CN114444931A

CN114444931A - Task allocation method and device combining RPA and AI

Info

Publication number: CN114444931A
Application number: CN202210094473.7A
Authority: CN
Inventors: 李检; 王瑞丰
Original assignee: Laiye Technology Beijing Co Ltd
Current assignee: Laiye Technology Beijing Co Ltd
Priority date: 2022-01-26
Filing date: 2022-01-26
Publication date: 2022-05-06

Abstract

The application provides a task allocation method and a device thereof combining Robot Process Automation (RPA) and Artificial Intelligence (AI). by acquiring tasks needing cooperative processing in an RPA robot process, the tasks needing cooperative processing are taken as target cooperative tasks; aiming at any candidate cooperative equipment, acquiring a task allocation parameter of the screened allocation target cooperative task of the candidate cooperative equipment; determining target cooperative equipment for executing the target cooperative task from the candidate cooperative equipment according to the task allocation parameters of each candidate cooperative equipment; and distributing the target cooperative task to the target cooperative equipment for execution. According to the method and the device, the target cooperative equipment is determined by acquiring the task allocation parameters of each candidate cooperative equipment, so that the situation that multiple items of work are overstocked on a certain staff is avoided, human resources can be more efficiently and reasonably utilized, a large amount of operation of managers is avoided, and the whole process is shorter in completion time and more efficient.

Description

Task allocation method and device combining RPA and AI

Technical Field

The present disclosure relates to the field of Robot Process Automation (RPA) and Artificial Intelligence (AI), and more particularly, to a method and an apparatus for allocating tasks by combining RPA and AI.

Background

Robot Process Automation (RPA) is a Process task that simulates human operations on a computer through specific robot software and automatically executes according to rules.

Artificial Intelligence (AI) is a technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human Intelligence.

In the related technology, a man-machine cooperation center is used as a manual operation entrance to link the cooperative work of a human and a robot, so that the links of manual checking, revision and the like of the process are realized. However, in many scenarios, if a cooperative task is randomly allocated to a corresponding cooperative employee for processing, the situation that part of the cooperative employees work in a pile-up state and part of the cooperative employees are idle often occurs, so that the task management cost is high.

Disclosure of Invention

The embodiment of the application provides a task allocation method combining RPA and AI to solve the problems in the related art, and the technical scheme is as follows:

in a first aspect, an embodiment of the present application provides a task allocation method combining an RPA and an AI, including: acquiring a task needing cooperative processing in an RPA robot flow, and taking the task needing cooperative processing as a target cooperative task; aiming at any candidate cooperative equipment, acquiring a task allocation parameter of the screened allocation target cooperative task of the candidate cooperative equipment; determining target cooperative equipment for executing the target cooperative task from the candidate cooperative equipment according to the task allocation parameters of each candidate cooperative equipment; and distributing the target cooperative task to the target cooperative equipment for execution.

In one embodiment, the obtaining of the task allocation parameter of the candidate cooperative device screened out to allocate the target cooperative task includes: acquiring historical task collaborative data of the candidate collaborative device; performing keyword identification on historical task collaborative data based on Natural Language Processing (NLP), and acquiring data items of the candidate collaborative equipment under each screening dimension and weights corresponding to the screening dimensions; and determining task allocation parameters of the candidate cooperative equipment according to the data items under each screening dimension and the corresponding weights of the data items.

In one embodiment, determining the task allocation parameter of the candidate cooperative device according to the data item and the corresponding weight of the data item in each screening dimension includes: for each screening dimension, performing weighted operation on the data item and the weight corresponding to the data item under each screening dimension, and determining a task allocation sub-parameter of the screening dimension; and determining task allocation parameters based on the task allocation sub-parameters of each screening dimension.

In one embodiment, the data items include a status data item and a task execution data item, where the obtaining of the data item of the candidate cooperative device in each filtering dimension and the weight corresponding to each filtering dimension includes: acquiring a state data item of the candidate cooperative equipment and a first weight corresponding to the state data item; and acquiring at least one task execution data item of the candidate cooperative device and the corresponding weight of the task execution data item.

In one embodiment, the task execution data item includes an average task execution time and a task accuracy, where obtaining weights corresponding to at least one task execution data item and the task execution data item of the candidate cooperative device includes: acquiring the average task execution time of the candidate cooperative equipment and a second weight corresponding to the average task execution time; and acquiring the task accuracy of the candidate cooperative device and a third weight corresponding to the task accuracy.

In one embodiment, determining a target cooperative device for executing a target cooperative task from candidate cooperative devices according to a task allocation parameter of each candidate cooperative device includes: and comparing the task allocation parameters of the candidate cooperative devices, and selecting the candidate cooperative device with the largest task allocation parameter as the target cooperative device.

In a second aspect, an embodiment of the present application provides a task allocation apparatus combining an RPA and an AI, where the apparatus includes: the first acquisition module is used for acquiring tasks needing cooperative processing in the RPA robot flow and taking the tasks needing cooperative processing as target cooperative tasks; a second obtaining module, configured to obtain, for any one candidate cooperative device, a task allocation parameter of the candidate cooperative device screened out the allocation target cooperative task; the determining module is used for determining target cooperative equipment for executing the target cooperative task from each candidate cooperative equipment according to the task allocation parameter of each candidate cooperative equipment; and the distribution module is used for distributing the target cooperative task to the target cooperative equipment for execution.

In one embodiment, the second obtaining module is further configured to: acquiring historical task collaborative data of the candidate collaborative device; performing keyword identification on historical task collaborative data based on Natural Language Processing (NLP), and acquiring data items of the candidate collaborative equipment under each screening dimension and weights corresponding to the screening dimensions; and determining task allocation parameters of the candidate cooperative equipment according to the data items under each screening dimension and the corresponding weights of the data items.

In one embodiment, the second obtaining module is further configured to: for each screening dimension, performing weighted operation on the data item and the weight corresponding to the data item under each screening dimension, and determining a task allocation sub-parameter of the screening dimension; and determining task allocation parameters based on the task allocation sub-parameters of each screening dimension.

In one embodiment, the second obtaining module is further configured to: acquiring a state data item of the candidate cooperative equipment and a first weight corresponding to the state data item; and acquiring at least one task execution data item of the candidate cooperative device and the corresponding weight of the task execution data item.

In one embodiment, the second obtaining module is further configured to: acquiring the average task execution time of the candidate cooperative equipment and a second weight corresponding to the average task execution time; and acquiring the task accuracy of the candidate cooperative device and a third weight corresponding to the task accuracy.

In one embodiment, the determining module is further configured to: and comparing the task allocation parameters of the candidate cooperative devices, and selecting the candidate cooperative device with the largest task allocation parameter as the target cooperative device.

In a third aspect, an embodiment of the present application provides an electronic device, including: a memory and a processor. Wherein the memory and the processor are in communication with each other through an internal connection path, the memory is configured to store instructions, and the processor is configured to execute the instructions stored by the memory, and when the processor executes the instructions stored by the memory, the processor is enabled to execute the task allocation method combining the RPA and the AI in any of the above-mentioned aspects.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program runs on a computer, the task allocation method combining the RPA and the AI in any one of the above-mentioned aspects is executed.

The advantages or beneficial effects in the above technical solution at least include:

according to the method and the device, the target cooperative equipment is determined by obtaining the task allocation parameters of each candidate cooperative equipment, the phenomenon that multiple items of work are overstocked on a certain staff is avoided, human resources can be more efficiently and reasonably utilized, a large number of operations of managers are avoided, the whole process is shorter and more efficient in completion time, and the subjective activity of the cooperative staff is improved in some scenes for calculating salary by task amount.

The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present application will be readily apparent by reference to the drawings and following detailed description.

Drawings

In the drawings, like reference characters designate like or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.

Fig. 1 is a flowchart illustrating a task allocation method combining RPA and AI according to the present application.

Fig. 2 is a schematic diagram of a target cooperative device for determining a target cooperative task according to the present application.

Fig. 3 is a flowchart illustrating another task allocation method combining RPA and AI according to the present application.

Fig. 4 is a general flowchart of a task allocation method combining RPA and AI according to the present application.

Fig. 5 is a schematic diagram of a task allocation apparatus combining RPA and AI according to the present application.

Fig. 6 is a schematic diagram of an electronic device shown in the present application.

Detailed Description

Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.

For ease of understanding, terms referred to in the present application will be first introduced.

In the description of the present application, the term "plurality" means two or more.

In the description of the application, the term "cooperative task" refers to that a human-computer cooperation center takes a task needing manual judgment and decision as a cooperative task in an automatic process, and distributes the task to a human, and the human provides accurate input for a robot through operations such as form information input, information secondary check and confirmation and the like, so that more and safer automatic opportunities are created.

In the description of the present application, the term "weight" refers to the degree of importance of a certain factor or index relative to a certain thing, which is different from the general specific gravity, and is not merely represented by the percentage of the certain factor or index, and it is emphasized that the relative degree of importance of the factor or index tends to contribute to degree or importance.

In the description of the present application, the term "RPA robot flow" refers to a flow in which an RPA robot automatically processes high-frequency services with clear rules and batched by simulating manual operation of a keyboard and mouse, and the RPA robot flow is suitable for operation flows with clear service rules and structured input and output in enterprises, such as boring, repeated and standardized work of reading mails, reconciliation and summary, checking files, generating files and reports, and the like, and can be completed by the RPA robot instead.

In the description of the present application, the term "candidate cooperative devices" refers to each device capable of processing the target cooperative task.

In the description of the present application, the term "task allocation parameter" refers to a parameter value representing a composite score of all screening dimensions of candidate cooperative devices determined from historical task cooperative data of each candidate cooperative device in order to facilitate determination of a target cooperative device that finally executes a target cooperative task.

In the description of the present application, the term "task assignment subparameter" refers to a parameter value in a dimension determined by a data item and a data item weight in a single dimension of each candidate cooperative device.

In the description of the present application, the term "target cooperative device" refers to a device that determines the most suitable device from among all candidate cooperative devices as a target cooperative device for performing a target cooperative task.

In the description of the present application, the term "historical task collaborative data" refers to data information of all collaborative tasks that have been completed by each candidate collaborative device within a set time period, such as information of execution start time, execution end time, execution result, accuracy and the like of a completed collaborative task.

In the description of the present application, the term "filtering dimension" refers to a data selection dimension when acquiring task allocation parameters of each candidate cooperative device, such as an average task processing duration dimension, a task processing accuracy dimension, and the like.

In the description of the present application, the term "task accuracy rate" refers to the processing accuracy rate of all the cooperative tasks completed by each candidate cooperative device, for example, if a candidate cooperative device processes 100 cooperative tasks within a set time period, where 99 cooperative tasks are executed correctly, and 1 cooperative task is executed incorrectly, the task accuracy rate of the candidate cooperative device is 99%.

These and other aspects of embodiments of the present application will be apparent from and elucidated with reference to the following description and drawings. In the description and drawings, particular embodiments of the application are disclosed in detail as being indicative of some of the ways in which the principles of the embodiments of the application may be practiced, but it is understood that the embodiments of the application are not limited correspondingly in scope. On the contrary, the embodiments of the application include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.

The following describes a task allocation method combining RPA and AI and an apparatus thereof according to an embodiment of the present application with reference to the drawings.

Fig. 1 is a flowchart of a task allocation method combining RPA and AI according to an embodiment of the present application, which is performed by an RPA robot, and as shown in fig. 1, the method may include the following steps:

and S101, acquiring the tasks needing the cooperative processing in the RPA robot flow, and taking the tasks needing the cooperative processing as target cooperative tasks.

The Robot Process Automation (RPA) automatically executes the Process task according to the rule by specific RPA robot software according to the operation of the RPA robot simulating human on the computer. And when the flow is executed to a task which needs manual judgment and decision, taking the task which needs cooperative processing as a target cooperative task. For example, the target cooperative task can be applied to a plurality of business fields, such as the building industry, the financial field, the human resource management, the internal service of the enterprise, and the like. Specifically, the target cooperative task may be an approval task that requires manual approval of summarized data acquired by the RPA robot, may be an audit task that requires manual approval of a cost form acquired by the RPA robot, and may be an approval task that requires manual approval of a leave request acquired by the RPA robot.

For example, when a certain RPA robot flow of company a performs task a, it needs to perform manual statistical confirmation on task a, and then task a is a target cooperative task of the RPA robot flow.

S102, aiming at any candidate cooperative equipment, acquiring task allocation parameters of the screened allocation target cooperative task of the candidate cooperative equipment.

All devices capable of processing the target cooperative task are taken as candidate cooperative devices. For example, say, the company a needs to perform manual statistical confirmation on the target collaborative task a, then all devices capable of processing the target collaborative task a are taken as candidate collaborative devices. For example, if 10 devices are capable of executing the target cooperative task a, all of the 10 devices are taken as candidate cooperative devices.

Fig. 2 is a schematic diagram of determining a target cooperative device corresponding to a target cooperative task, shown in the present application, and as shown in fig. 2, determining a plurality of candidate cooperative devices corresponding to the target cooperative task, and acquiring a task allocation parameter of each candidate cooperative device screened out to allocate the cooperative task. Optionally, the task allocation parameter may allocate a score to the task of the candidate cooperative device, and may also allocate a probability to the task of the candidate cooperative device. Optionally, the task allocation parameters may be obtained by combining the current-time status data of the employee corresponding to the candidate cooperative device, which is recorded by the candidate cooperative device, and the historical processing task data of the employee corresponding to the candidate cooperative device within a set time period.

As shown in fig. 2, if the plurality of candidate cooperative devices corresponding to the target cooperative task a are a candidate cooperative device a, a candidate cooperative device B, a candidate cooperative device C, a candidate cooperative device D, and a candidate cooperative device E, respectively, the task allocation parameters obtained respectively correspond to the candidate cooperative devices are a task allocation parameter a, a task allocation parameter B, a task allocation parameter C, a task allocation parameter D, and a task allocation parameter E, respectively.

S103, determining target cooperative equipment for executing the target cooperative task from each candidate cooperative equipment according to the task allocation parameters of each candidate cooperative equipment.

And according to the obtained task allocation parameters, determining candidate cooperative equipment of which the task allocation parameters meet set conditions from all the candidate cooperative equipment as target cooperative equipment for executing the target cooperative task. Alternatively, the setting condition may be that the task allocation parameter value is maximum, the task allocation parameter satisfies a setting threshold, and the like.

For example, as shown in fig. 2, if the task allocation parameter is a numerical value, the obtained task allocation parameter a, task allocation parameter b, task allocation parameter c, task allocation parameter d, and task allocation parameter e are compared, and the candidate cooperative device with the largest task allocation parameter numerical value is selected as the target cooperative device for executing the target cooperative task.

And S104, distributing the target cooperation task to the target cooperation device for execution.

And after the target cooperative equipment is determined, distributing the target cooperative task to the target cooperative equipment, and executing and finishing the target cooperative task by the target cooperative equipment.

For example, as shown in fig. 2, if the finally determined target cooperative device is the candidate cooperative device C, the target cooperative task a is sent to the candidate cooperative device C for execution.

The application provides a task allocation method combining RPA and AI, which comprises the steps of acquiring a task needing cooperative processing in an RPA robot flow, and taking the task needing cooperative processing as a target cooperative task; aiming at any candidate cooperative equipment, acquiring a task allocation parameter of the screened allocation target cooperative task of the candidate cooperative equipment; determining target cooperative equipment for executing the target cooperative task from the candidate cooperative equipment according to the task allocation parameters of each candidate cooperative equipment; and distributing the target cooperative task to the target cooperative equipment for execution. According to the method and the device, the target cooperative equipment is determined by obtaining the task allocation parameters of each candidate cooperative equipment, the phenomenon that multiple items of work are overstocked on a certain staff is avoided, human resources can be more efficiently and reasonably utilized, a large number of operations of managers are avoided, the whole process is shorter and more efficient in completion time, and the subjective activity of the cooperative staff is improved in some scenes for calculating salary by task amount.

Fig. 3 is a flowchart of a task allocation method combining RPA and AI according to an embodiment of the present application, which is performed by an RPA robot, and as shown in fig. 3, the method may include the following steps:

s301, acquiring the tasks needing the cooperative processing in the RPA robot flow, and taking the tasks needing the cooperative processing as target cooperative tasks.

As for the implementation manner of step S301, the implementation manners in the embodiments in the present application may be adopted, and are not described herein again.

S302, historical task collaborative data of the candidate collaborative device are obtained, keyword recognition is carried out on the historical task collaborative data based on Natural Language Processing (NLP), and data items of the candidate collaborative device under all screening dimensions and weights corresponding to all screening dimensions are obtained.

In order to accurately determine the most appropriate target cooperative device from the multiple candidate cooperative devices, historical task cooperative data of each candidate cooperative device is obtained, keyword recognition is performed on the historical task cooperative data of each candidate cooperative device based on Natural Language Processing (NLP), and data items of each candidate cooperative device in different screening dimensions and weights corresponding to the screening dimensions are obtained.

Alternatively, the data items may be status data items and task execution data items of the candidate cooperative devices. Wherein the status data item represents whether the candidate cooperative device is currently in a busy state or an idle state, and the task execution data item represents task execution data acquired from a history of executed tasks of the candidate cooperative device. Alternatively, the task execution data item may include an average task execution time and a task accuracy rate of the candidate cooperative devices within a set period of time.

Optionally, when selecting the screening dimension of the candidate collaborative device, the selection may be freely performed according to the actual situation, and the selection of three screening dimensions is taken as an example in the following of the application, and is respectively a current busy state dimension, a task average processing duration dimension, and a task processing accuracy dimension of the candidate collaborative device.

Illustratively, the status data items of the candidate cooperative devices and the corresponding first weights of the status data items are obtained. The status data item is denoted as Fx, and the first weight corresponding to the status data item is denoted as f. Wherein the status data item indicates whether the candidate cooperative device indicates that the corresponding employee is currently in a busy state or an idle state. In order to facilitate calculation of task allocation parameters of subsequent candidate cooperative devices, if a candidate cooperative device indicates that a corresponding employee is currently in a busy state, a first numerical value is given to a state data item; and if the candidate cooperative equipment indicates that the corresponding staff is in the idle state currently, giving a second numerical value to the state data item. Wherein the second value is greater than the first value. Alternatively, the first value may be set to 30 and the second value to 100.

And acquiring the average task execution time of the candidate cooperative equipment and a second weight corresponding to the average task execution time. For example, if a candidate cooperative device completes 2 cooperative tasks in total, where 1 cooperative task takes 100 minutes and another cooperative task takes 20 minutes, the average task execution time of the candidate cooperative device is 60 minutes. And recording the average task execution time of the candidate cooperative equipment as Ax, and recording a second weight corresponding to the average task execution time as a.

And acquiring the task accuracy of the candidate cooperative equipment and a third weight corresponding to the task accuracy. The task accuracy of the candidate cooperative device refers to the task accuracy of the staff corresponding to the candidate cooperative device completing all the completed tasks corresponding to the candidate cooperative device, for example, a candidate cooperative device processes 100 cooperative tasks in a set time period, where 99 cooperative tasks have a correct execution result, and 1 cooperative task has a wrong execution result, the task accuracy of the candidate cooperative device is 99%. And recording the task accuracy of the candidate cooperative equipment as Qx, and recording a third weight corresponding to the task accuracy as q.

Wherein, the sum of the first weight, the second weight and the third weight is 1.

And S303, determining task allocation parameters of the candidate cooperative equipment according to the data items under each screening dimension and the corresponding weights of the data items.

And determining task allocation parameters of the candidate cooperative equipment according to the obtained data items under the multiple screening dimensions and the corresponding weights of the data items.

As an implementation manner, task allocation sub-parameters of a plurality of screening dimensions are determined according to data items and weights under the screening dimensions. On the basis, taking a product Fx f of the state data item Fx and the corresponding first weight f as a first task allocation sub-parameter; taking the product Ax of the average task execution time Ax and the corresponding second weight a as a second task allocation sub-parameter; and taking the product Qx q of the task accuracy rate Qx and the corresponding third weight q as a third task allocation sub-parameter.

Determining task allocation parameters of the candidate cooperative devices based on the task allocation sub-parameters of the three screening dimensions, wherein a calculation formula of the task allocation parameters is as follows:

W＝Fx*f-Ax*a+Qx*q

in the above formula, W represents a task allocation parameter value of the candidate cooperative device; fx represents a status data item, and the status data item can be a first value when the candidate cooperative device is in a busy state or a second value when the candidate cooperative device is in an idle state; f represents a first weight corresponding to the state data item; ax represents the average task execution time of the cooperative task completed by the candidate cooperative device; a represents a second weight corresponding to the average task execution time; qx represents the task accuracy of the cooperative task completed by the candidate cooperative device; and q represents a third weight corresponding to the task accuracy.

Wherein, when calculating, the task accuracy does not need to be added with a percentile. Since the target cooperative device is determined according to the task allocation parameters obtained in the above equation, in some jobs in which remuneration is calculated according to the number of pieces, the efficiency is lower as the average task execution time is longer, and in the above equation, a minus sign is used before the average task execution time Ax in order to improve the subjective initiative of the employee.

Illustratively, if a candidate cooperative device is acquired to be in an idle state, a value of a corresponding state data item Fx is 100, a first weight corresponding to the state data item is 0.5, the average task execution time is 20 minutes, a second weight corresponding to the average task execution time is 0.2, the task accuracy is 98%, and a third weight corresponding to the task accuracy is 0.3, then a task allocation parameter of the candidate cooperative device is 100.5-20 × 0.2+98 × 0.3 — 75.4.

S304, comparing the task allocation parameters of the candidate cooperative devices, and selecting the candidate cooperative device with the largest task allocation parameter as the target cooperative device.

And comparing the task allocation parameters of all candidate cooperative devices according to the numerical value, and selecting the candidate cooperative device with the largest task allocation parameter as the target cooperative device. For example, if the task allocation parameters of 5 candidate cooperative devices corresponding to the target cooperative task a are 75.4, 72, 71, 68, and 53, respectively, the candidate cooperative device with the task allocation parameter of 75.4 is taken as the target cooperative device.

S305, distributing the target cooperation task to the target cooperation device for execution.

As for the implementation manner of step S305, the implementation manners in the embodiments in the present application may be adopted, and are not described herein again.

According to the method and the device, the task allocation parameters of each candidate cooperative device are determined through the data items with different dimensions and the corresponding weights, so that the target cooperative device is determined, the method for determining the target cooperative device is more accurate, the RPA robot can more efficiently and reasonably utilize human resources, and the whole process is shorter and more efficient in completion time.

Fig. 4 is a flowchart of a task allocation method combining RPA and AI according to an embodiment of the present application, which is performed by an RPA robot, and as shown in fig. 4, the method may include the following steps:

s401, acquiring the tasks needing the cooperative processing in the RPA robot flow, and taking the tasks needing the cooperative processing as target cooperative tasks.

As for the implementation manner of step S401, the implementation manners in the embodiments in the present application may be adopted, and are not described herein again.

S402, acquiring the state data item of each candidate cooperative device and a first weight corresponding to the state data item.

S403, acquiring the average task execution time of each candidate cooperative device and a second weight corresponding to the average task execution time.

S404, acquiring the task accuracy of each candidate cooperative device and a third weight corresponding to the task accuracy.

S405, determining task allocation sub-parameters under each screening dimension according to the data items under each screening dimension and the weight corresponding to the data items.

S406, for any candidate cooperative device, determining the task allocation parameters of the candidate cooperative device based on the task allocation sub-parameters of each screening dimension.

As to the implementation manners of steps S402 to S406, the implementation manners in the embodiments in the present application may be adopted, and are not described herein again.

S407, comparing the task allocation parameters of the candidate cooperative devices, and selecting the candidate cooperative device with the largest task allocation parameter as the target cooperative device.

And S408, distributing the target cooperative task to the target cooperative equipment for execution.

As for the implementation manners of steps S407 to S408, the implementation manners in the embodiments in the present application may be adopted, and are not described herein again.

Fig. 5 is a schematic diagram of the RPA and AI combined task allocation apparatus according to an embodiment of the present application, and as shown in fig. 5, the RPA and AI combined task allocation apparatus 500 includes a first obtaining module 51, a second obtaining module 52, a determining module 53, and an allocating module 54, where:

the first obtaining module 51 is configured to obtain a task that needs to be cooperatively processed in an RPA robot flow, and use the task that needs to be cooperatively processed as a target cooperative task.

A second obtaining module 52, configured to obtain, for any candidate cooperative device, a task allocation parameter of the candidate cooperative device screened out to allocate the target cooperative task.

The determining module 53 is configured to determine, according to the task allocation parameter of each candidate cooperative device, a target cooperative device that executes a target cooperative task from the candidate cooperative devices.

And an allocating module 54, configured to allocate the target cooperative task to the target cooperative device for execution.

Further, the second obtaining module 52 is further configured to: acquiring historical task collaborative data of the candidate collaborative device; performing keyword identification on historical task collaborative data based on Natural Language Processing (NLP), and acquiring data items of the candidate collaborative equipment under each screening dimension and weights corresponding to the screening dimensions; and determining task allocation parameters of the candidate cooperative equipment according to the data items under each screening dimension and the corresponding weights of the data items.

Further, the second obtaining module 52 is further configured to: for each screening dimension, performing weighted operation on the data item and the weight corresponding to the data item under each screening dimension, and determining a task allocation sub-parameter of the screening dimension; and determining task allocation parameters based on the task allocation sub-parameters of each screening dimension.

Further, the second obtaining module 52 is further configured to: acquiring a state data item of the candidate cooperative equipment and a first weight corresponding to the state data item; and acquiring at least one task execution data item of the candidate cooperative device and the corresponding weight of the task execution data item.

Further, the second obtaining module 52 is further configured to: acquiring the average task execution time of the candidate cooperative equipment and a second weight corresponding to the average task execution time; and acquiring the task accuracy of the candidate cooperative device and a third weight corresponding to the task accuracy.

Further, the determining module 53 is further configured to: and comparing the task allocation parameters of the candidate cooperative devices, and selecting the candidate cooperative device with the largest task allocation parameter as the target cooperative device.

Fig. 6 shows a block diagram of an electronic device according to an embodiment of the present application. As shown in fig. 6, the electronic apparatus includes: a memory 610 and a processor 620, the memory 610 having stored therein computer programs executable on the processor 620. The processor 620, when executing the computer program, implements the task allocation method combining RPA and AI in the above embodiments. The number of the memory 610 and the processor 620 may be one or more.

The electronic device further includes:

the communication interface 630 is used for communicating with an external device to perform data interactive transmission.

If the memory 610, the processor 620 and the communication interface 630 are implemented independently, the memory 610, the processor 620 and the communication interface 630 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.

Optionally, in an implementation, if the memory 610, the processor 620, and the communication interface 630 are integrated on a chip, the memory 610, the processor 620, and the communication interface 630 may complete communication with each other through an internal interface.

Embodiments of the present application provide a computer-readable storage medium, which stores a computer program, and when the program is executed by a processor, the computer program implements a task allocation method combining an RPA and an AI provided in an embodiment of the present application.

The embodiment of the present application further provides a chip, where the chip includes a processor, and is configured to call and run an instruction stored in a memory from the memory, so that a communication device equipped with the chip executes the task allocation method combining the RPA and the AI provided in the embodiment of the present application.

An embodiment of the present application further provides a chip, including: the processor is used for executing codes in the memory, and when the codes are executed, the processor is used for executing the task allocation method combining the RPA and the AI provided by the embodiment of the application.

It should be understood that the processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or any conventional processor or the like. It is noted that the processor may be an advanced reduced instruction set machine (ARM) architecture supported processor.

Further, optionally, the memory may include a read-only memory and a random access memory, and may further include a nonvolatile random access memory. The memory may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may include a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available. For example, Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct memory bus RAM (DR RAM).

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the present application are generated in whole or in part when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process. And the scope of the preferred embodiments of the present application includes other implementations in which functions may be performed out of the order shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the method of the above embodiments may be implemented by hardware that is configured to be instructed to perform the relevant steps by a program, which may be stored in a computer-readable storage medium, and which, when executed, includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module may also be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present application, and these should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for task allocation combining Robot Process Automation (RPA) and Artificial Intelligence (AI), performed by an RPA robot, the method comprising:

acquiring a task needing cooperative processing in an RPA robot flow, and taking the task needing cooperative processing as a target cooperative task;

acquiring a task allocation parameter of the candidate cooperative device screened out and allocated with the target cooperative task aiming at any candidate cooperative device;

determining target cooperative equipment for executing the target cooperative task from the candidate cooperative equipment according to the task allocation parameters of each candidate cooperative equipment;

and distributing the target cooperative task to the target cooperative equipment for execution.

2. The method according to claim 1, wherein the obtaining task allocation parameters of the candidate cooperative device screened out to allocate the target cooperative task comprises:

acquiring historical task collaborative data of the candidate collaborative device;

performing keyword identification on the historical task collaborative data based on Natural Language Processing (NLP), and acquiring data items of the candidate collaborative equipment under each screening dimension and weights corresponding to the screening dimensions;

and determining task allocation parameters of the candidate cooperative equipment according to the data items under each screening dimension and the weights corresponding to the data items.

3. The method according to claim 2, wherein the determining task allocation parameters of the candidate cooperative device according to the data items and the weights corresponding to the data items in each screening dimension includes:

for each screening dimension, performing weighted operation on the data item and the weight corresponding to the data item under each screening dimension, and determining a task allocation sub-parameter of the screening dimension;

and determining the task allocation parameters based on the task allocation sub-parameters of each screening dimension.

4. The method according to claim 2, wherein the data items include a status data item and a task execution data item, and wherein the obtaining the data items of the candidate cooperative device in each screening dimension and the weight corresponding to each screening dimension includes:

acquiring a state data item of the candidate cooperative equipment and a first weight corresponding to the state data item;

and acquiring at least one task execution data item of the candidate cooperative device and the weight corresponding to the task execution data item.

5. The method according to claim 4, wherein the task execution data items include an average task execution time and a task accuracy, and wherein the obtaining of the weight corresponding to at least one task execution data item of the candidate cooperative device and the task execution data item comprises:

acquiring the average task execution time of the candidate cooperative equipment and a second weight corresponding to the average task execution time;

and acquiring the task accuracy of the candidate cooperative device and a third weight corresponding to the task accuracy.

6. The method according to any one of claims 1 to 5, wherein the determining, from the candidate cooperative devices, a target cooperative device that executes the target cooperative task according to the task allocation parameter of each of the candidate cooperative devices, includes:

comparing the task allocation parameters of the candidate cooperative devices, and selecting the candidate cooperative device with the largest task allocation parameter as the target cooperative device.

7. A task allocation device that combines robot process automation, RPA, and artificial intelligence, AI, the device comprising:

the system comprises a first acquisition module, a first processing module and a second processing module, wherein the first acquisition module is used for acquiring tasks needing cooperative processing in an RPA robot flow and taking the tasks needing cooperative processing as target cooperative tasks;

a second obtaining module, configured to obtain, for any candidate cooperative device, a task allocation parameter for the candidate cooperative device screened out to allocate the target cooperative task;

a determining module, configured to determine, according to the task allocation parameter of each candidate cooperative device, a target cooperative device that executes the target cooperative task from each candidate cooperative device;

and the distribution module is used for distributing the target cooperative task to the target cooperative equipment for execution.

8. The apparatus of claim 7, wherein the second obtaining module is further configured to:

9. The apparatus of claim 8, wherein the second obtaining module is further configured to:

10. The apparatus of claim 8, wherein the second obtaining module is further configured to:

11. The apparatus of claim 10, wherein the second obtaining module is further configured to:

12. The apparatus of any of claims 7-11, wherein the determining module is further configured to:

13. An electronic device, comprising:

a processor and a memory, the memory having stored therein instructions that are loaded and executed by the processor to implement the method of any of claims 1-6.

14. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.