks are decomposed in a first-level and first-level manner, and finally processed by a single-point virtual robot, while mass data information needs to be analyzed, large tasks need to be decomposed, digested and executed, and current information is judged in the next step, the existing operating system often only provides the capability of basic transactions, and cannot provide the capability of direct calculation analysis and decision assistance from a bottom system kernel, when solving the actual problem of a certain scene, programming technicians are required to develop proper application programs to solve the problem of the actual service scene, the basic architecture cannot be quickly constructed, the process of quickly developing the service scene on the basic architecture is realized, and the processing efficiency of the actual service is influenced.
Disclosure of Invention
In view of this, the present invention provides a robot scheduling method and related devices, which are used to solve the problem in the prior art that when a practical problem of a certain scenario is faced, a programming technician is required to develop a suitable application program to solve the practical service scenario.
To achieve one or a part of or all of the above or other objects, the present invention provides a robot scheduling method, including: acquiring a user service request, wherein the service request comprises service scene information and task information;
constructing a basic behavior map knowledge base based on the service scene information;
matching a preset task execution flow in a preset template library according to the scene information and the task information;
and evaluating the matched preset task execution flow according to the basic behavior map knowledge base and a preset analysis decision model, acquiring a scheduling execution strategy, and finishing the scheduling of the robot based on the scheduling execution strategy.
Optionally, the step of constructing a basic behavior map knowledge base based on the service scenario information includes:
defining basic data contained in the service scenario information, wherein the basic data comprises: entity data, attribute data and entity relationship data; constructing a map structure based on the defined basic data;
and importing preset knowledge data into the map structure to obtain the basic behavior map knowledge base.
Optionally, the step of matching a task execution flow in a preset template library according to the scene information and the task information includes:
and selecting a target template from a preset template library according to the scene information and the task information, wherein the preset template library is constructed by combing, analyzing and constructing data results of historical tasks in different scenes, and the template comprises a task execution flow matched with the scene information and the task information.
Optionally, the step of selecting a target template in a template library according to the scene information and the task information includes:
screening out a task execution template corresponding to the scene information from the template library based on the scene information;
determining the task type based on the task information;
and determining the target template in the screened task execution templates according to the task type.
Optionally, the step of evaluating the matched preset task execution flow according to the basic behavior map knowledge base and the preset analysis decision model to obtain a scheduling execution policy includes:
decomposing the task information based on the scene information, and matching the decomposed task with the preset task execution flow to obtain subtasks corresponding to each flow node;
performing analysis evaluation decision on subtasks corresponding to each process node based on the preset analysis decision model, judging whether the subtasks need to be executed, if so, generating a scheduling execution strategy according to the distribution logic of the basic behavior map knowledge base, and scheduling a robot to execute the subtasks according to the scheduling execution strategy to obtain an execution report;
if not, terminating the subtask.
Optionally, the step of decomposing the task information based on the context information includes:
and decomposing the task information according to the entity relationship in the service scene information.
Optionally, the method further includes:
updating the preset analysis decision model based on the execution report.
In a second aspect, the present invention provides a robot scheduling system, the system comprising:
the system comprises a request receiving module, a service request processing module and a service processing module, wherein the request receiving module is used for acquiring a user service request, and the service request comprises service scene information and task information;
the map construction module is used for constructing a basic behavior map knowledge base based on the service scene information;
the matching module is used for matching a preset task execution flow in a preset template base according to the scene information and the task information;
and the scheduling module is used for evaluating the matched preset task execution flow according to the basic behavior map knowledge base and the preset analysis decision model to obtain a scheduling execution strategy and finishing the scheduling of the robot based on the scheduling execution strategy.
In a third aspect, the present application provides an electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is running, the machine-readable instructions, when executed by the processor, performing the steps of the robot scheduling method as described above.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, performs the steps of the robot scheduling method as described above.
The embodiment of the invention has the following beneficial effects:
acquiring service scene information and task information of a user; constructing a basic behavior map knowledge base based on the service scene information; matching task execution flows in a template base according to the scene information and the task information, and evaluating subtasks corresponding to each flow node according to the basic behavior map knowledge base and a preset analysis decision model to obtain a scheduling execution strategy, so that the scheduling process of the robot is completed, and the experience of technicians can be effectively converted into a knowledge data body which can be identified, calculated, reasoned and decided by a computer program; the method has the advantages that task execution flows are matched through a preset template base, a basic framework can be quickly constructed aiming at services under different scenes, the robot scheduling efficiency is improved, meanwhile, a multi-dimensional task scheduling engine constructed based on a behavior map can describe the relation between an execution body and an execution action from any granularity and multiple dimensionalities by using a network behavior execution body and an execution action unit supported by the network behavior execution body in a behavior map mode, the problems that the scheduling flow of the current task scheduling technology is solidified, the control granularity is coarse, the scene migration capability is insufficient, flexible expansion is difficult and the like are solved, and the knowledge processing of scheduling basic driving data is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
fig. 1 is a flowchart of a robot scheduling method according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a task decomposition process in a robot scheduling method according to an embodiment of the present disclosure;
FIG. 3 is a flowchart of another robot scheduling method provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a robot scheduling system according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a storage medium according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present application provides a robot scheduling method, including:
s101, acquiring a user service request, wherein the service request comprises service scene information and task information;
illustratively, a user inputs a user service request on an operating system of a supported virtual robot, and the operating system acquires the user service request and identifies the user service request to obtain a service scenario, i.e., service scenario information, to which the user service request is directed and specific tasks, i.e., task information, included in the user service request, where the task information includes a task type and task content.
S102, constructing a basic behavior map knowledge base based on the service scene information;
in a possible implementation, the step of building a basic behavior map knowledge base based on the service scenario information includes:
defining basic data contained in the service scenario information, wherein the basic data comprises: entity data, attribute data and entity relationship data, wherein the entities are divided into basic concept entities and network behavior entities;
constructing a map structure based on the defined basic data;
and importing preset knowledge data into the map structure to obtain the basic behavior map knowledge base.
Illustratively, when the service scenario is a security domain related scenario, the concrete process of abstracting and mapping the network behavior and asset entity related to the network security domain is as follows:
defining basic data contained in the service scenario information, wherein the basic data comprises: entity data, attribute data, and entity relationship data, the entity data comprising: a basic entity Er and a matter entity Ea; the data attribute is marked as P; the entity relationship data includes: empirical class relationship Rj, and factual relationship Rs.
The entities are divided into two types, one type is a basic concept entity, such as: IP, port, domain name, etc.; one is a network behavior class entity, such as: RDP blasting, http set request URL, elasticsearch unauthorized access vulnerability detection, etc.
When defining the basic data contained in the service scene information, the granularity defined by the entity can be adjusted at will according to the specific scene requirements to obtain the map structure;
filling specific knowledge data into the graph structure, wherein the specific knowledge data comprises the experience of technicians, such as: the asset inventory needs to perform domain name collection first, then perform IP, port service collection, etc., and the specific knowledge data further includes basic asset data, such as: IP, domain name, etc., to obtain the basic behavior map knowledge base.
S103, matching a preset task execution flow in a preset template library according to the scene information and the task information;
illustratively, after defining, editing and combing the historical tasks in different scenes, the scheduling engine is used for executing the historical tasks in different scenes to obtain the data results of the historical tasks in different scenes, the automatic affair work is carried out, and the automatic combing analysis is carried out on the data results to form the template library.
Illustratively, when a specific matter scene is realized, various kinds of knowledge are executed step by step according to a preset task execution flow in the template on the basis of the template.
S104, evaluating the matched preset task execution flow according to the basic behavior map knowledge base and a preset analysis decision model, acquiring a scheduling execution strategy, and finishing the scheduling of the robot based on the scheduling execution strategy; the preset analysis decision model comprises an empirical model, a common sense model and an algorithm model.
Illustratively, the basic behavior map knowledge base provides task distribution logic for specifying a robot for executing a task, and the preset analysis decision model is used for judging whether the task needs to be executed or not.
And when the preset analysis decision model judges that the current task needs to be executed, determining a robot for executing the task through the basic behavior map knowledge base, and further constructing a scheduling execution strategy.
The method comprises the steps of obtaining a user service request, wherein the service request comprises service scene information and task information; constructing a basic behavior map knowledge base based on the service scene information; selecting a target template from a template library according to the scene information and the task information, wherein the template library is constructed by combing, analyzing and constructing data results of historical tasks in different scenes, and the target template comprises a preset task execution flow, so that the experience of technicians is effectively converted into a knowledge data body which can be identified, calculated, reasoned and decided by a computer program; decomposing the task information based on the scene information, and matching the decomposed task with the preset task execution flow to obtain subtasks corresponding to each flow node; the method comprises the steps of evaluating subtasks corresponding to flow nodes according to a basic behavior map knowledge base and a preset analysis decision model to obtain a scheduling execution strategy, and completing a scheduling process of the robot based on the scheduling execution strategy, wherein the preset analysis decision model comprises an experience model, a common sense model and an algorithm model, a basic framework can be quickly constructed aiming at services under different scenes, the scheduling efficiency of the robot is improved, meanwhile, a multi-dimensional task scheduling engine constructed based on a behavior map can describe the relation between an execution body and an execution action from any granularity and multiple dimensions by using a network behavior execution body and an execution action unit supported by the network behavior execution body in a behavior map mode, the problems of solidification of a scheduling flow, coarse control granularity, insufficient scene migration capacity, difficulty in flexible expansion and the like of a current task scheduling technology are solved, and the knowledge processing of scheduling basic driving data is realized.
In a possible implementation manner, the step of matching a task execution flow in a preset template library according to the scene information and the task information includes:
and selecting a target template from a preset template library according to the scene information and the task information, wherein the preset template library is constructed by combing, analyzing and constructing data results of historical tasks in different scenes, and the template comprises a task execution flow matched with the scene information and the task information.
Illustratively, the template library is constructed by combing and analyzing data results of historical tasks in different scenes, and the target template comprises a preset task execution flow, so that experience of technicians is effectively converted into a knowledge data body which can be identified, calculated, reasoned and decided by a computer program
In a possible implementation manner, the step of selecting a target template in a template library according to the scene information and the task information includes:
screening all task execution templates corresponding to the scene information from the template library based on the scene information;
determining the task type based on the task information;
and determining the target template in all the screened task execution templates according to the task type.
Illustratively, all task execution templates corresponding to the scene information are screened from the template base based on basic attribute data such as scene identifiers in the scene information, coarse screening is performed in the template base for specific service scenes, all task execution templates corresponding to the service scenes are obtained after the coarse screening, fine screening is performed in all the screened task execution templates based on the task types, and the target template is determined.
For example, the templates in the template library may be stored in different partitions according to different service scenarios, that is, the templates corresponding to all task types of the same service scenario are stored in the same storage area, and when a specific service scenario is roughly screened, all task execution templates corresponding to the service scenario can be conveniently and quickly determined through the storage area and the storage data in the storage area.
In a possible implementation manner, the step of evaluating the subtasks corresponding to the process nodes according to the basic behavior map knowledge base and a preset analysis decision model includes:
importing entity data and entity relation data in the service scene information into the preset analysis decision model so that an experience model in the preset analysis decision model performs analysis evaluation decision on subtasks corresponding to each process node;
and importing the entity data, the entity relation data and the attribute data in the service scene information into the preset analysis decision model so that the common sense model in the preset analysis decision model performs analysis evaluation decision on the subtasks corresponding to the process nodes.
Illustratively, the empirical model evaluates task scheduling according to a (Er, ea) -Rj- > (Ea) chain, such as: according to experience, after scanning a domain name, an IP should be scanned; introducing a basic entity Er, a physical entity Ea and an experience relation Rj in the service scene information into the preset analysis decision model, so that the experience model in the preset analysis decision model performs analysis evaluation decision on subtasks corresponding to each process node;
the common sense model evaluates task scheduling according to the relationship of (Er) -Rs- (Er, P), such as: if the domain name bound by the IP is A, a plurality of the IP analyzed by A exist, and the general knowledge of CDN characteristics is met, so that a scanning port service task related to the IP should be terminated, the CDN is an operator acceleration node, and the scanning acceleration node has no practical significance; and importing the basic entity Er, the attribute data P and the factual relation Rs in the service scene information into the preset analysis decision model, so that the common sense model in the preset analysis decision model performs analysis evaluation decision on the subtasks corresponding to the process nodes.
In a possible implementation manner, as shown in fig. 2, the step of decomposing the task information based on the context information includes:
and decomposing the task information according to the entity relationship in the service scene information.
Illustratively, task scheduling is divided into two levels of scene tasks and entity subtasks, wherein the scene tasks are oriented to service scenes, namely, the initial scene of executing the event scheduling; the entity subtasks are subtasks constructed according to the affair entities decomposed from the scene tasks, and the decomposed tasks are matched with the preset task execution flows to obtain subtasks corresponding to the flow nodes.
In one possible embodiment, the method further comprises:
updating the pre-set analytical decision model based on the execution report.
Illustratively, when there are idle executable task resources in system resources, a scenario task is taken out from a queue in a scenario task pool, and a case entity task included in the scenario task is decomposed, for example: assuming that the scene task is a detection intranet risk point, acquiring all stock host node tasks of the current intranet according to the scene task in a decomposable way; analyzing and evaluating the decomposed task, judging whether the task needs to be executed, if so, generating a subtask, otherwise, terminating the task flow; if the subtask needs to be executed, distributing the subtask obtained by decomposition to a task execution pool for execution, wherein the task execution pool is a task queue executed by a final specific task unit, and dynamically reporting a task execution state and reporting a task execution result when the task is completed in the execution process to generate an execution report, namely, dynamic report content; and updating the preset analysis decision model according to the dynamic report content of the task execution state, and realizing dynamic feedback adjustment and scheduling logic.
In a possible implementation manner, the step of evaluating the matched preset task execution flow according to the basic behavior map knowledge base and a preset analysis decision model to obtain a scheduling execution policy includes:
decomposing the task information based on the scene information, and matching the decomposed task with the preset task execution flow to obtain subtasks corresponding to each flow node;
performing analysis evaluation decision on subtasks corresponding to each process node based on the preset analysis decision model, judging whether the subtasks need to be executed, if so, generating a scheduling execution strategy according to the distribution logic of the basic behavior map knowledge base, and scheduling a robot to execute the subtasks according to the scheduling execution strategy to obtain an execution report;
and if the execution is not required, terminating the subtask.
Illustratively, when judging whether the subtasks need to be executed, the empirical model and the common sense model judge whether the subtasks corresponding to the flow nodes are executed, and the algorithm model performs analysis, evaluation and decision-making on the subtasks corresponding to the flow nodes according to algorithms such as big data statistical analysis, machine learning, deep neural network learning and the like, so as to obtain accuracy data of the subtasks corresponding to the flow nodes.
As shown in fig. 3, a robot scheduling method includes:
the basic knowledge structure combing and defining specifically comprises application scene evaluation, case map modeling and basic case knowledge construction, wherein the application scene evaluation is equivalent to the process of identifying the user service request to obtain a service scene, namely service scene information, which the user service request aims at, and a specific task, namely task information, which the user service request contains, wherein the task information comprises a task type and task content, the case map modeling is equivalent to the process of defining basic data contained in the service scene information to obtain a map structure, and the basic case knowledge construction is equivalent to the process of filling specific knowledge data into the map structure to obtain a basic behavior map knowledge base;
the method comprises the following steps of driving a scheduling framework and defining a decision model, wherein the scheduling framework comprises a distributed driving framework, the distributed driving framework provides basic communication, storage and calculation support for task scheduling, and the distributed driving framework meets the following conditions: the module plugging mechanism is supported, the corresponding functional components can be started or unloaded according to the needs, and the integration and the expansion of new components are facilitated, such as: decision models, etc.; possess standard external integrated interface, including SDK, communication API etc. the system, equipment etc. that are relevant to the linkage functional unit of being convenient for, if: RESTful API interface, cross-platform SDK, etc.; supporting elastic capacity expansion, supporting a disk, bandwidth, computing power and the like, wherein the decision model definition is equivalent to the construction of the preset analysis decision model, and the preset analysis decision model comprises an empirical model, a common sense model and an algorithm model;
the dynamic feedback adjustment access is equivalent to the process of updating the preset analysis decision model based on the execution report.
In one possible embodiment, as shown in fig. 4, the present application provides a robot scheduling system, the system comprising:
a request receiving module 201, configured to obtain a user service request, where the service request includes service scenario information and task information;
the map construction module 202 is configured to construct a basic behavior map knowledge base based on the service scenario information;
the matching module 203 is configured to match a preset task execution flow in a preset template library according to the scene information and the task information;
and the scheduling module 204 is configured to evaluate the matched preset task execution flow according to the basic behavior map knowledge base and the preset analysis decision model to obtain a scheduling execution strategy, and complete scheduling of the robot based on the scheduling execution strategy.
Illustratively, the robot scheduling system receives various tasks in different scenes, at an execution end of the robot scheduling system, namely, a request receiving module, and a map construction process and a matching process are completed by an intelligent brain inside the robot scheduling system, wherein the intelligent brain comprises a map brain, a script brain, a affair brain, a task digestion brain and the like, the map brain is equivalent to the map construction module, and the matching module is equivalent to a brain used for matching a preset task execution flow in a preset template library according to scene information and task information.
Illustratively, in a script brain, a function code is used for interface definition, function modules are extracted in a standardized manner, each reaction is written by common action quantity to realize an overall framework of tasks, different logic reaction functions are written on the basis of the framework, and the virtual robot can be rapidly developed.
Illustratively, the request receiving module proposes and calls a case-affair task in a human-computer conversation of a user, executes the case-affair task after semantic analysis, adds the case-affair task to a case-affair brain, and the map building module builds a basic behavior map knowledge base based on the service scene information and then displays the basic behavior map knowledge base in the map brain; the matching module matches a preset task execution flow in a preset template library according to the scene information and the task information, a new cycle task is built based on the preset task execution flow, a corresponding matter task list is continuously read in a map brain, the progress is updated and displayed in a matter task progress organ of each Robot, the scheduling module evaluates the matched preset task execution flow according to the basic behavior map knowledge base and a preset analysis decision model, a scheduling execution strategy is obtained, and the scheduling of the Robot is completed based on the scheduling execution strategy.
In one possible implementation, as shown in fig. 5, an embodiment of the present application provides an electronic device 300, including: comprising a memory 310, a processor 320 and a computer program 311 stored in the memory 310 and executable on the processor 320, wherein the processor 320 implements, when executing the computer program 311: acquiring a user service request, wherein the service request comprises service scene information and task information; constructing a basic behavior map knowledge base based on the service scene information; matching a preset task execution flow in a preset template library according to the scene information and the task information; and evaluating the matched preset task execution flow according to the basic behavior map knowledge base and a preset analysis decision model, acquiring a scheduling execution strategy, and finishing the scheduling of the robot based on the scheduling execution strategy.
In one possible implementation, as shown in fig. 6, the present application provides a computer-readable storage medium 400, on which a computer program 411 is stored, where the computer program 411 implements, when executed by a processor: acquiring a user service request, wherein the service request comprises service scene information and task information; constructing a basic behavior map knowledge base based on the service scene information; matching a preset task execution flow in a preset template library according to the scene information and the task information; and evaluating the matched preset task execution flow according to the basic behavior map knowledge base and a preset analysis decision model, acquiring a scheduling execution strategy, and finishing the scheduling of the robot based on the scheduling execution strategy.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer-readable storage medium may be, for example but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It will be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general purpose computing device, they can be centralized in a single computing device or distributed over a network of multiple computing devices, and they can alternatively be implemented by program code executable by a computing device, so that they can be stored in a storage device and executed by a computing device, or they can be separately fabricated into various integrated circuit modules, or multiple modules or steps thereof can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A robot scheduling method, comprising:
acquiring a user service request, wherein the service request comprises service scene information and task information;
constructing a basic behavior map knowledge base based on the service scene information;
matching a preset task execution flow in a preset template library according to the scene information and the task information;
and evaluating the matched preset task execution flow according to the basic behavior map knowledge base and a preset analysis decision model, acquiring a scheduling execution strategy, and finishing the scheduling of the robot based on the scheduling execution strategy.
2. The robot scheduling method of claim 1, wherein the step of building a base behavior map knowledge base based on the business scenario information comprises:
defining basic data contained in the service scenario information, wherein the basic data comprises: entity data, attribute data and entity relationship data; constructing a map structure based on the defined basic data;
and importing preset knowledge data into the map structure to obtain the basic behavior map knowledge base.
3. The robot scheduling method of claim 1, wherein the step of matching a task execution flow in a preset template library according to the scene information and the task information comprises:
and selecting a target template from a preset template library according to the scene information and the task information, wherein the preset template library is constructed by combing, analyzing and constructing data results of historical tasks in different scenes, and the template comprises a task execution flow matched with the scene information and the task information.
4. The robot scheduling method of claim 3, wherein the step of selecting a target template in a template library based on the context information and the task information comprises:
screening out a task execution template corresponding to the scene information from the template library based on the scene information;
determining the task type based on the task information;
and determining the target template in the screened task execution templates according to the task type.
5. The robot scheduling method of claim 1, wherein the step of obtaining a scheduling execution strategy by evaluating the matched preset task execution flow according to the basic behavior map knowledge base and a preset analysis decision model comprises:
decomposing the task information based on the scene information, and matching the decomposed task with the preset task execution flow to obtain subtasks corresponding to each flow node;
performing analysis evaluation decision on subtasks corresponding to each process node based on the preset analysis decision model, judging whether the subtasks need to be executed, if so, generating a scheduling execution strategy according to the distribution logic of the basic behavior map knowledge base, and scheduling a robot to execute the subtasks according to the scheduling execution strategy to obtain an execution report;
and if the execution is not required, terminating the subtask.
6. The robot scheduling method of claim 5, wherein the step of decomposing the task information based on the context information comprises:
and decomposing the task information according to the entity relationship in the service scene information.
7. The robot scheduling method of claim 5, further comprising:
updating the preset analysis decision model based on the execution report.
8. A robot scheduling system, the system comprising:
the system comprises a request receiving module, a service request processing module and a service processing module, wherein the request receiving module is used for acquiring a user service request, and the service request comprises service scene information and task information;
the map construction module is used for constructing a basic behavior map knowledge base based on the service scene information;
the matching module is used for matching a preset task execution flow in a preset template library according to the scene information and the task information;
and the scheduling module is used for evaluating the matched preset task execution flow according to the basic behavior map knowledge base and the preset analysis decision model to obtain a scheduling execution strategy and finishing the scheduling of the robot based on the scheduling execution strategy.
9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the robot scheduling method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, performs the steps of the robot scheduling method according to any one of claims 1 to 7.
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| CN117391202B (en) * | 2023-12-13 | 2024-03-26 | 海乂知信息科技(南京)有限公司 | Method and system for obtaining analysis decision result based on central control knowledge graph |
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